commit b494f8ff5874d3e1f83614e5123988ac65a3c203 Author: yin-kangning <747919591@qq.com> Date: Fri Jun 21 10:06:54 2024 +0800 first commit diff --git a/11.jpg b/11.jpg new file mode 100644 index 0000000..1e0c9a0 Binary files /dev/null and b/11.jpg differ diff --git a/11.py b/11.py new file mode 100644 index 0000000..d67082a --- /dev/null +++ b/11.py @@ -0,0 +1,21 @@ +import torch +from algorithm.yolov5.models.common import DetectMultiBackend +import os +from algorithm.yolov5.models.yolo import Detect, Model,RotationDetect +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# model_state_dict = torch.load('weight/remote_sensing/oriented.pt') +model = DetectMultiBackend(weights='weight/remote_sensing/oriented.pt', device=device, dnn=True) +# model.load_state_dict(model_state_dict) +# model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight//remote_sensing/oriented.pt', force_reload=True) +print(RotationDetect()) +for m in model.modules(): + t = type(m) + print(t) + if Detect: + print(1) + m = RotationDetect() + +# print(model) + + diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..2bdae25 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Kadir Tuna + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/MyThreadFunc.py b/MyThreadFunc.py new file mode 100644 index 0000000..c47d1ad --- /dev/null +++ b/MyThreadFunc.py @@ -0,0 +1,53 @@ +import inspect +import ctypes +import threading + +class MyThreadFunc(object): + ''' + 手动终止线程的方法 + ''' + def __init__(self, func, argsTup): + self.myThread = threading.Thread(target=func, args=argsTup) + self.daemon = True + + def start(self): + print('线程启动') + self.result = self.myThread.start() + + def join(self): + self.myThread.join() + + def get_result(self): + try: + return self.result + except Exception as e: + return None + + def state(self): + status = self.myThread.is_alive() + print('线程状态: {0}'.format(status)) + return status + + def stop(self): + print('线程终止') + # self.myThread.join() + try: + for i in range(5): + self._async_raise(self.myThread.ident, SystemExit) + # time.sleep(1) + except Exception as e: + print(e) + + def _async_raise(self, tid, exctype): + """raises the exception, performs cleanup if needed""" + tid = ctypes.c_long(tid) + if not inspect.isclass(exctype): + exctype = type(exctype) + res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype)) + if res == 0: + raise ValueError("invalid thread id") + elif res != 1: + # """if it returns a number greater than one, you're in trouble, + # and you should call it again with exc=NULL to revert the effect""" + ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None) + raise SystemError("PyThreadState_SetAsyncExc failed") diff --git a/README.md b/README.md new file mode 100644 index 0000000..d02ebaf --- /dev/null +++ b/README.md @@ -0,0 +1,13 @@ +# 算法系统 + 算法系统在服务器后端运行代码 + + 运行方式，python app.py + +可以根据自己服务器配置咋app.py进行修改 + app.run(host='10.51.10.122',debug=True, port=5001) + + +模型下载：https://pan.baidu.com/s/12F5H4hC1VQIOAqLdCLzZtA?pwd=upup +提取码：upup + +下载后放在weight文件夹 \ No newline at end of file diff --git a/Scripts/activate b/Scripts/activate new file mode 100644 index 0000000..87d6cc6 --- /dev/null +++ b/Scripts/activate @@ -0,0 +1,83 @@ +# This file must be used with "source bin/activate" *from bash* +# you cannot run it directly + + +if [ "${BASH_SOURCE-}" = "$0" ]; then + echo "You must source this script: \$ source $0" >&2 + exit 33 +fi + +deactivate () { + unset -f pydoc >/dev/null 2>&1 || true + + # reset old environment variables + # ! [ -z ${VAR+_} ] returns true if VAR is declared at all + if ! [ -z "${_OLD_VIRTUAL_PATH:+_}" ] ; then + PATH="$_OLD_VIRTUAL_PATH" + export PATH + unset _OLD_VIRTUAL_PATH + fi + if ! [ -z "${_OLD_VIRTUAL_PYTHONHOME+_}" ] ; then + PYTHONHOME="$_OLD_VIRTUAL_PYTHONHOME" + export PYTHONHOME + unset _OLD_VIRTUAL_PYTHONHOME + fi + + # The hash command must be called to get it to forget past + # commands. Without forgetting past commands the $PATH changes + # we made may not be respected + hash -r 2>/dev/null + + if ! [ -z "${_OLD_VIRTUAL_PS1+_}" ] ; then + PS1="$_OLD_VIRTUAL_PS1" + export PS1 + unset _OLD_VIRTUAL_PS1 + fi + + unset VIRTUAL_ENV + if [ ! "${1-}" = "nondestructive" ] ; then + # Self destruct! + unset -f deactivate + fi +} + +# unset irrelevant variables +deactivate nondestructive + +VIRTUAL_ENV='C:\Users\ka\Desktop\Programming Projects\Python Projeleri\LiveVideoServer' +if ([ "$OSTYPE" = "cygwin" ] || [ "$OSTYPE" = "msys" ]) && $(command -v cygpath &> /dev/null) ; then + VIRTUAL_ENV=$(cygpath -u "$VIRTUAL_ENV") +fi +export VIRTUAL_ENV + +_OLD_VIRTUAL_PATH="$PATH" +PATH="$VIRTUAL_ENV/Scripts:$PATH" +export PATH + +# unset PYTHONHOME if set +if ! [ -z "${PYTHONHOME+_}" ] ; then + _OLD_VIRTUAL_PYTHONHOME="$PYTHONHOME" + unset PYTHONHOME +fi + +if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT-}" ] ; then + _OLD_VIRTUAL_PS1="${PS1-}" + if [ "x" != x ] ; then + PS1="() ${PS1-}" + else + PS1="(`basename \"$VIRTUAL_ENV\"`) ${PS1-}" + fi + export PS1 +fi + +# Make sure to unalias pydoc if it's already there +alias pydoc 2>/dev/null >/dev/null && unalias pydoc || true + +pydoc () { + python -m pydoc "$@" +} + +# The hash command must be called to get it to forget past +# commands. Without forgetting past commands the $PATH changes +# we made may not be respected +hash -r 2>/dev/null diff --git a/Scripts/activate.bat b/Scripts/activate.bat new file mode 100644 index 0000000..e3237e5 --- /dev/null +++ b/Scripts/activate.bat @@ -0,0 +1,39 @@ +@echo off + +set "VIRTUAL_ENV=C:\Users\ka\Desktop\Programming Projects\Python Projeleri\LiveVideoServer" + +if defined _OLD_VIRTUAL_PROMPT ( + set "PROMPT=%_OLD_VIRTUAL_PROMPT%" +) else ( + if not defined PROMPT ( + set "PROMPT=$P$G" + ) + if not defined VIRTUAL_ENV_DISABLE_PROMPT ( + set "_OLD_VIRTUAL_PROMPT=%PROMPT%" + ) +) +if not defined VIRTUAL_ENV_DISABLE_PROMPT ( + if "" NEQ "" ( + set "PROMPT=() %PROMPT%" + ) else ( + for %%d in ("%VIRTUAL_ENV%") do set "PROMPT=(%%~nxd) %PROMPT%" + ) +) + +REM Don't use () to avoid problems with them in %PATH% +if defined _OLD_VIRTUAL_PYTHONHOME goto ENDIFVHOME + set "_OLD_VIRTUAL_PYTHONHOME=%PYTHONHOME%" +:ENDIFVHOME + +set PYTHONHOME= + +REM if defined _OLD_VIRTUAL_PATH ( +if not defined _OLD_VIRTUAL_PATH goto ENDIFVPATH1 + set "PATH=%_OLD_VIRTUAL_PATH%" +:ENDIFVPATH1 +REM ) else ( +if defined _OLD_VIRTUAL_PATH goto ENDIFVPATH2 + set "_OLD_VIRTUAL_PATH=%PATH%" +:ENDIFVPATH2 + +set "PATH=%VIRTUAL_ENV%\Scripts;%PATH%" diff --git a/Scripts/activate.fish b/Scripts/activate.fish new file mode 100644 index 0000000..a9a5a31 --- /dev/null +++ b/Scripts/activate.fish @@ -0,0 +1,100 @@ +# This file must be used using `source bin/activate.fish` *within a running fish ( http://fishshell.com ) session*. +# Do not run it directly. + +function _bashify_path -d "Converts a fish path to something bash can recognize" + set fishy_path $argv + set bashy_path $fishy_path[1] + for path_part in $fishy_path[2..-1] + set bashy_path "$bashy_path:$path_part" + end + echo $bashy_path +end + +function _fishify_path -d "Converts a bash path to something fish can recognize" + echo $argv | tr ':' '\n' +end + +function deactivate -d 'Exit virtualenv mode and return to the normal environment.' + # reset old environment variables + if test -n "$_OLD_VIRTUAL_PATH" + # https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling + if test (echo $FISH_VERSION | head -c 1) -lt 3 + set -gx PATH (_fishify_path "$_OLD_VIRTUAL_PATH") + else + set -gx PATH $_OLD_VIRTUAL_PATH + end + set -e _OLD_VIRTUAL_PATH + end + + if test -n "$_OLD_VIRTUAL_PYTHONHOME" + set -gx PYTHONHOME "$_OLD_VIRTUAL_PYTHONHOME" + set -e _OLD_VIRTUAL_PYTHONHOME + end + + if test -n "$_OLD_FISH_PROMPT_OVERRIDE" + and functions -q _old_fish_prompt + # Set an empty local `$fish_function_path` to allow the removal of `fish_prompt` using `functions -e`. + set -l fish_function_path + + # Erase virtualenv's `fish_prompt` and restore the original. + functions -e fish_prompt + functions -c _old_fish_prompt fish_prompt + functions -e _old_fish_prompt + set -e _OLD_FISH_PROMPT_OVERRIDE + end + + set -e VIRTUAL_ENV + + if test "$argv[1]" != 'nondestructive' + # Self-destruct! + functions -e pydoc + functions -e deactivate + functions -e _bashify_path + functions -e _fishify_path + end +end + +# Unset irrelevant variables. +deactivate nondestructive + +set -gx VIRTUAL_ENV 'C:\Users\ka\Desktop\Programming Projects\Python Projeleri\LiveVideoServer' + +# https://github.com/fish-shell/fish-shell/issues/436 altered PATH handling +if test (echo $FISH_VERSION | head -c 1) -lt 3 + set -gx _OLD_VIRTUAL_PATH (_bashify_path $PATH) +else + set -gx _OLD_VIRTUAL_PATH $PATH +end +set -gx PATH "$VIRTUAL_ENV"'/Scripts' $PATH + +# Unset `$PYTHONHOME` if set. +if set -q PYTHONHOME + set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME + set -e PYTHONHOME +end + +function pydoc + python -m pydoc $argv +end + +if test -z "$VIRTUAL_ENV_DISABLE_PROMPT" + # Copy the current `fish_prompt` function as `_old_fish_prompt`. + functions -c fish_prompt _old_fish_prompt + + function fish_prompt + # Run the user's prompt first; it might depend on (pipe)status. + set -l prompt (_old_fish_prompt) + + # Prompt override provided? + # If not, just prepend the environment name. + if test -n '' + printf '(%s) ' '' + else + printf '(%s) ' (basename "$VIRTUAL_ENV") + end + + string join -- \n $prompt # handle multi-line prompts + end + + set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV" +end diff --git a/Scripts/activate.nu b/Scripts/activate.nu new file mode 100644 index 0000000..63a4f20 --- /dev/null +++ b/Scripts/activate.nu @@ -0,0 +1,41 @@ +# Setting all environment variables for the venv +let path-name = (if ((sys).host.name == "Windows") { "Path" } { "PATH" }) +let virtual-env = "C:\Users\ka\Desktop\Programming Projects\Python Projeleri\LiveVideoServer" +let bin = "Scripts" +let path-sep = ";" + +let old-path = ($nu.path | str collect ($path-sep)) + +let venv-path = ([$virtual-env $bin] | path join) +let new-path = ($nu.path | prepend $venv-path | str collect ($path-sep)) + +# environment variables that will be batched loaded to the virtual env +let new-env = ([ + [name, value]; + [$path-name $new-path] + [_OLD_VIRTUAL_PATH $old-path] + [VIRTUAL_ENV $virtual-env] +]) + +load-env $new-env + +# Creating the new prompt for the session +let virtual_prompt = (if ("" != "") { + "() " +} { + (build-string '(' ($virtual-env | path basename) ') ') +} +) + +# If there is no default prompt, then only the env is printed in the prompt +let new_prompt = (if ( config | select prompt | empty? ) { + ($"build-string '($virtual_prompt)'") +} { + ($"build-string '($virtual_prompt)' (config get prompt | str find-replace "build-string" "")") +}) +let-env PROMPT_COMMAND = $new_prompt + +# We are using alias as the function definitions because only aliases can be +# removed from the scope +alias pydoc = python -m pydoc +alias deactivate = source "C:\Users\ka\Desktop\Programming Projects\Python Projeleri\LiveVideoServer\Scripts\deactivate.nu" diff --git a/Scripts/activate.ps1 b/Scripts/activate.ps1 new file mode 100644 index 0000000..2057a7e --- /dev/null +++ b/Scripts/activate.ps1 @@ -0,0 +1,60 @@ +$script:THIS_PATH = $myinvocation.mycommand.path +$script:BASE_DIR = Split-Path (Resolve-Path "$THIS_PATH/..") -Parent + +function global:deactivate([switch] $NonDestructive) { + if (Test-Path variable:_OLD_VIRTUAL_PATH) { + $env:PATH = $variable:_OLD_VIRTUAL_PATH + Remove-Variable "_OLD_VIRTUAL_PATH" -Scope global + } + + if (Test-Path function:_old_virtual_prompt) { + $function:prompt = $function:_old_virtual_prompt + Remove-Item function:\_old_virtual_prompt + } + + if ($env:VIRTUAL_ENV) { + Remove-Item env:VIRTUAL_ENV -ErrorAction SilentlyContinue + } + + if (!$NonDestructive) { + # Self destruct! + Remove-Item function:deactivate + Remove-Item function:pydoc + } +} + +function global:pydoc { + python -m pydoc $args +} + +# unset irrelevant variables +deactivate -nondestructive + +$VIRTUAL_ENV = $BASE_DIR +$env:VIRTUAL_ENV = $VIRTUAL_ENV + +New-Variable -Scope global -Name _OLD_VIRTUAL_PATH -Value $env:PATH + +$env:PATH = "$env:VIRTUAL_ENV/Scripts;" + $env:PATH +if (!$env:VIRTUAL_ENV_DISABLE_PROMPT) { + function global:_old_virtual_prompt { + "" + } + $function:_old_virtual_prompt = $function:prompt + + if ("" -ne "") { + function global:prompt { + # Add the custom prefix to the existing prompt + $previous_prompt_value = & $function:_old_virtual_prompt + ("() " + $previous_prompt_value) + } + } + else { + function global:prompt { + # Add a prefix to the current prompt, but don't discard it. + $previous_prompt_value = & $function:_old_virtual_prompt + $new_prompt_value = "($( Split-Path $env:VIRTUAL_ENV -Leaf )) " + ($new_prompt_value + $previous_prompt_value) + } + } +} diff --git a/Scripts/activate_this.py b/Scripts/activate_this.py new file mode 100644 index 0000000..3d79a53 --- /dev/null +++ b/Scripts/activate_this.py @@ -0,0 +1,32 @@ +# -*- coding: utf-8 -*- +"""Activate virtualenv for current interpreter: + +Use exec(open(this_file).read(), {'__file__': this_file}). + +This can be used when you must use an existing Python interpreter, not the virtualenv bin/python. +""" +import os +import site +import sys + +try: + abs_file = os.path.abspath(__file__) +except NameError: + raise AssertionError("You must use exec(open(this_file).read(), {'__file__': this_file}))") + +bin_dir = os.path.dirname(abs_file) +base = bin_dir[: -len("Scripts") - 1] # strip away the bin part from the __file__, plus the path separator + +# prepend bin to PATH (this file is inside the bin directory) +os.environ["PATH"] = os.pathsep.join([bin_dir] + os.environ.get("PATH", "").split(os.pathsep)) +os.environ["VIRTUAL_ENV"] = base # virtual env is right above bin directory + +# add the virtual environments libraries to the host python import mechanism +prev_length = len(sys.path) +for lib in "..\Lib\site-packages".split(os.pathsep): + path = os.path.realpath(os.path.join(bin_dir, lib)) + site.addsitedir(path.decode("utf-8") if "" else path) +sys.path[:] = sys.path[prev_length:] + sys.path[0:prev_length] + +sys.real_prefix = sys.prefix +sys.prefix = base diff --git a/Scripts/convert-caffe2-to-onnx.exe b/Scripts/convert-caffe2-to-onnx.exe new file mode 100644 index 0000000..698531c Binary files /dev/null and b/Scripts/convert-caffe2-to-onnx.exe differ diff --git a/Scripts/convert-onnx-to-caffe2.exe b/Scripts/convert-onnx-to-caffe2.exe new file mode 100644 index 0000000..260a43e Binary files /dev/null and b/Scripts/convert-onnx-to-caffe2.exe differ diff --git a/Scripts/deactivate.bat b/Scripts/deactivate.bat new file mode 100644 index 0000000..7bbc568 --- /dev/null +++ b/Scripts/deactivate.bat @@ -0,0 +1,19 @@ +@echo off + +set VIRTUAL_ENV= + +REM Don't use () to avoid problems with them in %PATH% +if not defined _OLD_VIRTUAL_PROMPT goto ENDIFVPROMPT + set "PROMPT=%_OLD_VIRTUAL_PROMPT%" + set _OLD_VIRTUAL_PROMPT= +:ENDIFVPROMPT + +if not defined _OLD_VIRTUAL_PYTHONHOME goto ENDIFVHOME + set "PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%" + set _OLD_VIRTUAL_PYTHONHOME= +:ENDIFVHOME + +if not defined _OLD_VIRTUAL_PATH goto ENDIFVPATH + set "PATH=%_OLD_VIRTUAL_PATH%" + set _OLD_VIRTUAL_PATH= +:ENDIFVPATH diff --git a/Scripts/deactivate.nu b/Scripts/deactivate.nu new file mode 100644 index 0000000..4052438 --- /dev/null +++ b/Scripts/deactivate.nu @@ -0,0 +1,11 @@ +# Setting the old path +let path-name = (if ((sys).host.name == "Windows") { "Path" } { "PATH" }) +let-env $path-name = $nu.env._OLD_VIRTUAL_PATH + +# Unleting the environment variables that were created when activating the env +unlet-env VIRTUAL_ENV +unlet-env _OLD_VIRTUAL_PATH +unlet-env PROMPT_COMMAND + +unalias pydoc +unalias deactivate diff --git a/Scripts/f2py.exe b/Scripts/f2py.exe new file mode 100644 index 0000000..47cf221 Binary files /dev/null and b/Scripts/f2py.exe differ diff --git a/Scripts/flask.exe b/Scripts/flask.exe new file mode 100644 index 0000000..537927c Binary files /dev/null and b/Scripts/flask.exe differ diff --git a/Scripts/fonttools.exe b/Scripts/fonttools.exe new file mode 100644 index 0000000..9e6b55f Binary files /dev/null and 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0000000..880c2d7 Binary files /dev/null and b/Scripts/wheel3.9.exe differ diff --git a/Scripts/wheel3.exe b/Scripts/wheel3.exe new file mode 100644 index 0000000..880c2d7 Binary files /dev/null and b/Scripts/wheel3.exe differ diff --git a/algorithm/Car_recognition/.gitignore b/algorithm/Car_recognition/.gitignore new file mode 100644 index 0000000..6969f2c --- /dev/null +++ b/algorithm/Car_recognition/.gitignore @@ -0,0 +1,31 @@ +# .gitignore +# 首先忽略所有的文件 +* +# 但是不忽略目录 +!*/ +# 忽略一些指定的目录名 +ut/ +runs/ +.vscode/ +build/ +result1/ +mytest/ +*.pyc +# 不忽略下面指定的文件类型 +!*.cpp +!*.h +!*.hpp +!*.c +!.gitignore +!*.py +!*.sh +!*.npy +!*.jpg +!*.pt +!*.npy +!*.pth +!*.png +!*.yaml +!*.ttf +!*.txt +!*.md \ No newline at end of file diff --git a/algorithm/Car_recognition/README.md b/algorithm/Car_recognition/README.md new file mode 100644 index 0000000..b8f4712 --- /dev/null +++ b/algorithm/Car_recognition/README.md @@ -0,0 +1,70 @@ +## 车辆识别系统 + +**目前支持车辆检测+车牌检测识别** + +环境要求: python >=3.6 pytorch >=1.7 + +#### **图片测试demo:** + +``` +python Car_recognition.py --detect_model weights/detect.pt --rec_model weights/plate_rec_color.pth --image_path imgs --output result +``` + +测试文件夹imgs，结果保存再 result 文件夹中 + +![Image text](image/test.jpg) + +## **检测训练** + +1. **下载数据集：** [datasets](https://pan.baidu.com/s/1YSURJvo4v1N5x7NVsxEA_Q) 提取码：3s0j 数据从CCPD和CRPD数据集中选取并转换的 + 数据集格式为yolo格式： + + ``` + label x y w h pt1x pt1y pt2x pt2y pt3x pt3y pt4x pt4y + ``` + + 关键点依次是（左上，右上，右下，左下） + 坐标都是经过归一化，x,y是中心点除以图片宽高，w,h是框的宽高除以图片宽高，ptx，pty是关键点坐标除以宽高 + + 车辆标注不需要关键点 关键点全部置为-1即可 +2. **修改 data/widerface.yaml train和val路径,换成你的数据路径** + + ``` + train: /your/train/path #修改成你的路径 + val: /your/val/path #修改成你的路径 + # number of classes + nc: 3 #这里用的是3分类，0 单层车牌 1 双层车牌 2 车辆 + + # class names + names: [ 'single_plate','double_plate','Car'] + + ``` +3. **训练** + + ``` + python3 train.py --data data/plateAndCar.yaml --cfg models/yolov5n-0.5.yaml --weights weights/detect.pt --epoch 250 + ``` + + 结果存在run文件夹中 + +## **车牌识别训练** + +车牌识别训练链接如下： + +[车牌识别训练](https://github.com/we0091234/crnn_plate_recognition) + +## References + +* [https://github.com/we0091234/Chinese_license_plate_detection_recognition](https://github.com/we0091234/Chinese_license_plate_detection_recognition) +* [https://github.com/deepcam-cn/yolov5-face](https://github.com/deepcam-cn/yolov5-face) +* [https://github.com/meijieru/crnn.pytorch](https://github.com/meijieru/crnn.pytorch) + +## TODO + +车型，车辆颜色，品牌等。 + +## 联系 + +**有问题可以提issues 或者加qq群 823419837 询问** + +![Image text](image/README/1.png) diff --git a/algorithm/Car_recognition/car_detection.py b/algorithm/Car_recognition/car_detection.py new file mode 100644 index 0000000..ecf88e5 --- /dev/null +++ b/algorithm/Car_recognition/car_detection.py @@ -0,0 +1,286 @@ +# -*- coding: UTF-8 -*- +import argparse +import time +import os +import cv2 +import torch +from numpy import random +import copy +import numpy as np +from algorithm.Car_recognition.plate_recognition.plate_rec import get_plate_result,allFilePath,init_model,cv_imread +# from plate_recognition.plate_cls import cv_imread +from algorithm.Car_recognition.plate_recognition.double_plate_split_merge import get_split_merge +from algorithm.Car_recognition.plate_recognition.color_rec import plate_color_rec,init_color_model +from algorithm.Car_recognition.car_recognition.car_rec import init_car_rec_model,get_color_and_score +from algorithm.Car_recognition.utils.datasets import letterbox +from algorithm.Car_recognition.utils.general import check_img_size, non_max_suppression_face, scale_coords +from algorithm.Car_recognition.utils.cv_puttext import cv2ImgAddText + +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + + + +clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)] +danger=['危','险'] +object_color=[(0,255,255),(0,255,0),(255,255,0)] +class_type=['单层车牌','双层车牌','汽车'] + + +class CarDetection(): + + def __init__(self, video_path=None): + + # self.detect_model = detect_model + # self.plate_rec_model = plate_rec_model + # self.car_rec_model = car_rec_model + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + self.detect_model =torch.load('weight/traffic/best.pt', map_location=self.device)['model'].float().fuse() + # self.detect_model = load_model((os.getcwd()) + "/weight/traffic/detect.pt") #初始化检测模型 + self.plate_rec_model= init_model((os.getcwd()) + "/weight/traffic/plate_rec_color.pth") #初始化识别模型 + self.car_rec_model = init_car_rec_model((os.getcwd()) + "/weight/traffic/car_rec_color.pth") #初始化车辆识别模型 + + + + time_all = 0 + time_begin=time.time() + + # self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.imgsz = 384 + self.dataset = LoadImages(self.video_name,self.imgsz) + + def use_webcam(self, source): + + source = source + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + dict_list=detect_Recognition_plate(self.detect_model, img, self.device, self.plate_rec_model, car_rec_model=self.car_rec_model) + ori_img=draw_result(img,dict_list) + + ret, jpeg = cv2.imencode(".jpg", ori_img) + txt = str(dict_list) + + + return jpeg.tobytes(), txt + + + +def order_points(pts): #四个点安好左上 右上 右下 左下排列 + rect = np.zeros((4, 2), dtype = "float32") + s = pts.sum(axis = 1) + rect[0] = pts[np.argmin(s)] + rect[2] = pts[np.argmax(s)] + diff = np.diff(pts, axis = 1) + rect[1] = pts[np.argmin(diff)] + rect[3] = pts[np.argmax(diff)] + return rect + + +def four_point_transform(image, pts): #透视变换得到车牌小图 + rect = order_points(pts) + (tl, tr, br, bl) = rect + widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) + widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) + maxWidth = max(int(widthA), int(widthB)) + heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) + heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) + maxHeight = max(int(heightA), int(heightB)) + dst = np.array([ + [0, 0], + [maxWidth - 1, 0], + [maxWidth - 1, maxHeight - 1], + [0, maxHeight - 1]], dtype = "float32") + M = cv2.getPerspectiveTransform(rect, dst) + warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) + return warped + + +def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None): #返回到原图坐标 + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2, 4, 6]] -= pad[0] # x padding + coords[:, [1, 3, 5, 7]] -= pad[1] # y padding + coords[:, :8] /= gain + #clip_coords(coords, img0_shape) + coords[:, 0].clamp_(0, img0_shape[1]) # x1 + coords[:, 1].clamp_(0, img0_shape[0]) # y1 + coords[:, 2].clamp_(0, img0_shape[1]) # x2 + coords[:, 3].clamp_(0, img0_shape[0]) # y2 + coords[:, 4].clamp_(0, img0_shape[1]) # x3 + coords[:, 5].clamp_(0, img0_shape[0]) # y3 + coords[:, 6].clamp_(0, img0_shape[1]) # x4 + coords[:, 7].clamp_(0, img0_shape[0]) # y4 + # coords[:, 8].clamp_(0, img0_shape[1]) # x5 + # coords[:, 9].clamp_(0, img0_shape[0]) # y5 + return coords + +def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num,device,plate_rec_model,car_rec_model): + h,w,c = img.shape + result_dict={} + x1 = int(xyxy[0]) + y1 = int(xyxy[1]) + x2 = int(xyxy[2]) + y2 = int(xyxy[3]) + landmarks_np=np.zeros((4,2)) + rect=[x1,y1,x2,y2] + + if int(class_num) ==2: + car_roi_img = img[y1:y2,x1:x2] + car_color,color_conf=get_color_and_score(car_rec_model,car_roi_img,device) + result_dict['class_type']=class_type[int(class_num)] + result_dict['rect']=rect #车辆roi + result_dict['score']=conf #车牌区域检测得分 + result_dict['object_no']=int(class_num) + result_dict['car_color']=car_color + result_dict['color_conf']=color_conf + return result_dict + + for i in range(4): + point_x = int(landmarks[2 * i]) + point_y = int(landmarks[2 * i + 1]) + landmarks_np[i]=np.array([point_x,point_y]) + + class_label= int(class_num) #车牌的的类型0代表单牌，1代表双层车牌 + roi_img = four_point_transform(img,landmarks_np) #透视变换得到车牌小图 + if class_label: #判断是否是双层车牌，是双牌的话进行分割后然后拼接 + roi_img=get_split_merge(roi_img) + plate_number ,plate_color= get_plate_result(roi_img,device,plate_rec_model) #对车牌小图进行识别,得到颜色和车牌号 + for dan in danger: #只要出现‘危’或者‘险’就是危险品车牌 + if dan in plate_number: + plate_number='危险品' + # cv2.imwrite("roi.jpg",roi_img) + result_dict['class_type']=class_type[class_label] + result_dict['rect']=rect #车牌roi区域 + result_dict['landmarks']=landmarks_np.tolist() #车牌角点坐标 + result_dict['plate_no']=plate_number #车牌号 + result_dict['roi_height']=roi_img.shape[0] #车牌高度 + result_dict['plate_color']=plate_color #车牌颜色 + result_dict['object_no']=class_label #单双层 0单层 1双层 + result_dict['score']=conf #车牌区域检测得分 + return result_dict + + + +def detect_Recognition_plate(model, orgimg, device,plate_rec_model,car_rec_model=None): + # Load model + conf_thres = 0.3 + iou_thres = 0.5 + dict_list=[] + # orgimg = cv2.imread(image_path) # BGR + + img0 = copy.deepcopy(orgimg) + + img0 = np.transpose(img0, (2, 0, 1)) + + img = torch.from_numpy(img0) + assert orgimg is not None, 'Image Not Found ' + # print(model) + model.to(device) + img.to(device) + img = img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + + # Inference + pred = model(img)[0] + + # Apply NMS + pred = non_max_suppression_face(pred, conf_thres, iou_thres) + + + + # Process detections + for i, det in enumerate(pred): # detections per image + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round() + + # Print results + for c in det[:, -1].unique(): + n = (det[:, -1] == c).sum() # detections per class + + det[:, 5:13] = scale_coords_landmarks(img.shape[2:], det[:, 5:13], orgimg.shape).round() + + for j in range(det.size()[0]): + xyxy = det[j, :4].view(-1).tolist() + conf = det[j, 4].cpu().numpy() + landmarks = det[j, 5:13].view(-1).tolist() + class_num = det[j, 13].cpu().numpy() + result_dict = get_plate_rec_landmark(orgimg, xyxy, conf, landmarks, class_num,device,plate_rec_model,car_rec_model) + dict_list.append(result_dict) + return dict_list + # cv2.imwrite('result.jpg', orgimg) + +def draw_result(orgimg,dict_list): + result_str ="" + for result in dict_list: + rect_area = result['rect'] + object_no = result['object_no'] + if not object_no==2: + x,y,w,h = rect_area[0],rect_area[1],rect_area[2]-rect_area[0],rect_area[3]-rect_area[1] + padding_w = 0.05*w + padding_h = 0.11*h + rect_area[0]=max(0,int(x-padding_w)) + rect_area[1]=max(0,int(y-padding_h)) + rect_area[2]=min(orgimg.shape[1],int(rect_area[2]+padding_w)) + rect_area[3]=min(orgimg.shape[0],int(rect_area[3]+padding_h)) + + height_area = int(result['roi_height']/2) + landmarks=result['landmarks'] + result_p = result['plate_no'] + if result['object_no']==0:#单层 + result_p+=" "+result['plate_color'] + else: #双层 + result_p+=" "+result['plate_color']+"双层" + result_str+=result_p+" " + for i in range(4): #关键点 + cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1) + + if len(result)>=1: + if "危险品" in result_p: #如果是危险品车牌，文字就画在下面 + orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0],rect_area[3],(0,255,0),height_area) + else: + orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area) + else: + height_area=int((rect_area[3]-rect_area[1])/20) + car_color = result['car_color'] + car_color_str="车辆颜色:" + car_color_str+=car_color + orgimg=cv2ImgAddText(orgimg,car_color_str,rect_area[0],rect_area[1],(0,255,0),height_area) + + cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),object_color[object_no],2) #画框 + # print(result_str) + return orgimg + +def get_second(capture): + if capture.isOpened(): + rate = capture.get(5) # 帧速率 + FrameNumber = capture.get(7) # 视频文件的帧数 + duration = FrameNumber/rate # 帧速率/视频总帧数 是时间，除以60之后单位是分钟 + return int(rate),int(FrameNumber),int(duration) + diff --git a/algorithm/Car_recognition/car_recognition/car_rec.py b/algorithm/Car_recognition/car_recognition/car_rec.py new file mode 100644 index 0000000..d16613f --- /dev/null +++ b/algorithm/Car_recognition/car_recognition/car_rec.py @@ -0,0 +1,64 @@ +from algorithm.Car_recognition.car_recognition.myNet import myNet +import torch +import cv2 +import torch.nn.functional as F +import os +import numpy as np + +colors = ['黑色','蓝色','黄色','棕色','绿色','灰色','橙色','粉色','紫色','红色','白色'] +def init_car_rec_model(model_path): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + check_point = torch.load(model_path) + cfg= check_point['cfg'] + model = myNet(num_classes=11,cfg=cfg) + model.load_state_dict(check_point['state_dict']) + model.to(device) + model.eval() + return model + +def imge_processing(img,device): + img = cv2.resize(img,(64,64)) + img = img.transpose([2,0,1]) + img = torch.from_numpy(img).float().to(device) + img = img-127.5 + img = img.unsqueeze(0) + return img + +def allFilePath(rootPath,allFIleList): + fileList = os.listdir(rootPath) + for temp in fileList: + if os.path.isfile(os.path.join(rootPath,temp)): + allFIleList.append(os.path.join(rootPath,temp)) + else: + allFilePath(os.path.join(rootPath,temp),allFIleList) + +def get_color_and_score(model,img,device): + img = imge_processing(img,device) + result = model(img) + out =F.softmax( result) + _, predicted = torch.max(out.data, 1) + out=out.data.cpu().numpy().tolist()[0] + predicted = predicted.item() + car_color= colors[predicted] + color_conf = out[predicted] + # print(pic_,colors[predicted[0]]) + return car_color,color_conf + + +if __name__ == '__main__': + # root_file =r"/mnt/Gpan/BaiduNetdiskDownload/VehicleColour/VehicleColour/class/7" + root_file =r"imgs" + file_list=[] + allFilePath(root_file,file_list) + device = torch.device("cuda" if torch.cuda.is_available else "cpu") + model_path = r"/mnt/Gpan/Mydata/pytorchPorject/Car_system/car_color/color_model/0.8682285244554049_epoth_117_model.pth" + model = init_car_rec_model(model_path,device) + for pic_ in file_list: + img = cv2.imread(pic_) + # img = imge_processing(img,device) + color,conf = get_color_and_score(model,img,device) + print(pic_,color,conf) + + + + \ No newline at end of file diff --git a/algorithm/Car_recognition/car_recognition/myNet.py b/algorithm/Car_recognition/car_recognition/myNet.py new file mode 100644 index 0000000..76947ea --- /dev/null +++ b/algorithm/Car_recognition/car_recognition/myNet.py @@ -0,0 +1,95 @@ +import math + +import torch +import torch.nn as nn +from torch.autograd import Variable +from torchvision import models + + +__all__ = ['myNet','myResNet18'] + +# defaultcfg = { +# 11 : [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512], +# 13 : [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512], +# 16 : [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512], +# 19 : [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512], +# } +# myCfg = [32,'M',64,'M',96,'M',128,'M',192,'M',256] +myCfg = [32,'M',64,'M',96,'M',128,'M',256] +# myCfg = [8,'M',16,'M',32,'M',64,'M',96] +class myNet(nn.Module): + def __init__(self,cfg=None,num_classes=3): + super(myNet, self).__init__() + if cfg is None: + cfg = myCfg + self.feature = self.make_layers(cfg, True) + self.gap =nn.AdaptiveAvgPool2d((1,1)) + self.classifier = nn.Linear(cfg[-1], num_classes) + # self.classifier = nn.Conv2d(cfg[-1],num_classes,kernel_size=1,stride=1) + # self.bn_c= nn.BatchNorm2d(num_classes) + # self.flatten = nn.Flatten() + def make_layers(self, cfg, batch_norm=False): + layers = [] + in_channels = 3 + for i in range(len(cfg)): + if i == 0: + conv2d =nn.Conv2d(in_channels, cfg[i], kernel_size=5,stride =1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = cfg[i] + else : + if cfg[i] == 'M': + layers += [nn.MaxPool2d(kernel_size=3, stride=2,ceil_mode=True)] + else: + conv2d = nn.Conv2d(in_channels, cfg[i], kernel_size=3, padding=1,stride =1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = cfg[i] + return nn.Sequential(*layers) + + def forward(self, x): + y = self.feature(x) + y = nn.AvgPool2d(kernel_size=3, stride=1)(y) + y = y.view(x.size(0), -1) + y = self.classifier(y) + + # y = self.flatten(y) + return y + +class myResNet18(nn.Module): + def __init__(self,num_classes=1000): + super(myResNet18,self).__init__() + model_ft = models.resnet18(pretrained=True) + self.model =model_ft + self.model.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1,ceil_mode=True) + self.model.averagePool = nn.AvgPool2d((5,5),stride=1,ceil_mode=True) + self.cls=nn.Linear(512,num_classes) + + def forward(self,x): + x = self.model.conv1(x) + x = self.model.bn1(x) + x = self.model.relu(x) + x = self.model.maxpool(x) + + x = self.model.layer1(x) + x = self.model.layer2(x) + x = self.model.layer3(x) + x = self.model.layer4(x) + + x = self.model.averagePool(x) + x = x.view(x.size(0), -1) + x = self.cls(x) + + return x +if __name__ == '__main__': + net = myNet(num_classes=2) + # infeatures = net.cls.in_features + # net.cls=nn.Linear(infeatures,2) + x = torch.FloatTensor(16, 3, 64, 64) + y = net(x) + print(y.shape) + # print(net) \ No newline at end of file diff --git a/algorithm/Car_recognition/data/argoverse_hd.yaml b/algorithm/Car_recognition/data/argoverse_hd.yaml new file mode 100644 index 0000000..df7a936 --- /dev/null +++ b/algorithm/Car_recognition/data/argoverse_hd.yaml @@ -0,0 +1,21 @@ +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ +# Train command: python train.py --data argoverse_hd.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /argoverse +# /yolov5 + + +# download command/URL (optional) +download: bash data/scripts/get_argoverse_hd.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images +val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges +test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview + +# number of classes +nc: 8 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] diff --git a/algorithm/Car_recognition/data/coco.yaml b/algorithm/Car_recognition/data/coco.yaml new file mode 100644 index 0000000..b9da2bf --- /dev/null +++ b/algorithm/Car_recognition/data/coco.yaml @@ -0,0 +1,35 @@ +# COCO 2017 dataset http://cocodataset.org +# Train command: python train.py --data coco.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /coco +# /yolov5 + + +# download command/URL (optional) +download: bash data/scripts/get_coco.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../coco/train2017.txt # 118287 images +val: ../coco/val2017.txt # 5000 images +test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 + +# number of classes +nc: 80 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] + +# Print classes +# with open('data/coco.yaml') as f: +# d = yaml.load(f, Loader=yaml.FullLoader) # dict +# for i, x in enumerate(d['names']): +# print(i, x) diff --git a/algorithm/Car_recognition/data/coco128.yaml b/algorithm/Car_recognition/data/coco128.yaml new file mode 100644 index 0000000..c41bccf --- /dev/null +++ b/algorithm/Car_recognition/data/coco128.yaml @@ -0,0 +1,28 @@ +# COCO 2017 dataset http://cocodataset.org - first 128 training images +# Train command: python train.py --data coco128.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /coco128 +# /yolov5 + + +# download command/URL (optional) +download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../coco128/images/train2017/ # 128 images +val: ../coco128/images/train2017/ # 128 images + +# number of classes +nc: 80 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] diff --git a/algorithm/Car_recognition/data/hyp.finetune.yaml b/algorithm/Car_recognition/data/hyp.finetune.yaml new file mode 100644 index 0000000..1b84cff --- /dev/null +++ b/algorithm/Car_recognition/data/hyp.finetune.yaml @@ -0,0 +1,38 @@ +# Hyperparameters for VOC finetuning +# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + + +# Hyperparameter Evolution Results +# Generations: 306 +# P R mAP.5 mAP.5:.95 box obj cls +# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146 + +lr0: 0.0032 +lrf: 0.12 +momentum: 0.843 +weight_decay: 0.00036 +warmup_epochs: 2.0 +warmup_momentum: 0.5 +warmup_bias_lr: 0.05 +box: 0.0296 +cls: 0.243 +cls_pw: 0.631 +obj: 0.301 +obj_pw: 0.911 +iou_t: 0.2 +anchor_t: 2.91 +# anchors: 3.63 +fl_gamma: 0.0 +hsv_h: 0.0138 +hsv_s: 0.664 +hsv_v: 0.464 +degrees: 0.373 +translate: 0.245 +scale: 0.898 +shear: 0.602 +perspective: 0.0 +flipud: 0.00856 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.243 diff --git a/algorithm/Car_recognition/data/hyp.scratch.yaml b/algorithm/Car_recognition/data/hyp.scratch.yaml new file mode 100644 index 0000000..4973735 --- /dev/null +++ b/algorithm/Car_recognition/data/hyp.scratch.yaml @@ -0,0 +1,34 @@ +# Hyperparameters for COCO training from scratch +# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.5 # cls loss gain +landmark: 0.005 # landmark loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 1.0 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.5 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 0.5 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) diff --git a/algorithm/Car_recognition/data/plateAndCar.yaml b/algorithm/Car_recognition/data/plateAndCar.yaml new file mode 100644 index 0000000..ac39eb6 --- /dev/null +++ b/algorithm/Car_recognition/data/plateAndCar.yaml @@ -0,0 +1,19 @@ +# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ +# Train command: python train.py --data voc.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /VOC +# /yolov5 + + +# download command/URL (optional) +download: bash data/scripts/get_voc.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: /mnt/Gpan/Mydata/pytorchPorject/datasets/ccpd/train_car_plate/train_detect +val: /mnt/Gpan/Mydata/pytorchPorject/datasets/ccpd/train_car_plate/val_detect +# number of classes +nc: 3 + +# class names +names: [ 'single_plate','double_plate','car'] diff --git a/algorithm/Car_recognition/data/retinaface2yolo.py b/algorithm/Car_recognition/data/retinaface2yolo.py new file mode 100644 index 0000000..b51d382 --- /dev/null +++ b/algorithm/Car_recognition/data/retinaface2yolo.py @@ -0,0 +1,150 @@ +import os +import os.path +import sys +import torch +import torch.utils.data as data +import cv2 +import numpy as np + +class WiderFaceDetection(data.Dataset): + def __init__(self, txt_path, preproc=None): + self.preproc = preproc + self.imgs_path = [] + self.words = [] + f = open(txt_path,'r') + lines = f.readlines() + isFirst = True + labels = [] + for line in lines: + line = line.rstrip() + if line.startswith('#'): + if isFirst is True: + isFirst = False + else: + labels_copy = labels.copy() + self.words.append(labels_copy) + labels.clear() + path = line[2:] + path = txt_path.replace('label.txt','images/') + path + self.imgs_path.append(path) + else: + line = line.split(' ') + label = [float(x) for x in line] + labels.append(label) + + self.words.append(labels) + + def __len__(self): + return len(self.imgs_path) + + def __getitem__(self, index): + img = cv2.imread(self.imgs_path[index]) + height, width, _ = img.shape + + labels = self.words[index] + annotations = np.zeros((0, 15)) + if len(labels) == 0: + return annotations + for idx, label in enumerate(labels): + annotation = np.zeros((1, 15)) + # bbox + annotation[0, 0] = label[0] # x1 + annotation[0, 1] = label[1] # y1 + annotation[0, 2] = label[0] + label[2] # x2 + annotation[0, 3] = label[1] + label[3] # y2 + + # landmarks + annotation[0, 4] = label[4] # l0_x + annotation[0, 5] = label[5] # l0_y + annotation[0, 6] = label[7] # l1_x + annotation[0, 7] = label[8] # l1_y + annotation[0, 8] = label[10] # l2_x + annotation[0, 9] = label[11] # l2_y + annotation[0, 10] = label[13] # l3_x + annotation[0, 11] = label[14] # l3_y + annotation[0, 12] = label[16] # l4_x + annotation[0, 13] = label[17] # l4_y + if (annotation[0, 4]<0): + annotation[0, 14] = -1 + else: + annotation[0, 14] = 1 + + annotations = np.append(annotations, annotation, axis=0) + target = np.array(annotations) + if self.preproc is not None: + img, target = self.preproc(img, target) + + return torch.from_numpy(img), target + +def detection_collate(batch): + """Custom collate fn for dealing with batches of images that have a different + number of associated object annotations (bounding boxes). + + Arguments: + batch: (tuple) A tuple of tensor images and lists of annotations + + Return: + A tuple containing: + 1) (tensor) batch of images stacked on their 0 dim + 2) (list of tensors) annotations for a given image are stacked on 0 dim + """ + targets = [] + imgs = [] + for _, sample in enumerate(batch): + for _, tup in enumerate(sample): + if torch.is_tensor(tup): + imgs.append(tup) + elif isinstance(tup, type(np.empty(0))): + annos = torch.from_numpy(tup).float() + targets.append(annos) + + return (torch.stack(imgs, 0), targets) + +save_path = '/ssd_1t/derron/yolov5-face/data/widerface/train' +aa=WiderFaceDetection("/ssd_1t/derron/yolov5-face/data/widerface/widerface/train/label.txt") +for i in range(len(aa.imgs_path)): + print(i, aa.imgs_path[i]) + img = cv2.imread(aa.imgs_path[i]) + base_img = os.path.basename(aa.imgs_path[i]) + base_txt = os.path.basename(aa.imgs_path[i])[:-4] +".txt" + save_img_path = os.path.join(save_path, base_img) + save_txt_path = os.path.join(save_path, base_txt) + with open(save_txt_path, "w") as f: + height, width, _ = img.shape + labels = aa.words[i] + annotations = np.zeros((0, 14)) + if len(labels) == 0: + continue + for idx, label in enumerate(labels): + annotation = np.zeros((1, 14)) + # bbox + label[0] = max(0, label[0]) + label[1] = max(0, label[1]) + label[2] = min(width - 1, label[2]) + label[3] = min(height - 1, label[3]) + annotation[0, 0] = (label[0] + label[2] / 2) / width # cx + annotation[0, 1] = (label[1] + label[3] / 2) / height # cy + annotation[0, 2] = label[2] / width # w + annotation[0, 3] = label[3] / height # h + #if (label[2] -label[0]) < 8 or (label[3] - label[1]) < 8: + # img[int(label[1]):int(label[3]), int(label[0]):int(label[2])] = 127 + # continue + # landmarks + annotation[0, 4] = label[4] / width # l0_x + annotation[0, 5] = label[5] / height # l0_y + annotation[0, 6] = label[7] / width # l1_x + annotation[0, 7] = label[8] / height # l1_y + annotation[0, 8] = label[10] / width # l2_x + annotation[0, 9] = label[11] / height # l2_y + annotation[0, 10] = label[13] / width # l3_x + annotation[0, 11] = label[14] / height # l3_y + annotation[0, 12] = label[16] / width # l4_x + annotation[0, 13] = label[17] / height # l4_y + str_label="0 " + for i in range(len(annotation[0])): + str_label =str_label+" "+str(annotation[0][i]) + str_label = str_label.replace('[', '').replace(']', '') + str_label = str_label.replace(',', '') + '\n' + f.write(str_label) + cv2.imwrite(save_img_path, img) + diff --git a/algorithm/Car_recognition/data/scripts/get_argoverse_hd.sh b/algorithm/Car_recognition/data/scripts/get_argoverse_hd.sh new file mode 100644 index 0000000..caec61e --- /dev/null +++ b/algorithm/Car_recognition/data/scripts/get_argoverse_hd.sh @@ -0,0 +1,62 @@ +#!/bin/bash +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ +# Download command: bash data/scripts/get_argoverse_hd.sh +# Train command: python train.py --data argoverse_hd.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /argoverse +# /yolov5 + +# Download/unzip images +d='../argoverse/' # unzip directory +mkdir $d +url=https://argoverse-hd.s3.us-east-2.amazonaws.com/ +f=Argoverse-HD-Full.zip +curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background +wait # finish background tasks + +cd ../argoverse/Argoverse-1.1/ +ln -s tracking images + +cd ../Argoverse-HD/annotations/ + +python3 - "$@" <train.txt +cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt + +python3 - "$@" < 3: + print('Too many arguments were provided.') + print('Run command: python3 train2yolo.py /path/to/original/widerface/train [/path/to/save/widerface/train]') + exit(1) + original_path = sys.argv[1] + + if len(sys.argv) == 2: + if not os.path.isdir('widerface'): + os.mkdir('widerface') + if not os.path.isdir('widerface/train'): + os.mkdir('widerface/train') + + save_path = 'widerface/train' + else: + save_path = sys.argv[2] + + if not os.path.isfile(os.path.join(original_path, 'label.txt')): + print('Missing label.txt file.') + exit(1) + + aa = WiderFaceDetection(os.path.join(original_path, 'label.txt')) + + for i in range(len(aa.imgs_path)): + print(i, aa.imgs_path[i]) + img = cv2.imread(aa.imgs_path[i]) + base_img = os.path.basename(aa.imgs_path[i]) + base_txt = os.path.basename(aa.imgs_path[i])[:-4] + ".txt" + save_img_path = os.path.join(save_path, base_img) + save_txt_path = os.path.join(save_path, base_txt) + with open(save_txt_path, "w") as f: + height, width, _ = img.shape + labels = aa.words[i] + annotations = np.zeros((0, 14)) + if len(labels) == 0: + continue + for idx, label in enumerate(labels): + annotation = np.zeros((1, 14)) + # bbox + label[0] = max(0, label[0]) + label[1] = max(0, label[1]) + label[2] = min(width - 1, label[2]) + label[3] = min(height - 1, label[3]) + annotation[0, 0] = (label[0] + label[2] / 2) / width # cx + annotation[0, 1] = (label[1] + label[3] / 2) / height # cy + annotation[0, 2] = label[2] / width # w + annotation[0, 3] = label[3] / height # h + #if (label[2] -label[0]) < 8 or (label[3] - label[1]) < 8: + # img[int(label[1]):int(label[3]), int(label[0]):int(label[2])] = 127 + # continue + # landmarks + annotation[0, 4] = label[4] / width # l0_x + annotation[0, 5] = label[5] / height # l0_y + annotation[0, 6] = label[7] / width # l1_x + annotation[0, 7] = label[8] / height # l1_y + annotation[0, 8] = label[10] / width # l2_x + annotation[0, 9] = label[11] / height # l2_y + annotation[0, 10] = label[13] / width # l3_x + annotation[0, 11] = label[14] / height # l3_y + annotation[0, 12] = label[16] / width # l4_x + annotation[0, 13] = label[17] / height # l4_yca + str_label = "0 " + for i in range(len(annotation[0])): + str_label = str_label + " " + str(annotation[0][i]) + str_label = str_label.replace('[', '').replace(']', '') + str_label = str_label.replace(',', '') + '\n' + f.write(str_label) + cv2.imwrite(save_img_path, img) diff --git a/algorithm/Car_recognition/data/val2yolo.py b/algorithm/Car_recognition/data/val2yolo.py new file mode 100644 index 0000000..ddd8eda --- /dev/null +++ b/algorithm/Car_recognition/data/val2yolo.py @@ -0,0 +1,88 @@ +import os +import cv2 +import numpy as np +import shutil +import sys +from tqdm import tqdm + + +def xywh2xxyy(box): + x1 = box[0] + y1 = box[1] + x2 = box[0] + box[2] + y2 = box[1] + box[3] + return x1, x2, y1, y2 + + +def convert(size, box): + dw = 1. / (size[0]) + dh = 1. / (size[1]) + x = (box[0] + box[1]) / 2.0 - 1 + y = (box[2] + box[3]) / 2.0 - 1 + w = box[1] - box[0] + h = box[3] - box[2] + x = x * dw + w = w * dw + y = y * dh + h = h * dh + return x, y, w, h + + +def wider2face(root, phase='val', ignore_small=0): + data = {} + with open('{}/{}/label.txt'.format(root, phase), 'r') as f: + lines = f.readlines() + for line in tqdm(lines): + line = line.strip() + if '#' in line: + path = '{}/{}/images/{}'.format(root, phase, line.split()[-1]) + img = cv2.imread(path) + height, width, _ = img.shape + data[path] = list() + else: + box = np.array(line.split()[0:4], dtype=np.float32) # (x1,y1,w,h) + if box[2] < ignore_small or box[3] < ignore_small: + continue + box = convert((width, height), xywh2xxyy(box)) + label = '0 {} {} {} {} -1 -1 -1 -1 -1 -1 -1 -1 -1 -1'.format(round(box[0], 4), round(box[1], 4), + round(box[2], 4), round(box[3], 4)) + data[path].append(label) + return data + + +if __name__ == '__main__': + if len(sys.argv) == 1: + print('Missing path to WIDERFACE folder.') + print('Run command: python3 val2yolo.py /path/to/original/widerface [/path/to/save/widerface/val]') + exit(1) + elif len(sys.argv) > 3: + print('Too many arguments were provided.') + print('Run command: python3 val2yolo.py /path/to/original/widerface [/path/to/save/widerface/val]') + exit(1) + + root_path = sys.argv[1] + if not os.path.isfile(os.path.join(root_path, 'val', 'label.txt')): + print('Missing label.txt file.') + exit(1) + + if len(sys.argv) == 2: + if not os.path.isdir('widerface'): + os.mkdir('widerface') + if not os.path.isdir('widerface/val'): + os.mkdir('widerface/val') + + save_path = 'widerface/val' + else: + save_path = sys.argv[2] + + datas = wider2face(root_path, phase='val') + for idx, data in enumerate(datas.keys()): + pict_name = os.path.basename(data) + out_img = f'{save_path}/{idx}.jpg' + out_txt = f'{save_path}/{idx}.txt' + shutil.copyfile(data, out_img) + labels = datas[data] + f = open(out_txt, 'w') + for label in labels: + f.write(label + '\n') + f.close() diff --git a/algorithm/Car_recognition/data/val2yolo_for_test.py b/algorithm/Car_recognition/data/val2yolo_for_test.py new file mode 100644 index 0000000..0790c60 --- /dev/null +++ b/algorithm/Car_recognition/data/val2yolo_for_test.py @@ -0,0 +1,65 @@ +import os +import cv2 +import numpy as np +import shutil +from tqdm import tqdm + +root = '/ssd_1t/derron/WiderFace' + + +def xywh2xxyy(box): + x1 = box[0] + y1 = box[1] + x2 = box[0] + box[2] + y2 = box[1] + box[3] + return (x1, x2, y1, y2) + + +def convert(size, box): + dw = 1. / (size[0]) + dh = 1. / (size[1]) + x = (box[0] + box[1]) / 2.0 - 1 + y = (box[2] + box[3]) / 2.0 - 1 + w = box[1] - box[0] + h = box[3] - box[2] + x = x * dw + w = w * dw + y = y * dh + h = h * dh + return (x, y, w, h) + + +def wider2face(phase='val', ignore_small=0): + data = {} + with open('{}/{}/label.txt'.format(root, phase), 'r') as f: + lines = f.readlines() + for line in tqdm(lines): + line = line.strip() + if '#' in line: + path = '{}/{}/images/{}'.format(root, phase, os.path.basename(line)) + img = cv2.imread(path) + height, width, _ = img.shape + data[path] = list() + else: + box = np.array(line.split()[0:4], dtype=np.float32) # (x1,y1,w,h) + if box[2] < ignore_small or box[3] < ignore_small: + continue + box = convert((width, height), xywh2xxyy(box)) + label = '0 {} {} {} {} -1 -1 -1 -1 -1 -1 -1 -1 -1 -1'.format(round(box[0], 4), round(box[1], 4), + round(box[2], 4), round(box[3], 4)) + data[path].append(label) + return data + + +if __name__ == '__main__': + datas = wider2face('val') + for idx, data in enumerate(datas.keys()): + pict_name = os.path.basename(data) + out_img = 'widerface/val/images/{}'.format(pict_name) + out_txt = 'widerface/val/labels/{}.txt'.format(os.path.splitext(pict_name)[0]) + shutil.copyfile(data, out_img) + labels = datas[data] + f = open(out_txt, 'w') + for label in labels: + f.write(label + '\n') + f.close() diff --git a/algorithm/Car_recognition/data/voc.yaml b/algorithm/Car_recognition/data/voc.yaml new file mode 100644 index 0000000..851a9e0 --- /dev/null +++ b/algorithm/Car_recognition/data/voc.yaml @@ -0,0 +1,21 @@ +# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ +# Train command: python train.py --data voc.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /VOC +# /yolov5 + + +# download command/URL (optional) +download: bash data/scripts/get_voc.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../VOC/images/train/ # 16551 images +val: ../VOC/images/val/ # 4952 images + +# number of classes +nc: 20 + +# class names +names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', + 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ] diff --git a/algorithm/Car_recognition/data/widerface.yaml b/algorithm/Car_recognition/data/widerface.yaml new file mode 100644 index 0000000..d4b81fd --- /dev/null +++ b/algorithm/Car_recognition/data/widerface.yaml @@ -0,0 +1,19 @@ +# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ +# Train command: python train.py --data voc.yaml +# Default dataset location is next to /yolov5: +# /parent_folder +# /VOC +# /yolov5 + + +# download command/URL (optional) +download: bash data/scripts/get_voc.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: /mnt/Gpan/Mydata/pytorchPorject/yolov5-face/ccpd/train_detect +val: /mnt/Gpan/Mydata/pytorchPorject/yolov5-face/ccpd/val_detect +# number of classes +nc: 2 + +# class names +names: [ 'single','double'] diff --git a/algorithm/Car_recognition/demo.sh b/algorithm/Car_recognition/demo.sh new file mode 100644 index 0000000..cbb1826 --- /dev/null +++ b/algorithm/Car_recognition/demo.sh @@ -0,0 +1 @@ +python detect_plate.py --detect_model runs/train/exp22/weights/last.pt --rec_model /mnt/Gpan/Mydata/pytorchPorject/CRNN/newCrnn/crnn_plate_recognition/output/360CC/crnn/2022-12-02-22-29/checkpoints/checkpoint_71_acc_0.9524.pth --image_path mytest --img_size 384 \ No newline at end of file diff --git a/algorithm/Car_recognition/detect_demo.py b/algorithm/Car_recognition/detect_demo.py new file mode 100644 index 0000000..e08834f --- /dev/null +++ b/algorithm/Car_recognition/detect_demo.py @@ -0,0 +1,223 @@ +# -*- coding: UTF-8 -*- +import argparse +import time +import os +import cv2 +import torch +from numpy import random +import copy +import numpy as np +from models.experimental import attempt_load +from utils.datasets import letterbox +from utils.general import check_img_size, non_max_suppression_face, scale_coords + +from utils.torch_utils import time_synchronized +from utils.cv_puttext import cv2ImgAddText +from plate_recognition.plate_rec import get_plate_result,allFilePath,cv_imread + +from plate_recognition.double_plate_split_merge import get_split_merge + +clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)] + +def load_model(weights, device): + model = attempt_load(weights, map_location=device) # load FP32 model + return model + + +def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2, 4, 6]] -= pad[0] # x padding + coords[:, [1, 3, 5, 7]] -= pad[1] # y padding + coords[:, :10] /= gain + #clip_coords(coords, img0_shape) + coords[:, 0].clamp_(0, img0_shape[1]) # x1 + coords[:, 1].clamp_(0, img0_shape[0]) # y1 + coords[:, 2].clamp_(0, img0_shape[1]) # x2 + coords[:, 3].clamp_(0, img0_shape[0]) # y2 + coords[:, 4].clamp_(0, img0_shape[1]) # x3 + coords[:, 5].clamp_(0, img0_shape[0]) # y3 + coords[:, 6].clamp_(0, img0_shape[1]) # x4 + coords[:, 7].clamp_(0, img0_shape[0]) # y4 + # coords[:, 8].clamp_(0, img0_shape[1]) # x5 + # coords[:, 9].clamp_(0, img0_shape[0]) # y5 + return coords + + + + +def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num,device): + h,w,c = img.shape + result_dict={} + tl = 1 or round(0.002 * (h + w) / 2) + 1 # line/font thickness + + x1 = int(xyxy[0]) + y1 = int(xyxy[1]) + x2 = int(xyxy[2]) + y2 = int(xyxy[3]) + landmarks_np=np.zeros((4,2)) + rect=[x1,y1,x2,y2] + for i in range(4): + point_x = int(landmarks[2 * i]) + point_y = int(landmarks[2 * i + 1]) + landmarks_np[i]=np.array([point_x,point_y]) + + class_label= int(class_num) #车牌的的类型0代表单牌，1代表双层车牌 + result_dict['rect']=rect + result_dict['landmarks']=landmarks_np.tolist() + result_dict['class']=class_label + return result_dict + + + +def detect_plate(model, orgimg, device,img_size): + # Load model + # img_size = opt_img_size + conf_thres = 0.3 + iou_thres = 0.5 + dict_list=[] + # orgimg = cv2.imread(image_path) # BGR + img0 = copy.deepcopy(orgimg) + assert orgimg is not None, 'Image Not Found ' + h0, w0 = orgimg.shape[:2] # orig hw + r = img_size / max(h0, w0) # resize image to img_size + if r != 1: # always resize down, only resize up if training with augmentation + interp = cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR + img0 = cv2.resize(img0, (int(w0 * r), int(h0 * r)), interpolation=interp) + + imgsz = check_img_size(img_size, s=model.stride.max()) # check img_size + + img = letterbox(img0, new_shape=imgsz)[0] + # img =process_data(img0) + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1).copy() # BGR to RGB, to 3x416x416 + + # Run inference + t0 = time.time() + + img = torch.from_numpy(img).to(device) + img = img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + + # Inference + t1 = time_synchronized() + pred = model(img)[0] + t2=time_synchronized() + # print(f"infer time is {(t2-t1)*1000} ms") + + # Apply NMS + pred = non_max_suppression_face(pred, conf_thres, iou_thres) + + # print('img.shape: ', img.shape) + # print('orgimg.shape: ', orgimg.shape) + + # Process detections + for i, det in enumerate(pred): # detections per image + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round() + + # Print results + for c in det[:, -1].unique(): + n = (det[:, -1] == c).sum() # detections per class + + det[:, 5:13] = scale_coords_landmarks(img.shape[2:], det[:, 5:13], orgimg.shape).round() + + for j in range(det.size()[0]): + xyxy = det[j, :4].view(-1).tolist() + conf = det[j, 4].cpu().numpy() + landmarks = det[j, 5:13].view(-1).tolist() + class_num = det[j, 13].cpu().numpy() + result_dict = get_plate_rec_landmark(orgimg, xyxy, conf, landmarks, class_num,device) + dict_list.append(result_dict) + return dict_list + # cv2.imwrite('result.jpg', orgimg) + + + +def draw_result(orgimg,dict_list): + result_str ="" + for result in dict_list: + rect_area = result['rect'] + + x,y,w,h = rect_area[0],rect_area[1],rect_area[2]-rect_area[0],rect_area[3]-rect_area[1] + padding_w = 0.05*w + padding_h = 0.11*h + rect_area[0]=max(0,int(x-padding_w)) + rect_area[1]=max(0,int(y-padding_h)) + rect_area[2]=min(orgimg.shape[1],int(rect_area[2]+padding_w)) + rect_area[3]=min(orgimg.shape[0],int(rect_area[3]+padding_h)) + + + landmarks=result['landmarks'] + label=result['class'] + # result_str+=result+" " + for i in range(4): #关键点 + cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1) + cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),clors[label],2) #画框 + cv2.putText(img,str(label),(rect_area[0],rect_area[1]),cv2.FONT_HERSHEY_SIMPLEX,0.5,clors[label],2) + # orgimg=cv2ImgAddText(orgimg,label,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area) + # print(result_str) + return orgimg +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--detect_model', nargs='+', type=str, default='weights/detect.pt', help='model.pt path(s)') #检测模型 + parser.add_argument('--image_path', type=str, default='imgs', help='source') + parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--output', type=str, default='result1', help='source') + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # device =torch.device("cpu") + opt = parser.parse_args() + print(opt) + save_path = opt.output + count=0 + if not os.path.exists(save_path): + os.mkdir(save_path) + + detect_model = load_model(opt.detect_model, device) #初始化检测模型 + time_all = 0 + time_begin=time.time() + if not os.path.isfile(opt.image_path): #目录 + file_list=[] + allFilePath(opt.image_path,file_list) + for img_path in file_list: + + print(count,img_path) + time_b = time.time() + img =cv_imread(img_path) + + if img is None: + continue + if img.shape[-1]==4: + img=cv2.cvtColor(img,cv2.COLOR_BGRA2BGR) + # detect_one(model,img_path,device) + dict_list=detect_plate(detect_model, img, device,opt.img_size) + ori_img=draw_result(img,dict_list) + img_name = os.path.basename(img_path) + save_img_path = os.path.join(save_path,img_name) + time_e=time.time() + time_gap = time_e-time_b + if count: + time_all+=time_gap + cv2.imwrite(save_img_path,ori_img) + count+=1 + else: #单个图片 + print(count,opt.image_path,end=" ") + img =cv_imread(opt.image_path) + if img.shape[-1]==4: + img=cv2.cvtColor(img,cv2.COLOR_BGRA2BGR) + # detect_one(model,img_path,device) + dict_list=detect_plate(detect_model, img, device,opt.img_size) + ori_img=draw_result(img,dict_list) + img_name = os.path.basename(opt.image_path) + save_img_path = os.path.join(save_path,img_name) + cv2.imwrite(save_img_path,ori_img) + print(f"sumTime time is {time.time()-time_begin} s, average pic time is {time_all/(len(file_list)-1)}") \ No newline at end of file diff --git a/algorithm/Car_recognition/fonts/platech.ttf b/algorithm/Car_recognition/fonts/platech.ttf new file mode 100644 index 0000000..d66a970 Binary files /dev/null and b/algorithm/Car_recognition/fonts/platech.ttf differ diff --git a/algorithm/Car_recognition/image/README/1.png b/algorithm/Car_recognition/image/README/1.png new file mode 100644 index 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b/algorithm/Car_recognition/imgs/xue.jpg differ diff --git a/algorithm/Car_recognition/onnx_infer.py b/algorithm/Car_recognition/onnx_infer.py new file mode 100644 index 0000000..f5ccf59 --- /dev/null +++ b/algorithm/Car_recognition/onnx_infer.py @@ -0,0 +1,255 @@ +import onnxruntime +import numpy as np +import cv2 +import copy +import os +import argparse +from PIL import Image, ImageDraw, ImageFont +import time + +plateName=r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航危0123456789ABCDEFGHJKLMNPQRSTUVWXYZ险品" +mean_value,std_value=((0.588,0.193))#识别模型均值标准差 + +def decodePlate(preds): #识别后处理 + pre=0 + newPreds=[] + for i in range(len(preds)): + if preds[i]!=0 and preds[i]!=pre: + newPreds.append(preds[i]) + pre=preds[i] + plate="" + for i in newPreds: + plate+=plateName[int(i)] + return plate + # return newPreds + +def rec_pre_precessing(img,size=(48,168)): #识别前处理 + img =cv2.resize(img,(168,48)) + img = img.astype(np.float32) + img = (img/255-mean_value)/std_value #归一化 减均值 除标准差 + img = img.transpose(2,0,1) #h,w,c 转为 c,h,w + img = img.reshape(1,*img.shape) #channel,height,width转为batch,channel,height,channel + return img + +def get_plate_result(img,session_rec): #识别后处理 + img =rec_pre_precessing(img) + y_onnx = session_rec.run([session_rec.get_outputs()[0].name], {session_rec.get_inputs()[0].name: img})[0] + # print(y_onnx[0]) + index =np.argmax(y_onnx[0],axis=1) #找出概率最大的那个字符的序号 + # print(y_onnx[0]) + plate_no = decodePlate(index) + # plate_no = decodePlate(y_onnx[0]) + return plate_no + + +def allFilePath(rootPath,allFIleList): #遍历文件 + fileList = os.listdir(rootPath) + for temp in fileList: + if os.path.isfile(os.path.join(rootPath,temp)): + allFIleList.append(os.path.join(rootPath,temp)) + else: + allFilePath(os.path.join(rootPath,temp),allFIleList) + +def get_split_merge(img): #双层车牌进行分割后识别 + h,w,c = img.shape + img_upper = img[0:int(5/12*h),:] + img_lower = img[int(1/3*h):,:] + img_upper = cv2.resize(img_upper,(img_lower.shape[1],img_lower.shape[0])) + new_img = np.hstack((img_upper,img_lower)) + return new_img + + +def order_points(pts): # 关键点排列 按照（左上，右上，右下，左下）的顺序排列 + rect = np.zeros((4, 2), dtype = "float32") + s = pts.sum(axis = 1) + rect[0] = pts[np.argmin(s)] + rect[2] = pts[np.argmax(s)] + diff = np.diff(pts, axis = 1) + rect[1] = pts[np.argmin(diff)] + rect[3] = pts[np.argmax(diff)] + return rect + + +def four_point_transform(image, pts): #透视变换得到矫正后的图像，方便识别 + rect = order_points(pts) + (tl, tr, br, bl) = rect + widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) + widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) + maxWidth = max(int(widthA), int(widthB)) + heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) + heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) + maxHeight = max(int(heightA), int(heightB)) + dst = np.array([ + [0, 0], + [maxWidth - 1, 0], + [maxWidth - 1, maxHeight - 1], + [0, maxHeight - 1]], dtype = "float32") + M = cv2.getPerspectiveTransform(rect, dst) + warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) + + # return the warped image + return warped + +def my_letter_box(img,size=(640,640)): # + h,w,c = img.shape + r = min(size[0]/h,size[1]/w) + new_h,new_w = int(h*r),int(w*r) + top = int((size[0]-new_h)/2) + left = int((size[1]-new_w)/2) + + bottom = size[0]-new_h-top + right = size[1]-new_w-left + img_resize = cv2.resize(img,(new_w,new_h)) + img = cv2.copyMakeBorder(img_resize,top,bottom,left,right,borderType=cv2.BORDER_CONSTANT,value=(114,114,114)) + return img,r,left,top + +def xywh2xyxy(boxes): #xywh坐标变为 左上 ，右下坐标 x1,y1 x2,y2 + xywh =copy.deepcopy(boxes) + xywh[:,0]=boxes[:,0]-boxes[:,2]/2 + xywh[:,1]=boxes[:,1]-boxes[:,3]/2 + xywh[:,2]=boxes[:,0]+boxes[:,2]/2 + xywh[:,3]=boxes[:,1]+boxes[:,3]/2 + return xywh + +def my_nms(boxes,iou_thresh): #nms + index = np.argsort(boxes[:,4])[::-1] + keep = [] + while index.size >0: + i = index[0] + keep.append(i) + x1=np.maximum(boxes[i,0],boxes[index[1:],0]) + y1=np.maximum(boxes[i,1],boxes[index[1:],1]) + x2=np.minimum(boxes[i,2],boxes[index[1:],2]) + y2=np.minimum(boxes[i,3],boxes[index[1:],3]) + + w = np.maximum(0,x2-x1) + h = np.maximum(0,y2-y1) + + inter_area = w*h + union_area = (boxes[i,2]-boxes[i,0])*(boxes[i,3]-boxes[i,1])+(boxes[index[1:],2]-boxes[index[1:],0])*(boxes[index[1:],3]-boxes[index[1:],1]) + iou = inter_area/(union_area-inter_area) + idx = np.where(iou<=iou_thresh)[0] + index = index[idx+1] + return keep + +def restore_box(boxes,r,left,top): #返回原图上面的坐标 + boxes[:,[0,2,5,7,9,11]]-=left + boxes[:,[1,3,6,8,10,12]]-=top + + boxes[:,[0,2,5,7,9,11]]/=r + boxes[:,[1,3,6,8,10,12]]/=r + return boxes + +def detect_pre_precessing(img,img_size): #检测前处理 + img,r,left,top=my_letter_box(img,img_size) + # cv2.imwrite("1.jpg",img) + img =img[:,:,::-1].transpose(2,0,1).copy().astype(np.float32) + img=img/255 + img=img.reshape(1,*img.shape) + return img,r,left,top + +def post_precessing(dets,r,left,top,conf_thresh=0.3,iou_thresh=0.5):#检测后处理 + choice = dets[:,:,4]>conf_thresh + dets=dets[choice] + dets[:,13:15]*=dets[:,4:5] + box = dets[:,:4] + boxes = xywh2xyxy(box) + score= np.max(dets[:,13:15],axis=-1,keepdims=True) + index = np.argmax(dets[:,13:15],axis=-1).reshape(-1,1) + output = np.concatenate((boxes,score,dets[:,5:13],index),axis=1) + reserve_=my_nms(output,iou_thresh) + output=output[reserve_] + output = restore_box(output,r,left,top) + return output + +def rec_plate(outputs,img0,session_rec): #识别车牌 + dict_list=[] + for output in outputs: + result_dict={} + rect=output[:4].tolist() + land_marks = output[5:13].reshape(4,2) + roi_img = four_point_transform(img0,land_marks) + label = int(output[-1]) + score = output[4] + if label==1: #代表是双层车牌 + roi_img = get_split_merge(roi_img) + plate_no = get_plate_result(roi_img,session_rec) + result_dict['rect']=rect + result_dict['landmarks']=land_marks.tolist() + result_dict['plate_no']=plate_no + result_dict['roi_height']=roi_img.shape[0] + dict_list.append(result_dict) + return dict_list + +def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20): #将识别结果画在图上 + if (isinstance(img, np.ndarray)): #判断是否OpenCV图片类型 + img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + draw = ImageDraw.Draw(img) + fontText = ImageFont.truetype( + "fonts/platech.ttf", textSize, encoding="utf-8") + draw.text((left, top), text, textColor, font=fontText) + return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) + +def draw_result(orgimg,dict_list): + result_str ="" + for result in dict_list: + rect_area = result['rect'] + + x,y,w,h = rect_area[0],rect_area[1],rect_area[2]-rect_area[0],rect_area[3]-rect_area[1] + padding_w = 0.05*w + padding_h = 0.11*h + rect_area[0]=max(0,int(x-padding_w)) + rect_area[1]=min(orgimg.shape[1],int(y-padding_h)) + rect_area[2]=max(0,int(rect_area[2]+padding_w)) + rect_area[3]=min(orgimg.shape[0],int(rect_area[3]+padding_h)) + + height_area = result['roi_height'] + landmarks=result['landmarks'] + result = result['plate_no'] + result_str+=result+" " + for i in range(4): #关键点 + cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1) + cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(0,0,255),2) #画框 + if len(result)>=1: + orgimg=cv2ImgAddText(orgimg,result,rect_area[0]-height_area,rect_area[1]-height_area-10,(255,0,0),height_area) + print(result_str) + return orgimg + +if __name__ == "__main__": + begin = time.time() + parser = argparse.ArgumentParser() + parser.add_argument('--detect_model',type=str, default=r'weights/plate_detect.onnx', help='model.pt path(s)') #检测模型 + parser.add_argument('--rec_model', type=str, default='weights/plate_rec.onnx', help='model.pt path(s)')#识别模型 + parser.add_argument('--image_path', type=str, default='imgs', help='source') + parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--output', type=str, default='result1', help='source') + opt = parser.parse_args() + file_list = [] + allFilePath(opt.image_path,file_list) + providers = ['CPUExecutionProvider'] + clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)] + img_size = (opt.img_size,opt.img_size) + session_detect = onnxruntime.InferenceSession(opt.detect_model, providers=providers ) + session_rec = onnxruntime.InferenceSession(opt.rec_model, providers=providers ) + if not os.path.exists(opt.output): + os.mkdir(opt.output) + save_path = opt.output + count = 0 + for pic_ in file_list: + count+=1 + print(count,pic_,end=" ") + img=cv2.imread(pic_) + img0 = copy.deepcopy(img) + img,r,left,top = detect_pre_precessing(img,img_size) #检测前处理 + # print(img.shape) + y_onnx = session_detect.run([session_detect.get_outputs()[0].name], {session_detect.get_inputs()[0].name: img})[0] + outputs = post_precessing(y_onnx,r,left,top) #检测后处理 + result_list=rec_plate(outputs,img0,session_rec) + ori_img = draw_result(img0,result_list) + img_name = os.path.basename(pic_) + save_img_path = os.path.join(save_path,img_name) + cv2.imwrite(save_img_path,ori_img) + print(f"总共耗时{time.time()-begin} s") + + + \ No newline at end of file diff --git a/algorithm/Car_recognition/openvino_infer.py b/algorithm/Car_recognition/openvino_infer.py new file mode 100644 index 0000000..032c65e --- /dev/null +++ b/algorithm/Car_recognition/openvino_infer.py @@ -0,0 +1,342 @@ +import cv2 +import matplotlib.pyplot as plt +import numpy as np +from openvino.runtime import Core +import os +import time +import copy +from PIL import Image, ImageDraw, ImageFont +import argparse + +def cv_imread(path): + img=cv2.imdecode(np.fromfile(path,dtype=np.uint8),-1) + return img + +def allFilePath(rootPath,allFIleList): + fileList = os.listdir(rootPath) + for temp in fileList: + if os.path.isfile(os.path.join(rootPath,temp)): + # if temp.endswith("jpg"): + allFIleList.append(os.path.join(rootPath,temp)) + else: + allFilePath(os.path.join(rootPath,temp),allFIleList) + +mean_value,std_value=((0.588,0.193))#识别模型均值标准差 +plateName=r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航危0123456789ABCDEFGHJKLMNPQRSTUVWXYZ险品" + +def rec_pre_precessing(img,size=(48,168)): #识别前处理 + img =cv2.resize(img,(168,48)) + img = img.astype(np.float32) + img = (img/255-mean_value)/std_value + img = img.transpose(2,0,1) + img = img.reshape(1,*img.shape) + return img + +def decodePlate(preds): #识别后处理 + pre=0 + newPreds=[] + preds=preds.astype(np.int8)[0] + for i in range(len(preds)): + if preds[i]!=0 and preds[i]!=pre: + newPreds.append(preds[i]) + pre=preds[i] + plate="" + for i in newPreds: + plate+=plateName[int(i)] + return plate + +def load_model(onnx_path): + ie = Core() + model_onnx = ie.read_model(model=onnx_path) + compiled_model_onnx = ie.compile_model(model=model_onnx, device_name="CPU") + output_layer_onnx = compiled_model_onnx.output(0) + return compiled_model_onnx,output_layer_onnx + +def get_plate_result(img,rec_model,rec_output): + img =rec_pre_precessing(img) + # time_b = time.time() + res_onnx = rec_model([img])[rec_output] + # time_e= time.time() + index =np.argmax(res_onnx,axis=-1) #找出最大概率的那个字符的序号 + plate_no = decodePlate(index) + # print(f'{plate_no},time is {time_e-time_b}') + return plate_no + + +def get_split_merge(img): #双层车牌进行分割后识别 + h,w,c = img.shape + img_upper = img[0:int(5/12*h),:] + img_lower = img[int(1/3*h):,:] + img_upper = cv2.resize(img_upper,(img_lower.shape[1],img_lower.shape[0])) + new_img = np.hstack((img_upper,img_lower)) + return new_img + + +def order_points(pts): + rect = np.zeros((4, 2), dtype = "float32") + s = pts.sum(axis = 1) + rect[0] = pts[np.argmin(s)] + rect[2] = pts[np.argmax(s)] + diff = np.diff(pts, axis = 1) + rect[1] = pts[np.argmin(diff)] + rect[3] = pts[np.argmax(diff)] + return rect + + +def four_point_transform(image, pts): + rect = order_points(pts) + (tl, tr, br, bl) = rect + widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) + widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) + maxWidth = max(int(widthA), int(widthB)) + heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) + heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) + maxHeight = max(int(heightA), int(heightB)) + dst = np.array([ + [0, 0], + [maxWidth - 1, 0], + [maxWidth - 1, maxHeight - 1], + [0, maxHeight - 1]], dtype = "float32") + M = cv2.getPerspectiveTransform(rect, dst) + warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) + + # return the warped image + return warped + +def my_letter_box(img,size=(640,640)): + h,w,c = img.shape + r = min(size[0]/h,size[1]/w) + new_h,new_w = int(h*r),int(w*r) + top = int((size[0]-new_h)/2) + left = int((size[1]-new_w)/2) + + bottom = size[0]-new_h-top + right = size[1]-new_w-left + img_resize = cv2.resize(img,(new_w,new_h)) + img = cv2.copyMakeBorder(img_resize,top,bottom,left,right,borderType=cv2.BORDER_CONSTANT,value=(114,114,114)) + return img,r,left,top + +def xywh2xyxy(boxes): + xywh =copy.deepcopy(boxes) + xywh[:,0]=boxes[:,0]-boxes[:,2]/2 + xywh[:,1]=boxes[:,1]-boxes[:,3]/2 + xywh[:,2]=boxes[:,0]+boxes[:,2]/2 + xywh[:,3]=boxes[:,1]+boxes[:,3]/2 + return xywh + +def my_nms(boxes,iou_thresh): + index = np.argsort(boxes[:,4])[::-1] + keep = [] + while index.size >0: + i = index[0] + keep.append(i) + x1=np.maximum(boxes[i,0],boxes[index[1:],0]) + y1=np.maximum(boxes[i,1],boxes[index[1:],1]) + x2=np.minimum(boxes[i,2],boxes[index[1:],2]) + y2=np.minimum(boxes[i,3],boxes[index[1:],3]) + + w = np.maximum(0,x2-x1) + h = np.maximum(0,y2-y1) + + inter_area = w*h + union_area = (boxes[i,2]-boxes[i,0])*(boxes[i,3]-boxes[i,1])+(boxes[index[1:],2]-boxes[index[1:],0])*(boxes[index[1:],3]-boxes[index[1:],1]) + iou = inter_area/(union_area-inter_area) + idx = np.where(iou<=iou_thresh)[0] + index = index[idx+1] + return keep + +def restore_box(boxes,r,left,top): + boxes[:,[0,2,5,7,9,11]]-=left + boxes[:,[1,3,6,8,10,12]]-=top + + boxes[:,[0,2,5,7,9,11]]/=r + boxes[:,[1,3,6,8,10,12]]/=r + return boxes + +def detect_pre_precessing(img,img_size): + img,r,left,top=my_letter_box(img,img_size) + # cv2.imwrite("1.jpg",img) + img =img[:,:,::-1].transpose(2,0,1).copy().astype(np.float32) + img=img/255 + img=img.reshape(1,*img.shape) + return img,r,left,top + +def post_precessing(dets,r,left,top,conf_thresh=0.3,iou_thresh=0.5):#检测后处理 + choice = dets[:,:,4]>conf_thresh + dets=dets[choice] + dets[:,13:15]*=dets[:,4:5] + box = dets[:,:4] + boxes = xywh2xyxy(box) + score= np.max(dets[:,13:15],axis=-1,keepdims=True) + index = np.argmax(dets[:,13:15],axis=-1).reshape(-1,1) + output = np.concatenate((boxes,score,dets[:,5:13],index),axis=1) + reserve_=my_nms(output,iou_thresh) + output=output[reserve_] + output = restore_box(output,r,left,top) + return output + +def rec_plate(outputs,img0,rec_model,rec_output): + dict_list=[] + for output in outputs: + result_dict={} + rect=output[:4].tolist() + land_marks = output[5:13].reshape(4,2) + roi_img = four_point_transform(img0,land_marks) + label = int(output[-1]) + if label==1: #代表是双层车牌 + roi_img = get_split_merge(roi_img) + plate_no = get_plate_result(roi_img,rec_model,rec_output) #得到车牌识别结果 + result_dict['rect']=rect + result_dict['landmarks']=land_marks.tolist() + result_dict['plate_no']=plate_no + result_dict['roi_height']=roi_img.shape[0] + dict_list.append(result_dict) + return dict_list + + + +def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20): + if (isinstance(img, np.ndarray)): #判断是否OpenCV图片类型 + img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + draw = ImageDraw.Draw(img) + fontText = ImageFont.truetype( + "fonts/platech.ttf", textSize, encoding="utf-8") + draw.text((left, top), text, textColor, font=fontText) + return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) + +def draw_result(orgimg,dict_list): + result_str ="" + for result in dict_list: + rect_area = result['rect'] + + x,y,w,h = rect_area[0],rect_area[1],rect_area[2]-rect_area[0],rect_area[3]-rect_area[1] + padding_w = 0.05*w + padding_h = 0.11*h + rect_area[0]=max(0,int(x-padding_w)) + rect_area[1]=min(orgimg.shape[1],int(y-padding_h)) + rect_area[2]=max(0,int(rect_area[2]+padding_w)) + rect_area[3]=min(orgimg.shape[0],int(rect_area[3]+padding_h)) + + height_area = result['roi_height'] + landmarks=result['landmarks'] + result = result['plate_no'] + result_str+=result+" " + # for i in range(4): #关键点 + # cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1) + + if len(result)>=6: + cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(0,0,255),2) #画框 + orgimg=cv2ImgAddText(orgimg,result,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area) + # print(result_str) + return orgimg + +def get_second(capture): + if capture.isOpened(): + rate = capture.get(5) # 帧速率 + FrameNumber = capture.get(7) # 视频文件的帧数 + duration = FrameNumber/rate # 帧速率/视频总帧数 是时间，除以60之后单位是分钟 + return int(rate),int(FrameNumber),int(duration) + +if __name__=="__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--detect_model',type=str, default=r'weights/plate_detect.onnx', help='model.pt path(s)') #检测模型 + parser.add_argument('--rec_model', type=str, default='weights/plate_rec.onnx', help='model.pt path(s)')#识别模型 + parser.add_argument('--image_path', type=str, default='imgs', help='source') + parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--output', type=str, default='result1', help='source') + opt = parser.parse_args() + file_list=[] + file_folder=opt.image_path + allFilePath(file_folder,file_list) + rec_onnx_path =opt.rec_model + detect_onnx_path=opt.detect_model + rec_model,rec_output=load_model(rec_onnx_path) + detect_model,detect_output=load_model(detect_onnx_path) + count=0 + img_size=(opt.img_size,opt.img_size) + begin=time.time() + save_path=opt.output + if not os.path.exists(save_path): + os.mkdir(save_path) + for pic_ in file_list: + + count+=1 + print(count,pic_,end=" ") + img=cv2.imread(pic_) + time_b = time.time() + if img.shape[-1]==4: + img = cv2.cvtColor(img,cv2.COLOR_BGRA2BGR) + img0 = copy.deepcopy(img) + img,r,left,top = detect_pre_precessing(img,img_size) #检测前处理 + # print(img.shape) + det_result = detect_model([img])[detect_output] + outputs = post_precessing(det_result,r,left,top) #检测后处理 + time_1 = time.time() + result_list=rec_plate(outputs,img0,rec_model,rec_output) + time_e= time.time() + print(f'耗时 {time_e-time_b} s') + ori_img = draw_result(img0,result_list) + img_name = os.path.basename(pic_) + save_img_path = os.path.join(save_path,img_name) + + cv2.imwrite(save_img_path,ori_img) +print(f"总共耗时{time.time()-begin} s") + + # video_name = r"plate.mp4" + # capture=cv2.VideoCapture(video_name) + # fourcc = cv2.VideoWriter_fourcc(*'MP4V') + # fps = capture.get(cv2.CAP_PROP_FPS) # 帧数 + # width, height = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) # 宽高 + # out = cv2.VideoWriter('2result.mp4', fourcc, fps, (width, height)) # 写入视频 + # frame_count = 0 + # fps_all=0 + # rate,FrameNumber,duration=get_second(capture) + # # with open("example.csv",mode='w',newline='') as example_file: + # # fieldnames = ['车牌', '时间'] + # # writer = csv.DictWriter(example_file, fieldnames=fieldnames, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) + # # writer.writeheader() + # if capture.isOpened(): + # while True: + # t1 = cv2.getTickCount() + # frame_count+=1 + # ret,img=capture.read() + # if not ret: + # break + # # if frame_count%rate==0: + # img0 = copy.deepcopy(img) + # img,r,left,top = detect_pre_precessing(img,img_size) #检测前处理 + # # print(img.shape) + # det_result = detect_model([img])[detect_output] + # outputs = post_precessing(det_result,r,left,top) #检测后处理 + # result_list=rec_plate(outputs,img0,rec_model,rec_output) + # ori_img = draw_result(img0,result_list) + # t2 =cv2.getTickCount() + # infer_time =(t2-t1)/cv2.getTickFrequency() + # fps=1.0/infer_time + # fps_all+=fps + # str_fps = f'fps:{fps:.4f}' + # out.write(ori_img) + # cv2.putText(ori_img,str_fps,(20,20),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2) + # cv2.imshow("haha",ori_img) + # cv2.waitKey(1) + + # # current_time = int(frame_count/FrameNumber*duration) + # # sec = current_time%60 + # # minute = current_time//60 + # # for result_ in result_list: + # # plate_no = result_['plate_no'] + # # if not is_car_number(pattern_str,plate_no): + # # continue + # # print(f'车牌号:{plate_no},时间:{minute}分{sec}秒') + # # time_str =f'{minute}分{sec}秒' + # # writer.writerow({"车牌":plate_no,"时间":time_str}) + # # out.write(ori_img) + + + # else: + # print("失败") + # capture.release() + # out.release() + # cv2.destroyAllWindows() + # print(f"all frame is {frame_count},average fps is {fps_all/frame_count}") + diff --git a/algorithm/Car_recognition/plate_recognition/color_rec.py b/algorithm/Car_recognition/plate_recognition/color_rec.py new file mode 100644 index 0000000..3c48816 --- /dev/null +++ b/algorithm/Car_recognition/plate_recognition/color_rec.py @@ -0,0 +1,74 @@ +import warnings +import cv2 +import torch +import numpy as np +import torch.nn as nn +from torchvision import transforms +from algorithm.Car_recognition.plate_recognition.plateNet import MyNet_color + + +class MyNet(nn.Module): + def __init__(self, class_num=6): + super(MyNet, self).__init__() + self.class_num = class_num + self.backbone = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(5, 5), stride=(1, 1)), # 0 + torch.nn.BatchNorm2d(16), + nn.ReLU(), + nn.MaxPool2d(kernel_size=(2, 2)), + nn.Dropout(0), + nn.Flatten(), + nn.Linear(480, 64), + nn.Dropout(0), + nn.ReLU(), + nn.Linear(64, class_num), + nn.Dropout(0), + nn.Softmax(1) + ) + + def forward(self, x): + logits = self.backbone(x) + + return logits + + +def init_color_model(model_path,device): + + # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # print("color_rec_device:", device) + # PATH = 'E:\study\plate\Chinese_license_plate_detection_recognition-main\weights\color_classify.pth' # 定义模型路径 + class_num = 6 + warnings.filterwarnings('ignore') + net = MyNet_color(class_num) + net.load_state_dict(torch.load(model_path, map_location=torch.device(device))) + net.eval().to(device) + modelc = net + + return modelc + + +def plate_color_rec(img,model,device): + class_name = ['黑色', '蓝色', '', '绿色', '白色', '黄色'] + data_input = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + image = cv2.resize(data_input, (34, 9)) + image = np.transpose(image, (2, 0, 1)) + img = image / 255 + img = torch.tensor(img) + + normalize = transforms.Normalize(mean=[0.4243, 0.4947, 0.434], + std=[0.2569, 0.2478, 0.2174]) + img = normalize(img) + img = torch.unsqueeze(img, dim=0).to(device).float() + xx = model(img) + + return class_name[int(torch.argmax(xx, dim=1)[0])] + + +if __name__ == '__main__': + class_name = ['black', 'blue', 'danger', 'green', 'white', 'yellow'] + data_input = cv2.imread("/mnt/Gpan/Mydata/pytorchPorject/myCrnnPlate/images/test.jpg") # (高，宽，通道(B，G，R)),（H,W,C） + device = torch.device("cuda" if torch.cuda.is_available else "cpu") + model = init_color_model("/mnt/Gpan/Mydata/pytorchPorject/Chinese_license_plate_detection_recognition/weights/color_classify.pth",device) + color_code = plate_color_rec(data_input,model,device) + print(color_code) + print(class_name[color_code]) diff --git a/algorithm/Car_recognition/plate_recognition/double_plate_split_merge.py b/algorithm/Car_recognition/plate_recognition/double_plate_split_merge.py new file mode 100644 index 0000000..24c6537 --- /dev/null +++ b/algorithm/Car_recognition/plate_recognition/double_plate_split_merge.py @@ -0,0 +1,15 @@ +import os +import cv2 +import numpy as np +def get_split_merge(img): + h,w,c = img.shape + img_upper = img[0:int(5/12*h),:] + img_lower = img[int(1/3*h):,:] + img_upper = cv2.resize(img_upper,(img_lower.shape[1],img_lower.shape[0])) + new_img = np.hstack((img_upper,img_lower)) + return new_img + +if __name__=="__main__": + img = cv2.imread("double_plate/tmp8078.png") + new_img =get_split_merge(img) + cv2.imwrite("double_plate/new.jpg",new_img) diff --git a/algorithm/Car_recognition/plate_recognition/plateNet.py b/algorithm/Car_recognition/plate_recognition/plateNet.py new file mode 100644 index 0000000..b4e8979 --- /dev/null +++ b/algorithm/Car_recognition/plate_recognition/plateNet.py @@ -0,0 +1,210 @@ +import torch.nn as nn +import torch +import torch.nn.functional as F + + +class myNet_ocr(nn.Module): + def __init__(self,cfg=None,num_classes=78,export=False): + super(myNet_ocr, self).__init__() + if cfg is None: + cfg =[32,32,64,64,'M',128,128,'M',196,196,'M',256,256] + # cfg =[32,32,'M',64,64,'M',128,128,'M',256,256] + self.feature = self.make_layers(cfg, True) + self.export = export + # self.classifier = nn.Linear(cfg[-1], num_classes) + # self.loc = nn.MaxPool2d((2, 2), (5, 1), (0, 1),ceil_mode=True) + # self.loc = nn.AvgPool2d((2, 2), (5, 2), (0, 1),ceil_mode=False) + self.loc = nn.MaxPool2d((5, 2), (1, 1),(0,1),ceil_mode=False) + self.newCnn=nn.Conv2d(cfg[-1],num_classes,1,1) + # self.newBn=nn.BatchNorm2d(num_classes) + def make_layers(self, cfg, batch_norm=False): + layers = [] + in_channels = 3 + for i in range(len(cfg)): + if i == 0: + conv2d =nn.Conv2d(in_channels, cfg[i], kernel_size=5,stride =1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = cfg[i] + else : + if cfg[i] == 'M': + layers += [nn.MaxPool2d(kernel_size=3, stride=2,ceil_mode=True)] + else: + conv2d = nn.Conv2d(in_channels, cfg[i], kernel_size=3, padding=(1,1),stride =1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = cfg[i] + return nn.Sequential(*layers) + + def forward(self, x): + x = self.feature(x) + x=self.loc(x) + x=self.newCnn(x) + # x=self.newBn(x) + if self.export: + conv = x.squeeze(2) # b *512 * width + conv = conv.transpose(2,1) # [w, b, c] + conv =conv.argmax(dim=2) + return conv + else: + b, c, h, w = x.size() + assert h == 1, "the height of conv must be 1" + conv = x.squeeze(2) # b *512 * width + conv = conv.permute(2, 0, 1) # [w, b, c] + # output = F.log_softmax(self.rnn(conv), dim=2) + output = torch.softmax(conv, dim=2) + return output + +myCfg = [32,'M',64,'M',96,'M',128,'M',256] +class myNet(nn.Module): + def __init__(self,cfg=None,num_classes=3): + super(myNet, self).__init__() + if cfg is None: + cfg = myCfg + self.feature = self.make_layers(cfg, True) + self.classifier = nn.Linear(cfg[-1], num_classes) + def make_layers(self, cfg, batch_norm=False): + layers = [] + in_channels = 3 + for i in range(len(cfg)): + if i == 0: + conv2d =nn.Conv2d(in_channels, cfg[i], kernel_size=5,stride =1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = cfg[i] + else : + if cfg[i] == 'M': + layers += [nn.MaxPool2d(kernel_size=3, stride=2,ceil_mode=True)] + else: + conv2d = nn.Conv2d(in_channels, cfg[i], kernel_size=3, padding=1,stride =1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = cfg[i] + return nn.Sequential(*layers) + + def forward(self, x): + x = self.feature(x) + x = nn.AvgPool2d(kernel_size=3, stride=1)(x) + x = x.view(x.size(0), -1) + y = self.classifier(x) + return y + + +class MyNet_color(nn.Module): + def __init__(self, class_num=6): + super(MyNet_color, self).__init__() + self.class_num = class_num + self.backbone = nn.Sequential( + nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(5, 5), stride=(1, 1)), # 0 + torch.nn.BatchNorm2d(16), + nn.ReLU(), + nn.MaxPool2d(kernel_size=(2, 2)), + nn.Dropout(0), + nn.Flatten(), + nn.Linear(480, 64), + nn.Dropout(0), + nn.ReLU(), + nn.Linear(64, class_num), + nn.Dropout(0), + nn.Softmax(1) + ) + + def forward(self, x): + logits = self.backbone(x) + + return logits + + +class myNet_ocr_color(nn.Module): + def __init__(self,cfg=None,num_classes=78,export=False,color_num=None): + super(myNet_ocr_color, self).__init__() + if cfg is None: + cfg =[32,32,64,64,'M',128,128,'M',196,196,'M',256,256] + # cfg =[32,32,'M',64,64,'M',128,128,'M',256,256] + self.feature = self.make_layers(cfg, True) + self.export = export + self.color_num=color_num + self.conv_out_num=12 #颜色第一个卷积层输出通道12 + if self.color_num: + self.conv1=nn.Conv2d(cfg[-1],self.conv_out_num,kernel_size=3,stride=2) + self.bn1=nn.BatchNorm2d(self.conv_out_num) + self.relu1=nn.ReLU(inplace=True) + self.gap =nn.AdaptiveAvgPool2d(output_size=1) + self.color_classifier=nn.Conv2d(self.conv_out_num,self.color_num,kernel_size=1,stride=1) + self.color_bn = nn.BatchNorm2d(self.color_num) + self.flatten = nn.Flatten() + # self.relu = nn.ReLU(inplace=True) + # self.classifier = nn.Linear(cfg[-1], num_classes) + # self.loc = nn.MaxPool2d((2, 2), (5, 1), (0, 1),ceil_mode=True) + # self.loc = nn.AvgPool2d((2, 2), (5, 2), (0, 1),ceil_mode=False) + self.loc = nn.MaxPool2d((5, 2), (1, 1),(0,1),ceil_mode=False) + self.newCnn=nn.Conv2d(cfg[-1],num_classes,1,1) + # self.newBn=nn.BatchNorm2d(num_classes) + def make_layers(self, cfg, batch_norm=False): + layers = [] + in_channels = 3 + for i in range(len(cfg)): + if i == 0: + conv2d =nn.Conv2d(in_channels, cfg[i], kernel_size=5,stride =1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = cfg[i] + else : + if cfg[i] == 'M': + layers += [nn.MaxPool2d(kernel_size=3, stride=2,ceil_mode=True)] + else: + conv2d = nn.Conv2d(in_channels, cfg[i], kernel_size=3, padding=(1,1),stride =1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = cfg[i] + return nn.Sequential(*layers) + + def forward(self, x): + x = self.feature(x) + if self.color_num: + x_color=self.conv1(x) + x_color=self.bn1(x_color) + x_color =self.relu1(x_color) + x_color = self.color_classifier(x_color) + x_color = self.color_bn(x_color) + x_color =self.gap(x_color) + x_color = self.flatten(x_color) + x=self.loc(x) + x=self.newCnn(x) + + if self.export: + conv = x.squeeze(2) # b *512 * width + conv = conv.transpose(2,1) # [w, b, c] + if self.color_num: + return conv,x_color + return conv + else: + b, c, h, w = x.size() + assert h == 1, "the height of conv must be 1" + conv = x.squeeze(2) # b *512 * width + conv = conv.permute(2, 0, 1) # [w, b, c] + output = F.log_softmax(conv, dim=2) + if self.color_num: + return output,x_color + return output + + + + +if __name__ == '__main__': + x = torch.randn(1,3,48,216) + model = myNet_ocr(num_classes=78,export=True) + out = model(x) + print(out.shape) \ No newline at end of file diff --git a/algorithm/Car_recognition/plate_recognition/plate_rec.py b/algorithm/Car_recognition/plate_recognition/plate_rec.py new file mode 100644 index 0000000..aca9e22 --- /dev/null +++ b/algorithm/Car_recognition/plate_recognition/plate_rec.py @@ -0,0 +1,103 @@ +from algorithm.Car_recognition.plate_recognition.plateNet import myNet_ocr,myNet_ocr_color +import torch +import torch.nn as nn +import cv2 +import numpy as np +import os +import time +import sys + +def cv_imread(path): #可以读取中文路径的图片 + img=cv2.imdecode(np.fromfile(path,dtype=np.uint8),-1) + return img + +def allFilePath(rootPath,allFIleList): + fileList = os.listdir(rootPath) + for temp in fileList: + if os.path.isfile(os.path.join(rootPath,temp)): + if temp.endswith('.jpg') or temp.endswith('.png') or temp.endswith('.JPG'): + allFIleList.append(os.path.join(rootPath,temp)) + else: + allFilePath(os.path.join(rootPath,temp),allFIleList) +device = torch.device('cuda') if torch.cuda.is_available() else torch.device("cpu") +plateName=r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航危0123456789ABCDEFGHJKLMNPQRSTUVWXYZ险品" +color_list=['黑色','蓝色','绿色','白色','黄色'] +mean_value,std_value=(0.588,0.193) +def decodePlate(preds): + pre=0 + newPreds=[] + for i in range(len(preds)): + if preds[i]!=0 and preds[i]!=pre: + newPreds.append(preds[i]) + pre=preds[i] + return newPreds + +def image_processing(img,device): + img = cv2.resize(img, (168,48)) + img = np.reshape(img, (48, 168, 3)) + + # normalize + img = img.astype(np.float32) + img = (img / 255. - mean_value) / std_value + img = img.transpose([2, 0, 1]) + img = torch.from_numpy(img) + + img = img.to(device) + img = img.view(1, *img.size()) + return img + +def get_plate_result(img,device,model): + input = image_processing(img,device) + preds,color_preds = model(input) + preds =preds.argmax(dim=2) #找出概率最大的那个字符 + color_preds = color_preds.argmax(dim=-1) + # print(preds) + preds=preds.view(-1).detach().cpu().numpy() + color_preds=color_preds.item() + newPreds=decodePlate(preds) + plate="" + for i in newPreds: + plate+=plateName[i] + # if not (plate[0] in plateName[1:44] ): + # return "" + return plate,color_list[color_preds] + +def init_model(model_path): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # print( print(sys.path)) + # model_path ="plate_recognition/model/checkpoint_61_acc_0.9715.pth" + check_point = torch.load(model_path,map_location=device) + model_state=check_point['state_dict'] + cfg=check_point['cfg'] + model_path = os.sep.join([sys.path[0],model_path]) + model = myNet_ocr_color(num_classes=len(plateName),export=True,cfg=cfg,color_num=len(color_list)) + + model.load_state_dict(model_state) + model.to(device) + model.eval() + return model + +# model = init_model(device) +if __name__ == '__main__': + + image_path ="images/tmp2424.png" + testPath = r"double_plate" + fileList=[] + allFilePath(testPath,fileList) +# result = get_plate_result(image_path,device) +# print(result) + model = init_model(device) + right=0 + begin = time.time() + for imge_path in fileList: + plate=get_plate_result(imge_path) + plate_ori = imge_path.split('/')[-1].split('_')[0] + # print(plate,"---",plate_ori) + if(plate==plate_ori): + + right+=1 + else: + print(plate_ori,"--->",plate,imge_path) + end=time.time() + print("sum:%d ,right:%d , accuracy: %f, time: %f"%(len(fileList),right,right/len(fileList),end-begin)) + diff --git a/algorithm/Car_recognition/requirements.txt b/algorithm/Car_recognition/requirements.txt new file mode 100644 index 0000000..7b04708 --- /dev/null +++ b/algorithm/Car_recognition/requirements.txt @@ -0,0 +1,47 @@ +asttokens +backcall +charset-normalizer +cycler +dataclasses +debugpy +decorator +executing +fonttools +idna +ipykernel +ipython +jedi +jupyter-client +jupyter-core +kiwisolver +matplotlib +matplotlib-inline +nest-asyncio +numpy +opencv-python +packaging +pandas +parso +pickleshare +Pillow +prompt-toolkit +psutil +pure-eval +Pygments +pyparsing +python-dateutil +pytz +PyYAML +pyzmq +requests +scipy +seaborn +six +stack-data +thop +tornado +tqdm +traitlets +typing-extensions +urllib3 +wcwidth \ No newline at end of file diff --git a/algorithm/Car_recognition/test.py b/algorithm/Car_recognition/test.py new file mode 100644 index 0000000..59d1831 --- /dev/null +++ b/algorithm/Car_recognition/test.py @@ -0,0 +1,336 @@ +import argparse +import json +import os +from pathlib import Path +from threading import Thread + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +from models.experimental import attempt_load +from utils.datasets import create_dataloader +from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, box_iou, \ + non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, non_max_suppression_face +from utils.loss import compute_loss +from utils.metrics import ap_per_class, ConfusionMatrix +from utils.plots import plot_images, output_to_target, plot_study_txt +from utils.torch_utils import select_device, time_synchronized + + +def test(data, + weights=None, + batch_size=32, + imgsz=640, + conf_thres=0.001, + iou_thres=0.6, # for NMS + save_json=False, + single_cls=False, + augment=False, + verbose=False, + model=None, + dataloader=None, + save_dir=Path(''), # for saving images + save_txt=False, # for auto-labelling + save_hybrid=False, # for hybrid auto-labelling + save_conf=False, # save auto-label confidences + plots=True, + log_imgs=0): # number of logged images + + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device = next(model.parameters()).device # get model device + + else: # called directly + set_logging() + device = select_device(opt.device, batch_size=batch_size) + + # Directories + save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = attempt_load(weights, map_location=device) # load FP32 model + imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + + # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99 + # if device.type != 'cpu' and torch.cuda.device_count() > 1: + # model = nn.DataParallel(model) + + # Half + half = device.type != 'cpu' # half precision only supported on CUDA + if half: + model.half() + + # Configure + model.eval() + is_coco = data.endswith('coco.yaml') # is COCO dataset + with open(data) as f: + data = yaml.load(f, Loader=yaml.FullLoader) # model dict + check_dataset(data) # check + nc = 1 if single_cls else int(data['nc']) # number of classes + iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Logging + log_imgs, wandb = min(log_imgs, 100), None # ceil + try: + import wandb # Weights & Biases + except ImportError: + log_imgs = 0 + + # Dataloader + if not training: + img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img + _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once + path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images + dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[0] + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)} + coco91class = coco80_to_coco91_class() + s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') + p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. + loss = torch.zeros(3, device=device) + jdict, stats, ap, ap_class, wandb_images = [], [], [], [], [] + for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): + img = img.to(device, non_blocking=True) + img = img.half() if half else img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + targets = targets.to(device) + nb, _, height, width = img.shape # batch size, channels, height, width + + with torch.no_grad(): + # Run model + t = time_synchronized() + inf_out, train_out = model(img, augment=augment) # inference and training outputs + t0 += time_synchronized() - t + + # Compute loss + if training: + loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls + + # Run NMS + targets[:, 2:6] *= torch.Tensor([width, height, width, height]).to(device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + t = time_synchronized() + #output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb) + output = non_max_suppression_face(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb) + t1 += time_synchronized() - t + + # Statistics per image + for si, pred in enumerate(output): + pred = torch.cat((pred[:, :5], pred[:, 13:]), 1) # throw landmark in thresh + labels = targets[targets[:, 0] == si, 1:] + nl = len(labels) + tcls = labels[:, 0].tolist() if nl else [] # target class + path = Path(paths[si]) + seen += 1 + + if len(pred) == 0: + if nl: + stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) + continue + + # Predictions + predn = pred.clone() + scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred + + # Append to text file + if save_txt: + gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh + for *xyxy, conf, cls in predn.tolist(): + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + # W&B logging + if plots and len(wandb_images) < log_imgs: + box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name)) + + # Append to pycocotools JSON dictionary + if save_json: + # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ... + image_id = int(path.stem) if path.stem.isnumeric() else path.stem + box = xyxy2xywh(predn[:, :4]) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + for p, b in zip(pred.tolist(), box.tolist()): + jdict.append({'image_id': image_id, + 'category_id': coco91class[int(p[15])] if is_coco else int(p[15]), + 'bbox': [round(x, 3) for x in b], + 'score': round(p[4], 5)}) + + # Assign all predictions as incorrect + correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) + if nl: + detected = [] # target indices + tcls_tensor = labels[:, 0] + + # target boxes + tbox = xywh2xyxy(labels[:, 1:5]) + scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels + if plots: + confusion_matrix.process_batch(pred, torch.cat((labels[:, 0:1], tbox), 1)) + + # Per target class + for cls in torch.unique(tcls_tensor): + ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices + pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices + + # Search for detections + if pi.shape[0]: + # Prediction to target ious + ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices + + # Append detections + detected_set = set() + for j in (ious > iouv[0]).nonzero(as_tuple=False): + d = ti[i[j]] # detected target + if d.item() not in detected_set: + detected_set.add(d.item()) + detected.append(d) + correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn + if len(detected) == nl: # all targets already located in image + break + + # Append statistics (correct, conf, pcls, tcls) + stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) + + # Plot images + if plots and batch_i < 3: + f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels + Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start() + f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions + Thread(target=plot_images, args=(img, output_to_target(output), paths, f, names), daemon=True).start() + + # Compute statistics + stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) + p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95] + mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() + nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class + else: + nt = torch.zeros(1) + + # Print results + pf = '%20s' + '%12.3g' * 6 # print format + print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) + + # Print results per class + if verbose and nc > 1 and len(stats): + for i, c in enumerate(ap_class): + print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) + + # Print speeds + t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple + if not training: + print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + if wandb and wandb.run: + wandb.log({"Images": wandb_images}) + wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]}) + + # Save JSON + if save_json and len(jdict): + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights + anno_json = '../coco/annotations/instances_val2017.json' # annotations json + pred_json = str(save_dir / f"{w}_predictions.json") # predictions json + print('\nEvaluating pycocotools mAP... saving %s...' % pred_json) + with open(pred_json, 'w') as f: + json.dump(jdict, f) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + anno = COCO(anno_json) # init annotations api + pred = anno.loadRes(pred_json) # init predictions api + eval = COCOeval(anno, pred, 'bbox') + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) + except Exception as e: + print(f'pycocotools unable to run: {e}') + + # Return results + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + print(f"Results saved to {save_dir}{s}") + model.float() # for training + maps = np.zeros(nc) + map + for i, c in enumerate(ap_class): + maps[c] = ap[i] + return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(prog='test.py') + parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path') + parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch') + parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS') + parser.add_argument('--task', default='val', help="'val', 'test', 'study'") + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--verbose', action='store_true', help='report mAP by class') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') + parser.add_argument('--project', default='runs/test', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + opt = parser.parse_args() + opt.save_json |= opt.data.endswith('coco.yaml') + opt.data = check_file(opt.data) # check file + print(opt) + + if opt.task in ['val', 'test']: # run normally + test(opt.data, + opt.weights, + opt.batch_size, + opt.img_size, + opt.conf_thres, + opt.iou_thres, + opt.save_json, + opt.single_cls, + opt.augment, + opt.verbose, + save_txt=opt.save_txt | opt.save_hybrid, + save_hybrid=opt.save_hybrid, + save_conf=opt.save_conf, + ) + + elif opt.task == 'study': # run over a range of settings and save/plot + for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: + f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to + x = list(range(320, 800, 64)) # x axis + y = [] # y axis + for i in x: # img-size + print('\nRunning %s point %s...' % (f, i)) + r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, + plots=False) + y.append(r + t) # results and times + np.savetxt(f, y, fmt='%10.4g') # save + os.system('zip -r study.zip study_*.txt') + plot_study_txt(f, x) # plot diff --git a/algorithm/Car_recognition/test_widerface.py b/algorithm/Car_recognition/test_widerface.py new file mode 100644 index 0000000..31bb113 --- /dev/null +++ b/algorithm/Car_recognition/test_widerface.py @@ -0,0 +1,170 @@ +import argparse +import glob +import time +from pathlib import Path + +import os +import cv2 +import torch +import torch.backends.cudnn as cudnn +from numpy import random +import numpy as np +from models.experimental import attempt_load +from utils.datasets import letterbox +from utils.general import check_img_size, check_requirements, non_max_suppression_face, apply_classifier, \ + scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path +from utils.plots import plot_one_box +from utils.torch_utils import select_device, load_classifier, time_synchronized +from tqdm import tqdm + +def dynamic_resize(shape, stride=64): + max_size = max(shape[0], shape[1]) + if max_size % stride != 0: + max_size = (int(max_size / stride) + 1) * stride + return max_size + +def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2, 4, 6, 8]] -= pad[0] # x padding + coords[:, [1, 3, 5, 7, 9]] -= pad[1] # y padding + coords[:, :10] /= gain + #clip_coords(coords, img0_shape) + coords[:, 0].clamp_(0, img0_shape[1]) # x1 + coords[:, 1].clamp_(0, img0_shape[0]) # y1 + coords[:, 2].clamp_(0, img0_shape[1]) # x2 + coords[:, 3].clamp_(0, img0_shape[0]) # y2 + coords[:, 4].clamp_(0, img0_shape[1]) # x3 + coords[:, 5].clamp_(0, img0_shape[0]) # y3 + coords[:, 6].clamp_(0, img0_shape[1]) # x4 + coords[:, 7].clamp_(0, img0_shape[0]) # y4 + coords[:, 8].clamp_(0, img0_shape[1]) # x5 + coords[:, 9].clamp_(0, img0_shape[0]) # y5 + return coords + +def show_results(img, xywh, conf, landmarks, class_num): + h,w,c = img.shape + tl = 1 or round(0.002 * (h + w) / 2) + 1 # line/font thickness + x1 = int(xywh[0] * w - 0.5 * xywh[2] * w) + y1 = int(xywh[1] * h - 0.5 * xywh[3] * h) + x2 = int(xywh[0] * w + 0.5 * xywh[2] * w) + y2 = int(xywh[1] * h + 0.5 * xywh[3] * h) + cv2.rectangle(img, (x1,y1), (x2, y2), (0,255,0), thickness=tl, lineType=cv2.LINE_AA) + + clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)] + + for i in range(5): + point_x = int(landmarks[2 * i] * w) + point_y = int(landmarks[2 * i + 1] * h) + cv2.circle(img, (point_x, point_y), tl+1, clors[i], -1) + + tf = max(tl - 1, 1) # font thickness + label = str(int(class_num)) + ': ' + str(conf)[:5] + cv2.putText(img, label, (x1, y1 - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + return img + +def detect(model, img0): + stride = int(model.stride.max()) # model stride + imgsz = opt.img_size + if imgsz <= 0: # original size + imgsz = dynamic_resize(img0.shape) + imgsz = check_img_size(imgsz, s=64) # check img_size + img = letterbox(img0, imgsz)[0] + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + img = torch.from_numpy(img).to(device) + img = img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + + # Inference + pred = model(img, augment=opt.augment)[0] + # Apply NMS + pred = non_max_suppression_face(pred, opt.conf_thres, opt.iou_thres)[0] + gn = torch.tensor(img0.shape)[[1, 0, 1, 0]].to(device) # normalization gain whwh + gn_lks = torch.tensor(img0.shape)[[1, 0, 1, 0, 1, 0, 1, 0, 1, 0]].to(device) # normalization gain landmarks + boxes = [] + h, w, c = img0.shape + if pred is not None: + pred[:, :4] = scale_coords(img.shape[2:], pred[:, :4], img0.shape).round() + pred[:, 5:15] = scale_coords_landmarks(img.shape[2:], pred[:, 5:15], img0.shape).round() + for j in range(pred.size()[0]): + xywh = (xyxy2xywh(pred[j, :4].view(1, 4)) / gn).view(-1) + xywh = xywh.data.cpu().numpy() + conf = pred[j, 4].cpu().numpy() + landmarks = (pred[j, 5:15].view(1, 10) / gn_lks).view(-1).tolist() + class_num = pred[j, 15].cpu().numpy() + x1 = int(xywh[0] * w - 0.5 * xywh[2] * w) + y1 = int(xywh[1] * h - 0.5 * xywh[3] * h) + x2 = int(xywh[0] * w + 0.5 * xywh[2] * w) + y2 = int(xywh[1] * h + 0.5 * xywh[3] * h) + boxes.append([x1, y1, x2-x1, y2-y1, conf]) + return boxes + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp5/weights/last.pt', help='model.pt path(s)') + parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.02, help='object confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') + parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') + parser.add_argument('--project', default='runs/detect', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--save_folder', default='./widerface_evaluate/widerface_txt/', type=str, help='Dir to save txt results') + parser.add_argument('--dataset_folder', default='../WiderFace/val/images/', type=str, help='dataset path') + parser.add_argument('--folder_pict', default='/yolov5-face/data/widerface/val/wider_val.txt', type=str, help='folder_pict') + opt = parser.parse_args() + print(opt) + + # changhy : read folder_pict + pict_folder = {} + with open(opt.folder_pict, 'r') as f: + lines = f.readlines() + for line in lines: + line = line.strip().split('/') + pict_folder[line[-1]] = line[-2] + + # Load model + device = select_device(opt.device) + model = attempt_load(opt.weights, map_location=device) # load FP32 model + with torch.no_grad(): + # testing dataset + testset_folder = opt.dataset_folder + + for image_path in tqdm(glob.glob(os.path.join(testset_folder, '*'))): + if image_path.endswith('.txt'): + continue + img0 = cv2.imread(image_path) # BGR + if img0 is None: + print(f'ignore : {image_path}') + continue + boxes = detect(model, img0) + # -------------------------------------------------------------------- + image_name = os.path.basename(image_path) + txt_name = os.path.splitext(image_name)[0] + ".txt" + save_name = os.path.join(opt.save_folder, pict_folder[image_name], txt_name) + dirname = os.path.dirname(save_name) + if not os.path.isdir(dirname): + os.makedirs(dirname) + with open(save_name, "w") as fd: + file_name = os.path.basename(save_name)[:-4] + "\n" + bboxs_num = str(len(boxes)) + "\n" + fd.write(file_name) + fd.write(bboxs_num) + for box in boxes: + fd.write('%d %d %d %d %.03f' % (box[0], box[1], box[2], box[3], box[4] if box[4] <= 1 else 1) + '\n') + print('done.') diff --git a/algorithm/Car_recognition/train.py b/algorithm/Car_recognition/train.py new file mode 100644 index 0000000..e6dea20 --- /dev/null +++ b/algorithm/Car_recognition/train.py @@ -0,0 +1,602 @@ +import argparse +import logging +import math +import os +import random +import time +from pathlib import Path +from threading import Thread +from warnings import warn + +import numpy as np +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torch.optim.lr_scheduler as lr_scheduler +import torch.utils.data +import yaml +from torch.cuda import amp +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +import test # import test.py to get mAP after each epoch +from models.experimental import attempt_load +from models.yolo import Model +from utils.autoanchor import check_anchors +from utils.face_datasets import create_dataloader +from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ + fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ + print_mutation, set_logging +from utils.google_utils import attempt_download +from utils.loss import compute_loss +from utils.plots import plot_images, plot_labels, plot_results, plot_evolution +from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first + +logger = logging.getLogger(__name__) +begin_save=1 +try: + import wandb +except ImportError: + wandb = None + logger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)") + + +def train(hyp, opt, device, tb_writer=None, wandb=None): + logger.info(f'Hyperparameters {hyp}') + save_dir, epochs, batch_size, total_batch_size, weights, rank = \ + Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank + + # Directories + wdir = save_dir / 'weights' + wdir.mkdir(parents=True, exist_ok=True) # make dir + last = wdir / 'last.pt' + best = wdir / 'best.pt' + results_file = save_dir / 'results.txt' + + # Save run settings + with open(save_dir / 'hyp.yaml', 'w') as f: + yaml.dump(hyp, f, sort_keys=False) + with open(save_dir / 'opt.yaml', 'w') as f: + yaml.dump(vars(opt), f, sort_keys=False) + + # Configure + plots = not opt.evolve # create plots + cuda = device.type != 'cpu' + init_seeds(2 + rank) + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict + with torch_distributed_zero_first(rank): + check_dataset(data_dict) # check + train_path = data_dict['train'] + test_path = data_dict['val'] + nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes + names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names + assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check + + # Model + pretrained = weights.endswith('.pt') + if pretrained: + with torch_distributed_zero_first(rank): + attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location=device) # load checkpoint + if hyp.get('anchors'): + ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor + model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create + exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys + state_dict = ckpt['model'].float().state_dict() # to FP32 + state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(state_dict, strict=False) # load + logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report + else: + model = Model(opt.cfg, ch=3, nc=nc).to(device) # create + + # Freeze + freeze = [] # parameter names to freeze (full or partial) + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + if any(x in k for x in freeze): + print('freezing %s' % k) + v.requires_grad = False + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay + + pg0, pg1, pg2 = [], [], [] # optimizer parameter groups + for k, v in model.named_modules(): + if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): + pg2.append(v.bias) # biases + if isinstance(v, nn.BatchNorm2d): + pg0.append(v.weight) # no decay + elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): + pg1.append(v.weight) # apply decay + + if opt.adam: + optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum + else: + optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) + + optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay + optimizer.add_param_group({'params': pg2}) # add pg2 (biases) + logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) + del pg0, pg1, pg2 + + # Scheduler https://arxiv.org/pdf/1812.01187.pdf + # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR + lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf'] # cosine + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # plot_lr_scheduler(optimizer, scheduler, epochs) + + # Logging + if wandb and wandb.run is None: + opt.hyp = hyp # add hyperparameters + wandb_run = wandb.init(config=opt, resume="allow", + project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, + name=save_dir.stem, + id=ckpt.get('wandb_id') if 'ckpt' in locals() else None) + loggers = {'wandb': wandb} # loggers dict + + # Resume + start_epoch, best_fitness = 0, 0.0 + if pretrained: + # Optimizer + if ckpt['optimizer'] is not None: + optimizer.load_state_dict(ckpt['optimizer']) + best_fitness = 0 + + # Results + if ckpt.get('training_results') is not None: + with open(results_file, 'w') as file: + file.write(ckpt['training_results']) # write results.txt + + # Epochs + # start_epoch = ckpt['epoch'] + 1 + if opt.resume: + assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) + if epochs < start_epoch: + logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % + (weights, ckpt['epoch'], epochs)) + epochs += ckpt['epoch'] # finetune additional epochs + + del ckpt, state_dict + + # Image sizes + gs = int(max(model.stride)) # grid size (max stride) + imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples + + # DP mode + if cuda and rank == -1 and torch.cuda.device_count() > 1: + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and rank != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + logger.info('Using SyncBatchNorm()') + + # EMA + ema = ModelEMA(model) if rank in [-1, 0] else None + + # DDP mode + if cuda and rank != -1: + model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank) + + # Trainloader + dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, + hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, + world_size=opt.world_size, workers=opt.workers, + image_weights=opt.image_weights) + mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class + nb = len(dataloader) # number of batches + assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) + + # Process 0 + if rank in [-1, 0]: + ema.updates = start_epoch * nb // accumulate # set EMA updates + testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, # testloader + hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, + rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5)[0] + + if not opt.resume: + labels = np.concatenate(dataset.labels, 0) + c = torch.tensor(labels[:, 0]) # classes + # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency + # model._initialize_biases(cf.to(device)) + if plots: + plot_labels(labels, save_dir, loggers) + if tb_writer: + tb_writer.add_histogram('classes', c, 0) + + # Anchors + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) + + # Model parameters + hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = amp.GradScaler(enabled=cuda) + logger.info('Image sizes %g train, %g test\n' + 'Using %g dataloader workers\nLogging results to %s\n' + 'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs)) + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + model.train() + + # Update image weights (optional) + if opt.image_weights: + # Generate indices + if rank in [-1, 0]: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + # Broadcast if DDP + if rank != -1: + indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int() + dist.broadcast(indices, 0) + if rank != 0: + dataset.indices = indices.cpu().numpy() + + # Update mosaic border + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(5, device=device) # mean losses + if rank != -1: + dataloader.sampler.set_epoch(epoch) + pbar = enumerate(dataloader) + logger.info(('\n' + '%10s' * 9) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'landmark', 'total', 'targets', 'img_size')) + if rank in [-1, 0]: + pbar = tqdm(pbar, total=nb) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + with amp.autocast(enabled=cuda): + pred = model(imgs) # forward + loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size + if rank != -1: + loss *= opt.world_size # gradient averaged between devices in DDP mode + + # Backward + scaler.scale(loss).backward() + + # Optimize + if ni % accumulate == 0: + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + # Print + if rank in [-1, 0]: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) + s = ('%10s' * 2 + '%10.4g' * 7) % ( + '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) + pbar.set_description(s) + + # Plot + if plots and ni < 3: + f = save_dir / f'train_batch{ni}.jpg' # filename + Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() + # if tb_writer: + # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) + # tb_writer.add_graph(model, imgs) # add model to tensorboard + elif plots and ni == 3 and wandb: + wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]}) + + # end batch ------------------------------------------------------------------------------------------------ + # end epoch ---------------------------------------------------------------------------------------------------- + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard + scheduler.step() + + # DDP process 0 or single-GPU + if rank in [-1, 0] and epoch > begin_save: + # mAP + if ema: + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) + final_epoch = epoch + 1 == epochs + if not opt.notest or final_epoch: # Calculate mAP + results, maps, times = test.test(opt.data, + batch_size=total_batch_size, + imgsz=imgsz_test, + model=ema.ema, + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=save_dir, + plots=False, + log_imgs=opt.log_imgs if wandb else 0) + + # Write + with open(results_file, 'a') as f: + f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + if len(opt.name) and opt.bucket: + os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) + + # Log + tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss + 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', + 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss + 'x/lr0', 'x/lr1', 'x/lr2'] # params + for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): + if tb_writer: + tb_writer.add_scalar(tag, x, epoch) # tensorboard + if wandb: + wandb.log({tag: x}) # W&B + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + if fi > best_fitness: + best_fitness = fi + + # Save model + save = (not opt.nosave) or (final_epoch and not opt.evolve) + if save: + with open(results_file, 'r') as f: # create checkpoint + ckpt = {'epoch': epoch, + 'best_fitness': best_fitness, + 'training_results': f.read(), + 'model': ema.ema, + 'optimizer': None if final_epoch else optimizer.state_dict(), + 'wandb_id': wandb_run.id if wandb else None} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + ckpt_best = { + 'epoch': epoch, + 'best_fitness': best_fitness, + # 'training_results': f.read(), + 'model': ema.ema, + # 'optimizer': None if final_epoch else optimizer.state_dict(), + # 'wandb_id': wandb_run.id if wandb else None + } + torch.save(ckpt_best, best) + del ckpt + # end epoch ---------------------------------------------------------------------------------------------------- + # end training + + if rank in [-1, 0]: + # Strip optimizers + final = best if best.exists() else last # final model + for f in [last, best]: + if f.exists(): + strip_optimizer(f) # strip optimizers + if opt.bucket: + os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload + + # Plots + if plots: + plot_results(save_dir=save_dir) # save as results.png + if wandb: + files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png'] + wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files + if (save_dir / f).exists()]}) + if opt.log_artifacts: + wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem) + + # Test best.pt + logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) + if opt.data.endswith('coco.yaml') and nc == 80: # if COCO + for conf, iou, save_json in ([0.25, 0.45, False], [0.001, 0.65, True]): # speed, mAP tests + results, _, _ = test.test(opt.data, + batch_size=total_batch_size, + imgsz=imgsz_test, + conf_thres=conf, + iou_thres=iou, + model=attempt_load(final, device).half(), + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=save_dir, + save_json=save_json, + plots=False) + + else: + dist.destroy_process_group() + + wandb.run.finish() if wandb and wandb.run else None + torch.cuda.empty_cache() + return results + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path') + parser.add_argument('--data', type=str, default='data/widerface.yaml', help='data.yaml path') + parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=120) + parser.add_argument('--batch-size', type=int, default=32, help='total batch size for all GPUs') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--notest', action='store_true', help='only test final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') + parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', default=True, help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') + parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100') + parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model') + parser.add_argument('--workers', type=int, default=4, help='maximum number of dataloader workers') + parser.add_argument('--project', default='runs/train', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + opt = parser.parse_args() + + # Set DDP variables + opt.total_batch_size = opt.batch_size + opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 + set_logging(opt.global_rank) + if opt.global_rank in [-1, 0]: + check_git_status() + + # Resume + if opt.resume: # resume an interrupted run + ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path + assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' + with open(Path(ckpt).parent.parent / 'opt.yaml') as f: + opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader)) # replace + opt.cfg, opt.weights, opt.resume = '', ckpt, True + logger.info('Resuming training from %s' % ckpt) + else: + # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') + opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files + assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) + opt.name = 'evolve' if opt.evolve else opt.name + opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if opt.local_rank != -1: + assert torch.cuda.device_count() > opt.local_rank + torch.cuda.set_device(opt.local_rank) + device = torch.device('cuda', opt.local_rank) + dist.init_process_group(backend='nccl', init_method='env://') # distributed backend + assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' + opt.batch_size = opt.total_batch_size // opt.world_size + + # Hyperparameters + with open(opt.hyp) as f: + hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps + if 'box' not in hyp: + warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' % + (opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120')) + hyp['box'] = hyp.pop('giou') + + # Train + logger.info(opt) + if not opt.evolve: + tb_writer = None # init loggers + if opt.global_rank in [-1, 0]: + logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/') + tb_writer = SummaryWriter(opt.save_dir) # Tensorboard + train(hyp, opt, device, tb_writer, wandb) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr + 'box': (1, 0.02, 0.2), # box loss gain + 'cls': (1, 0.2, 4.0), # cls loss gain + 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (0, 0.1, 0.7), # IoU training threshold + 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (1, 0.0, 0.9), # image scale (+/- gain) + 'shear': (1, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (1, 0.0, 1.0), # image mixup (probability) + 'mixup': (1, 0.0, 1.0)} # image mixup (probability) + + assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' + opt.notest, opt.nosave = True, True # only test/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here + if opt.bucket: + os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists + + for _ in range(300): # generations to evolve + if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt('evolve.txt', ndmin=2) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() # weights + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([x[0] for x in meta.values()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 7] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device, wandb=wandb) + + # Write mutation results + print_mutation(hyp.copy(), results, yaml_file, opt.bucket) + + # Plot results + plot_evolution(yaml_file) + print(f'Hyperparameter evolution complete. Best results saved as: {yaml_file}\n' + f'Command to train a new model with these hyperparameters: $ python train.py --hyp {yaml_file}') diff --git a/algorithm/Car_recognition/utils/__init__.py b/algorithm/Car_recognition/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/Car_recognition/utils/activations.py b/algorithm/Car_recognition/utils/activations.py new file mode 100644 index 0000000..aa3ddf0 --- /dev/null +++ b/algorithm/Car_recognition/utils/activations.py @@ -0,0 +1,72 @@ +# Activation functions + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +# SiLU https://arxiv.org/pdf/1606.08415.pdf ---------------------------------------------------------------------------- +class SiLU(nn.Module): # export-friendly version of nn.SiLU() + @staticmethod + def forward(x): + return x * torch.sigmoid(x) + + +class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() + @staticmethod + def forward(x): + # return x * F.hardsigmoid(x) # for torchscript and CoreML + return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX + + +class MemoryEfficientSwish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x * torch.sigmoid(x) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + return grad_output * (sx * (1 + x * (1 - sx))) + + def forward(self, x): + return self.F.apply(x) + + +# Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- +class Mish(nn.Module): + @staticmethod + def forward(x): + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + return self.F.apply(x) + + +# FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- +class FReLU(nn.Module): + def __init__(self, c1, k=3): # ch_in, kernel + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + return torch.max(x, self.bn(self.conv(x))) diff --git a/algorithm/Car_recognition/utils/autoanchor.py b/algorithm/Car_recognition/utils/autoanchor.py new file mode 100644 index 0000000..5dba9f1 --- /dev/null +++ b/algorithm/Car_recognition/utils/autoanchor.py @@ -0,0 +1,155 @@ +# Auto-anchor utils + +import numpy as np +import torch +import yaml +from scipy.cluster.vq import kmeans +from tqdm import tqdm + +from utils.general import colorstr + + +def check_anchor_order(m): + # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary + a = m.anchor_grid.prod(-1).view(-1) # anchor area + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da.sign() != ds.sign(): # same order + print('Reversing anchor order') + m.anchors[:] = m.anchors.flip(0) + m.anchor_grid[:] = m.anchor_grid.flip(0) + + +def check_anchors(dataset, model, thr=4.0, imgsz=640): + # Check anchor fit to data, recompute if necessary + prefix = colorstr('autoanchor: ') + print(f'\n{prefix}Analyzing anchors... ', end='') + m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh + + def metric(k): # compute metric + r = wh[:, None] / k[None] + x = torch.min(r, 1. / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1. / thr).float().mean() # best possible recall + return bpr, aat + + bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2)) + print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') + if bpr < 0.98: # threshold to recompute + print('. Attempting to improve anchors, please wait...') + na = m.anchor_grid.numel() // 2 # number of anchors + new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + new_bpr = metric(new_anchors.reshape(-1, 2))[0] + if new_bpr > bpr: # replace anchors + new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors) + m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid) # for inference + m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss + check_anchor_order(m) + print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') + else: + print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') + print('') # newline + + +def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ Creates kmeans-evolved anchors from training dataset + + Arguments: + path: path to dataset *.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + thr = 1. / thr + prefix = colorstr('autoanchor: ') + + def metric(k, wh): # compute metrics + r = wh[:, None] / k[None] + x = torch.min(r, 1. / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k): + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr + print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr') + print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' + f'past_thr={x[x > thr].mean():.3f}-mean: ', end='') + for i, x in enumerate(k): + print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg + return k + + if isinstance(path, str): # *.yaml file + with open(path) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict + from utils.datasets import LoadImagesAndLabels + dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) + else: + dataset = path # dataset + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') + wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels + # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 + + # Kmeans calculation + print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') + s = wh.std(0) # sigmas for whitening + k, dist = kmeans(wh / s, n, iter=30) # points, mean distance + k *= s + wh = torch.tensor(wh, dtype=torch.float32) # filtered + wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered + k = print_results(k) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + npr = np.random + f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma + pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' + if verbose: + print_results(k) + + return print_results(k) diff --git a/algorithm/Car_recognition/utils/aws/__init__.py b/algorithm/Car_recognition/utils/aws/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/Car_recognition/utils/aws/mime.sh b/algorithm/Car_recognition/utils/aws/mime.sh new file mode 100644 index 0000000..c319a83 --- /dev/null +++ b/algorithm/Car_recognition/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/algorithm/Car_recognition/utils/aws/resume.py b/algorithm/Car_recognition/utils/aws/resume.py new file mode 100644 index 0000000..faad8d2 --- /dev/null +++ b/algorithm/Car_recognition/utils/aws/resume.py @@ -0,0 +1,37 @@ +# Resume all interrupted trainings in yolov5/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +sys.path.append('./') # to run '$ python *.py' files in subdirectories + +port = 0 # --master_port +path = Path('').resolve() +for last in path.rglob('*/**/last.pt'): + ckpt = torch.load(last) + if ckpt['optimizer'] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / 'opt.yaml') as f: + opt = yaml.load(f, Loader=yaml.SafeLoader) + + # Get device count + d = opt['device'].split(',') # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' + else: # single-GPU + cmd = f'python train.py --resume {last}' + + cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/algorithm/Car_recognition/utils/aws/userdata.sh b/algorithm/Car_recognition/utils/aws/userdata.sh new file mode 100644 index 0000000..890606b --- /dev/null +++ b/algorithm/Car_recognition/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolov5 ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5 + cd yolov5 + bash data/scripts/get_coco.sh && echo "Data done." & + sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/algorithm/Car_recognition/utils/cv_puttext.py b/algorithm/Car_recognition/utils/cv_puttext.py new file mode 100644 index 0000000..21c4712 --- /dev/null +++ b/algorithm/Car_recognition/utils/cv_puttext.py @@ -0,0 +1,22 @@ +import cv2 +import numpy as np +from PIL import Image, ImageDraw, ImageFont + +def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20): + if (isinstance(img, np.ndarray)): #判断是否OpenCV图片类型 + img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + draw = ImageDraw.Draw(img) + fontText = ImageFont.truetype( + "algorithm/Car_recognition/fonts/platech.ttf", textSize, encoding="utf-8") + draw.text((left, top), text, textColor, font=fontText) + return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) + +if __name__ == '__main__': + imgPath = "result.jpg" + img = cv2.imread(imgPath) + + saveImg = cv2ImgAddText(img, '中国加油！', 50, 100, (255, 0, 0), 50) + + # cv2.imshow('display',saveImg) + cv2.imwrite('save.jpg',saveImg) + # cv2.waitKey() \ No newline at end of file diff --git a/algorithm/Car_recognition/utils/datasets.py b/algorithm/Car_recognition/utils/datasets.py new file mode 100644 index 0000000..56d4190 --- /dev/null +++ b/algorithm/Car_recognition/utils/datasets.py @@ -0,0 +1,1019 @@ +# Dataset utils and dataloaders + +import glob +import logging +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from threading import Thread + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image, ExifTags +from torch.utils.data import Dataset +from tqdm import tqdm + +from algorithm.Car_recognition.utils.general import xyxy2xywh, xywh2xyxy, xywhn2xyxy, clean_str +from algorithm.Car_recognition.utils.torch_utils import torch_distributed_zero_first + +# Parameters +help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes +vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes +logger = logging.getLogger(__name__) + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == 'Orientation': + break + + +def get_hash(files): + # Returns a single hash value of a list of files + return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) + + +def exif_size(img): + # Returns exif-corrected PIL size + s = img.size # (width, height) + try: + rotation = dict(img._getexif().items())[orientation] + if rotation == 6: # rotation 270 + s = (s[1], s[0]) + elif rotation == 8: # rotation 90 + s = (s[1], s[0]) + except: + pass + + return s + + +def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, + rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''): + # Make sure only the first process in DDP process the dataset first, and the following others can use the cache + with torch_distributed_zero_first(rank): + dataset = LoadImagesAndLabels(path, imgsz, batch_size, + augment=augment, # augment images + hyp=hyp, # augmentation hyperparameters + rect=rect, # rectangular training + cache_images=cache, + single_cls=opt.single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix) + + batch_size = min(batch_size, len(dataset)) + nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None + loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader + # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader() + dataloader = loader(dataset, + batch_size=batch_size, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn) + return dataloader, dataset + + +class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): + """ Dataloader that reuses workers + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + return len(self.batch_sampler.sampler) + + def __iter__(self): + for i in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler(object): + """ Sampler that repeats forever + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + self.sampler = sampler + + def __iter__(self): + while True: + yield from iter(self.sampler) + + +class LoadImages: # for inference + def __init__(self, path, img_size=640): + p = str(Path(path)) # os-agnostic + p = os.path.abspath(p) # absolute path + if '*' in p: + files = sorted(glob.glob(p, recursive=True)) # glob + elif os.path.isdir(p): + files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir + elif os.path.isfile(p): + files = [p] # files + else: + raise Exception(f'ERROR: {p} does not exist') + + images = [x for x in files if x.split('.')[-1].lower() in img_formats] + videos = [x for x in files if x.split('.')[-1].lower() in vid_formats] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = 'image' + if any(videos): + self.new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, f'No images or videos found in {p}. ' \ + f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}' + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = 'video' + ret_val, img0 = self.cap.read() + if not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + else: + path = self.files[self.count] + self.new_video(path) + ret_val, img0 = self.cap.read() + + self.frame += 1 + print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='') + + else: + # Read image + self.count += 1 + img0 = cv2.imread(path) # BGR + assert img0 is not None, 'Image Not Found ' + path + print(f'image {self.count}/{self.nf} {path}: ', end='') + + # Padded resize + img = letterbox(img0, new_shape=self.img_size)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return path, img, img0, self.cap + + def new_video(self, path): + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + def __len__(self): + return self.nf # number of files + + +class LoadWebcam: # for inference + def __init__(self, pipe='0', img_size=640): + self.img_size = img_size + + if pipe.isnumeric(): + pipe = eval(pipe) # local camera + # pipe = 'rtsp://192.168.1.64/1' # IP camera + # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login + # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera + + self.pipe = pipe + self.cap = cv2.VideoCapture(pipe) # video capture object + self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if cv2.waitKey(1) == ord('q'): # q to quit + self.cap.release() + cv2.destroyAllWindows() + raise StopIteration + + # Read frame + if self.pipe == 0: # local camera + ret_val, img0 = self.cap.read() + img0 = cv2.flip(img0, 1) # flip left-right + else: # IP camera + n = 0 + while True: + n += 1 + self.cap.grab() + if n % 30 == 0: # skip frames + ret_val, img0 = self.cap.retrieve() + if ret_val: + break + + # Print + assert ret_val, f'Camera Error {self.pipe}' + img_path = 'webcam.jpg' + print(f'webcam {self.count}: ', end='') + + # Padded resize + img = letterbox(img0, new_shape=self.img_size)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return img_path, img, img0, None + + def __len__(self): + return 0 + + +class LoadStreams: # multiple IP or RTSP cameras + def __init__(self, sources='streams.txt', img_size=640): + self.mode = 'stream' + self.img_size = img_size + + if os.path.isfile(sources): + with open(sources, 'r') as f: + sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] + else: + sources = [sources] + + n = len(sources) + self.imgs = [None] * n + self.sources = [clean_str(x) for x in sources] # clean source names for later + for i, s in enumerate(sources): + # Start the thread to read frames from the video stream + print(f'{i + 1}/{n}: {s}... ', end='') + cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s) + assert cap.isOpened(), f'Failed to open {s}' + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) % 100 + _, self.imgs[i] = cap.read() # guarantee first frame + thread = Thread(target=self.update, args=([i, cap]), daemon=True) + print(f' success ({w}x{h} at {fps:.2f} FPS).') + thread.start() + print('') # newline + + # check for common shapes + s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + if not self.rect: + print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') + + def update(self, index, cap): + # Read next stream frame in a daemon thread + n = 0 + while cap.isOpened(): + n += 1 + # _, self.imgs[index] = cap.read() + cap.grab() + if n == 4: # read every 4th frame + _, self.imgs[index] = cap.retrieve() + n = 0 + time.sleep(0.01) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + img0 = self.imgs.copy() + if cv2.waitKey(1) == ord('q'): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + # Letterbox + img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0] + + # Stack + img = np.stack(img, 0) + + # Convert + img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 + img = np.ascontiguousarray(img) + + return self.sources, img, img0, None + + def __len__(self): + return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + # Define label paths as a function of image paths + sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings + return [x.replace(sa, sb, 1).replace('.' + x.split('.')[-1], '.txt') for x in img_paths] + + +class LoadImagesAndLabels(Dataset): # for training/testing + def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, + cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''): + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / '**' / '*.*'), recursive=True) + elif p.is_file(): # file + with open(p, 'r') as t: + t = t.read().strip().splitlines() + parent = str(p.parent) + os.sep + f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path + else: + raise Exception(f'{prefix}{p} does not exist') + self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats]) + assert self.img_files, f'{prefix}No images found' + except Exception as e: + raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}') + + # Check cache + self.label_files = img2label_paths(self.img_files) # labels + cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') # cached labels + if cache_path.is_file(): + cache = torch.load(cache_path) # load + if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache: # changed + cache = self.cache_labels(cache_path, prefix) # re-cache + else: + cache = self.cache_labels(cache_path, prefix) # cache + + # Display cache + [nf, nm, ne, nc, n] = cache.pop('results') # found, missing, empty, corrupted, total + desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" + tqdm(None, desc=prefix + desc, total=n, initial=n) + assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}' + + # Read cache + cache.pop('hash') # remove hash + labels, shapes = zip(*cache.values()) + self.labels = list(labels) + self.shapes = np.array(shapes, dtype=np.float64) + self.img_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + if single_cls: + for x in self.labels: + x[:, 0] = 0 + + n = len(shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = range(n) + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.img_files = [self.img_files[i] for i in irect] + self.label_files = [self.label_files[i] for i in irect] + self.labels = [self.labels[i] for i in irect] + self.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride + + # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) + self.imgs = [None] * n + if cache_images: + gb = 0 # Gigabytes of cached images + self.img_hw0, self.img_hw = [None] * n, [None] * n + results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) # 8 threads + pbar = tqdm(enumerate(results), total=n) + for i, x in pbar: + self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i) + gb += self.imgs[i].nbytes + pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)' + + def cache_labels(self, path=Path('./labels.cache'), prefix=''): + # Cache dataset labels, check images and read shapes + x = {} # dict + nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate + pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) + for i, (im_file, lb_file) in enumerate(pbar): + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels' + + # verify labels + if os.path.isfile(lb_file): + nf += 1 # label found + with open(lb_file, 'r') as f: + l = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + if len(l): + assert l.shape[1] == 5, 'labels require 5 columns each' + assert (l >= 0).all(), 'negative labels' + assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels' + assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels' + else: + ne += 1 # label empty + l = np.zeros((0, 5), dtype=np.float32) + else: + nm += 1 # label missing + l = np.zeros((0, 5), dtype=np.float32) + x[im_file] = [l, shape] + except Exception as e: + nc += 1 + print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}') + + pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' for images and labels... " \ + f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" + + if nf == 0: + print(f'{prefix}WARNING: No labels found in {path}. See {help_url}') + + x['hash'] = get_hash(self.label_files + self.img_files) + x['results'] = [nf, nm, ne, nc, i + 1] + torch.save(x, path) # save for next time + logging.info(f'{prefix}New cache created: {path}') + return x + + def __len__(self): + return len(self.img_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + if mosaic: + # Load mosaic + img, labels = load_mosaic(self, index) + shapes = None + + # MixUp https://arxiv.org/pdf/1710.09412.pdf + if random.random() < hyp['mixup']: + img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1)) + r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0 + img = (img * r + img2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + + else: + # Load image + img, (h0, w0), (h, w) = load_image(self, index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + # Augment imagespace + if not mosaic: + img, labels = random_perspective(img, labels, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + # Augment colorspace + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Apply cutouts + # if random.random() < 0.9: + # labels = cutout(img, labels) + + nL = len(labels) # number of labels + if nL: + labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh + labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 + labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 + + if self.augment: + # flip up-down + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nL: + labels[:, 2] = 1 - labels[:, 2] + + # flip left-right + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nL: + labels[:, 1] = 1 - labels[:, 1] + + labels_out = torch.zeros((nL, 6)) + if nL: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.img_files[index], shapes + + @staticmethod + def collate_fn(batch): + img, label, path, shapes = zip(*batch) # transposed + for i, l in enumerate(label): + l[:, 0] = i # add target image index for build_targets() + return torch.stack(img, 0), torch.cat(label, 0), path, shapes + + @staticmethod + def collate_fn4(batch): + img, label, path, shapes = zip(*batch) # transposed + n = len(shapes) // 4 + img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] + + ho = torch.tensor([[0., 0, 0, 1, 0, 0]]) + wo = torch.tensor([[0., 0, 1, 0, 0, 0]]) + s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale + for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW + i *= 4 + if random.random() < 0.5: + im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[ + 0].type(img[i].type()) + l = label[i] + else: + im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2) + l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s + img4.append(im) + label4.append(l) + + for i, l in enumerate(label4): + l[:, 0] = i # add target image index for build_targets() + + return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def load_image(self, index): + # loads 1 image from dataset, returns img, original hw, resized hw + img = self.imgs[index] + if img is None: # not cached + path = self.img_files[index] + img = cv2.imread(path) # BGR + assert img is not None, 'Image Not Found ' + path + h0, w0 = img.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # resize image to img_size + if r != 1: # always resize down, only resize up if training with augmentation + interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) + return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized + else: + return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized + + +def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) + dtype = img.dtype # uint8 + + x = np.arange(0, 256, dtype=np.int16) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) + cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed + + # Histogram equalization + # if random.random() < 0.2: + # for i in range(3): + # img[:, :, i] = cv2.equalizeHist(img[:, :, i]) + + +def load_mosaic(self, index): + # loads images in a 4-mosaic + + labels4 = [] + s = self.img_size + yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)] # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels = self.labels[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + labels4.append(labels) + + # Concat/clip labels + if len(labels4): + labels4 = np.concatenate(labels4, 0) + np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_perspective + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4 = random_perspective(img4, labels4, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img4, labels4 + + +def load_mosaic9(self, index): + # loads images in a 9-mosaic + + labels9 = [] + s = self.img_size + indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(8)] # 8 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, index) + + # place img in img9 + if i == 0: # center + img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + h0, w0 = h, w + c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates + elif i == 1: # top + c = s, s - h, s + w, s + elif i == 2: # top right + c = s + wp, s - h, s + wp + w, s + elif i == 3: # right + c = s + w0, s, s + w0 + w, s + h + elif i == 4: # bottom right + c = s + w0, s + hp, s + w0 + w, s + hp + h + elif i == 5: # bottom + c = s + w0 - w, s + h0, s + w0, s + h0 + h + elif i == 6: # bottom left + c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h + elif i == 7: # left + c = s - w, s + h0 - h, s, s + h0 + elif i == 8: # top left + c = s - w, s + h0 - hp - h, s, s + h0 - hp + + padx, pady = c[:2] + x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords + + # Labels + labels = self.labels[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + labels9.append(labels) + + # Image + img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] + hp, wp = h, w # height, width previous + + # Offset + yc, xc = [int(random.uniform(0, s)) for x in self.mosaic_border] # mosaic center x, y + img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] + + # Concat/clip labels + if len(labels9): + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + + np.clip(labels9[:, 1:], 0, 2 * s, out=labels9[:, 1:]) # use with random_perspective + # img9, labels9 = replicate(img9, labels9) # replicate + + # Augment + img9, labels9 = random_perspective(img9, labels9, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img9, labels9 + + +def replicate(img, labels): + # Replicate labels + h, w = img.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return img, labels + + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): + # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = img.shape[0] + border[0] * 2 # shape(h,w,c) + width = img.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -img.shape[1] / 2 # x translation (pixels) + C[1, 2] = -img.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(img[:, :, ::-1]) # base + # ax[1].imshow(img2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + if n: + # warp points + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + if perspective: + xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale + else: # affine + xy = xy[:, :2].reshape(n, 8) + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # # apply angle-based reduction of bounding boxes + # radians = a * math.pi / 180 + # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 + # x = (xy[:, 2] + xy[:, 0]) / 2 + # y = (xy[:, 3] + xy[:, 1]) / 2 + # w = (xy[:, 2] - xy[:, 0]) * reduction + # h = (xy[:, 3] - xy[:, 1]) * reduction + # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T + + # clip boxes + xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) + xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T) + targets = targets[i] + targets[:, 1:5] = xy[i] + + return img, targets + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) + # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates + + +def cutout(image, labels): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + def bbox_ioa(box1, box2): + # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 + box2 = box2.transpose() + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ + (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 + + # Intersection over box2 area + return inter_area / box2_area + + # create random masks + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def create_folder(path='./new'): + # Create folder + if os.path.exists(path): + shutil.rmtree(path) # delete output folder + os.makedirs(path) # make new output folder + + +def flatten_recursive(path='../coco128'): + # Flatten a recursive directory by bringing all files to top level + new_path = Path(path + '_flat') + create_folder(new_path) + for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_boxes('../coco128') + # Convert detection dataset into classification dataset, with one directory per class + + path = Path(path) # images dir + shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None # remove existing + files = list(path.rglob('*.*')) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in img_formats: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file, 'r') as f: + lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' + + +def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0)): # from utils.datasets import *; autosplit('../coco128') + """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + # Arguments + path: Path to images directory + weights: Train, val, test weights (list) + """ + path = Path(path) # images dir + files = list(path.rglob('*.*')) + n = len(files) # number of files + indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files + [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing + for i, img in tqdm(zip(indices, files), total=n): + if img.suffix[1:] in img_formats: + with open(path / txt[i], 'a') as f: + f.write(str(img) + '\n') # add image to txt file diff --git a/algorithm/Car_recognition/utils/general.py b/algorithm/Car_recognition/utils/general.py new file mode 100644 index 0000000..7a54f6f --- /dev/null +++ b/algorithm/Car_recognition/utils/general.py @@ -0,0 +1,647 @@ +# General utils + +import glob +import logging +import math +import os +import random +import re +import subprocess +import time +from pathlib import Path + +import cv2 +import numpy as np +import torch +import torchvision +import yaml + +from algorithm.Car_recognition.utils.google_utils import gsutil_getsize +from algorithm.Car_recognition.utils.metrics import fitness +from algorithm.Car_recognition.utils.torch_utils import init_torch_seeds + +# Settings +torch.set_printoptions(linewidth=320, precision=5, profile='long') +np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) +os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads + + +def set_logging(rank=-1): + logging.basicConfig( + format="%(message)s", + level=logging.INFO if rank in [-1, 0] else logging.WARN) + + +def init_seeds(seed=0): + # Initialize random number generator (RNG) seeds + random.seed(seed) + np.random.seed(seed) + init_torch_seeds(seed) + + +def get_latest_run(search_dir='.'): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) + return max(last_list, key=os.path.getctime) if last_list else '' + + +def check_online(): + # Check internet connectivity + import socket + try: + socket.create_connection(("1.1.1.1", 53)) # check host accesability + return True + except OSError: + return False + + +def check_git_status(): + # Recommend 'git pull' if code is out of date + print(colorstr('github: '), end='') + try: + assert Path('.git').exists(), 'skipping check (not a git repository)' + assert not Path('/workspace').exists(), 'skipping check (Docker image)' # not Path('/.dockerenv').exists() + assert check_online(), 'skipping check (offline)' + + cmd = 'git fetch && git config --get remote.origin.url' # github repo url + url = subprocess.check_output(cmd, shell=True).decode()[:-1] + cmd = 'git rev-list $(git rev-parse --abbrev-ref HEAD)..origin/master --count' # commits behind + n = int(subprocess.check_output(cmd, shell=True)) + if n > 0: + print(f"⚠️ WARNING: code is out of date by {n} {'commits' if n > 1 else 'commmit'}. " + f"Use 'git pull' to update or 'git clone {url}' to download latest.") + else: + print(f'up to date with {url} ✅') + except Exception as e: + print(e) + + +def check_requirements(file='requirements.txt'): + # Check installed dependencies meet requirements + import pkg_resources + requirements = pkg_resources.parse_requirements(Path(file).open()) + requirements = [x.name + ''.join(*x.specs) if len(x.specs) else x.name for x in requirements] + pkg_resources.require(requirements) # DistributionNotFound or VersionConflict exception if requirements not met + + +def check_img_size(img_size, s=32): + # Verify img_size is a multiple of stride s + new_size = make_divisible(img_size, int(s)) # ceil gs-multiple + if new_size != img_size: + print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size)) + return new_size + + +def check_file(file): + # Search for file if not found + if os.path.isfile(file) or file == '': + return file + else: + files = glob.glob('./**/' + file, recursive=True) # find file + assert len(files), 'File Not Found: %s' % file # assert file was found + assert len(files) == 1, "Multiple files match '%s', specify exact path: %s" % (file, files) # assert unique + return files[0] # return file + + +def check_dataset(dict): + # Download dataset if not found locally + val, s = dict.get('val'), dict.get('download') + if val and len(val): + val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path + if not all(x.exists() for x in val): + print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()]) + if s and len(s): # download script + print('Downloading %s ...' % s) + if s.startswith('http') and s.endswith('.zip'): # URL + f = Path(s).name # filename + torch.hub.download_url_to_file(s, f) + r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip + else: # bash script + r = os.system(s) + print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value + else: + raise Exception('Dataset not found.') + + +def make_divisible(x, divisor): + # Returns x evenly divisible by divisor + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + # Cleans a string by replacing special characters with underscore _ + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + # lambda function for sinusoidal ramp from y1 to y2 + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +def colorstr(*input): + # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') + *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string + colors = {'black': '\033[30m', # basic colors + 'red': '\033[31m', + 'green': '\033[32m', + 'yellow': '\033[33m', + 'blue': '\033[34m', + 'magenta': '\033[35m', + 'cyan': '\033[36m', + 'white': '\033[37m', + 'bright_black': '\033[90m', # bright colors + 'bright_red': '\033[91m', + 'bright_green': '\033[92m', + 'bright_yellow': '\033[93m', + 'bright_blue': '\033[94m', + 'bright_magenta': '\033[95m', + 'bright_cyan': '\033[96m', + 'bright_white': '\033[97m', + 'end': '\033[0m', # misc + 'bold': '\033[1m', + 'underline': '\033[4m'} + return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] + + +def labels_to_class_weights(labels, nc=80): + # Get class weights (inverse frequency) from training labels + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(np.int) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights) + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + # Produces image weights based on class_weights and image contents + class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels]) + image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) + # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample + return image_weights + + +def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) + # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] + return x + + +def xyxy2xywh(x): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center + y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center + y[:, 2] = x[:, 2] - x[:, 0] # width + y[:, 3] = x[:, 3] - x[:, 1] # height + return y + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def xywhn2xyxy(x, w=640, h=640, padw=32, padh=32): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x + y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y + y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x + y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y + return y + + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + clip_coords(coords, img0_shape) + return coords + + +def clip_coords(boxes, img_shape): + # Clip bounding xyxy bounding boxes to image shape (height, width) + boxes[:, 0].clamp_(0, img_shape[1]) # x1 + boxes[:, 1].clamp_(0, img_shape[0]) # y1 + boxes[:, 2].clamp_(0, img_shape[1]) # x2 + boxes[:, 3].clamp_(0, img_shape[0]) # y2 + + +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-9): + # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ + (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union + if GIoU or DIoU or CIoU: + # convex (smallest enclosing box) width + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared + if DIoU: + return iou - rho2 / c2 # DIoU + elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * \ + torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) + with torch.no_grad(): + alpha = v / ((1 + eps) - iou + v) + return iou - (rho2 / c2 + v * alpha) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU + else: + return iou # IoU + + +def box_iou(box1, box2): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - + torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + # iou = inter / (area1 + area2 - inter) + return inter / (area1[:, None] + area2 - inter) + + +def wh_iou(wh1, wh2): + # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + # iou = inter / (area1 + area2 - inter) + return inter / (wh1.prod(2) + wh2.prod(2) - inter) + +def jaccard_diou(box_a, box_b, iscrowd:bool=False): + use_batch = True + if box_a.dim() == 2: + use_batch = False + box_a = box_a[None, ...] + box_b = box_b[None, ...] + + inter = intersect(box_a, box_b) + area_a = ((box_a[:, :, 2]-box_a[:, :, 0]) * + (box_a[:, :, 3]-box_a[:, :, 1])).unsqueeze(2).expand_as(inter) # [A,B] + area_b = ((box_b[:, :, 2]-box_b[:, :, 0]) * + (box_b[:, :, 3]-box_b[:, :, 1])).unsqueeze(1).expand_as(inter) # [A,B] + union = area_a + area_b - inter + x1 = ((box_a[:, :, 2]+box_a[:, :, 0]) / 2).unsqueeze(2).expand_as(inter) + y1 = ((box_a[:, :, 3]+box_a[:, :, 1]) / 2).unsqueeze(2).expand_as(inter) + x2 = ((box_b[:, :, 2]+box_b[:, :, 0]) / 2).unsqueeze(1).expand_as(inter) + y2 = ((box_b[:, :, 3]+box_b[:, :, 1]) / 2).unsqueeze(1).expand_as(inter) + + t1 = box_a[:, :, 1].unsqueeze(2).expand_as(inter) + b1 = box_a[:, :, 3].unsqueeze(2).expand_as(inter) + l1 = box_a[:, :, 0].unsqueeze(2).expand_as(inter) + r1 = box_a[:, :, 2].unsqueeze(2).expand_as(inter) + + t2 = box_b[:, :, 1].unsqueeze(1).expand_as(inter) + b2 = box_b[:, :, 3].unsqueeze(1).expand_as(inter) + l2 = box_b[:, :, 0].unsqueeze(1).expand_as(inter) + r2 = box_b[:, :, 2].unsqueeze(1).expand_as(inter) + + cr = torch.max(r1, r2) + cl = torch.min(l1, l2) + ct = torch.min(t1, t2) + cb = torch.max(b1, b2) + D = (((x2 - x1)**2 + (y2 - y1)**2) / ((cr-cl)**2 + (cb-ct)**2 + 1e-7)) + out = inter / area_a if iscrowd else inter / (union + 1e-7) - D ** 0.7 + return out if use_batch else out.squeeze(0) + + +def non_max_suppression_face(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()): + """Performs Non-Maximum Suppression (NMS) on inference results + Returns: + detections with shape: nx6 (x1, y1, x2, y2, conf, cls) + """ + + nc = prediction.shape[2] - 13 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + multi_label=False + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0, 14), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 13), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 13] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 13:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, landmarks, cls) + if multi_label: + i, j = (x[:, 13:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 13, None], x[i, 5:13] ,j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 13:].max(1, keepdim=True) + x = torch.cat((box, conf, x[:, 5:13], j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # If none remain process next image + n = x.shape[0] # number of boxes + if not n: + continue + + # Batched NMS + c = x[:, 13:14] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + #if i.shape[0] > max_det: # limit detections + # i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + break # time limit exceeded + + return output + + +def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()): + """Performs Non-Maximum Suppression (NMS) on inference results + + Returns: + detections with shape: nx6 (x1, y1, x2, y2, conf, cls) + """ + + nc = prediction.shape[2] - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + # (pixels) minimum and maximum box width and height + min_wh, max_wh = 2, 4096 + #max_det = 300 # maximum number of detections per image + #max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[ + conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + #elif n > max_nms: # excess boxes + # x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + x = x[x[:, 4].argsort(descending=True)] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + #if i.shape[0] > max_det: # limit detections + # i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f'WARNING: NMS time limit {time_limit}s exceeded') + break # time limit exceeded + + return output + + +def strip_optimizer(f='weights/best.pt', s=''): # from utils.general import *; strip_optimizer() + # Strip optimizer from 'f' to finalize training, optionally save as 's' + x = torch.load(f, map_location=torch.device('cpu')) + for key in 'optimizer', 'training_results', 'wandb_id': + x[key] = None + x['epoch'] = -1 + x['model'].half() # to FP16 + for p in x['model'].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1E6 # filesize + print('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', mb)) + + +def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): + # Print mutation results to evolve.txt (for use with train.py --evolve) + a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys + b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values + c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) + + if bucket: + url = 'gs://%s/evolve.txt' % bucket + if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0): + os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local + + with open('evolve.txt', 'a') as f: # append result + f.write(c + b + '\n') + x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows + x = x[np.argsort(-fitness(x))] # sort + np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness + + # Save yaml + for i, k in enumerate(hyp.keys()): + hyp[k] = float(x[0, i + 7]) + with open(yaml_file, 'w') as f: + results = tuple(x[0, :7]) + c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n') + yaml.dump(hyp, f, sort_keys=False) + + if bucket: + os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload + + +def apply_classifier(x, model, img, im0): + # applies a second stage classifier to yolo outputs + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_coords(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for j, a in enumerate(d): # per item + cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + # cv2.imwrite('test%i.jpg' % j, cutout) + + # BGR to RGB, to 3x416x416 + im = im[:, :, ::-1].transpose(2, 0, 1) + im = np.ascontiguousarray( + im, dtype=np.float32) # uint8 to float32 + im /= 255.0 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device) + ).argmax(1) # classifier prediction + # retain matching class detections + x[i] = x[i][pred_cls1 == pred_cls2] + + return x + + +def increment_path(path, exist_ok=True, sep=''): + # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc. + path = Path(path) # os-agnostic + if (path.exists() and exist_ok) or (not path.exists()): + return str(path) + else: + dirs = glob.glob(f"{path}{sep}*") # similar paths + matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] + i = [int(m.groups()[0]) for m in matches if m] # indices + n = max(i) + 1 if i else 2 # increment number + return f"{path}{sep}{n}" # update path diff --git a/algorithm/Car_recognition/utils/google_app_engine/additional_requirements.txt b/algorithm/Car_recognition/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 0000000..5fcc305 --- /dev/null +++ b/algorithm/Car_recognition/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,4 @@ +# add these requirements in your app on top of the existing ones +pip==18.1 +Flask==1.0.2 +gunicorn==19.9.0 diff --git a/algorithm/Car_recognition/utils/google_app_engine/app.yaml b/algorithm/Car_recognition/utils/google_app_engine/app.yaml new file mode 100644 index 0000000..ac29d10 --- /dev/null +++ b/algorithm/Car_recognition/utils/google_app_engine/app.yaml @@ -0,0 +1,14 @@ +runtime: custom +env: flex + +service: yolov5app + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 \ No newline at end of file diff --git a/algorithm/Car_recognition/utils/google_utils.py b/algorithm/Car_recognition/utils/google_utils.py new file mode 100644 index 0000000..024dc78 --- /dev/null +++ b/algorithm/Car_recognition/utils/google_utils.py @@ -0,0 +1,122 @@ +# Google utils: https://cloud.google.com/storage/docs/reference/libraries + +import os +import platform +import subprocess +import time +from pathlib import Path + +import requests +import torch + + +def gsutil_getsize(url=''): + # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du + s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') + return eval(s.split(' ')[0]) if len(s) else 0 # bytes + + +def attempt_download(file, repo='ultralytics/yolov5'): + # Attempt file download if does not exist + file = Path(str(file).strip().replace("'", '').lower()) + + if not file.exists(): + try: + response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api + assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] + tag = response['tag_name'] # i.e. 'v1.0' + except: # fallback plan + assets = ['yolov5.pt', 'yolov5.pt', 'yolov5l.pt', 'yolov5x.pt'] + tag = subprocess.check_output('git tag', shell=True).decode('utf-8').split('\n')[-2] + + name = file.name + if name in assets: + msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' + redundant = False # second download option + try: # GitHub + url = f'https://github.com/{repo}/releases/download/{tag}/{name}' + print(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, file) + assert file.exists() and file.stat().st_size > 1E6 # check + except Exception as e: # GCP + print(f'Download error: {e}') + assert redundant, 'No secondary mirror' + url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' + print(f'Downloading {url} to {file}...') + os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) + finally: + if not file.exists() or file.stat().st_size < 1E6: # check + file.unlink(missing_ok=True) # remove partial downloads + print(f'ERROR: Download failure: {msg}') + print('') + return + + +def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'): + # Downloads a file from Google Drive. from yolov5.utils.google_utils import *; gdrive_download() + t = time.time() + file = Path(file) + cookie = Path('cookie') # gdrive cookie + print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') + file.unlink(missing_ok=True) # remove existing file + cookie.unlink(missing_ok=True) # remove existing cookie + + # Attempt file download + out = "NUL" if platform.system() == "Windows" else "/dev/null" + os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') + if os.path.exists('cookie'): # large file + s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' + else: # small file + s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' + r = os.system(s) # execute, capture return + cookie.unlink(missing_ok=True) # remove existing cookie + + # Error check + if r != 0: + file.unlink(missing_ok=True) # remove partial + print('Download error ') # raise Exception('Download error') + return r + + # Unzip if archive + if file.suffix == '.zip': + print('unzipping... ', end='') + os.system(f'unzip -q {file}') # unzip + file.unlink() # remove zip to free space + + print(f'Done ({time.time() - t:.1f}s)') + return r + + +def get_token(cookie="./cookie"): + with open(cookie) as f: + for line in f: + if "download" in line: + return line.split()[-1] + return "" + +# def upload_blob(bucket_name, source_file_name, destination_blob_name): +# # Uploads a file to a bucket +# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python +# +# storage_client = storage.Client() +# bucket = storage_client.get_bucket(bucket_name) +# blob = bucket.blob(destination_blob_name) +# +# blob.upload_from_filename(source_file_name) +# +# print('File {} uploaded to {}.'.format( +# source_file_name, +# destination_blob_name)) +# +# +# def download_blob(bucket_name, source_blob_name, destination_file_name): +# # Uploads a blob from a bucket +# storage_client = storage.Client() +# bucket = storage_client.get_bucket(bucket_name) +# blob = bucket.blob(source_blob_name) +# +# blob.download_to_filename(destination_file_name) +# +# print('Blob {} downloaded to {}.'.format( +# source_blob_name, +# destination_file_name)) diff --git a/algorithm/Car_recognition/utils/infer_utils.py b/algorithm/Car_recognition/utils/infer_utils.py new file mode 100644 index 0000000..9dc428c --- /dev/null +++ b/algorithm/Car_recognition/utils/infer_utils.py @@ -0,0 +1,36 @@ +import torch + + + +def decode_infer(output, stride): + # logging.info(torch.tensor(output.shape[0])) + # logging.info(output.shape) + # # bz is batch-size + # bz = tuple(torch.tensor(output.shape[0])) + # gridsize = tuple(torch.tensor(output.shape[-1])) + # logging.info(gridsize) + sh = torch.tensor(output.shape) + bz = sh[0] + gridsize = sh[-1] + + output = output.permute(0, 2, 3, 1) + output = output.view(bz, gridsize, gridsize, self.gt_per_grid, 5+self.numclass) + x1y1, x2y2, conf, prob = torch.split( + output, [2, 2, 1, self.numclass], dim=4) + + shiftx = torch.arange(0, gridsize, dtype=torch.float32) + shifty = torch.arange(0, gridsize, dtype=torch.float32) + shifty, shiftx = torch.meshgrid([shiftx, shifty]) + shiftx = shiftx.unsqueeze(-1).repeat(bz, 1, 1, self.gt_per_grid) + shifty = shifty.unsqueeze(-1).repeat(bz, 1, 1, self.gt_per_grid) + + xy_grid = torch.stack([shiftx, shifty], dim=4).cuda() + x1y1 = (xy_grid+0.5-torch.exp(x1y1))*stride + x2y2 = (xy_grid+0.5+torch.exp(x2y2))*stride + + xyxy = torch.cat((x1y1, x2y2), dim=4) + conf = torch.sigmoid(conf) + prob = torch.sigmoid(prob) + output = torch.cat((xyxy, conf, prob), 4) + output = output.view(bz, -1, 5+self.numclass) + return output \ No newline at end of file diff --git a/algorithm/Car_recognition/utils/loss.py b/algorithm/Car_recognition/utils/loss.py new file mode 100644 index 0000000..5fd23a3 --- /dev/null +++ b/algorithm/Car_recognition/utils/loss.py @@ -0,0 +1,304 @@ +# Loss functions + +import torch +import torch.nn as nn +import numpy as np +from utils.general import bbox_iou +from utils.torch_utils import is_parallel + + +def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 + # return positive, negative label smoothing BCE targets + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=0.05): + super(BCEBlurWithLogitsLoss, self).__init__() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class FocalLoss(nn.Module): + # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super(FocalLoss, self).__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super(QFocalLoss, self).__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + +class WingLoss(nn.Module): + def __init__(self, w=10, e=2): + super(WingLoss, self).__init__() + # https://arxiv.org/pdf/1711.06753v4.pdf Figure 5 + self.w = w + self.e = e + self.C = self.w - self.w * np.log(1 + self.w / self.e) + + def forward(self, x, t, sigma=1): + weight = torch.ones_like(t) + weight[torch.where(t==-1)] = 0 + diff = weight * (x - t) + abs_diff = diff.abs() + flag = (abs_diff.data < self.w).float() + y = flag * self.w * torch.log(1 + abs_diff / self.e) + (1 - flag) * (abs_diff - self.C) + return y.sum() + +class LandmarksLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=1.0): + super(LandmarksLoss, self).__init__() + self.loss_fcn = WingLoss()#nn.SmoothL1Loss(reduction='sum') + self.alpha = alpha + + def forward(self, pred, truel, mask): + loss = self.loss_fcn(pred*mask, truel*mask) + return loss / (torch.sum(mask) + 10e-14) + + +def compute_loss(p, targets, model): # predictions, targets, model + device = targets.device + lcls, lbox, lobj, lmark = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + tcls, tbox, indices, anchors, tlandmarks, lmks_mask = build_targets(p, targets, model) # targets + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) # weight=model.class_weights) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + landmarks_loss = LandmarksLoss(1.0) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + cp, cn = smooth_BCE(eps=0.0) + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + # Losses + nt = 0 # number of targets + no = len(p) # number of outputs + balance = [4.0, 1.0, 0.4] if no == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6 + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + nt += n # cumulative targets + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + if model.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 13:], cn, device=device) # targets + t[range(n), tcls[i]] = cp + lcls += BCEcls(ps[:, 13:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + #landmarks loss + #plandmarks = ps[:,5:13].sigmoid() * 8. - 4. + plandmarks = ps[:,5:13] + + plandmarks[:, 0:2] = plandmarks[:, 0:2] * anchors[i] + plandmarks[:, 2:4] = plandmarks[:, 2:4] * anchors[i] + plandmarks[:, 4:6] = plandmarks[:, 4:6] * anchors[i] + plandmarks[:, 6:8] = plandmarks[:, 6:8] * anchors[i] + # plandmarks[:, 8:10] = plandmarks[:,8:10] * anchors[i] + + lmark += landmarks_loss(plandmarks, tlandmarks[i], lmks_mask[i]) + + + lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss + + s = 3 / no # output count scaling + lbox *= h['box'] * s + lobj *= h['obj'] * s * (1.4 if no == 4 else 1.) + lcls *= h['cls'] * s + lmark *= h['landmark'] * s + + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + lmark + return loss * bs, torch.cat((lbox, lobj, lcls, lmark, loss)).detach() + + +def build_targets(p, targets, model): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + na, nt = det.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch, landmarks, lmks_mask = [], [], [], [], [], [] + #gain = torch.ones(7, device=targets.device) # normalized to gridspace gain + gain = torch.ones(15, device=targets.device) + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(det.nl): + anchors = det.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + #landmarks 10 + gain[6:14] = torch.tensor(p[i].shape)[[3, 2, 3, 2, 3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 14].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + #landmarks + lks = t[:,6:14] + #lks_mask = lks > 0 + #lks_mask = lks_mask.float() + lks_mask = torch.where(lks < 0, torch.full_like(lks, 0.), torch.full_like(lks, 1.0)) + + #应该是关键点的坐标除以anch的宽高才对，便于模型学习。使用gwh会导致不同关键点的编码不同，没有统一的参考标准 + + lks[:, [0, 1]] = (lks[:, [0, 1]] - gij) + lks[:, [2, 3]] = (lks[:, [2, 3]] - gij) + lks[:, [4, 5]] = (lks[:, [4, 5]] - gij) + lks[:, [6, 7]] = (lks[:, [6, 7]] - gij) + # lks[:, [8, 9]] = (lks[:, [8, 9]] - gij) + + ''' + #anch_w = torch.ones(5, device=targets.device).fill_(anchors[0][0]) + #anch_wh = torch.ones(5, device=targets.device) + anch_f_0 = (a == 0).unsqueeze(1).repeat(1, 5) + anch_f_1 = (a == 1).unsqueeze(1).repeat(1, 5) + anch_f_2 = (a == 2).unsqueeze(1).repeat(1, 5) + lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_0, lks[:, [0, 2, 4, 6, 8]] / anchors[0][0], lks[:, [0, 2, 4, 6, 8]]) + lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_1, lks[:, [0, 2, 4, 6, 8]] / anchors[1][0], lks[:, [0, 2, 4, 6, 8]]) + lks[:, [0, 2, 4, 6, 8]] = torch.where(anch_f_2, lks[:, [0, 2, 4, 6, 8]] / anchors[2][0], lks[:, [0, 2, 4, 6, 8]]) + + lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_0, lks[:, [1, 3, 5, 7, 9]] / anchors[0][1], lks[:, [1, 3, 5, 7, 9]]) + lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_1, lks[:, [1, 3, 5, 7, 9]] / anchors[1][1], lks[:, [1, 3, 5, 7, 9]]) + lks[:, [1, 3, 5, 7, 9]] = torch.where(anch_f_2, lks[:, [1, 3, 5, 7, 9]] / anchors[2][1], lks[:, [1, 3, 5, 7, 9]]) + + #new_lks = lks[lks_mask>0] + #print('new_lks: min --- ', torch.min(new_lks), ' max --- ', torch.max(new_lks)) + + lks_mask_1 = torch.where(lks < -3, torch.full_like(lks, 0.), torch.full_like(lks, 1.0)) + lks_mask_2 = torch.where(lks > 3, torch.full_like(lks, 0.), torch.full_like(lks, 1.0)) + + lks_mask_new = lks_mask * lks_mask_1 * lks_mask_2 + lks_mask_new[:, 0] = lks_mask_new[:, 0] * lks_mask_new[:, 1] + lks_mask_new[:, 1] = lks_mask_new[:, 0] * lks_mask_new[:, 1] + lks_mask_new[:, 2] = lks_mask_new[:, 2] * lks_mask_new[:, 3] + lks_mask_new[:, 3] = lks_mask_new[:, 2] * lks_mask_new[:, 3] + lks_mask_new[:, 4] = lks_mask_new[:, 4] * lks_mask_new[:, 5] + lks_mask_new[:, 5] = lks_mask_new[:, 4] * lks_mask_new[:, 5] + lks_mask_new[:, 6] = lks_mask_new[:, 6] * lks_mask_new[:, 7] + lks_mask_new[:, 7] = lks_mask_new[:, 6] * lks_mask_new[:, 7] + lks_mask_new[:, 8] = lks_mask_new[:, 8] * lks_mask_new[:, 9] + lks_mask_new[:, 9] = lks_mask_new[:, 8] * lks_mask_new[:, 9] + ''' + lks_mask_new = lks_mask + lmks_mask.append(lks_mask_new) + landmarks.append(lks) + #print('lks: ', lks.size()) + + return tcls, tbox, indices, anch, landmarks, lmks_mask diff --git a/algorithm/Car_recognition/utils/metrics.py b/algorithm/Car_recognition/utils/metrics.py new file mode 100644 index 0000000..99d5bcf --- /dev/null +++ b/algorithm/Car_recognition/utils/metrics.py @@ -0,0 +1,200 @@ +# Model validation metrics + +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from . import general + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]): + """ Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes = np.unique(target_cls) + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + pr_score = 0.1 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898 + s = [unique_classes.shape[0], tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95) + ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = (target_cls == c).sum() # number of labels + n_p = i.sum() # number of predictions + + if n_p == 0 or n_l == 0: + continue + else: + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + 1e-16) # recall curve + r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0]) # r at pr_score, negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0]) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) + if plot and (j == 0): + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 score (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + 1e-16) + + if plot: + plot_pr_curve(px, py, ap, save_dir, names) + + return p, r, ap, f1, unique_classes.astype('int32') + + +def compute_ap(recall, precision): + """ Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + # Returns + Average precision, precision curve, recall curve + """ + + # Append sentinel values to beginning and end + mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) + mpre = np.concatenate(([1.], precision, [0.])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = 'interp' # methods: 'continuous', 'interp' + if method == 'interp': + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix + def __init__(self, nc, conf=0.25, iou_thres=0.45): + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = general.box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(np.int16) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[gc, detection_classes[m1[j]]] += 1 # correct + else: + self.matrix[gc, self.nc] += 1 # background FP + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[self.nc, dc] += 1 # background FN + + def matrix(self): + return self.matrix + + def plot(self, save_dir='', names=()): + try: + import seaborn as sn + + array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig = plt.figure(figsize=(12, 9), tight_layout=True) + sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size + labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels + sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, + xticklabels=names + ['background FN'] if labels else "auto", + yticklabels=names + ['background FP'] if labels else "auto").set_facecolor((1, 1, 1)) + fig.axes[0].set_xlabel('True') + fig.axes[0].set_ylabel('Predicted') + fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) + except Exception as e: + pass + + def print(self): + for i in range(self.nc + 1): + print(' '.join(map(str, self.matrix[i]))) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + +def plot_pr_curve(px, py, ap, save_dir='.', names=()): + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # show mAP in legend if < 10 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0]) # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) + ax.set_xlabel('Recall') + ax.set_ylabel('Precision') + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250) diff --git a/algorithm/Car_recognition/utils/plots.py b/algorithm/Car_recognition/utils/plots.py new file mode 100644 index 0000000..41adef8 --- /dev/null +++ b/algorithm/Car_recognition/utils/plots.py @@ -0,0 +1,413 @@ +# Plotting utils + +import glob +import math +import os +import random +from copy import copy +from pathlib import Path + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import torch +import yaml +from PIL import Image, ImageDraw +from scipy.signal import butter, filtfilt + +from algorithm.Car_recognition.utils.general import xywh2xyxy, xyxy2xywh +from algorithm.Car_recognition.utils.metrics import fitness + +# Settings +matplotlib.rc('font', **{'size': 11}) +matplotlib.use('Agg') # for writing to files only + + +def color_list(): + # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb + def hex2rgb(h): + return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + + return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']] + + +def hist2d(x, y, n=100): + # 2d histogram used in labels.png and evolve.png + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype='low', analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def plot_one_box(x, img, color=None, label=None, line_thickness=None): + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + +def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() + # Compares the two methods for width-height anchor multiplication + # https://github.com/ultralytics/yolov3/issues/168 + x = np.arange(-4.0, 4.0, .1) + ya = np.exp(x) + yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 + + fig = plt.figure(figsize=(6, 3), tight_layout=True) + plt.plot(x, ya, '.-', label='YOLOv3') + plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2') + plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6') + plt.xlim(left=-4, right=4) + plt.ylim(bottom=0, top=6) + plt.xlabel('input') + plt.ylabel('output') + plt.grid() + plt.legend() + fig.savefig('comparison.png', dpi=200) + + +def output_to_target(output): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] + targets = [] + for i, o in enumerate(output): + for *box, conf, cls in o.cpu().numpy(): + targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) + return np.array(targets) + + +def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16): + # Plot image grid with labels + + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + # un-normalise + if np.max(images[0]) <= 1: + images *= 255 + + tl = 3 # line thickness + tf = max(tl - 1, 1) # font thickness + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs ** 0.5) # number of subplots (square) + + # Check if we should resize + scale_factor = max_size / max(h, w) + if scale_factor < 1: + h = math.ceil(scale_factor * h) + w = math.ceil(scale_factor * w) + + # colors = color_list() # list of colors + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, img in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + + block_x = int(w * (i // ns)) + block_y = int(h * (i % ns)) + + img = img.transpose(1, 2, 0) + if scale_factor < 1: + img = cv2.resize(img, (w, h)) + + mosaic[block_y:block_y + h, block_x:block_x + w, :] = img + if len(targets) > 0: + image_targets = targets[targets[:, 0] == i] + boxes = xywh2xyxy(image_targets[:, 2:6]).T + classes = image_targets[:, 1].astype('int') + labels = image_targets.shape[1] == 6 # labels if no conf column + conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale_factor < 1: # absolute coords need scale if image scales + boxes *= scale_factor + boxes[[0, 2]] += block_x + boxes[[1, 3]] += block_y + for j, box in enumerate(boxes.T): + cls = int(classes[j]) + # color = colors[cls % len(colors)] + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j]) + plot_one_box(box, mosaic, label=label, color=None, line_thickness=tl) + + # Draw image filename labels + if paths: + label = Path(paths[i]).name[:40] # trim to 40 char + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf, + lineType=cv2.LINE_AA) + + # Image border + cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3) + + if fname: + r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size + mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA) + # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save + Image.fromarray(mosaic).save(fname) # PIL save + return mosaic + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): + # Plot LR simulating training for full epochs + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]['lr']) + plt.plot(y, '.-', label='LR') + plt.xlabel('epoch') + plt.ylabel('LR') + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / 'LR.png', dpi=200) + plt.close() + + +def plot_test_txt(): # from utils.plots import *; plot_test() + # Plot test.txt histograms + x = np.loadtxt('test.txt', dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect('equal') + plt.savefig('hist2d.png', dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig('hist1d.png', dpi=200) + + +def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() + # Plot targets.txt histograms + x = np.loadtxt('targets.txt', dtype=np.float32).T + s = ['x targets', 'y targets', 'width targets', 'height targets'] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig('targets.jpg', dpi=200) + + +def plot_study_txt(path='study/', x=None): # from utils.plots import *; plot_study_txt() + # Plot study.txt generated by test.py + fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) + ax = ax.ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]: + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] + for i in range(7): + ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8, + label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) + + ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') + + ax2.grid() + ax2.set_yticks(np.arange(30, 60, 5)) + ax2.set_xlim(0, 30) + ax2.set_ylim(29, 51) + ax2.set_xlabel('GPU Speed (ms/img)') + ax2.set_ylabel('COCO AP val') + ax2.legend(loc='lower right') + plt.savefig('test_study.png', dpi=300) + + +def plot_labels(labels, save_dir=Path(''), loggers=None): + # plot dataset labels + print('Plotting labels... ') + c, b = labels[:, 0], labels[:, 1:5].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + colors = color_list() + x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) + + # seaborn correlogram + sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use('svg') # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + ax[0].set_xlabel('classes') + sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) + sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + # for cls, *box in labels[:1000]: + # ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10]) # plot + ax[1].imshow(img) + ax[1].axis('off') + + for a in [0, 1, 2, 3]: + for s in ['top', 'right', 'left', 'bottom']: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / 'labels.jpg', dpi=200) + matplotlib.use('Agg') + plt.close() + + # loggers + for k, v in loggers.items() or {}: + if k == 'wandb' and v: + v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}) + + +def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() + # Plot hyperparameter evolution results in evolve.txt + with open(yaml_file) as f: + hyp = yaml.load(f, Loader=yaml.SafeLoader) + x = np.loadtxt('evolve.txt', ndmin=2) + f = fitness(x) + # weights = (f - f.min()) ** 2 # for weighted results + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc('font', **{'size': 8}) + for i, (k, v) in enumerate(hyp.items()): + y = x[:, i + 7] + # mu = (y * weights).sum() / weights.sum() # best weighted result + mu = y[f.argmax()] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none') + plt.plot(mu, f.max(), 'k+', markersize=15) + plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print('%15s: %.3g' % (k, mu)) + plt.savefig('evolve.png', dpi=200) + print('\nPlot saved as evolve.png') + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=''): + # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] + files = list(Path(save_dir).glob('frames*.txt')) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = (results[0] - results[0].min()) # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace('frames_', '') + a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) + a.set_title(s[i]) + a.set_xlabel('time (s)') + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ['top', 'right']: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print('Warning: Plotting error for %s; %s' % (f, e)) + + ax[1].legend() + plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) + + +def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay() + # Plot training 'results*.txt', overlaying train and val losses + s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends + t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles + for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) + ax = ax.ravel() + for i in range(5): + for j in [i, i + 5]: + y = results[j, x] + ax[i].plot(x, y, marker='.', label=s[j]) + # y_smooth = butter_lowpass_filtfilt(y) + # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) + + ax[i].set_title(t[i]) + ax[i].legend() + ax[i].set_ylabel(f) if i == 0 else None # add filename + fig.savefig(f.replace('.txt', '.png'), dpi=200) + + +def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''): + # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp') + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall', + 'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95'] + if bucket: + # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] + files = ['results%g.txt' % x for x in id] + c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id) + os.system(c) + else: + files = list(Path(save_dir).glob('results*.txt')) + assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + for i in range(10): + y = results[i, x] + if i in [0, 1, 2, 5, 6, 7]: + y[y == 0] = np.nan # don't show zero loss values + # y /= y[0] # normalize + label = labels[fi] if len(labels) else f.stem + ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8) + ax[i].set_title(s[i]) + # if i in [5, 6, 7]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print('Warning: Plotting error for %s; %s' % (f, e)) + + ax[1].legend() + fig.savefig(Path(save_dir) / 'results.png', dpi=200) diff --git a/algorithm/Car_recognition/utils/torch_utils.py b/algorithm/Car_recognition/utils/torch_utils.py new file mode 100644 index 0000000..2cb09e7 --- /dev/null +++ b/algorithm/Car_recognition/utils/torch_utils.py @@ -0,0 +1,294 @@ +# PyTorch utils + +import logging +import math +import os +import subprocess +import time +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path + +import torch +import torch.backends.cudnn as cudnn +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +try: + import thop # for FLOPS computation +except ImportError: + thop = None +logger = logging.getLogger(__name__) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + """ + Decorator to make all processes in distributed training wait for each local_master to do something. + """ + if local_rank not in [-1, 0]: + torch.distributed.barrier() + yield + if local_rank == 0: + torch.distributed.barrier() + + +def init_torch_seeds(seed=0): + # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html + torch.manual_seed(seed) + if seed == 0: # slower, more reproducible + cudnn.benchmark, cudnn.deterministic = False, True + else: # faster, less reproducible + cudnn.benchmark, cudnn.deterministic = True, False + + +def git_describe(): + # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe + if Path('.git').exists(): + return subprocess.check_output('git describe --tags --long --always', shell=True).decode('utf-8')[:-1] + else: + return '' + + +def select_device(device='', batch_size=None): + # device = 'cpu' or '0' or '0,1,2,3' + s = f'YOLOv5 {git_describe()} torch {torch.__version__} ' # string + cpu = device.lower() == 'cpu' + if cpu: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable + assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability + + cuda = not cpu and torch.cuda.is_available() + if cuda: + n = torch.cuda.device_count() + if n > 1 and batch_size: # check that batch_size is compatible with device_count + assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' + space = ' ' * len(s) + for i, d in enumerate(device.split(',') if device else range(n)): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB + else: + s += 'CPU\n' + + logger.info(s) # skip a line + return torch.device('cuda:0' if cuda else 'cpu') + + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(x, ops, n=100, device=None): + # profile a pytorch module or list of modules. Example usage: + # x = torch.randn(16, 3, 640, 640) # input + # m1 = lambda x: x * torch.sigmoid(x) + # m2 = nn.SiLU() + # profile(x, [m1, m2], n=100) # profile speed over 100 iterations + + device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + x = x.to(device) + x.requires_grad = True + print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '') + print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}") + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, 'to') else m # device + m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type + dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS + except: + flops = 0 + + for _ in range(n): + t[0] = time_synchronized() + y = m(x) + t[1] = time_synchronized() + try: + _ = y.sum().backward() + t[2] = time_synchronized() + except: # no backward method + t[2] = float('nan') + dtf += (t[1] - t[0]) * 1000 / n # ms per op forward + dtb += (t[2] - t[1]) * 1000 / n # ms per op backward + + s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' + s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' + p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters + print(f'{p:12.4g}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') + + +def is_parallel(model): + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def intersect_dicts(da, db, exclude=()): + # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values + return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape} + + +def initialize_weights(model): + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + # Finds layer indices matching module class 'mclass' + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + # Return global model sparsity + a, b = 0., 0. + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + # Prune model to requested global sparsity + import torch.nn.utils.prune as prune + print('Pruning model... ', end='') + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name='weight', amount=amount) # prune + prune.remove(m, 'weight') # make permanent + print(' %.3g global sparsity' % sparsity(model)) + + +def fuse_conv_and_bn(conv, bn): + # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + fusedconv = nn.Conv2d(conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + groups=conv.groups, + bias=True).requires_grad_(False).to(conv.weight.device) + + # prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size())) + + # prepare spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, img_size=640): + # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace('module_list.', '') + print('%5g %40s %9s %12g %20s %10.3g %10.3g' % + (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + + try: # FLOPS + from thop import profile + stride = int(model.stride.max()) if hasattr(model, 'stride') else 32 + img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input + flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS + img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float + fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS + except (ImportError, Exception): + fs = '' + + logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") + + +def load_classifier(name='resnet101', n=2): + # Loads a pretrained model reshaped to n-class output + model = torchvision.models.__dict__[name](pretrained=True) + + # ResNet model properties + # input_size = [3, 224, 224] + # input_space = 'RGB' + # input_range = [0, 1] + # mean = [0.485, 0.456, 0.406] + # std = [0.229, 0.224, 0.225] + + # Reshape output to n classes + filters = model.fc.weight.shape[1] + model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) + model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) + model.fc.out_features = n + return model + + +def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) + # scales img(bs,3,y,x) by ratio constrained to gs-multiple + if ratio == 1.0: + return img + else: + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize + if not same_shape: # pad/crop img + h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] + return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith('_') or k in exclude: + continue + else: + setattr(a, k, v) + + +class ModelEMA: + """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models + Keep a moving average of everything in the model state_dict (parameters and buffers). + This is intended to allow functionality like + https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + A smoothed version of the weights is necessary for some training schemes to perform well. + This class is sensitive where it is initialized in the sequence of model init, + GPU assignment and distributed training wrappers. + """ + + def __init__(self, model, decay=0.9999, updates=0): + # Create EMA + self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA + # if next(model.parameters()).device.type != 'cpu': + # self.ema.half() # FP16 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay(self.updates) + + msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: + v *= d + v += (1. - d) * msd[k].detach() + + def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) diff --git a/algorithm/Car_recognition/utils/wandb_logging/__init__.py b/algorithm/Car_recognition/utils/wandb_logging/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/Car_recognition/utils/wandb_logging/log_dataset.py b/algorithm/Car_recognition/utils/wandb_logging/log_dataset.py new file mode 100644 index 0000000..d7a521f --- /dev/null +++ b/algorithm/Car_recognition/utils/wandb_logging/log_dataset.py @@ -0,0 +1,24 @@ +import argparse + +import yaml + +from wandb_utils import WandbLogger + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def create_dataset_artifact(opt): + with open(opt.data) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') + parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') + parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') + opt = parser.parse_args() + opt.resume = False # Explicitly disallow resume check for dataset upload job + + create_dataset_artifact(opt) diff --git a/algorithm/Car_recognition/utils/wandb_logging/wandb_utils.py b/algorithm/Car_recognition/utils/wandb_logging/wandb_utils.py new file mode 100644 index 0000000..d8f50ae --- /dev/null +++ b/algorithm/Car_recognition/utils/wandb_logging/wandb_utils.py @@ -0,0 +1,306 @@ +import json +import sys +from pathlib import Path + +import torch +import yaml +from tqdm import tqdm + +sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path +from utils.datasets import LoadImagesAndLabels +from utils.datasets import img2label_paths +from utils.general import colorstr, xywh2xyxy, check_dataset + +try: + import wandb + from wandb import init, finish +except ImportError: + wandb = None + +WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' + + +def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): + return from_string[len(prefix):] + + +def check_wandb_config_file(data_config_file): + wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path + if Path(wandb_config).is_file(): + return wandb_config + return data_config_file + + +def get_run_info(run_path): + run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) + run_id = run_path.stem + project = run_path.parent.stem + model_artifact_name = 'run_' + run_id + '_model' + return run_id, project, model_artifact_name + + +def check_wandb_resume(opt): + process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None + if isinstance(opt.resume, str): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + if opt.global_rank not in [-1, 0]: # For resuming DDP runs + run_id, project, model_artifact_name = get_run_info(opt.resume) + api = wandb.Api() + artifact = api.artifact(project + '/' + model_artifact_name + ':latest') + modeldir = artifact.download() + opt.weights = str(Path(modeldir) / "last.pt") + return True + return None + + +def process_wandb_config_ddp_mode(opt): + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict + train_dir, val_dir = None, None + if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) + train_dir = train_artifact.download() + train_path = Path(train_dir) / 'data/images/' + data_dict['train'] = str(train_path) + + if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): + api = wandb.Api() + val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) + val_dir = val_artifact.download() + val_path = Path(val_dir) / 'data/images/' + data_dict['val'] = str(val_path) + if train_dir or val_dir: + ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') + with open(ddp_data_path, 'w') as f: + yaml.dump(data_dict, f) + opt.data = ddp_data_path + + +class WandbLogger(): + def __init__(self, opt, name, run_id, data_dict, job_type='Training'): + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict + # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call + if isinstance(opt.resume, str): # checks resume from artifact + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + run_id, project, model_artifact_name = get_run_info(opt.resume) + model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name + assert wandb, 'install wandb to resume wandb runs' + # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config + self.wandb_run = wandb.init(id=run_id, project=project, resume='allow') + opt.resume = model_artifact_name + elif self.wandb: + self.wandb_run = wandb.init(config=opt, + resume="allow", + project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, + name=name, + job_type=job_type, + id=run_id) if not wandb.run else wandb.run + if self.wandb_run: + if self.job_type == 'Training': + if not opt.resume: + wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict + # Info useful for resuming from artifacts + self.wandb_run.config.opt = vars(opt) + self.wandb_run.config.data_dict = wandb_data_dict + self.data_dict = self.setup_training(opt, data_dict) + if self.job_type == 'Dataset Creation': + self.data_dict = self.check_and_upload_dataset(opt) + else: + prefix = colorstr('wandb: ') + print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") + + def check_and_upload_dataset(self, opt): + assert wandb, 'Install wandb to upload dataset' + check_dataset(self.data_dict) + config_path = self.log_dataset_artifact(opt.data, + opt.single_cls, + 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) + print("Created dataset config file ", config_path) + with open(config_path) as f: + wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader) + return wandb_data_dict + + def setup_training(self, opt, data_dict): + self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + modeldir, _ = self.download_model_artifact(opt) + if modeldir: + self.weights = Path(modeldir) / "last.pt" + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( + self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ + config.opt['hyp'] + data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume + if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download + self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), + opt.artifact_alias) + self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), + opt.artifact_alias) + self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None + if self.train_artifact_path is not None: + train_path = Path(self.train_artifact_path) / 'data/images/' + data_dict['train'] = str(train_path) + if self.val_artifact_path is not None: + val_path = Path(self.val_artifact_path) / 'data/images/' + data_dict['val'] = str(val_path) + self.val_table = self.val_artifact.get("val") + self.map_val_table_path() + if self.val_artifact is not None: + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + return data_dict + + def download_dataset_artifact(self, path, alias): + if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): + dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) + assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" + datadir = dataset_artifact.download() + return datadir, dataset_artifact + return None, None + + def download_model_artifact(self, opt): + if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): + model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") + assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' + modeldir = model_artifact.download() + epochs_trained = model_artifact.metadata.get('epochs_trained') + total_epochs = model_artifact.metadata.get('total_epochs') + assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( + total_epochs) + return modeldir, model_artifact + return None, None + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ + 'original_url': str(path), + 'epochs_trained': epoch + 1, + 'save period': opt.save_period, + 'project': opt.project, + 'total_epochs': opt.epochs, + 'fitness_score': fitness_score + }) + model_artifact.add_file(str(path / 'last.pt'), name='last.pt') + wandb.log_artifact(model_artifact, + aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) + print("Saving model artifact on epoch ", epoch + 1) + + def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): + with open(data_file) as f: + data = yaml.load(f, Loader=yaml.SafeLoader) # data dict + nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) + names = {k: v for k, v in enumerate(names)} # to index dictionary + self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['train']), names, name='train') if data.get('train') else None + self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( + data['val']), names, name='val') if data.get('val') else None + if data.get('train'): + data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') + if data.get('val'): + data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') + path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path + data.pop('download', None) + with open(path, 'w') as f: + yaml.dump(data, f) + + if self.job_type == 'Training': # builds correct artifact pipeline graph + self.wandb_run.use_artifact(self.val_artifact) + self.wandb_run.use_artifact(self.train_artifact) + self.val_artifact.wait() + self.val_table = self.val_artifact.get('val') + self.map_val_table_path() + else: + self.wandb_run.log_artifact(self.train_artifact) + self.wandb_run.log_artifact(self.val_artifact) + return path + + def map_val_table_path(self): + self.val_table_map = {} + print("Mapping dataset") + for i, data in enumerate(tqdm(self.val_table.data)): + self.val_table_map[data[3]] = data[0] + + def create_dataset_table(self, dataset, class_to_id, name='dataset'): + # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging + artifact = wandb.Artifact(name=name, type="dataset") + img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None + img_files = tqdm(dataset.img_files) if not img_files else img_files + for img_file in img_files: + if Path(img_file).is_dir(): + artifact.add_dir(img_file, name='data/images') + labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) + artifact.add_dir(labels_path, name='data/labels') + else: + artifact.add_file(img_file, name='data/images/' + Path(img_file).name) + label_file = Path(img2label_paths([img_file])[0]) + artifact.add_file(str(label_file), + name='data/labels/' + label_file.name) if label_file.exists() else None + table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) + for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): + height, width = shapes[0] + labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height]) + box_data, img_classes = [], {} + for cls, *xyxy in labels[:, 1:].tolist(): + cls = int(cls) + box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": cls, + "box_caption": "%s" % (class_to_id[cls]), + "scores": {"acc": 1}, + "domain": "pixel"}) + img_classes[cls] = class_to_id[cls] + boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space + table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), + Path(paths).name) + artifact.add(table, name) + return artifact + + def log_training_progress(self, predn, path, names): + if self.val_table and self.result_table: + class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) + box_data = [] + total_conf = 0 + for *xyxy, conf, cls in predn.tolist(): + if conf >= 0.25: + box_data.append( + {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"}) + total_conf = total_conf + conf + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + id = self.val_table_map[Path(path).name] + self.result_table.add_data(self.current_epoch, + id, + wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), + total_conf / max(1, len(box_data)) + ) + + def log(self, log_dict): + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self, best_result=False): + if self.wandb_run: + wandb.log(self.log_dict) + self.log_dict = {} + if self.result_artifact: + train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") + self.result_artifact.add(train_results, 'result') + wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), + ('best' if best_result else '')]) + self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) + self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") + + def finish_run(self): + if self.wandb_run: + if self.log_dict: + wandb.log(self.log_dict) + wandb.run.finish() diff --git a/algorithm/Car_recognition/widerface_evaluate/README.md b/algorithm/Car_recognition/widerface_evaluate/README.md new file mode 100644 index 0000000..95952b7 --- /dev/null +++ b/algorithm/Car_recognition/widerface_evaluate/README.md @@ -0,0 +1,27 @@ +# WiderFace-Evaluation +Python Evaluation Code for [Wider Face Dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) + + +## Usage + + +##### before evaluating .... + +```` +python3 setup.py build_ext --inplace +```` + +##### evaluating + +**GroungTruth:** `wider_face_val.mat`, `wider_easy_val.mat`, `wider_medium_val.mat`,`wider_hard_val.mat` + +```` +python3 evaluation.py -p -g +```` + +## Bugs & Problems +please issue + +## Acknowledgements + +some code borrowed from Sergey Karayev diff --git a/algorithm/Car_recognition/widerface_evaluate/box_overlaps.c b/algorithm/Car_recognition/widerface_evaluate/box_overlaps.c new file mode 100644 index 0000000..9a890a0 --- /dev/null +++ b/algorithm/Car_recognition/widerface_evaluate/box_overlaps.c @@ -0,0 +1,7813 @@ +/* Generated by Cython 0.29.21 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [ + "/home/deepcam/miniconda3/lib/python3.7/site-packages/numpy/core/include/numpy/arrayobject.h", + "/home/deepcam/miniconda3/lib/python3.7/site-packages/numpy/core/include/numpy/ufuncobject.h" + ], + "include_dirs": [ + "/home/deepcam/miniconda3/lib/python3.7/site-packages/numpy/core/include" + ], + "name": "bbox", + "sources": [ + "box_overlaps.pyx" + ] + }, + "module_name": "bbox" +} +END: Cython Metadata */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) + #error Cython requires Python 2.6+ or Python 3.3+. +#else +#define CYTHON_ABI "0_29_21" +#define CYTHON_HEX_VERSION 0x001D15F0 +#define CYTHON_FUTURE_DIVISION 0 +#include +#ifndef offsetof + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#define __PYX_COMMA , +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x02070000 + #define HAVE_LONG_LONG + #endif +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_PYTYPE_LOOKUP + #define CYTHON_USE_PYTYPE_LOOKUP 0 + #if PY_VERSION_HEX < 0x03050000 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef 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Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#ifndef METH_STACKLESS + #define METH_STACKLESS 0 +#endif +#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) + #ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + #endif + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); + typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast + #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1 + #define PyMem_RawMalloc(n) PyMem_Malloc(n) + #define PyMem_RawRealloc(p, n) PyMem_Realloc(p, n) + #define PyMem_RawFree(p) PyMem_Free(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif +#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#elif PY_VERSION_HEX >= 0x03060000 + #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() +#elif PY_VERSION_HEX >= 0x03000000 + #define __Pyx_PyThreadState_Current PyThreadState_GET() +#else + #define __Pyx_PyThreadState_Current _PyThreadState_Current +#endif +#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) +#include "pythread.h" +#define Py_tss_NEEDS_INIT 0 +typedef int Py_tss_t; +static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { + *key = PyThread_create_key(); + return 0; +} +static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { + Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); + *key = Py_tss_NEEDS_INIT; + return key; +} +static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { + PyObject_Free(key); +} +static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { + return *key != Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { + PyThread_delete_key(*key); + *key = Py_tss_NEEDS_INIT; +} +static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { + return PyThread_set_key_value(*key, value); +} +static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { + return PyThread_get_key_value(*key); +} +#endif +#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) +#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) +#else +#define __Pyx_PyDict_NewPresized(n) PyDict_New() +#endif +#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS +#define __Pyx_PyDict_GetItemStr(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) +#else +#define __Pyx_PyDict_GetItemStr(dict, name) PyDict_GetItem(dict, name) +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) + #else + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) + #endif +#else + #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#ifndef PyObject_Unicode + #define PyObject_Unicode PyObject_Str +#endif +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#if PY_VERSION_HEX >= 0x030900A4 + #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size) +#else + #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt) + #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size) +#endif +#if CYTHON_ASSUME_SAFE_MACROS + #define __Pyx_PySequence_SIZE(seq) Py_SIZE(seq) +#else + #define __Pyx_PySequence_SIZE(seq) PySequence_Size(seq) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif +#else + #define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef __Pyx_PyAsyncMethodsStruct + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; +#endif + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif + +#define __PYX_MARK_ERR_POS(f_index, lineno) \ + { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; } +#define __PYX_ERR(f_index, lineno, Ln_error) \ + { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; } + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__bbox +#define __PYX_HAVE_API__bbox +/* Early includes */ +#include +#include +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" + + /* NumPy API declarations from "numpy/__init__.pxd" */ + +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS) +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8) +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) { + return (size_t) i < (size_t) limit; +} +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) + #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value)) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s)) +#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) { + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b); +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +#define __Pyx_PySequence_Tuple(obj)\ + (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj)) +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_ASSUME_SAFE_MACROS +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ +static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; } + +static PyObject *__pyx_m = NULL; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_cython_runtime = NULL; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +/* Header.proto */ +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "box_overlaps.pyx", + "__init__.pxd", + "type.pxd", +}; +/* BufferFormatStructs.proto */ +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":697 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":698 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":699 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":700 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":704 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":705 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":706 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":707 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":711 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":712 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":721 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":722 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":723 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":725 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":726 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":727 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":729 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":730 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":732 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":733 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":734 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; 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r = NULL; __Pyx_DECREF(tmp);}} while(0) + +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +/* GetBuiltinName.proto */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +/* RaiseArgTupleInvalid.proto */ +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +/* RaiseDoubleKeywords.proto */ +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +/* ParseKeywords.proto */ +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +/* ArgTypeTest.proto */ +#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ + ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\ + __Pyx__ArgTypeTest(obj, type, name, exact)) +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); 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+#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ + (VAR) = __pyx_dict_cached_value;\ + } else {\ + (VAR) = __pyx_dict_cached_value = (LOOKUP);\ + __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ + }\ +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); +#else +#define __PYX_GET_DICT_VERSION(dict) (0) +#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) +#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); +#endif + +/* GetModuleGlobalName.proto */ +#if CYTHON_USE_DICT_VERSIONS +#define __Pyx_GetModuleGlobalName(var, name) {\ + static PY_UINT64_T __pyx_dict_version = 0;\ + static PyObject *__pyx_dict_cached_value = NULL;\ + (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ + (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ + __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +#define __Pyx_GetModuleGlobalNameUncached(var, name) {\ + PY_UINT64_T __pyx_dict_version;\ + PyObject *__pyx_dict_cached_value;\ + (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ +} +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); +#else +#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) +#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); +#endif + +/* PyObjectCall.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +/* ExtTypeTest.proto */ +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +/* BufferIndexError.proto */ +static void __Pyx_RaiseBufferIndexError(int axis); + +#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1) +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current; +#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#define __Pyx_PyErr_Occurred() PyErr_Occurred() +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL) +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) +#else +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#endif +#else +#define __Pyx_PyErr_Clear() PyErr_Clear() +#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif + +/* DictGetItem.proto */ +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key); +#define __Pyx_PyObject_Dict_GetItem(obj, name)\ + (likely(PyDict_CheckExact(obj)) ?\ + __Pyx_PyDict_GetItem(obj, name) : PyObject_GetItem(obj, name)) +#else +#define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#define __Pyx_PyObject_Dict_GetItem(obj, name) PyObject_GetItem(obj, name) +#endif + +/* RaiseTooManyValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +/* RaiseNeedMoreValuesToUnpack.proto */ +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +/* RaiseNoneIterError.proto */ +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +/* RaiseException.proto */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#define __Pyx_BUILD_ASSERT_EXPR(cond)\ + (sizeof(char [1 - 2*!(cond)]) - 1) +#ifndef Py_MEMBER_SIZE +#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) +#endif + static size_t __pyx_pyframe_localsplus_offset = 0; + #include "frameobject.h" + #define __Pxy_PyFrame_Initialize_Offsets()\ + ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ + (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) + #define __Pyx_PyFrame_GetLocalsplus(frame)\ + (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) +#endif + +/* PyObjectCallMethO.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +/* PyObjectCallOneArg.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +/* GetTopmostException.proto */ +#if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); +#endif + +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* TypeImport.proto */ +#ifndef __PYX_HAVE_RT_ImportType_proto +#define __PYX_HAVE_RT_ImportType_proto +enum __Pyx_ImportType_CheckSize { + __Pyx_ImportType_CheckSize_Error = 0, + __Pyx_ImportType_CheckSize_Warn = 1, + __Pyx_ImportType_CheckSize_Ignore = 2 +}; +static PyTypeObject *__Pyx_ImportType(PyObject* module, const char *module_name, const char *class_name, size_t size, enum __Pyx_ImportType_CheckSize check_size); +#endif + +/* Import.proto */ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +/* CLineInTraceback.proto */ +#ifdef CYTHON_CLINE_IN_TRACEBACK +#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0) +#else +static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line); +#endif + +/* CodeObjectCache.proto */ +typedef struct { + PyCodeObject* code_object; + int code_line; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +/* AddTraceback.proto */ +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +/* BufferStructDeclare.proto */ +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value); + +/* RealImag.proto */ +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) + #if 1 + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +/* Arithmetic.proto */ +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) + #if 1 + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *); + +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +/* CIntFromPy.proto */ +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +/* FastTypeChecks.proto */ +#if CYTHON_COMPILING_IN_CPYTHON +#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); +static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); +#else +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) +#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) +#endif +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) + +/* CheckBinaryVersion.proto */ +static int __Pyx_check_binary_version(void); + +/* InitStrings.proto */ +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'cython' */ + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'cpython.mem' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'bbox' */ +static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_4bbox_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_4bbox_DTYPE_t), { 0 }, 0, 'R', 0, 0 }; +#define __Pyx_MODULE_NAME "bbox" +extern int __pyx_module_is_main_bbox; +int __pyx_module_is_main_bbox = 0; + +/* Implementation of 'bbox' */ +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_RuntimeError; +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_ImportError; +static const char __pyx_k_K[] = "K"; +static const char __pyx_k_N[] = "N"; +static const char __pyx_k_k[] = "k"; +static const char __pyx_k_n[] = "n"; +static const char __pyx_k_ih[] = "ih"; +static const char __pyx_k_iw[] = "iw"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_ua[] = "ua"; +static const char __pyx_k_bbox[] = "bbox"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_name[] = "__name__"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_DTYPE[] = "DTYPE"; +static const char __pyx_k_boxes[] = "boxes"; +static const char __pyx_k_dtype[] = "dtype"; +static const char __pyx_k_float[] = "float"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_box_area[] = "box_area"; +static const char __pyx_k_overlaps[] = "overlaps"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_query_boxes[] = "query_boxes"; +static const char __pyx_k_RuntimeError[] = "RuntimeError"; +static const char __pyx_k_bbox_overlaps[] = "bbox_overlaps"; +static const char __pyx_k_box_overlaps_pyx[] = "box_overlaps.pyx"; +static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; +static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; +static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; +static PyObject *__pyx_n_s_DTYPE; +static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; +static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; +static PyObject *__pyx_n_s_ImportError; +static PyObject *__pyx_n_s_K; +static PyObject *__pyx_n_s_N; +static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; +static PyObject *__pyx_n_s_RuntimeError; +static PyObject *__pyx_n_s_ValueError; +static PyObject *__pyx_n_s_bbox; +static PyObject *__pyx_n_s_bbox_overlaps; 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if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 15, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_2); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_bbox_overlaps, __pyx_t_2) < 0) __PYX_ERR(0, 15, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + + /* "box_overlaps.pyx":1 + * # -------------------------------------------------------- # <<<<<<<<<<<<<< + * # Fast R-CNN + * # Copyright (c) 2015 Microsoft + */ + __pyx_t_2 = __Pyx_PyDict_NewPresized(0); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 1, __pyx_L1_error) + __Pyx_GOTREF(__pyx_t_2); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_2) < 0) __PYX_ERR(0, 1, __pyx_L1_error) + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + + /* "../../../miniconda3/lib/python3.7/site-packages/numpy/__init__.pxd":967 + * raise ImportError("numpy.core.umath failed to import") + * + * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< + * try: + * _import_umath() + */ + + /*--- Wrapped vars code ---*/ + + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + __Pyx_XDECREF(__pyx_t_2); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init bbox", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_CLEAR(__pyx_m); + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init bbox"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if CYTHON_PEP489_MULTI_PHASE_INIT + return (__pyx_m != NULL) ? 0 : -1; + #elif PY_MAJOR_VERSION >= 3 + return __pyx_m; + #else + return; + #endif +} + +/* --- Runtime support code --- */ +/* Refnanny */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule(modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, "RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +/* PyObjectGetAttrStr */ +#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#endif + +/* GetBuiltinName */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +/* RaiseArgTupleInvalid */ +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; + const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +/* RaiseDoubleKeywords */ +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +/* ParseKeywords */ +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +/* ArgTypeTest */ +static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + else if (exact) { + #if PY_MAJOR_VERSION == 2 + if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(__Pyx_TypeCheck(obj, type))) return 1; + } + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); + return 0; +} + +/* IsLittleEndian */ +static CYTHON_INLINE int __Pyx_Is_Little_Endian(void) +{ + union { + uint32_t u32; + uint8_t u8[4]; + } S; + S.u32 = 0x01020304; + return S.u8[0] == 4; +} + +/* BufferFormatCheck */ +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t <= '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case '?': return "'bool'"; + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number, ndim; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ndim = ctx->head->field->type->ndim; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_Is_Little_Endian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + CYTHON_FALLTHROUGH; + case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && + (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + CYTHON_FALLTHROUGH; + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} + +/* BufferGetAndValidate */ + static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (unlikely(info->buf == NULL)) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} +static void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static int __Pyx__GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + buf->buf = NULL; + if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) { + __Pyx_ZeroBuffer(buf); + return -1; + } + if (unlikely(buf->ndim != nd)) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if (unlikely((size_t)buf->itemsize != dtype->size)) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_SafeReleaseBuffer(buf); + return -1; +} + +/* PyDictVersioning */ + #if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; +} +static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { + PyObject **dictptr = NULL; + Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; + if (offset) { +#if CYTHON_COMPILING_IN_CPYTHON + dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); +#else + dictptr = _PyObject_GetDictPtr(obj); +#endif + } + return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; +} +static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { + PyObject *dict = Py_TYPE(obj)->tp_dict; + if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) + return 0; + return obj_dict_version == __Pyx_get_object_dict_version(obj); +} +#endif + +/* GetModuleGlobalName */ + #if CYTHON_USE_DICT_VERSIONS +static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) +#else +static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) +#endif +{ + PyObject *result; +#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 + result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } else if (unlikely(PyErr_Occurred())) { + return NULL; + } +#else + result = PyDict_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } +#endif +#else + result = PyObject_GetItem(__pyx_d, name); + __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) + if (likely(result)) { + return __Pyx_NewRef(result); + } + PyErr_Clear(); +#endif + return __Pyx_GetBuiltinName(name); +} + +/* PyObjectCall */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* ExtTypeTest */ + static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(__Pyx_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +/* BufferIndexError */ + static void __Pyx_RaiseBufferIndexError(int axis) { + PyErr_Format(PyExc_IndexError, + "Out of bounds on buffer access (axis %d)", axis); +} + +/* PyErrFetchRestore */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +} +#endif + +/* DictGetItem */ + #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + if (unlikely(PyTuple_Check(key))) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) { + PyErr_SetObject(PyExc_KeyError, args); + Py_DECREF(args); + } + } else { + PyErr_SetObject(PyExc_KeyError, key); + } + } + return NULL; + } + Py_INCREF(value); + return value; +} +#endif + +/* RaiseTooManyValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +/* RaiseNeedMoreValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +/* RaiseNoneIterError */ + static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +/* RaiseException */ + #if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_PyThreadState_assign + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } + if (cause) { + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +/* PyCFunctionFastCall */ + #if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + int flags = PyCFunction_GET_FLAGS(func); + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS))); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) { + return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL); + } else { + return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs); + } +} +#endif + +/* PyFunctionFastCall */ + #if CYTHON_FAST_PYCALL +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = __Pyx_PyFrame_GetLocalsplus(f); + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, (int)nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif +#endif + +/* PyObjectCallMethO */ + #if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +/* PyObjectCallOneArg */ + #if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif + if (likely(PyCFunction_Check(func))) { + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +/* GetTopmostException */ + #if CYTHON_USE_EXC_INFO_STACK +static _PyErr_StackItem * +__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) +{ + _PyErr_StackItem *exc_info = tstate->exc_info; + while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) && + exc_info->previous_item != NULL) + { + exc_info = exc_info->previous_item; + } + return exc_info; +} +#endif + +/* SaveResetException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); + *type = exc_info->exc_type; + *value = exc_info->exc_value; + *tb = exc_info->exc_traceback; + #else + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + #endif + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + #if CYTHON_USE_EXC_INFO_STACK + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = type; + exc_info->exc_value = value; + exc_info->exc_traceback = tb; + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* PyErrExceptionMatches */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; icurexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + if (unlikely(PyTuple_Check(err))) + return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err); + return __Pyx_PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetException */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) +#endif +{ + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + #if CYTHON_USE_EXC_INFO_STACK + { + _PyErr_StackItem *exc_info = tstate->exc_info; + tmp_type = exc_info->exc_type; + tmp_value = exc_info->exc_value; + tmp_tb = exc_info->exc_traceback; + exc_info->exc_type = local_type; + exc_info->exc_value = local_value; + exc_info->exc_traceback = local_tb; + } + #else + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + #endif + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* TypeImport */ + #ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(PyObject *module, const char *module_name, const char *class_name, + size_t size, enum __Pyx_ImportType_CheckSize check_size) +{ + PyObject *result = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + result = PyObject_GetAttrString(module, class_name); + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if ((size_t)basicsize < size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + goto bad; + } + if (check_size == __Pyx_ImportType_CheckSize_Error && (size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + goto bad; + } + else if (check_size == __Pyx_ImportType_CheckSize_Warn && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility. " + "Expected %zd from C header, got %zd from PyObject", + module_name, class_name, size, basicsize); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(result); + return NULL; +} +#endif + +/* Import */ + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_MAJOR_VERSION < 3 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_MAJOR_VERSION < 3 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, (PyObject *)NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_MAJOR_VERSION < 3 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +/* CLineInTraceback */ + #ifndef CYTHON_CLINE_IN_TRACEBACK +static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) { + PyObject *use_cline; + PyObject *ptype, *pvalue, *ptraceback; +#if CYTHON_COMPILING_IN_CPYTHON + PyObject **cython_runtime_dict; +#endif + if (unlikely(!__pyx_cython_runtime)) { + return c_line; + } + __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); +#if CYTHON_COMPILING_IN_CPYTHON + cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); + if (likely(cython_runtime_dict)) { + __PYX_PY_DICT_LOOKUP_IF_MODIFIED( + use_cline, *cython_runtime_dict, + __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) + } else +#endif + { + PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); + if (use_cline_obj) { + use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True; + Py_DECREF(use_cline_obj); + } else { + PyErr_Clear(); + use_cline = NULL; + } + } + if (!use_cline) { + c_line = 0; + PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False); + } + else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) { + c_line = 0; + } + __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback); + return c_line; +} +#endif + +/* CodeObjectCache */ + static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +/* AddTraceback */ + #include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + PyThreadState *tstate = __Pyx_PyThreadState_Current; + if (c_line) { + c_line = __Pyx_CLineForTraceback(tstate, c_line); + } + py_code = __pyx_find_code_object(c_line ? -c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? -c_line : py_line, py_code); + } + py_frame = PyFrame_New( + tstate, /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if ((0)) {} + view->obj = NULL; + Py_DECREF(obj); +} +#endif + + + /* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_int(unsigned int value) { + const unsigned int neg_one = (unsigned int) ((unsigned int) 0 - (unsigned int) 1), const_zero = (unsigned int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(unsigned int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(unsigned int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(unsigned int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(unsigned int), + little, !is_unsigned); + } +} + +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = (float)(1.0) / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = (float)(1.0) / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_float(a, a); + case 3: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); + case 4: + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = powf(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2f(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_float(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +/* Declarations */ + #if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +/* Arithmetic */ + #if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = (double)(1.0) / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = (double)(1.0) / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } + } + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + return __Pyx_c_prod_double(a, a); + case 3: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); + case 4: + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } else if (b.imag == 0) { + z.real = pow(a.real, b.real); + z.imag = 0; + return z; + } else if (a.real > 0) { + r = a.real; + theta = 0; + } else { + r = -a.real; + theta = atan2(0.0, -1.0); + } + } else { + r = __Pyx_c_abs_double(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) ((enum NPY_TYPES) 0 - (enum NPY_TYPES) 1), const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE unsigned int __Pyx_PyInt_As_unsigned_int(PyObject *x) { + const unsigned int neg_one = (unsigned int) ((unsigned int) 0 - (unsigned int) 1), const_zero = (unsigned int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(unsigned int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (unsigned int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (unsigned int) 0; + case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, digits[0]) + case 2: + if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 2 * PyLong_SHIFT) { + return (unsigned int) (((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 3 * PyLong_SHIFT) { + return (unsigned int) (((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) >= 4 * PyLong_SHIFT) { + return (unsigned int) (((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (unsigned int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(unsigned int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (unsigned int) 0; + case -1: __PYX_VERIFY_RETURN_INT(unsigned int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(unsigned int, digit, +digits[0]) + case -2: + if (8 * sizeof(unsigned int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(unsigned int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + return (unsigned int) ((((((unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(unsigned int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(unsigned int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + return (unsigned int) ((((((((unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(unsigned int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) { + return (unsigned int) (((unsigned int)-1)*(((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(unsigned int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(unsigned int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(unsigned int) - 1 > 4 * PyLong_SHIFT) { + return (unsigned int) ((((((((((unsigned int)digits[3]) << PyLong_SHIFT) | (unsigned int)digits[2]) << PyLong_SHIFT) | (unsigned int)digits[1]) << PyLong_SHIFT) | (unsigned int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(unsigned int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(unsigned int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(unsigned int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + unsigned int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (unsigned int) -1; + } + } else { + unsigned int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (unsigned int) -1; + val = __Pyx_PyInt_As_unsigned_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to unsigned int"); + return (unsigned int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to unsigned int"); + return (unsigned int) -1; +} + +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_IntOrLong(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +/* FastTypeChecks */ + #if CYTHON_COMPILING_IN_CPYTHON +static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { + while (a) { + a = a->tp_base; + if (a == b) + return 1; + } + return b == &PyBaseObject_Type; +} +static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { + PyObject *mro; + if (a == b) return 1; + mro = a->tp_mro; + if (likely(mro)) { + Py_ssize_t i, n; + n = PyTuple_GET_SIZE(mro); + for (i = 0; i < n; i++) { + if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) + return 1; + } + return 0; + } + return __Pyx_InBases(a, b); +} +#if PY_MAJOR_VERSION == 2 +static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { + PyObject *exception, *value, *tb; + int res; + __Pyx_PyThreadState_declare + __Pyx_PyThreadState_assign + __Pyx_ErrFetch(&exception, &value, &tb); + res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + if (!res) { + res = PyObject_IsSubclass(err, exc_type2); + if (unlikely(res == -1)) { + PyErr_WriteUnraisable(err); + res = 0; + } + } + __Pyx_ErrRestore(exception, value, tb); + return res; +} +#else +static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { + int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0; + if (!res) { + res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); + } + return res; +} +#endif +static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { + Py_ssize_t i, n; + assert(PyExceptionClass_Check(exc_type)); + n = PyTuple_GET_SIZE(tuple); +#if PY_MAJOR_VERSION >= 3 + for (i=0; ip) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + if (PyObject_Hash(*t->p) == -1) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +#if !CYTHON_PEP393_ENABLED +static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +} +#else +static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { + if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (likely(PyUnicode_IS_ASCII(o))) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +} +#endif +#endif +static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { + return __Pyx_PyUnicode_AsStringAndSize(o, length); + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) { + int retval; + if (unlikely(!x)) return -1; + retval = __Pyx_PyObject_IsTrue(x); + Py_DECREF(x); + return retval; +} +static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) { +#if PY_MAJOR_VERSION >= 3 + if (PyLong_Check(result)) { + if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1, + "__int__ returned non-int (type %.200s). " + "The ability to return an instance of a strict subclass of int " + "is deprecated, and may be removed in a future version of Python.", + Py_TYPE(result)->tp_name)) { + Py_DECREF(result); + return NULL; + } + return result; + } +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + type_name, type_name, Py_TYPE(result)->tp_name); + Py_DECREF(result); + return NULL; +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS + PyNumberMethods *m; +#endif + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x) || PyLong_Check(x))) +#else + if (likely(PyLong_Check(x))) +#endif + return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS + m = Py_TYPE(x)->tp_as_number; + #if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = m->nb_int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = m->nb_long(x); + } + #else + if (likely(m && m->nb_int)) { + name = "int"; + res = m->nb_int(x); + } + #endif +#else + if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { + res = PyNumber_Int(x); + } +#endif + if (likely(res)) { +#if PY_MAJOR_VERSION < 3 + if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { +#else + if (unlikely(!PyLong_CheckExact(res))) { +#endif + return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(b); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { + return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/algorithm/Car_recognition/widerface_evaluate/evaluation.py b/algorithm/Car_recognition/widerface_evaluate/evaluation.py new file mode 100644 index 0000000..554f278 --- /dev/null +++ b/algorithm/Car_recognition/widerface_evaluate/evaluation.py @@ -0,0 +1,303 @@ +""" +WiderFace evaluation code +author: wondervictor +mail: tianhengcheng@gmail.com +copyright@wondervictor +""" + +import os +import tqdm +import pickle +import argparse +import numpy as np +from scipy.io import loadmat +from bbox import bbox_overlaps +from IPython import embed + + +def get_gt_boxes(gt_dir): + """ gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)""" + + gt_mat = loadmat(os.path.join(gt_dir, 'wider_face_val.mat')) + hard_mat = loadmat(os.path.join(gt_dir, 'wider_hard_val.mat')) + medium_mat = loadmat(os.path.join(gt_dir, 'wider_medium_val.mat')) + easy_mat = loadmat(os.path.join(gt_dir, 'wider_easy_val.mat')) + + facebox_list = gt_mat['face_bbx_list'] + event_list = gt_mat['event_list'] + file_list = gt_mat['file_list'] + + hard_gt_list = hard_mat['gt_list'] + medium_gt_list = medium_mat['gt_list'] + easy_gt_list = easy_mat['gt_list'] + + return facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list + + +def get_gt_boxes_from_txt(gt_path, cache_dir): + + cache_file = os.path.join(cache_dir, 'gt_cache.pkl') + if os.path.exists(cache_file): + f = open(cache_file, 'rb') + boxes = pickle.load(f) + f.close() + return boxes + + f = open(gt_path, 'r') + state = 0 + lines = f.readlines() + lines = list(map(lambda x: x.rstrip('\r\n'), lines)) + boxes = {} + print(len(lines)) + f.close() + current_boxes = [] + current_name = None + for line in lines: + if state == 0 and '--' in line: + state = 1 + current_name = line + continue + if state == 1: + state = 2 + continue + + if state == 2 and '--' in line: + state = 1 + boxes[current_name] = np.array(current_boxes).astype('float32') + current_name = line + current_boxes = [] + continue + + if state == 2: + box = [float(x) for x in line.split(' ')[:4]] + current_boxes.append(box) + continue + + f = open(cache_file, 'wb') + pickle.dump(boxes, f) + f.close() + return boxes + + +def read_pred_file(filepath): + + with open(filepath, 'r') as f: + lines = f.readlines() + img_file = lines[0].rstrip('\n\r') + lines = lines[2:] + + # b = lines[0].rstrip('\r\n').split(' ')[:-1] + # c = float(b) + # a = map(lambda x: [[float(a[0]), float(a[1]), float(a[2]), float(a[3]), float(a[4])] for a in x.rstrip('\r\n').split(' ')], lines) + boxes = [] + for line in lines: + line = line.rstrip('\r\n').split(' ') + if line[0] == '': + continue + # a = float(line[4]) + boxes.append([float(line[0]), float(line[1]), float(line[2]), float(line[3]), float(line[4])]) + boxes = np.array(boxes) + # boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], lines))).astype('float') + return img_file.split('/')[-1], boxes + + +def get_preds(pred_dir): + events = os.listdir(pred_dir) + boxes = dict() + pbar = tqdm.tqdm(events) + + for event in pbar: + pbar.set_description('Reading Predictions ') + event_dir = os.path.join(pred_dir, event) + event_images = os.listdir(event_dir) + current_event = dict() + for imgtxt in event_images: + imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt)) + current_event[imgname.rstrip('.jpg')] = _boxes + boxes[event] = current_event + return boxes + + +def norm_score(pred): + """ norm score + pred {key: [[x1,y1,x2,y2,s]]} + """ + + max_score = 0 + min_score = 1 + + for _, k in pred.items(): + for _, v in k.items(): + if len(v) == 0: + continue + _min = np.min(v[:, -1]) + _max = np.max(v[:, -1]) + max_score = max(_max, max_score) + min_score = min(_min, min_score) + + diff = max_score - min_score + for _, k in pred.items(): + for _, v in k.items(): + if len(v) == 0: + continue + v[:, -1] = (v[:, -1] - min_score)/diff + + +def image_eval(pred, gt, ignore, iou_thresh): + """ single image evaluation + pred: Nx5 + gt: Nx4 + ignore: + """ + + _pred = pred.copy() + _gt = gt.copy() + pred_recall = np.zeros(_pred.shape[0]) + recall_list = np.zeros(_gt.shape[0]) + proposal_list = np.ones(_pred.shape[0]) + + _pred[:, 2] = _pred[:, 2] + _pred[:, 0] + _pred[:, 3] = _pred[:, 3] + _pred[:, 1] + _gt[:, 2] = _gt[:, 2] + _gt[:, 0] + _gt[:, 3] = _gt[:, 3] + _gt[:, 1] + + overlaps = bbox_overlaps(_pred[:, :4], _gt) + + for h in range(_pred.shape[0]): + + gt_overlap = overlaps[h] + max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax() + if max_overlap >= iou_thresh: + if ignore[max_idx] == 0: + recall_list[max_idx] = -1 + proposal_list[h] = -1 + elif recall_list[max_idx] == 0: + recall_list[max_idx] = 1 + + r_keep_index = np.where(recall_list == 1)[0] + pred_recall[h] = len(r_keep_index) + return pred_recall, proposal_list + + +def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall): + pr_info = np.zeros((thresh_num, 2)).astype('float') + for t in range(thresh_num): + + thresh = 1 - (t+1)/thresh_num + r_index = np.where(pred_info[:, 4] >= thresh)[0] + if len(r_index) == 0: + pr_info[t, 0] = 0 + pr_info[t, 1] = 0 + else: + r_index = r_index[-1] + p_index = np.where(proposal_list[:r_index+1] == 1)[0] + pr_info[t, 0] = len(p_index) + pr_info[t, 1] = pred_recall[r_index] + return pr_info + + +def dataset_pr_info(thresh_num, pr_curve, count_face): + _pr_curve = np.zeros((thresh_num, 2)) + for i in range(thresh_num): + _pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0] + _pr_curve[i, 1] = pr_curve[i, 1] / count_face + return _pr_curve + + +def voc_ap(rec, prec): + + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def evaluation(pred, gt_path, iou_thresh=0.5): + pred = get_preds(pred) + norm_score(pred) + facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path) + event_num = len(event_list) + thresh_num = 1000 + settings = ['easy', 'medium', 'hard'] + setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list] + aps = [] + for setting_id in range(3): + # different setting + gt_list = setting_gts[setting_id] + count_face = 0 + pr_curve = np.zeros((thresh_num, 2)).astype('float') + # [hard, medium, easy] + pbar = tqdm.tqdm(range(event_num)) + for i in pbar: + pbar.set_description('Processing {}'.format(settings[setting_id])) + event_name = str(event_list[i][0][0]) + img_list = file_list[i][0] + pred_list = pred[event_name] + sub_gt_list = gt_list[i][0] + # img_pr_info_list = np.zeros((len(img_list), thresh_num, 2)) + gt_bbx_list = facebox_list[i][0] + + for j in range(len(img_list)): + pred_info = pred_list[str(img_list[j][0][0])] + + gt_boxes = gt_bbx_list[j][0].astype('float') + keep_index = sub_gt_list[j][0] + count_face += len(keep_index) + + if len(gt_boxes) == 0 or len(pred_info) == 0: + continue + ignore = np.zeros(gt_boxes.shape[0]) + if len(keep_index) != 0: + ignore[keep_index-1] = 1 + pred_recall, proposal_list = image_eval(pred_info, gt_boxes, ignore, iou_thresh) + + _img_pr_info = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall) + + pr_curve += _img_pr_info + pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face) + + propose = pr_curve[:, 0] + recall = pr_curve[:, 1] + + ap = voc_ap(recall, propose) + aps.append(ap) + + print("==================== Results ====================") + print("Easy Val AP: {}".format(aps[0])) + print("Medium Val AP: {}".format(aps[1])) + print("Hard Val AP: {}".format(aps[2])) + print("=================================================") + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument('-p', '--pred', default="./widerface_txt/") + parser.add_argument('-g', '--gt', default='./ground_truth/') + + args = parser.parse_args() + evaluation(args.pred, args.gt) + + + + + + + + + + + + diff --git a/algorithm/Car_recognition/widerface_evaluate/setup.py b/algorithm/Car_recognition/widerface_evaluate/setup.py new file mode 100644 index 0000000..74dba05 --- /dev/null +++ b/algorithm/Car_recognition/widerface_evaluate/setup.py @@ -0,0 +1,13 @@ +""" +WiderFace evaluation code +author: wondervictor +mail: tianhengcheng@gmail.com +copyright@wondervictor +""" + +from distutils.core import setup, Extension +from Cython.Build import cythonize +import numpy + +package = Extension('bbox', ['box_overlaps.pyx'], include_dirs=[numpy.get_include()]) +setup(ext_modules=cythonize([package])) diff --git a/algorithm/Remote_sense/__pycache__/remote_sense.cpython-39.pyc b/algorithm/Remote_sense/__pycache__/remote_sense.cpython-39.pyc new file mode 100644 index 0000000..389bcca Binary files /dev/null and b/algorithm/Remote_sense/__pycache__/remote_sense.cpython-39.pyc differ diff --git a/algorithm/Remote_sense/nms_rotated/__init__.py b/algorithm/Remote_sense/nms_rotated/__init__.py new file mode 100644 index 0000000..9768d17 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/__init__.py @@ -0,0 +1,3 @@ +from .nms_rotated_wrapper import obb_nms, poly_nms + +__all__ = ['obb_nms', 'poly_nms'] diff --git a/algorithm/Remote_sense/nms_rotated/__pycache__/__init__.cpython-39.pyc b/algorithm/Remote_sense/nms_rotated/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..c5e4e9b Binary files /dev/null and b/algorithm/Remote_sense/nms_rotated/__pycache__/__init__.cpython-39.pyc differ diff --git a/algorithm/Remote_sense/nms_rotated/__pycache__/nms_rotated_wrapper.cpython-39.pyc b/algorithm/Remote_sense/nms_rotated/__pycache__/nms_rotated_wrapper.cpython-39.pyc new file mode 100644 index 0000000..e57434e Binary files /dev/null and b/algorithm/Remote_sense/nms_rotated/__pycache__/nms_rotated_wrapper.cpython-39.pyc differ diff --git a/algorithm/Remote_sense/nms_rotated/build/lib.linux-x86_64-cpython-39/nms_rotated_ext.cpython-39-x86_64-linux-gnu.so b/algorithm/Remote_sense/nms_rotated/build/lib.linux-x86_64-cpython-39/nms_rotated_ext.cpython-39-x86_64-linux-gnu.so new file mode 100644 index 0000000..159b4a0 Binary files /dev/null and b/algorithm/Remote_sense/nms_rotated/build/lib.linux-x86_64-cpython-39/nms_rotated_ext.cpython-39-x86_64-linux-gnu.so differ diff --git a/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/.ninja_deps b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/.ninja_deps new file mode 100644 index 0000000..5e37101 Binary files /dev/null and b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/.ninja_deps differ diff --git a/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/.ninja_log b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/.ninja_log new file mode 100644 index 0000000..7bd2544 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/.ninja_log @@ -0,0 +1,5 @@ +# ninja log v5 +0 7660 1704361209621634480 /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/build/temp.linux-x86_64-cpython-39/src/nms_rotated_cpu.o b029b47685232f9e +2 14294 1704361216249269110 /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/build/temp.linux-x86_64-cpython-39/src/poly_nms_cuda.o 4266f53ebf8b72b5 +1 15314 1704361217265213406 /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/build/temp.linux-x86_64-cpython-39/src/nms_rotated_cuda.o 9e95c868baf04fe9 +1 17587 1704361219549088475 /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/build/temp.linux-x86_64-cpython-39/src/nms_rotated_ext.o 745e423397cd77c2 diff --git a/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/build.ninja b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/build.ninja new file mode 100644 index 0000000..595e82e --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/build.ninja @@ -0,0 +1,31 @@ +ninja_required_version = 1.3 +cxx = c++ +nvcc = /usr/local/cuda-11.1/bin/nvcc + +cflags = -pthread -B /home/ykn/anaconda3/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /home/ykn/anaconda3/include -I/home/ykn/anaconda3/include -fPIC -O2 -isystem /home/ykn/anaconda3/include -fPIC -DWITH_CUDA -I/home/ykn/anaconda3/lib/python3.9/site-packages/torch/include -I/home/ykn/anaconda3/lib/python3.9/site-packages/torch/include/torch/csrc/api/include -I/home/ykn/anaconda3/lib/python3.9/site-packages/torch/include/TH -I/home/ykn/anaconda3/lib/python3.9/site-packages/torch/include/THC -I/usr/local/cuda-11.1/include -I/home/ykn/anaconda3/include/python3.9 -c +post_cflags = -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=nms_rotated_ext -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 +cuda_cflags = -DWITH_CUDA -I/home/ykn/anaconda3/lib/python3.9/site-packages/torch/include -I/home/ykn/anaconda3/lib/python3.9/site-packages/torch/include/torch/csrc/api/include -I/home/ykn/anaconda3/lib/python3.9/site-packages/torch/include/TH -I/home/ykn/anaconda3/lib/python3.9/site-packages/torch/include/THC -I/usr/local/cuda-11.1/include -I/home/ykn/anaconda3/include/python3.9 -c +cuda_post_cflags = -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options ''"'"'-fPIC'"'"'' -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NAME=nms_rotated_ext -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_75,code=compute_75 -gencode=arch=compute_75,code=sm_75 -std=c++14 +ldflags = + +rule compile + command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags + depfile = $out.d + deps = gcc + +rule cuda_compile + depfile = $out.d + deps = gcc + command = $nvcc --generate-dependencies-with-compile --dependency-output $out.d $cuda_cflags -c $in -o $out $cuda_post_cflags + + + +build /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/build/temp.linux-x86_64-cpython-39/src/nms_rotated_cpu.o: compile /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/src/nms_rotated_cpu.cpp +build /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/build/temp.linux-x86_64-cpython-39/src/nms_rotated_cuda.o: cuda_compile /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/src/nms_rotated_cuda.cu +build /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/build/temp.linux-x86_64-cpython-39/src/nms_rotated_ext.o: compile /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/src/nms_rotated_ext.cpp +build /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/build/temp.linux-x86_64-cpython-39/src/poly_nms_cuda.o: cuda_compile /home/ykn/algorithm_system/yolov5-obb-oriented-object-detection-master/utils/nms_rotated/src/poly_nms_cuda.cu + + + + + diff --git a/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/src/nms_rotated_cpu.o b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/src/nms_rotated_cpu.o new file mode 100644 index 0000000..6161a45 Binary files /dev/null and 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b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/src/poly_nms_cuda.o new file mode 100644 index 0000000..45c49c6 Binary files /dev/null and b/algorithm/Remote_sense/nms_rotated/build/temp.linux-x86_64-cpython-39/src/poly_nms_cuda.o differ diff --git a/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/PKG-INFO b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/PKG-INFO new file mode 100644 index 0000000..2af9f16 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/PKG-INFO @@ -0,0 +1,3 @@ +Metadata-Version: 2.1 +Name: nms-rotated +Version: 0.0.0 diff --git a/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/SOURCES.txt b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/SOURCES.txt new file mode 100644 index 0000000..8e475c6 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/SOURCES.txt @@ -0,0 +1,10 @@ +setup.py +nms_rotated.egg-info/PKG-INFO +nms_rotated.egg-info/SOURCES.txt +nms_rotated.egg-info/dependency_links.txt +nms_rotated.egg-info/not-zip-safe +nms_rotated.egg-info/top_level.txt +src/nms_rotated_cpu.cpp +src/nms_rotated_cuda.cu +src/nms_rotated_ext.cpp +src/poly_nms_cuda.cu \ No newline at end of file diff --git a/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/dependency_links.txt b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/not-zip-safe b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/not-zip-safe new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/not-zip-safe @@ -0,0 +1 @@ + diff --git a/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/top_level.txt b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/top_level.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/nms_rotated.egg-info/top_level.txt @@ -0,0 +1 @@ + diff --git a/algorithm/Remote_sense/nms_rotated/nms_rotated_ext.cpython-39-x86_64-linux-gnu.so b/algorithm/Remote_sense/nms_rotated/nms_rotated_ext.cpython-39-x86_64-linux-gnu.so new file mode 100644 index 0000000..159b4a0 Binary files /dev/null and b/algorithm/Remote_sense/nms_rotated/nms_rotated_ext.cpython-39-x86_64-linux-gnu.so differ diff --git a/algorithm/Remote_sense/nms_rotated/nms_rotated_wrapper.py b/algorithm/Remote_sense/nms_rotated/nms_rotated_wrapper.py new file mode 100644 index 0000000..afba7bd --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/nms_rotated_wrapper.py @@ -0,0 +1,78 @@ +import numpy as np +import torch + +from . import nms_rotated_ext + +def obb_nms(dets, scores, iou_thr, device_id=None): + """ + RIoU NMS - iou_thr. + Args: + dets (tensor/array): (num, [cx cy w h θ]) θ∈[-pi/2, pi/2) + scores (tensor/array): (num) + iou_thr (float): (1) + Returns: + dets (tensor): (n_nms, [cx cy w h θ]) + inds (tensor): (n_nms), nms index of dets + """ + if isinstance(dets, torch.Tensor): + is_numpy = False + dets_th = dets + elif isinstance(dets, np.ndarray): + is_numpy = True + device = 'cpu' if device_id is None else f'cuda:{device_id}' + dets_th = torch.from_numpy(dets).to(device) + else: + raise TypeError('dets must be eithr a Tensor or numpy array, ' + f'but got {type(dets)}') + + if dets_th.numel() == 0: # len(dets) + inds = dets_th.new_zeros(0, dtype=torch.int64) + else: + # same bug will happen when bboxes is too small + too_small = dets_th[:, [2, 3]].min(1)[0] < 0.001 # [n] + if too_small.all(): # all the bboxes is too small + inds = dets_th.new_zeros(0, dtype=torch.int64) + else: + ori_inds = torch.arange(dets_th.size(0)) # 0 ~ n-1 + ori_inds = ori_inds[~too_small] + dets_th = dets_th[~too_small] # (n_filter, 5) + scores = scores[~too_small] + + inds = nms_rotated_ext.nms_rotated(dets_th, scores, iou_thr) + inds = ori_inds[inds] + + if is_numpy: + inds = inds.cpu().numpy() + return dets[inds, :], inds + + +def poly_nms(dets, iou_thr, device_id=None): + if isinstance(dets, torch.Tensor): + is_numpy = False + dets_th = dets + elif isinstance(dets, np.ndarray): + is_numpy = True + device = 'cpu' if device_id is None else f'cuda:{device_id}' + dets_th = torch.from_numpy(dets).to(device) + else: + raise TypeError('dets must be eithr a Tensor or numpy array, ' + f'but got {type(dets)}') + + if dets_th.device == torch.device('cpu'): + raise NotImplementedError + inds = nms_rotated_ext.nms_poly(dets_th.float(), iou_thr) + + if is_numpy: + inds = inds.cpu().numpy() + return dets[inds, :], inds + +if __name__ == '__main__': + rboxes_opencv = torch.tensor(([136.6, 111.6, 200, 100, -60], + [136.6, 111.6, 100, 200, -30], + [100, 100, 141.4, 141.4, -45], + [100, 100, 141.4, 141.4, -45])) + rboxes_longedge = torch.tensor(([136.6, 111.6, 200, 100, -60], + [136.6, 111.6, 200, 100, 120], + [100, 100, 141.4, 141.4, 45], + [100, 100, 141.4, 141.4, 135])) + \ No newline at end of file diff --git a/algorithm/Remote_sense/nms_rotated/setup.py b/algorithm/Remote_sense/nms_rotated/setup.py new file mode 100644 index 0000000..a3ee967 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/setup.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python +import os +import subprocess +import time +from setuptools import find_packages, setup + +import torch +from torch.utils.cpp_extension import (BuildExtension, CppExtension, + CUDAExtension) +def make_cuda_ext(name, module, sources, sources_cuda=[]): + + define_macros = [] + extra_compile_args = {'cxx': []} + + if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1': + define_macros += [('WITH_CUDA', None)] + extension = CUDAExtension + extra_compile_args['nvcc'] = [ + '-D__CUDA_NO_HALF_OPERATORS__', + '-D__CUDA_NO_HALF_CONVERSIONS__', + '-D__CUDA_NO_HALF2_OPERATORS__', + ] + sources += sources_cuda + else: + print(f'Compiling {name} without CUDA') + extension = CppExtension + # raise EnvironmentError('CUDA is required to compile MMDetection!') + + return extension( + name=f'{module}.{name}', + sources=[os.path.join(*module.split('.'), p) for p in sources], + define_macros=define_macros, + extra_compile_args=extra_compile_args) + +# python setup.py develop +if __name__ == '__main__': + #write_version_py() + setup( + name='nms_rotated', + ext_modules=[ + make_cuda_ext( + name='nms_rotated_ext', + module='', + sources=[ + 'src/nms_rotated_cpu.cpp', + 'src/nms_rotated_ext.cpp' + ], + sources_cuda=[ + 'src/nms_rotated_cuda.cu', + 'src/poly_nms_cuda.cu', + ]), + ], + cmdclass={'build_ext': BuildExtension}, + zip_safe=False) \ No newline at end of file diff --git a/algorithm/Remote_sense/nms_rotated/src/box_iou_rotated_utils.h b/algorithm/Remote_sense/nms_rotated/src/box_iou_rotated_utils.h new file mode 100644 index 0000000..c017e17 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/src/box_iou_rotated_utils.h @@ -0,0 +1,360 @@ +// Mortified from +// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/box_iou_rotated +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#pragma once + +#include +#include + +#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1 +// Designates functions callable from the host (CPU) and the device (GPU) +#define HOST_DEVICE __host__ __device__ +#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__ +#else +#include +#define HOST_DEVICE +#define HOST_DEVICE_INLINE HOST_DEVICE inline +#endif + + +template +struct RotatedBox { + T x_ctr, y_ctr, w, h, a; +}; + +template +struct Point { + T x, y; + HOST_DEVICE_INLINE Point(const T& px = 0, const T& py = 0) : x(px), y(py) {} + HOST_DEVICE_INLINE Point operator+(const Point& p) const { + return Point(x + p.x, y + p.y); + } + HOST_DEVICE_INLINE Point& operator+=(const Point& p) { + x += p.x; + y += p.y; + return *this; + } + HOST_DEVICE_INLINE Point operator-(const Point& p) const { + return Point(x - p.x, y - p.y); + } + HOST_DEVICE_INLINE Point operator*(const T coeff) const { + return Point(x * coeff, y * coeff); + } +}; + +template +HOST_DEVICE_INLINE T dot_2d(const Point& A, const Point& B) { + return A.x * B.x + A.y * B.y; +} + +// R: result type. can be different from input type +template +HOST_DEVICE_INLINE R cross_2d(const Point& A, const Point& B) { + return static_cast(A.x) * static_cast(B.y) - + static_cast(B.x) * static_cast(A.y); +} + +template +HOST_DEVICE_INLINE void get_rotated_vertices( + const RotatedBox& box, + Point (&pts)[4]) { + // M_PI / 180. == 0.01745329251 + //double theta = box.a * 0.01745329251; ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + double theta = box.a; + T cosTheta2 = (T)cos(theta) * 0.5f; + T sinTheta2 = (T)sin(theta) * 0.5f; + + // y: top --> down; x: left --> right + pts[0].x = box.x_ctr + sinTheta2 * box.h + cosTheta2 * box.w; + pts[0].y = box.y_ctr + cosTheta2 * box.h - sinTheta2 * box.w; + pts[1].x = box.x_ctr - sinTheta2 * box.h + cosTheta2 * box.w; + pts[1].y = box.y_ctr - cosTheta2 * box.h - sinTheta2 * box.w; + pts[2].x = 2 * box.x_ctr - pts[0].x; + pts[2].y = 2 * box.y_ctr - pts[0].y; + pts[3].x = 2 * box.x_ctr - pts[1].x; + pts[3].y = 2 * box.y_ctr - pts[1].y; +} + +template +HOST_DEVICE_INLINE int get_intersection_points( + const Point (&pts1)[4], + const Point (&pts2)[4], + Point (&intersections)[24]) { + // Line vector + // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1] + Point vec1[4], vec2[4]; + for (int i = 0; i < 4; i++) { + vec1[i] = pts1[(i + 1) % 4] - pts1[i]; + vec2[i] = pts2[(i + 1) % 4] - pts2[i]; + } + + // Line test - test all line combos for intersection + int num = 0; // number of intersections + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + // Solve for 2x2 Ax=b + T det = cross_2d(vec2[j], vec1[i]); + + // This takes care of parallel lines + if (fabs(det) <= 1e-14) { + continue; + } + + auto vec12 = pts2[j] - pts1[i]; + + T t1 = cross_2d(vec2[j], vec12) / det; + T t2 = cross_2d(vec1[i], vec12) / det; + + if (t1 >= 0.0f && t1 <= 1.0f && t2 >= 0.0f && t2 <= 1.0f) { + intersections[num++] = pts1[i] + vec1[i] * t1; + } + } + } + + // Check for vertices of rect1 inside rect2 + { + const auto& AB = vec2[0]; + const auto& DA = vec2[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + // assume ABCD is the rectangle, and P is the point to be judged + // P is inside ABCD iff. P's projection on AB lies within AB + // and P's projection on AD lies within AD + + auto AP = pts1[i] - pts2[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) && + (APdotAD <= ADdotAD)) { + intersections[num++] = pts1[i]; + } + } + } + + // Reverse the check - check for vertices of rect2 inside rect1 + { + const auto& AB = vec1[0]; + const auto& DA = vec1[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + auto AP = pts2[i] - pts1[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) && + (APdotAD <= ADdotAD)) { + intersections[num++] = pts2[i]; + } + } + } + + return num; +} + +template +HOST_DEVICE_INLINE int convex_hull_graham( + const Point (&p)[24], + const int& num_in, + Point (&q)[24], + bool shift_to_zero = false) { + assert(num_in >= 2); + + // Step 1: + // Find point with minimum y + // if more than 1 points have the same minimum y, + // pick the one with the minimum x. + int t = 0; + for (int i = 1; i < num_in; i++) { + if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) { + t = i; + } + } + auto& start = p[t]; // starting point + + // Step 2: + // Subtract starting point from every points (for sorting in the next step) + for (int i = 0; i < num_in; i++) { + q[i] = p[i] - start; + } + + // Swap the starting point to position 0 + auto tmp = q[0]; + q[0] = q[t]; + q[t] = tmp; + + // Step 3: + // Sort point 1 ~ num_in according to their relative cross-product values + // (essentially sorting according to angles) + // If the angles are the same, sort according to their distance to origin + T dist[24]; +#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1 + // compute distance to origin before sort, and sort them together with the + // points + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } + + // CUDA version + // In the future, we can potentially use thrust + // for sorting here to improve speed (though not guaranteed) + for (int i = 1; i < num_in - 1; i++) { + for (int j = i + 1; j < num_in; j++) { + T crossProduct = cross_2d(q[i], q[j]); + if ((crossProduct < -1e-6) || + (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) { + auto q_tmp = q[i]; + q[i] = q[j]; + q[j] = q_tmp; + auto dist_tmp = dist[i]; + dist[i] = dist[j]; + dist[j] = dist_tmp; + } + } + } +#else + // CPU version + std::sort( + q + 1, q + num_in, [](const Point& A, const Point& B) -> bool { + T temp = cross_2d(A, B); + if (fabs(temp) < 1e-6) { + return dot_2d(A, A) < dot_2d(B, B); + } else { + return temp > 0; + } + }); + // compute distance to origin after sort, since the points are now different. + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } +#endif + + // Step 4: + // Make sure there are at least 2 points (that don't overlap with each other) + // in the stack + int k; // index of the non-overlapped second point + for (k = 1; k < num_in; k++) { + if (dist[k] > 1e-8) { + break; + } + } + if (k == num_in) { + // We reach the end, which means the convex hull is just one point + q[0] = p[t]; + return 1; + } + q[1] = q[k]; + int m = 2; // 2 points in the stack + // Step 5: + // Finally we can start the scanning process. + // When a non-convex relationship between the 3 points is found + // (either concave shape or duplicated points), + // we pop the previous point from the stack + // until the 3-point relationship is convex again, or + // until the stack only contains two points + for (int i = k + 1; i < num_in; i++) { + while (m > 1) { + auto q1 = q[i] - q[m - 2], q2 = q[m - 1] - q[m - 2]; + // cross_2d() uses FMA and therefore computes round(round(q1.x*q2.y) - + // q2.x*q1.y) So it may not return 0 even when q1==q2. Therefore we + // compare round(q1.x*q2.y) and round(q2.x*q1.y) directly. (round means + // round to nearest floating point). + if (q1.x * q2.y >= q2.x * q1.y) + m--; + else + break; + } + // Using double also helps, but float can solve the issue for now. + // while (m > 1 && cross_2d(q[i] - q[m - 2], q[m - 1] - q[m - 2]) + // >= 0) { + // m--; + // } + q[m++] = q[i]; + } + + // Step 6 (Optional): + // In general sense we need the original coordinates, so we + // need to shift the points back (reverting Step 2) + // But if we're only interested in getting the area/perimeter of the shape + // We can simply return. + if (!shift_to_zero) { + for (int i = 0; i < m; i++) { + q[i] += start; + } + } + + return m; +} + +template +HOST_DEVICE_INLINE T polygon_area(const Point (&q)[24], const int& m) { + if (m <= 2) { + return 0; + } + + T area = 0; + for (int i = 1; i < m - 1; i++) { + area += fabs(cross_2d(q[i] - q[0], q[i + 1] - q[0])); + } + + return area / 2.0; +} + +template +HOST_DEVICE_INLINE T rotated_boxes_intersection( + const RotatedBox& box1, + const RotatedBox& box2) { + // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned + // from rotated_rect_intersection_pts + Point intersectPts[24], orderedPts[24]; + + Point pts1[4]; + Point pts2[4]; + get_rotated_vertices(box1, pts1); + get_rotated_vertices(box2, pts2); + + int num = get_intersection_points(pts1, pts2, intersectPts); + + if (num <= 2) { + return 0.0; + } + + // Convex Hull to order the intersection points in clockwise order and find + // the contour area. + int num_convex = convex_hull_graham(intersectPts, num, orderedPts, true); + return polygon_area(orderedPts, num_convex); +} + + +template +HOST_DEVICE_INLINE T +single_box_iou_rotated(T const* const box1_raw, T const* const box2_raw) { + // shift center to the middle point to achieve higher precision in result + RotatedBox box1, box2; + auto center_shift_x = (box1_raw[0] + box2_raw[0]) / 2.0; + auto center_shift_y = (box1_raw[1] + box2_raw[1]) / 2.0; + box1.x_ctr = box1_raw[0] - center_shift_x; + box1.y_ctr = box1_raw[1] - center_shift_y; + box1.w = box1_raw[2]; + box1.h = box1_raw[3]; + box1.a = box1_raw[4]; + box2.x_ctr = box2_raw[0] - center_shift_x; + box2.y_ctr = box2_raw[1] - center_shift_y; + box2.w = box2_raw[2]; + box2.h = box2_raw[3]; + box2.a = box2_raw[4]; + + T area1 = box1.w * box1.h; + T area2 = box2.w * box2.h; + if (area1 < 1e-14 || area2 < 1e-14) { + return 0.f; + } + + T intersection = rotated_boxes_intersection(box1, box2); + T iou = intersection / (area1 + area2 - intersection); + return iou; +} diff --git a/algorithm/Remote_sense/nms_rotated/src/nms_rotated_cpu.cpp b/algorithm/Remote_sense/nms_rotated/src/nms_rotated_cpu.cpp new file mode 100644 index 0000000..185e9a4 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/src/nms_rotated_cpu.cpp @@ -0,0 +1,74 @@ +// Modified from +// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/nms_rotated +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include "box_iou_rotated_utils.h" + + +template +at::Tensor nms_rotated_cpu_kernel( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + // nms_rotated_cpu_kernel is modified from torchvision's nms_cpu_kernel, + // however, the code in this function is much shorter because + // we delegate the IoU computation for rotated boxes to + // the single_box_iou_rotated function in box_iou_rotated_utils.h + AT_ASSERTM(dets.device().is_cpu(), "dets must be a CPU tensor"); + AT_ASSERTM(scores.device().is_cpu(), "scores must be a CPU tensor"); + AT_ASSERTM( + dets.scalar_type() == scores.scalar_type(), + "dets should have the same type as scores"); + + if (dets.numel() == 0) { + return at::empty({0}, dets.options().dtype(at::kLong)); + } + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + + auto ndets = dets.size(0); + at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte)); + at::Tensor keep_t = at::zeros({ndets}, dets.options().dtype(at::kLong)); + + auto suppressed = suppressed_t.data_ptr(); + auto keep = keep_t.data_ptr(); + auto order = order_t.data_ptr(); + + int64_t num_to_keep = 0; + + for (int64_t _i = 0; _i < ndets; _i++) { + auto i = order[_i]; + if (suppressed[i] == 1) { + continue; + } + + keep[num_to_keep++] = i; + + for (int64_t _j = _i + 1; _j < ndets; _j++) { + auto j = order[_j]; + if (suppressed[j] == 1) { + continue; + } + + auto ovr = single_box_iou_rotated( + dets[i].data_ptr(), dets[j].data_ptr()); + if (ovr >= iou_threshold) { + suppressed[j] = 1; + } + } + } + return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep); +} + +at::Tensor nms_rotated_cpu( + // input must be contiguous + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + auto result = at::empty({0}, dets.options()); + + AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "nms_rotated", [&] { + result = nms_rotated_cpu_kernel(dets, scores, iou_threshold); + }); + return result; +} diff --git a/algorithm/Remote_sense/nms_rotated/src/nms_rotated_cuda.cu b/algorithm/Remote_sense/nms_rotated/src/nms_rotated_cuda.cu new file mode 100644 index 0000000..84d5acf --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/src/nms_rotated_cuda.cu @@ -0,0 +1,134 @@ +// Modified from +// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/nms_rotated +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include +#include +#include +#include "box_iou_rotated_utils.h" + +int const threadsPerBlock = sizeof(unsigned long long) * 8; + +template +__global__ void nms_rotated_cuda_kernel( + const int n_boxes, + const float iou_threshold, + const T* dev_boxes, + unsigned long long* dev_mask) { + // nms_rotated_cuda_kernel is modified from torchvision's nms_cuda_kernel + + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + // Compared to nms_cuda_kernel, where each box is represented with 4 values + // (x1, y1, x2, y2), each rotated box is represented with 5 values + // (x_center, y_center, width, height, angle_degrees) here. + __shared__ T block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const T* cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + // Instead of devIoU used by original horizontal nms, here + // we use the single_box_iou_rotated function from box_iou_rotated_utils.h + if (single_box_iou_rotated(cur_box, block_boxes + i * 5) > + iou_threshold) { + t |= 1ULL << i; + } + } + const int col_blocks = at::cuda::ATenCeilDiv(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + + +at::Tensor nms_rotated_cuda( + // input must be contiguous + const at::Tensor& dets, + const at::Tensor& scores, + float iou_threshold) { + // using scalar_t = float; + AT_ASSERTM(dets.is_cuda(), "dets must be a CUDA tensor"); + AT_ASSERTM(scores.is_cuda(), "scores must be a CUDA tensor"); + at::cuda::CUDAGuard device_guard(dets.device()); + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + auto dets_sorted = dets.index_select(0, order_t); + + auto dets_num = dets.size(0); + + const int col_blocks = + at::cuda::ATenCeilDiv(static_cast(dets_num), threadsPerBlock); + + at::Tensor mask = + at::empty({dets_num * col_blocks}, dets.options().dtype(at::kLong)); + + dim3 blocks(col_blocks, col_blocks); + dim3 threads(threadsPerBlock); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES( + dets_sorted.scalar_type(), "nms_rotated_kernel_cuda", [&] { + nms_rotated_cuda_kernel<<>>( + dets_num, + iou_threshold, + dets_sorted.data_ptr(), + (unsigned long long*)mask.data_ptr()); + }); + + at::Tensor mask_cpu = mask.to(at::kCPU); + unsigned long long* mask_host = + (unsigned long long*)mask_cpu.data_ptr(); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = + at::empty({dets_num}, dets.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data_ptr(); + + int num_to_keep = 0; + for (int i = 0; i < dets_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long* p = mask_host + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + AT_CUDA_CHECK(cudaGetLastError()); + return order_t.index( + {keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep) + .to(order_t.device(), keep.scalar_type())}); +} diff --git a/algorithm/Remote_sense/nms_rotated/src/nms_rotated_ext.cpp b/algorithm/Remote_sense/nms_rotated/src/nms_rotated_ext.cpp new file mode 100644 index 0000000..287338f --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/src/nms_rotated_ext.cpp @@ -0,0 +1,60 @@ +// Modified from +// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/nms_rotated +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include + + +#ifdef WITH_CUDA +at::Tensor nms_rotated_cuda( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold); + +at::Tensor poly_nms_cuda( + const at::Tensor boxes, + float nms_overlap_thresh); +#endif + +at::Tensor nms_rotated_cpu( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold); + + +inline at::Tensor nms_rotated( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + assert(dets.device().is_cuda() == scores.device().is_cuda()); + if (dets.device().is_cuda()) { +#ifdef WITH_CUDA + return nms_rotated_cuda( + dets.contiguous(), scores.contiguous(), iou_threshold); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + return nms_rotated_cpu(dets.contiguous(), scores.contiguous(), iou_threshold); +} + + +inline at::Tensor nms_poly( + const at::Tensor& dets, + const float iou_threshold) { + if (dets.device().is_cuda()) { +#ifdef WITH_CUDA + if (dets.numel() == 0) + return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)); + return poly_nms_cuda(dets, iou_threshold); +#else + AT_ERROR("POLY_NMS is not compiled with GPU support"); +#endif + } + AT_ERROR("POLY_NMS is not implemented on CPU"); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("nms_rotated", &nms_rotated, "nms for rotated bboxes"); + m.def("nms_poly", &nms_poly, "nms for poly bboxes"); +} diff --git a/algorithm/Remote_sense/nms_rotated/src/poly_nms_cpu.cpp b/algorithm/Remote_sense/nms_rotated/src/poly_nms_cpu.cpp new file mode 100644 index 0000000..75af948 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/src/poly_nms_cpu.cpp @@ -0,0 +1,5 @@ +#include + +template +at::Tensor poly_nms_cpu_kernel(const at::Tensor& dets, const float threshold) { + diff --git a/algorithm/Remote_sense/nms_rotated/src/poly_nms_cuda.cu b/algorithm/Remote_sense/nms_rotated/src/poly_nms_cuda.cu new file mode 100644 index 0000000..efa3286 --- /dev/null +++ b/algorithm/Remote_sense/nms_rotated/src/poly_nms_cuda.cu @@ -0,0 +1,262 @@ +#include +#include + +#include +#include + +#include +#include + +#define CUDA_CHECK(condition) \ + /* Code block avoids redefinition of cudaError_t error */ \ + do { \ + cudaError_t error = condition; \ + if (error != cudaSuccess) { \ + std::cout << cudaGetErrorString(error) << std::endl; \ + } \ + } while (0) + +#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) +int const threadsPerBlock = sizeof(unsigned long long) * 8; + + +#define maxn 10 +const double eps=1E-8; + +__device__ inline int sig(float d){ + return(d>eps)-(d<-eps); +} + +__device__ inline int point_eq(const float2 a, const float2 b) { + return sig(a.x - b.x) == 0 && sig(a.y - b.y)==0; +} + +__device__ inline void point_swap(float2 *a, float2 *b) { + float2 temp = *a; + *a = *b; + *b = temp; +} + +__device__ inline void point_reverse(float2 *first, float2* last) +{ + while ((first!=last)&&(first!=--last)) { + point_swap (first,last); + ++first; + } +} + +__device__ inline float cross(float2 o,float2 a,float2 b){ //叉积 + return(a.x-o.x)*(b.y-o.y)-(b.x-o.x)*(a.y-o.y); +} +__device__ inline float area(float2* ps,int n){ + ps[n]=ps[0]; + float res=0; + for(int i=0;i0) pp[m++]=p[i]; + if(sig(cross(a,b,p[i]))!=sig(cross(a,b,p[i+1]))) + lineCross(a,b,p[i],p[i+1],pp[m++]); + } + n=0; + for(int i=0;i1&&p[n-1]==p[0])n--; + while(n>1&&point_eq(p[n-1], p[0]))n--; +} + +//---------------华丽的分隔线-----------------// +//返回三角形oab和三角形ocd的有向交面积,o是原点// +__device__ inline float intersectArea(float2 a,float2 b,float2 c,float2 d){ + float2 o = make_float2(0,0); + int s1=sig(cross(o,a,b)); + int s2=sig(cross(o,c,d)); + if(s1==0||s2==0)return 0.0;//退化，面积为0 + // if(s1==-1) swap(a,b); + // if(s2==-1) swap(c,d); + if (s1 == -1) point_swap(&a, &b); + if (s2 == -1) point_swap(&c, &d); + float2 p[10]={o,a,b}; + int n=3; + float2 pp[maxn]; + polygon_cut(p,n,o,c,pp); + polygon_cut(p,n,c,d,pp); + polygon_cut(p,n,d,o,pp); + float res=fabs(area(p,n)); + if(s1*s2==-1) res=-res;return res; +} +//求两多边形的交面积 +__device__ inline float intersectArea(float2*ps1,int n1,float2*ps2,int n2){ + if(area(ps1,n1)<0) point_reverse(ps1,ps1+n1); + if(area(ps2,n2)<0) point_reverse(ps2,ps2+n2); + ps1[n1]=ps1[0]; + ps2[n2]=ps2[0]; + float res=0; + for(int i=0;i nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = THCCeilDiv(n_polys, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +// boxes is a N x 9 tensor +at::Tensor poly_nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) { + + at::DeviceGuard guard(boxes.device()); + + using scalar_t = float; + AT_ASSERTM(boxes.device().is_cuda(), "boxes must be a CUDA tensor"); + auto scores = boxes.select(1, 8); + auto order_t = std::get<1>(scores.sort(0, /*descending=*/true)); + auto boxes_sorted = boxes.index_select(0, order_t); + + int boxes_num = boxes.size(0); + + const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock); + + scalar_t* boxes_dev = boxes_sorted.data_ptr(); + + THCState *state = at::globalContext().lazyInitCUDA(); + + unsigned long long* mask_dev = NULL; + + mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long)); + + dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock), + THCCeilDiv(boxes_num, threadsPerBlock)); + dim3 threads(threadsPerBlock); + poly_nms_kernel<<>>(boxes_num, + nms_overlap_thresh, + boxes_dev, + mask_dev); + + std::vector mask_host(boxes_num * col_blocks); + THCudaCheck(cudaMemcpyAsync( + &mask_host[0], + mask_dev, + sizeof(unsigned long long) * boxes_num * col_blocks, + cudaMemcpyDeviceToHost, + at::cuda::getCurrentCUDAStream() + )); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data_ptr(); + + int num_to_keep = 0; + for (int i = 0; i < boxes_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long *p = &mask_host[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + THCudaFree(state, mask_dev); + + return order_t.index({ + keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to( + order_t.device(), keep.scalar_type())}); +} + diff --git a/algorithm/Remote_sense/remote_sense.py b/algorithm/Remote_sense/remote_sense.py new file mode 100644 index 0000000..3f71e15 --- /dev/null +++ b/algorithm/Remote_sense/remote_sense.py @@ -0,0 +1,637 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn +import torch.nn.functional as F +import torchvision +from algorithm.yolov5.models.common import DetectMultiBackend + +from algorithm.Remote_sense.nms_rotated import nms_rotated_ext + + +from PIL import Image, ImageDraw, ImageFont + +pi = 3.141592 + +class Remote_Sense(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + + #self.model = torch.load('weight/remote_sensing/oriented.pt', map_location=self.device)['model'].float().fuse() + + self.model = DetectMultiBackend(weights='weight/remote_sensing/oriented.pt', dnn=True, rotation = True) + # self.model.Detect.rotations = True + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.imgsz = 2048 + + self.dataset = LoadImages(path =self.video_name, img_size = self.imgsz) + + self.names = self.model.names + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + + def class_to_label(self, x): + return self.classes[int(x)] + + def get_frame(self): + + colors = Colors() + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + image = im0s[0].copy() + else: + image = im0s.copy() + img = image[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + + img0 = img.copy() + + img = torch.tensor(img0) + + img = img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + img = img.to(self.device) + self.model.to(self.device) + result = self.model(img) + + pred = result[0] + + pred = non_max_suppression_obb(pred, conf_thres=0.1, iou_thres=0.2, multi_label=True, max_det=1000) + + # print(pred) + txt = "" + for i, det in enumerate(pred): # per image + pred_poly = rbox2poly(det[:, :5]) # (n, [x1 y1 x2 y2 x3 y3 x4 y4]) + annotator = Annotator(image, line_width=3, example=str(self.names)) + if len(det): + pred_poly = scale_polys(img.shape[2:], pred_poly, image.shape) + det = torch.cat((pred_poly, det[:, -2:]), dim=1) # (n, [poly conf cls]) + + # Print results + for c in det[:, -1].unique(): + n = (det[:, -1] == c).sum() # detections per class + txt += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + + for *poly, conf, cls in reversed(det): + c = int(cls) + label = None + # print(poly, label) + annotator.poly_label(poly, label, color=colors(c, True)) + + im0 = annotator.result() + + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", im0) + + return jpeg.tobytes(), txt + + +class Colors: + # Ultralytics color palette https://ultralytics.com/ + def __init__(self): + # hex = matplotlib.colors.TABLEAU_COLORS.values() + hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', + '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') + self.palette = [self.hex2rgb(f'#{c}') for c in hexs] + self.n = len(self.palette) + + def __call__(self, i, bgr=False): + c = self.palette[int(i) % self.n] + return (c[2], c[1], c[0]) if bgr else c + + @staticmethod + def hex2rgb(h): # rgb order (PIL) + return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + +class Annotator: + + def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): + assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' + self.pil = pil or not is_ascii(example) or is_chinese(example) + if self.pil: # use PIL + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.im_cv2 = im + self.draw = ImageDraw.Draw(self.im) + self.font = 'Arial.Unicode.ttf' + else: # use cv2 + self.im = im + self.im_cv2 = im + self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width + + def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)): + # Add one xyxy box to image with label + if self.pil or not is_ascii(label): + self.draw.rectangle(box, width=self.lw, outline=color) # box + if label: + w, h = self.font.getsize(label) # text width, height + outside = box[1] - h >= 0 # label fits outside box + self.draw.rectangle([box[0], + box[1] - h if outside else box[1], + box[0] + w + 1, + box[1] + 1 if outside else box[1] + h + 1], fill=color) + # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0 + self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font) + else: # cv2 + p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3])) + cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA) + if label: + tf = max(self.lw - 1, 1) # font thickness + w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height + outside = p1[1] - h - 3 >= 0 # label fits outside box + p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3 + cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled + cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color, + thickness=tf, lineType=cv2.LINE_AA) + + def poly_label(self, poly, label='', color=(128, 128, 128), txt_color=(255, 255, 255)): + if isinstance(poly, torch.Tensor): + poly = poly.cpu().numpy() + if isinstance(poly[0], torch.Tensor): + poly = [x.cpu().numpy() for x in poly] + polygon_list = np.array([(poly[0], poly[1]), (poly[2], poly[3]), \ + (poly[4], poly[5]), (poly[6], poly[7])], np.int32) + cv2.drawContours(image=self.im_cv2, contours=[polygon_list], contourIdx=-1, color=color, thickness=self.lw) + if label: + tf = max(self.lw - 1, 1) # font thicknes + xmax, xmin, ymax, ymin = max(poly[0::2]), min(poly[0::2]), max(poly[1::2]), min(poly[1::2]) + x_label, y_label = int((xmax + xmin)/2), int((ymax + ymin)/2) + w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height + cv2.rectangle( + self.im_cv2, + (x_label, y_label), + (x_label + w + 1, y_label + int(1.5*h)), + color, -1, cv2.LINE_AA + ) + cv2.putText(self.im_cv2, label, (x_label, y_label + h), 0, self.lw / 3, txt_color, thickness=tf, lineType=cv2.LINE_AA) + self.im = self.im_cv2 if isinstance(self.im_cv2, Image.Image) else Image.fromarray(self.im_cv2) + + def rectangle(self, xy, fill=None, outline=None, width=1): + # Add rectangle to image (PIL-only) + self.draw.rectangle(xy, fill, outline, width) + + def text(self, xy, text, txt_color=(255, 255, 255)): + # Add text to image (PIL-only) + w, h = self.font.getsize(text) # text width, height + self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font) + + def result(self): + # Return annotated image as array + return np.asarray(self.im) + + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + +def rbox2poly_single(rrect): + """ + rrect:[x_ctr,y_ctr,w,h,angle] + to + poly:[x0,y0,x1,y1,x2,y2,x3,y3] + """ + x_ctr, y_ctr, width, height, angle = rrect[:5] + tl_x, tl_y, br_x, br_y = -width/2, -height/2, width/2, height/2 + rect = np.array([[tl_x, br_x, br_x, tl_x], [tl_y, tl_y, br_y, br_y]]) + R = np.array([[np.cos(angle), -np.sin(angle)], + [np.sin(angle), np.cos(angle)]]) + poly = R.dot(rect) + x0, x1, x2, x3 = poly[0, :4] + x_ctr + y0, y1, y2, y3 = poly[1, :4] + y_ctr + poly = np.array([x0, y0, x1, y1, x2, y2, x3, y3], dtype=np.float32) + poly = get_best_begin_point_single(poly) + return poly + +def is_ascii(s=''): + # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) + s = str(s) # convert list, tuple, None, etc. to str + return len(s.encode().decode('ascii', 'ignore')) == len(s) +def is_chinese(s='人工智能'): + import re + # Is string composed of any Chinese characters? + return re.search('[\u4e00-\u9fff]', s) + +def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): + # Rescale boxes (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + clip_boxes(boxes, img0_shape) + return boxes + +def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + segments[:, 0] -= pad[0] # x padding + segments[:, 1] -= pad[1] # y padding + segments /= gain + clip_segments(segments, img0_shape) + if normalize: + segments[:, 0] /= img0_shape[1] # width + segments[:, 1] /= img0_shape[0] # height + return segments + +def clip_boxes(boxes, shape): + # Clip boxes (xyxy) to image shape (height, width) + if isinstance(boxes, torch.Tensor): # faster individually + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 + else: # np.array (faster grouped) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + + +def clip_segments(segments, shape): + # Clip segments (xy1,xy2,...) to image shape (height, width) + if isinstance(segments, torch.Tensor): # faster individually + segments[:, 0].clamp_(0, shape[1]) # x + segments[:, 1].clamp_(0, shape[0]) # y + else: # np.array (faster grouped) + segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x + segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y + + + +def masks2segments(masks, strategy='largest'): + # Convert masks(n,160,160) into segments(n,xy) + segments = [] + for x in masks.int().cpu().numpy().astype('uint8'): + c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + if c: + if strategy == 'concat': # concatenate all segments + c = np.concatenate([x.reshape(-1, 2) for x in c]) + elif strategy == 'largest': # select largest segment + c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) + else: + c = np.zeros((0, 2)) # no segments found + segments.append(c.astype('float32')) + return segments + +def process_mask(protos, masks_in, bboxes, shape, upsample=False): + """ + Crop before upsample. + proto_out: [mask_dim, mask_h, mask_w] + out_masks: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape:input_image_size, (h, w) + + return: h, w, n + """ + + c, mh, mw = protos.shape # CHW + ih, iw = shape + # print(masks_in.shape, protos.shape) + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW + + downsampled_bboxes = bboxes.clone() + downsampled_bboxes[:, 0] *= mw / iw + downsampled_bboxes[:, 2] *= mw / iw + downsampled_bboxes[:, 3] *= mh / ih + downsampled_bboxes[:, 1] *= mh / ih + + masks = crop_mask(masks, downsampled_bboxes) # CHW + if upsample: + masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + return masks.gt_(0.5) + + +def crop_mask(masks, boxes): + """ + "Crop" predicted masks by zeroing out everything not in the predicted bbox. + Vectorized by Chong (thanks Chong). + + Args: + - masks should be a size [h, w, n] tensor of masks + - boxes should be a size [n, 4] tensor of bbox coords in relative point form + """ + + n, h, w = masks.shape + x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n) + r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1) + c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1) + + return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) + + + + + +def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): + """ + img1_shape: model input shape, [h, w] + img0_shape: origin pic shape, [h, w, 3] + masks: [h, w, num] + """ + # Rescale coordinates (xyxy) from im1_shape to im0_shape + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + # masks = masks.permute(2, 0, 1).contiguous() + # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] + # masks = masks.permute(1, 2, 0).contiguous() + + masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) + + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + +def non_max_suppression_obb(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, + labels=(), max_det=1500): + """Runs Non-Maximum Suppression (NMS) on inference results_obb + Args: + prediction (tensor): (b, n_all_anchors, [cx cy l s obj num_cls theta_cls]) + agnostic (bool): True = NMS will be applied between elements of different categories + labels : () or + + Returns: + list of detections, len=batch_size, on (n,7) tensor per image [xylsθ, conf, cls] θ ∈ [-pi/2, pi/2) + """ + + nc = prediction.shape[2] - 5 - 180 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + class_index = nc + 5 + + # Checks + assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + + # Settings + max_wh = 4096 # min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 30.0 # seconds to quit after + # redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + + t = time.time() + output = [torch.zeros((0, 7), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence, (tensor): (n_conf_thres, [cx cy l s obj num_cls theta_cls]) + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:class_index] *= x[:, 4:5] # conf = obj_conf * cls_conf + + _, theta_pred = torch.max(x[:, class_index:], 1, keepdim=True) # [n_conf_thres, 1] θ ∈ int[0, 179] + theta_pred = (theta_pred - 90) / 180 * pi # [n_conf_thres, 1] θ ∈ [-pi/2, pi/2) + + # Detections matrix nx7 (xyls, θ, conf, cls) θ ∈ [-pi/2, pi/2) + if multi_label: + i, j = (x[:, 5:class_index] > conf_thres).nonzero(as_tuple=False).T # () + x = torch.cat((x[i, :4], theta_pred[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:class_index].max(1, keepdim=True) + x = torch.cat((x[:, :4], theta_pred, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 6:7] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 5].argsort(descending=True)[:max_nms]] # sort by confidence + + # Batched NMS + c = x[:, 6:7] * (0 if agnostic else max_wh) # classes + rboxes = x[:, :5].clone() + rboxes[:, :2] = rboxes[:, :2] + c # rboxes (offset by class) + scores = x[:, 5] # scores + _, i = obb_nms(rboxes, scores, iou_thres) + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f'WARNING: NMS time limit {time_limit}s exceeded') + break # time limit exceeded + + return output + +def obb_nms(dets, scores, iou_thr, device_id=None): + """ + RIoU NMS - iou_thr. + Args: + dets (tensor/array): (num, [cx cy w h θ]) θ∈[-pi/2, pi/2) + scores (tensor/array): (num) + iou_thr (float): (1) + Returns: + dets (tensor): (n_nms, [cx cy w h θ]) + inds (tensor): (n_nms), nms index of dets + """ + if isinstance(dets, torch.Tensor): + is_numpy = False + dets_th = dets + elif isinstance(dets, np.ndarray): + is_numpy = True + device = 'cpu' if device_id is None else f'cuda:{device_id}' + dets_th = torch.from_numpy(dets).to(device) + else: + raise TypeError('dets must be eithr a Tensor or numpy array, ' + f'but got {type(dets)}') + + if dets_th.numel() == 0: # len(dets) + inds = dets_th.new_zeros(0, dtype=torch.int64) + else: + # same bug will happen when bboxes is too small + too_small = dets_th[:, [2, 3]].min(1)[0] < 0.001 # [n] + if too_small.all(): # all the bboxes is too small + inds = dets_th.new_zeros(0, dtype=torch.int64) + else: + ori_inds = torch.arange(dets_th.size(0)) # 0 ~ n-1 + ori_inds = ori_inds[~too_small] + dets_th = dets_th[~too_small] # (n_filter, 5) + scores = scores[~too_small] + + inds = nms_rotated_ext.nms_rotated(dets_th, scores, iou_thr) + inds = ori_inds[inds] + + if is_numpy: + inds = inds.cpu().numpy() + return dets[inds, :], inds + + +def poly_nms(dets, iou_thr, device_id=None): + if isinstance(dets, torch.Tensor): + is_numpy = False + dets_th = dets + elif isinstance(dets, np.ndarray): + is_numpy = True + device = 'cpu' if device_id is None else f'cuda:{device_id}' + dets_th = torch.from_numpy(dets).to(device) + else: + raise TypeError('dets must be eithr a Tensor or numpy array, ' + f'but got {type(dets)}') + + if dets_th.device == torch.device('cpu'): + raise NotImplementedError + inds = nms_rotated_ext.nms_poly(dets_th.float(), iou_thr) + + if is_numpy: + inds = inds.cpu().numpy() + return dets[inds, :], inds + + +def box_iou(box1, box2, eps=1e-7): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) + + +def rbox2poly(obboxes): + """ + Trans rbox format to poly format. + Args: + rboxes (array/tensor): (num_gts, [cx cy l s θ]) θ∈[-pi/2, pi/2) + + Returns: + polys (array/tensor): (num_gts, [x1 y1 x2 y2 x3 y3 x4 y4]) + """ + if isinstance(obboxes, torch.Tensor): + center, w, h, theta = obboxes[:, :2], obboxes[:, 2:3], obboxes[:, 3:4], obboxes[:, 4:5] + Cos, Sin = torch.cos(theta), torch.sin(theta) + + vector1 = torch.cat( + (w/2 * Cos, -w/2 * Sin), dim=-1) + vector2 = torch.cat( + (-h/2 * Sin, -h/2 * Cos), dim=-1) + point1 = center + vector1 + vector2 + point2 = center + vector1 - vector2 + point3 = center - vector1 - vector2 + point4 = center - vector1 + vector2 + order = obboxes.shape[:-1] + return torch.cat( + (point1, point2, point3, point4), dim=-1).reshape(*order, 8) + else: + center, w, h, theta = np.split(obboxes, (2, 3, 4), axis=-1) + Cos, Sin = np.cos(theta), np.sin(theta) + + vector1 = np.concatenate( + [w/2 * Cos, -w/2 * Sin], axis=-1) + vector2 = np.concatenate( + [-h/2 * Sin, -h/2 * Cos], axis=-1) + + point1 = center + vector1 + vector2 + point2 = center + vector1 - vector2 + point3 = center - vector1 - vector2 + point4 = center - vector1 + vector2 + order = obboxes.shape[:-1] + return np.concatenate( + [point1, point2, point3, point4], axis=-1).reshape(*order, 8) + +def scale_polys(img1_shape, polys, img0_shape, ratio_pad=None): + # ratio_pad: [(h_raw, w_raw), (hw_ratios, wh_paddings)] + # Rescale coords (xyxyxyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = resized / raw + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] # h_ratios + pad = ratio_pad[1] # wh_paddings + + polys[:, [0, 2, 4, 6]] -= pad[0] # x padding + polys[:, [1, 3, 5, 7]] -= pad[1] # y padding + polys[:, :8] /= gain # Rescale poly shape to img0_shape + #clip_polys(polys, img0_shape) + return polys \ No newline at end of file diff --git a/algorithm/Unetliversegmaster/README.md b/algorithm/Unetliversegmaster/README.md new file mode 100644 index 0000000..6f5b456 --- /dev/null +++ b/algorithm/Unetliversegmaster/README.md @@ -0,0 +1,9 @@ +# Unet_liver_seg +使用Unet进行MRI肝脏图像分割 +# Dependencies +Python >= 3.7 +opencv-python +Pillow == 7.0.0 +torch == 1.4.0 +torchsummary == 1.5.1 +torchvision == 0.4.2 \ No newline at end of file diff --git a/algorithm/Unetliversegmaster/__pycache__/common_tools.cpython-38.pyc b/algorithm/Unetliversegmaster/__pycache__/common_tools.cpython-38.pyc new file mode 100644 index 0000000..86233a3 Binary files /dev/null and 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a/algorithm/Unetliversegmaster/__pycache__/unet.cpython-38.pyc b/algorithm/Unetliversegmaster/__pycache__/unet.cpython-38.pyc new file mode 100644 index 0000000..b12130c Binary files /dev/null and b/algorithm/Unetliversegmaster/__pycache__/unet.cpython-38.pyc differ diff --git a/algorithm/Unetliversegmaster/__pycache__/unet.cpython-39.pyc b/algorithm/Unetliversegmaster/__pycache__/unet.cpython-39.pyc new file mode 100644 index 0000000..17b5373 Binary files /dev/null and b/algorithm/Unetliversegmaster/__pycache__/unet.cpython-39.pyc differ diff --git a/algorithm/Unetliversegmaster/common_tools.py b/algorithm/Unetliversegmaster/common_tools.py new file mode 100644 index 0000000..aab80c0 --- /dev/null +++ b/algorithm/Unetliversegmaster/common_tools.py @@ -0,0 +1,75 @@ +# -*- coding: utf-8 -*- +""" +# @file name : common_tools.py +# @author : Peter +# @date : 2020-02-03 14:10:00 +# @brief : 通用函数 +""" + +import numpy as np +import torch +import random +import torchvision.transforms as transforms +from PIL import Image + + +def transform_invert(img_, transform_train): + """ + 将data 进行反transfrom操作 + :param img_: tensor + :param transform_train: torchvision.transforms + :return: PIL image + """ + if 'Normalize' in str(transform_train): + norm_transform = list(filter(lambda x: isinstance(x, transforms.Normalize), transform_train.transforms)) + mean = torch.tensor(norm_transform[0].mean, dtype=img_.dtype, device=img_.device) + std = torch.tensor(norm_transform[0].std, dtype=img_.dtype, device=img_.device) + img_.mul_(std[:, None, None]).add_(mean[:, None, None]) + + img_ = img_.transpose(0, 2).transpose(0, 1) # C*H*W --> H*W*C + if 'ToTensor' in str(transform_train): + # img_ = np.array(img_) * 255 + img_ = img_.detach().numpy() * 255 + + if img_.shape[2] == 3: + img_ = Image.fromarray(img_.astype('uint8')).convert('RGB') + elif img_.shape[2] == 1: + img_ = Image.fromarray(img_.astype('uint8').squeeze()) + else: + raise Exception("Invalid img shape, expected 1 or 3 in axis 2, but got {}!".format(img_.shape[2]) ) + + return img_ + + +def set_seed(seed): + """ + 进行随机种子的设置 + :param seed: 种子数 + :return: 无 + """ + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + + +def rand_crop(data, label, img_w, img_h): + width1 = random.randint(0, data.size[0] - img_w) + height1 = random.randint(0, data.size[1] - img_h) + width2 = width1 + img_w + height2 = height1 + img_h + + data = data.crop((width1, height1, width2, height2)) + label = label.crop((width1, height1, width2, height2)) + + return data, label + + + + + + + + + + diff --git a/algorithm/Unetliversegmaster/data/predict/predict_20240108_172807_.png b/algorithm/Unetliversegmaster/data/predict/predict_20240108_172807_.png new file mode 100644 index 0000000..0c63661 Binary files /dev/null and b/algorithm/Unetliversegmaster/data/predict/predict_20240108_172807_.png differ diff --git a/algorithm/Unetliversegmaster/data/val/Data/P13_T1_00028.png 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"/home/shared/wy/flask_web/Unet_liver_seg-master/data/val/Data/P2_T1_00018.png" + + self.imgs = imgs + self.transform = transform + self.target_transform = target_transform + + def __getitem__(self, index): + # x_path, y_path = self.imgs[index] + x_path = self.imgs + img_x = Image.open(x_path).convert('L') + # img_y = Image.open(y_path).convert('L') + if self.transform is not None: + img_x = self.transform(img_x) + # if self.target_transform is not None: + # img_y = self.target_transform(img_y) + return img_x + + def __len__(self): + return len(self.imgs) diff --git a/algorithm/Unetliversegmaster/main.py b/algorithm/Unetliversegmaster/main.py new file mode 100644 index 0000000..ff62a0c --- /dev/null +++ b/algorithm/Unetliversegmaster/main.py @@ -0,0 +1,196 @@ +import torch +import argparse +import cv2 +import os +import numpy as np +import matplotlib.pyplot as plt +from torch.utils.data import DataLoader +from torch import nn, optim +from torchvision.transforms import transforms +from unet import Unet +from dataset import LiverDataset +from common_tools import transform_invert + + +def makedir(dir): + if not os.path.exists(dir): + os.mkdir(dir) + + +val_interval = 1 +# 是否使用cuda +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# 均为灰度图像，只需要转换为tensor +x_transforms = transforms.ToTensor() +y_transforms = transforms.ToTensor() + +train_curve = list() +valid_curve = list() + + +def train_model(model, criterion, optimizer, dataload, num_epochs=100): + makedir('./model') + model_path = "./model/weights_100.pth" + if os.path.exists(model_path): + model.load_state_dict(torch.load(model_path, map_location=device)) + start_epoch = 20 + print('加载成功！') + else: + start_epoch = 0 + print('无保存模型，将从头开始训练！') + + for epoch in range(start_epoch+1, num_epochs): + print('Epoch {}/{}'.format(epoch, num_epochs)) + print('-' * 10) + dt_size = len(dataload.dataset) + epoch_loss = 0 + step = 0 + for x, y in dataload: + step += 1 + inputs = x.to(device) + labels = y.to(device) + # zero the parameter gradients + optimizer.zero_grad() + # forward + outputs = model(inputs) + loss = criterion(outputs, labels) + loss.backward() + optimizer.step() + epoch_loss += loss.item() + train_curve.append(loss.item()) + print("%d/%d,train_loss:%0.3f" % (step, (dt_size - 1) // dataload.batch_size + 1, loss.item())) + print("epoch %d loss:%0.3f" % (epoch, epoch_loss/step)) + if (epoch + 1) % 50 == 0: + torch.save(model.state_dict(), './model/weights_%d.pth' % (epoch + 1)) + + # Validate the model + valid_dataset = LiverDataset("data/val", transform=x_transforms, target_transform=y_transforms) + valid_loader = DataLoader(valid_dataset, batch_size=4, shuffle=True) + if (epoch + 2) % val_interval == 0: + loss_val = 0. + model.eval() + with torch.no_grad(): + step_val = 0 + for x, y in valid_loader: + step_val += 1 + x = x.type(torch.FloatTensor) + inputs = x.to(device) + labels = y.to(device) + outputs = model(inputs) + loss = criterion(outputs, labels) + loss_val += loss.item() + + valid_curve.append(loss_val) + print("epoch %d valid_loss:%0.3f" % (epoch, loss_val / step_val)) + + train_x = range(len(train_curve)) + train_y = train_curve + + train_iters = len(dataload) + valid_x = np.arange(1, len( + valid_curve) + 1) * train_iters * val_interval # 由于valid中记录的是EpochLoss，需要对记录点进行转换到iterations + valid_y = valid_curve + + plt.plot(train_x, train_y, label='Train') + plt.plot(valid_x, valid_y, label='Valid') + + plt.legend(loc='upper right') + plt.ylabel('loss value') + plt.xlabel('Iteration') + plt.show() + return model + + +# 训练模型 +def train(args): + model = Unet(1, 1).to(device) + batch_size = args.batch_size + criterion = nn.BCEWithLogitsLoss() + optimizer = optim.Adam(model.parameters()) + liver_dataset = LiverDataset("./data/train", transform=x_transforms, target_transform=y_transforms) + dataloaders = DataLoader(liver_dataset, batch_size=batch_size, shuffle=True, num_workers=4) + train_model(model, criterion, optimizer, dataloaders) + + +# 显示模型的输出结果 +def test(args): + model = Unet(1, 1) + model.load_state_dict(torch.load(args.ckpt, map_location='cuda')) + liver_dataset = LiverDataset("data/val", transform=x_transforms, target_transform=y_transforms) + dataloaders = DataLoader(liver_dataset, batch_size=1) + + save_root = './data/predict' + + model.eval() + plt.ion() + index = 0 + with torch.no_grad(): + for x, ground in dataloaders: + x = x.type(torch.FloatTensor) + y = model(x) + x = torch.squeeze(x) + x = x.unsqueeze(0) + ground = torch.squeeze(ground) + ground = ground.unsqueeze(0) + img_ground = transform_invert(ground, y_transforms) + img_x = transform_invert(x, x_transforms) + img_y = torch.squeeze(y).numpy() + # cv2.imshow('img', img_y) + src_path = os.path.join(save_root, "predict_%d_s.png" % index) + save_path = os.path.join(save_root, "predict_%d_o.png" % index) + ground_path = os.path.join(save_root, "predict_%d_g.png" % index) + img_ground.save(ground_path) + # img_x.save(src_path) + cv2.imwrite(save_path, img_y * 255) + index = index + 1 + # plt.imshow(img_y) + # plt.pause(0.5) + # plt.show() + + +# 计算Dice系数 +def dice_calc(args): + root = './data/predict' + nums = len(os.listdir(root)) // 3 + dice = list() + dice_mean = 0 + for i in range(nums): + ground_path = os.path.join(root, "predict_%d_g.png" % i) + predict_path = os.path.join(root, "predict_%d_o.png" % i) + img_ground = cv2.imread(ground_path) + img_predict = cv2.imread(predict_path) + intersec = 0 + x = 0 + y = 0 + for w in range(256): + for h in range(256): + intersec += img_ground.item(w, h, 1) * img_predict.item(w, h, 1) / (255 * 255) + x += img_ground.item(w, h, 1) / 255 + y += img_predict.item(w, h, 1) / 255 + if x + y == 0: + current_dice = 1 + else: + current_dice = round(2 * intersec / (x + y), 3) + dice_mean += current_dice + dice.append(current_dice) + dice_mean /= len(dice) + print(dice) + print(round(dice_mean, 3)) + + +if __name__ == '__main__': + #参数解析 + parse = argparse.ArgumentParser() + parse.add_argument("--action", type=str, help="train, test or dice", default="test") + parse.add_argument("--batch_size", type=int, default=4) + parse.add_argument("--ckpt", type=str, help="the path of model weight file", default="./model/weights_100.pth") + # parse.add_argument("--ckpt", type=str, help="the path of model weight file") + args = parse.parse_args() + + if args.action == "train": + train(args) + elif args.action == "test": + test(args) + elif args.action == "dice": + dice_calc(args) \ No newline at end of file diff --git a/algorithm/Unetliversegmaster/main_wy.py b/algorithm/Unetliversegmaster/main_wy.py new file mode 100644 index 0000000..22e0f7f --- /dev/null +++ b/algorithm/Unetliversegmaster/main_wy.py @@ -0,0 +1,53 @@ +import torch +import argparse +import cv2 +import os +import numpy as np +import matplotlib.pyplot as plt +from torch.utils.data import DataLoader +from torch import nn, optim +from torchvision.transforms import transforms +from unet import Unet +from dataset import LiverDataset +from common_tools import transform_invert +import PIL.Image as Image +from datetime import datetime + +class ImageSegmentation: + + val_interval = 1 + # 是否使用cuda + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # 均为灰度图像，只需要转换为tensor + x_transforms = transforms.ToTensor() + y_transforms = transforms.ToTensor() + + + # 显示模型的输出结果 + def test(): + model = Unet(1, 1) + model.load_state_dict(torch.load('./model/weights_100.pth', map_location='cuda')) + x_path = "/home/shared/wy/flask_web/Unet_liver_seg-master/data/val/Data/P8_T1_00070.png" + img_x = Image.open(x_path).convert('L') + img_x = x_transforms(img_x) + + save_root = './data/predict' + + # 获取当前时间 + current_time = datetime.now() + + # 将当前时间格式化为字符串 + time_str = current_time.strftime("%Y%m%d_%H%M%S") + model.eval() + plt.ion() + with torch.no_grad(): + img_x = img_x.unsqueeze(0) + x = img_x.type(torch.FloatTensor) + y = model(x) + x = torch.squeeze(x) + x = x.unsqueeze(0) + img_x = transform_invert(x, x_transforms) + img_y = torch.squeeze(y).numpy() + save_path = os.path.join(save_root, "predict_%s_.png" % time_str) + cv2.imwrite(save_path, img_y * 255) \ No newline at end of file diff --git a/algorithm/Unetliversegmaster/model/weights_100.pth b/algorithm/Unetliversegmaster/model/weights_100.pth new file mode 100644 index 0000000..d99aa00 Binary files /dev/null and b/algorithm/Unetliversegmaster/model/weights_100.pth differ diff --git a/algorithm/Unetliversegmaster/model/weights_50.pth b/algorithm/Unetliversegmaster/model/weights_50.pth new file mode 100644 index 0000000..65f2bc6 Binary files /dev/null and b/algorithm/Unetliversegmaster/model/weights_50.pth differ diff --git a/algorithm/Unetliversegmaster/test.py b/algorithm/Unetliversegmaster/test.py new file mode 100644 index 0000000..70150ec --- /dev/null +++ b/algorithm/Unetliversegmaster/test.py @@ -0,0 +1,63 @@ +import torch +import argparse +import cv2 +import os +import numpy as np +import matplotlib.pyplot as plt +from torch.utils.data import DataLoader +from torch import nn, optim +from torchvision.transforms import transforms +from unet import Unet +from dataset import LiverDataset +from common_tools import transform_invert +import PIL.Image as Image + +val_interval = 1 +# 是否使用cuda +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +# 均为灰度图像，只需要转换为tensor +x_transforms = transforms.ToTensor() +y_transforms = transforms.ToTensor() + + +# 显示模型的输出结果 +def test(args): + model = Unet(1, 1) + model.load_state_dict(torch.load(args.ckpt, map_location='cuda')) + image_path = "/home/shared/wy/flask_web/Unet_liver_seg-master/data/val_data/Data/P1_T1_00038.png" + img_x = Image.open(image_path).convert('L') + img_x1 = x_transforms(img_x) + dataloaders = DataLoader(img_x, batch_size=1) + + save_root = './data/predict' + + model.eval() + plt.ion() + index = 0 + with torch.no_grad(): + + x = img_x1.type(torch.FloatTensor) + y = model(x) + x = torch.squeeze(x) + x = x.unsqueeze(0) + img_x = transform_invert(x, x_transforms) + img_y = torch.squeeze(y).numpy() + save_path = os.path.join(save_root, "predict_%d_re.png" % index) + cv2.imwrite(save_path, img_y * 255) + index = index + 1 + +if __name__ == '__main__': + #参数解析 + parse = argparse.ArgumentParser() + parse.add_argument("--action", type=str, help="train, test or dice", default="test") + parse.add_argument("--ckpt", type=str, help="the path of model weight file", default="./model/weights_100.pth") + # parse.add_argument("--ckpt", type=str, help="the path of model weight file") + args = parse.parse_args() + + if args.action == "train": + train(args) + elif args.action == "test": + test(args) + elif args.action == "dice": + dice_calc(args) diff --git a/algorithm/Unetliversegmaster/unet.py b/algorithm/Unetliversegmaster/unet.py new file mode 100644 index 0000000..a010b18 --- /dev/null +++ b/algorithm/Unetliversegmaster/unet.py @@ -0,0 +1,87 @@ +import torch +from torch import nn + + +class DoubleConv(nn.Module): + def __init__(self, in_ch, out_ch): + super(DoubleConv, self).__init__() + self.conv = nn.Sequential( + nn.Conv2d(in_ch, out_ch, 3, padding=1), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + nn.Conv2d(out_ch, out_ch, 3, padding=1), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True) + ) + + def forward(self, input): + return self.conv(input) + + +class Unet(nn.Module): + def __init__(self, in_ch, out_ch): + super(Unet, self).__init__() + + self.conv1 = DoubleConv(in_ch, 64) + self.pool1 = nn.MaxPool2d(2) + self.conv2 = DoubleConv(64, 128) + self.pool2 = nn.MaxPool2d(2) + self.conv3 = DoubleConv(128, 256) + self.pool3 = nn.MaxPool2d(2) + self.conv4 = DoubleConv(256, 512) + self.pool4 = nn.MaxPool2d(2) + self.conv5 = DoubleConv(512, 1024) + self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2) + self.conv6 = DoubleConv(1024, 512) + self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2) + self.conv7 = DoubleConv(512, 256) + self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2) + self.conv8 = DoubleConv(256, 128) + self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2) + self.conv9 = DoubleConv(128, 64) + self.conv10 = nn.Conv2d(64, out_ch, 1) + self.dropout = nn.Dropout(p=0.2) + + def forward(self, x): + c1 = self.conv1(x) + p1 = self.pool1(c1) + p1 = self.dropout(p1) + c2 = self.conv2(p1) + p2 = self.pool2(c2) + p2 = self.dropout(p2) + c3 = self.conv3(p2) + p3 = self.pool3(c3) + p3 = self.dropout(p3) + c4 = self.conv4(p3) + p4 = self.pool4(c4) + p4 = self.dropout(p4) + c5 = self.conv5(p4) + up_6 = self.up6(c5) + merge6 = torch.cat([up_6, c4], dim=1) + merge6 = self.dropout(merge6) + c6 = self.conv6(merge6) + up_7 = self.up7(c6) + merge7 = torch.cat([up_7, c3], dim=1) + merge7 = self.dropout(merge7) + c7 = self.conv7(merge7) + up_8 = self.up8(c7) + merge8 = torch.cat([up_8, c2], dim=1) + merge8 = self.dropout(merge8) + c8 = self.conv8(merge8) + up_9 = self.up9(c8) + merge9 = torch.cat([up_9, c1], dim=1) + merge9 = self.dropout(merge9) + c9 = self.conv9(merge9) + c10 = self.conv10(c9) + # out = nn.Sigmoid()(c10) + return c10 + + + + + + + + + + diff --git a/algorithm/detect_emotion/__pycache__/emo_face_detection.cpython-39.pyc b/algorithm/detect_emotion/__pycache__/emo_face_detection.cpython-39.pyc new file mode 100644 index 0000000..e9d269e Binary files /dev/null and b/algorithm/detect_emotion/__pycache__/emo_face_detection.cpython-39.pyc differ diff --git a/algorithm/detect_emotion/__pycache__/emotion_detection.cpython-38.pyc b/algorithm/detect_emotion/__pycache__/emotion_detection.cpython-38.pyc new file mode 100644 index 0000000..673d9ee Binary files /dev/null and b/algorithm/detect_emotion/__pycache__/emotion_detection.cpython-38.pyc differ diff --git a/algorithm/detect_emotion/__pycache__/emotion_detection.cpython-39.pyc b/algorithm/detect_emotion/__pycache__/emotion_detection.cpython-39.pyc new file mode 100644 index 0000000..7a083fe Binary files /dev/null and b/algorithm/detect_emotion/__pycache__/emotion_detection.cpython-39.pyc differ diff --git a/algorithm/detect_emotion/__pycache__/face_detection.cpython-39.pyc b/algorithm/detect_emotion/__pycache__/face_detection.cpython-39.pyc new file mode 100644 index 0000000..5d656a1 Binary files /dev/null and b/algorithm/detect_emotion/__pycache__/face_detection.cpython-39.pyc differ diff --git a/algorithm/detect_emotion/__pycache__/photo.cpython-38.pyc b/algorithm/detect_emotion/__pycache__/photo.cpython-38.pyc new file mode 100644 index 0000000..1975534 Binary files /dev/null and b/algorithm/detect_emotion/__pycache__/photo.cpython-38.pyc differ diff --git a/algorithm/detect_emotion/deploy.prototxt.txt b/algorithm/detect_emotion/deploy.prototxt.txt new file mode 100644 index 0000000..905580e --- /dev/null +++ b/algorithm/detect_emotion/deploy.prototxt.txt @@ -0,0 +1,1789 @@ +input: "data" +input_shape { + dim: 1 + dim: 3 + dim: 300 + dim: 300 +} + +layer { + name: "data_bn" + type: "BatchNorm" + bottom: "data" + top: "data_bn" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "data_scale" + type: "Scale" + bottom: "data_bn" + top: "data_bn" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_h" + type: "Convolution" + bottom: "data_bn" + top: "conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + pad: 3 + kernel_size: 7 + stride: 2 + weight_filler { + type: "msra" + variance_norm: FAN_OUT + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "conv1_bn_h" + type: "BatchNorm" + bottom: "conv1_h" + top: "conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "conv1_scale_h" + type: "Scale" + bottom: "conv1_h" + top: "conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "conv1_relu" + type: "ReLU" + bottom: "conv1_h" + top: "conv1_h" +} +layer { + name: "conv1_pool" + type: "Pooling" + bottom: "conv1_h" + top: "conv1_pool" + pooling_param { + kernel_size: 3 + stride: 2 + } +} +layer { + name: "layer_64_1_conv1_h" + type: "Convolution" + bottom: "conv1_pool" + top: "layer_64_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_64_1_bn2_h" + type: "BatchNorm" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_64_1_scale2_h" + type: "Scale" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_64_1_relu2" + type: "ReLU" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv1_h" +} +layer { + name: "layer_64_1_conv2_h" + type: "Convolution" + bottom: "layer_64_1_conv1_h" + top: "layer_64_1_conv2_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 32 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_64_1_sum" + type: "Eltwise" + bottom: "layer_64_1_conv2_h" + bottom: "conv1_pool" + top: "layer_64_1_sum" +} +layer { + name: "layer_128_1_bn1_h" + type: "BatchNorm" + bottom: "layer_64_1_sum" + top: "layer_128_1_bn1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_128_1_scale1_h" + type: "Scale" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_bn1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_128_1_relu1" + type: "ReLU" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_bn1_h" +} +layer { + name: "layer_128_1_conv1_h" + type: "Convolution" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_bn2" + type: "BatchNorm" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_128_1_scale2" + type: "Scale" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_128_1_relu2" + type: "ReLU" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv1_h" +} +layer { + name: "layer_128_1_conv2" + type: "Convolution" + bottom: "layer_128_1_conv1_h" + top: "layer_128_1_conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_conv_expand_h" + type: "Convolution" + bottom: "layer_128_1_bn1_h" + top: "layer_128_1_conv_expand_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_128_1_sum" + type: "Eltwise" + bottom: "layer_128_1_conv2" + bottom: "layer_128_1_conv_expand_h" + top: "layer_128_1_sum" +} +layer { + name: "layer_256_1_bn1" + type: "BatchNorm" + bottom: "layer_128_1_sum" + top: "layer_256_1_bn1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_256_1_scale1" + type: "Scale" + bottom: "layer_256_1_bn1" + top: "layer_256_1_bn1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_256_1_relu1" + type: "ReLU" + bottom: "layer_256_1_bn1" + top: "layer_256_1_bn1" +} +layer { + name: "layer_256_1_conv1" + type: "Convolution" + bottom: "layer_256_1_bn1" + top: "layer_256_1_conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_bn2" + type: "BatchNorm" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_256_1_scale2" + type: "Scale" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_256_1_relu2" + type: "ReLU" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv1" +} +layer { + name: "layer_256_1_conv2" + type: "Convolution" + bottom: "layer_256_1_conv1" + top: "layer_256_1_conv2" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_conv_expand" + type: "Convolution" + bottom: "layer_256_1_bn1" + top: "layer_256_1_conv_expand" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_256_1_sum" + type: "Eltwise" + bottom: "layer_256_1_conv2" + bottom: "layer_256_1_conv_expand" + top: "layer_256_1_sum" +} +layer { + name: "layer_512_1_bn1" + type: "BatchNorm" + bottom: "layer_256_1_sum" + top: "layer_512_1_bn1" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_512_1_scale1" + type: "Scale" + bottom: "layer_512_1_bn1" + top: "layer_512_1_bn1" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_512_1_relu1" + type: "ReLU" + bottom: "layer_512_1_bn1" + top: "layer_512_1_bn1" +} +layer { + name: "layer_512_1_conv1_h" + type: "Convolution" + bottom: "layer_512_1_bn1" + top: "layer_512_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 128 + bias_term: false + pad: 1 + kernel_size: 3 + stride: 1 # 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_bn2_h" + type: "BatchNorm" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "layer_512_1_scale2_h" + type: "Scale" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "layer_512_1_relu2" + type: "ReLU" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv1_h" +} +layer { + name: "layer_512_1_conv2_h" + type: "Convolution" + bottom: "layer_512_1_conv1_h" + top: "layer_512_1_conv2_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 2 # 1 + kernel_size: 3 + stride: 1 + dilation: 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_conv_expand_h" + type: "Convolution" + bottom: "layer_512_1_bn1" + top: "layer_512_1_conv_expand_h" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + convolution_param { + num_output: 256 + bias_term: false + pad: 0 + kernel_size: 1 + stride: 1 # 2 + weight_filler { + type: "msra" + } + bias_filler { + type: "constant" + value: 0.0 + } + } +} +layer { + name: "layer_512_1_sum" + type: "Eltwise" + bottom: "layer_512_1_conv2_h" + bottom: "layer_512_1_conv_expand_h" + top: "layer_512_1_sum" +} +layer { + name: "last_bn_h" + type: "BatchNorm" + bottom: "layer_512_1_sum" + top: "layer_512_1_sum" + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } + param { + lr_mult: 0.0 + } +} +layer { + name: "last_scale_h" + type: "Scale" + bottom: "layer_512_1_sum" + top: "layer_512_1_sum" + param { + lr_mult: 1.0 + decay_mult: 1.0 + } + param { + lr_mult: 2.0 + decay_mult: 1.0 + } + scale_param { + bias_term: true + } +} +layer { + name: "last_relu" + type: "ReLU" + bottom: "layer_512_1_sum" + top: "fc7" +} + +layer { + name: "conv6_1_h" + type: "Convolution" + bottom: "fc7" + top: "conv6_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_1_relu" + type: "ReLU" + bottom: "conv6_1_h" + top: "conv6_1_h" +} +layer { + name: "conv6_2_h" + type: "Convolution" + bottom: "conv6_1_h" + top: "conv6_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 256 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_relu" + type: "ReLU" + bottom: "conv6_2_h" + top: "conv6_2_h" +} +layer { + name: "conv7_1_h" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv7_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_1_relu" + type: "ReLU" + bottom: "conv7_1_h" + top: "conv7_1_h" +} +layer { + name: "conv7_2_h" + type: "Convolution" + bottom: "conv7_1_h" + top: "conv7_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + stride: 2 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_relu" + type: "ReLU" + bottom: "conv7_2_h" + top: "conv7_2_h" +} +layer { + name: "conv8_1_h" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv8_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_1_relu" + type: "ReLU" + bottom: "conv8_1_h" + top: "conv8_1_h" +} +layer { + name: "conv8_2_h" + type: "Convolution" + bottom: "conv8_1_h" + top: "conv8_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_relu" + type: "ReLU" + bottom: "conv8_2_h" + top: "conv8_2_h" +} +layer { + name: "conv9_1_h" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv9_1_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 64 + pad: 0 + kernel_size: 1 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_1_relu" + type: "ReLU" + bottom: "conv9_1_h" + top: "conv9_1_h" +} +layer { + name: "conv9_2_h" + type: "Convolution" + bottom: "conv9_1_h" + top: "conv9_2_h" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 128 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_relu" + type: "ReLU" + bottom: "conv9_2_h" + top: "conv9_2_h" +} +layer { + name: "conv4_3_norm" + type: "Normalize" + bottom: "layer_256_1_bn1" + top: "conv4_3_norm" + norm_param { + across_spatial: false + scale_filler { + type: "constant" + value: 20 + } + channel_shared: false + } +} +layer { + name: "conv4_3_norm_mbox_loc" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_loc_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_loc" + top: "conv4_3_norm_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_loc_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_loc_perm" + top: "conv4_3_norm_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf" + type: "Convolution" + bottom: "conv4_3_norm" + top: "conv4_3_norm_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv4_3_norm_mbox_conf_perm" + type: "Permute" + bottom: "conv4_3_norm_mbox_conf" + top: "conv4_3_norm_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv4_3_norm_mbox_conf_flat" + type: "Flatten" + bottom: "conv4_3_norm_mbox_conf_perm" + top: "conv4_3_norm_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv4_3_norm_mbox_priorbox" + type: "PriorBox" + bottom: "conv4_3_norm" + bottom: "data" + top: "conv4_3_norm_mbox_priorbox" + prior_box_param { + min_size: 30.0 + max_size: 60.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 8 + offset: 0.5 + } +} +layer { + name: "fc7_mbox_loc" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_loc_perm" + type: "Permute" + bottom: "fc7_mbox_loc" + top: "fc7_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_loc_flat" + type: "Flatten" + bottom: "fc7_mbox_loc_perm" + top: "fc7_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_conf" + type: "Convolution" + bottom: "fc7" + top: "fc7_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "fc7_mbox_conf_perm" + type: "Permute" + bottom: "fc7_mbox_conf" + top: "fc7_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "fc7_mbox_conf_flat" + type: "Flatten" + bottom: "fc7_mbox_conf_perm" + top: "fc7_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "fc7_mbox_priorbox" + type: "PriorBox" + bottom: "fc7" + bottom: "data" + top: "fc7_mbox_priorbox" + prior_box_param { + min_size: 60.0 + max_size: 111.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 16 + offset: 0.5 + } +} +layer { + name: "conv6_2_mbox_loc" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv6_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_loc_perm" + type: "Permute" + bottom: "conv6_2_mbox_loc" + top: "conv6_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv6_2_mbox_loc_perm" + top: "conv6_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_conf" + type: "Convolution" + bottom: "conv6_2_h" + top: "conv6_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv6_2_mbox_conf_perm" + type: "Permute" + bottom: "conv6_2_mbox_conf" + top: "conv6_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv6_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv6_2_mbox_conf_perm" + top: "conv6_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv6_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv6_2_h" + bottom: "data" + top: "conv6_2_mbox_priorbox" + prior_box_param { + min_size: 111.0 + max_size: 162.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 32 + offset: 0.5 + } +} +layer { + name: "conv7_2_mbox_loc" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv7_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 24 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_loc_perm" + type: "Permute" + bottom: "conv7_2_mbox_loc" + top: "conv7_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv7_2_mbox_loc_perm" + top: "conv7_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_conf" + type: "Convolution" + bottom: "conv7_2_h" + top: "conv7_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 12 # 126 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv7_2_mbox_conf_perm" + type: "Permute" + bottom: "conv7_2_mbox_conf" + top: "conv7_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv7_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv7_2_mbox_conf_perm" + top: "conv7_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv7_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv7_2_h" + bottom: "data" + top: "conv7_2_mbox_priorbox" + prior_box_param { + min_size: 162.0 + max_size: 213.0 + aspect_ratio: 2 + aspect_ratio: 3 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 64 + offset: 0.5 + } +} +layer { + name: "conv8_2_mbox_loc" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv8_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_loc_perm" + type: "Permute" + bottom: "conv8_2_mbox_loc" + top: "conv8_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv8_2_mbox_loc_perm" + top: "conv8_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_conf" + type: "Convolution" + bottom: "conv8_2_h" + top: "conv8_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv8_2_mbox_conf_perm" + type: "Permute" + bottom: "conv8_2_mbox_conf" + top: "conv8_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv8_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv8_2_mbox_conf_perm" + top: "conv8_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv8_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv8_2_h" + bottom: "data" + top: "conv8_2_mbox_priorbox" + prior_box_param { + min_size: 213.0 + max_size: 264.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 100 + offset: 0.5 + } +} +layer { + name: "conv9_2_mbox_loc" + type: "Convolution" + bottom: "conv9_2_h" + top: "conv9_2_mbox_loc" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 16 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_loc_perm" + type: "Permute" + bottom: "conv9_2_mbox_loc" + top: "conv9_2_mbox_loc_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_loc_flat" + type: "Flatten" + bottom: "conv9_2_mbox_loc_perm" + top: "conv9_2_mbox_loc_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_conf" + type: "Convolution" + bottom: "conv9_2_h" + top: "conv9_2_mbox_conf" + param { + lr_mult: 1 + decay_mult: 1 + } + param { + lr_mult: 2 + decay_mult: 0 + } + convolution_param { + num_output: 8 # 84 + pad: 1 + kernel_size: 3 + stride: 1 + weight_filler { + type: "xavier" + } + bias_filler { + type: "constant" + value: 0 + } + } +} +layer { + name: "conv9_2_mbox_conf_perm" + type: "Permute" + bottom: "conv9_2_mbox_conf" + top: "conv9_2_mbox_conf_perm" + permute_param { + order: 0 + order: 2 + order: 3 + order: 1 + } +} +layer { + name: "conv9_2_mbox_conf_flat" + type: "Flatten" + bottom: "conv9_2_mbox_conf_perm" + top: "conv9_2_mbox_conf_flat" + flatten_param { + axis: 1 + } +} +layer { + name: "conv9_2_mbox_priorbox" + type: "PriorBox" + bottom: "conv9_2_h" + bottom: "data" + top: "conv9_2_mbox_priorbox" + prior_box_param { + min_size: 264.0 + max_size: 315.0 + aspect_ratio: 2 + flip: true + clip: false + variance: 0.1 + variance: 0.1 + variance: 0.2 + variance: 0.2 + step: 300 + offset: 0.5 + } +} +layer { + name: "mbox_loc" + type: "Concat" + bottom: "conv4_3_norm_mbox_loc_flat" + bottom: "fc7_mbox_loc_flat" + bottom: "conv6_2_mbox_loc_flat" + bottom: "conv7_2_mbox_loc_flat" + bottom: "conv8_2_mbox_loc_flat" + bottom: "conv9_2_mbox_loc_flat" + top: "mbox_loc" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_conf" + type: "Concat" + bottom: "conv4_3_norm_mbox_conf_flat" + bottom: "fc7_mbox_conf_flat" + bottom: "conv6_2_mbox_conf_flat" + bottom: "conv7_2_mbox_conf_flat" + bottom: "conv8_2_mbox_conf_flat" + bottom: "conv9_2_mbox_conf_flat" + top: "mbox_conf" + concat_param { + axis: 1 + } +} +layer { + name: "mbox_priorbox" + type: "Concat" + bottom: "conv4_3_norm_mbox_priorbox" + bottom: "fc7_mbox_priorbox" + bottom: "conv6_2_mbox_priorbox" + bottom: "conv7_2_mbox_priorbox" + bottom: "conv8_2_mbox_priorbox" + bottom: "conv9_2_mbox_priorbox" + top: "mbox_priorbox" + concat_param { + axis: 2 + } +} + +layer { + name: "mbox_conf_reshape" + type: "Reshape" + bottom: "mbox_conf" + top: "mbox_conf_reshape" + reshape_param { + shape { + dim: 0 + dim: -1 + dim: 2 + } + } +} +layer { + name: "mbox_conf_softmax" + type: "Softmax" + bottom: "mbox_conf_reshape" + top: "mbox_conf_softmax" + softmax_param { + axis: 2 + } +} +layer { + name: "mbox_conf_flatten" + type: "Flatten" + bottom: "mbox_conf_softmax" + top: "mbox_conf_flatten" + flatten_param { + axis: 1 + } +} + +layer { + name: "detection_out" + type: "DetectionOutput" + bottom: "mbox_loc" + bottom: "mbox_conf_flatten" + bottom: "mbox_priorbox" + top: "detection_out" + include { + phase: TEST + } + detection_output_param { + num_classes: 2 + share_location: true + background_label_id: 0 + nms_param { + nms_threshold: 0.45 + top_k: 400 + } + code_type: CENTER_SIZE + keep_top_k: 200 + confidence_threshold: 0.01 + } +} diff --git a/algorithm/detect_emotion/emotion_detection.py b/algorithm/detect_emotion/emotion_detection.py new file mode 100644 index 0000000..82898e5 --- /dev/null +++ b/algorithm/detect_emotion/emotion_detection.py @@ -0,0 +1,59 @@ +import sys + +from algorithm.detect_emotion.rmn import RMN +from PIL import Image +import cv2 +import matplotlib.pyplot as plt # plt 用于显示图片 +from read_data import LoadImages, LoadStreams +import torch +import time +import torch.backends.cudnn as cudnn + +class Emotion_Detection(): + def __init__(self,video_path=None, model = None): + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.dataset = LoadImages(self.video_name) + self.face_detector = model + self.emotion_model = RMN(face_detector = self.face_detector) + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + cudnn.benchmark = True + self.dataset = LoadStreams(source) + + def get_frame(self): + + for im0s in self.dataset: + + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + + results = self.emotion_model.detect_emotion_for_single_frame(img) + + keyword_to_remove = 'proba_list' + image = self.emotion_model.draw(img, results) + + for dictionary in results: + if keyword_to_remove in dictionary: + del dictionary[keyword_to_remove] + # print(results) + + + ret, jpeg = cv2.imencode(".jpg", image) + return jpeg.tobytes(), '' + + +# x.Emotion_result(picpath="666666.png") diff --git a/algorithm/detect_emotion/pretrained_ckpt b/algorithm/detect_emotion/pretrained_ckpt new file mode 100644 index 0000000..d7e56e1 Binary files /dev/null and b/algorithm/detect_emotion/pretrained_ckpt differ diff --git a/algorithm/detect_emotion/res10_300x300_ssd_iter_140000.caffemodel b/algorithm/detect_emotion/res10_300x300_ssd_iter_140000.caffemodel new file mode 100644 index 0000000..809dfd7 Binary files /dev/null and b/algorithm/detect_emotion/res10_300x300_ssd_iter_140000.caffemodel differ diff --git a/algorithm/detect_emotion/rmn/__init__.py b/algorithm/detect_emotion/rmn/__init__.py new file mode 100644 index 0000000..3c7327e --- /dev/null +++ b/algorithm/detect_emotion/rmn/__init__.py @@ -0,0 +1,308 @@ +import os +import glob +import json +import cv2 +import numpy as np +import torch +from torchvision.transforms import transforms + +from algorithm.detect_emotion.rmn.models import densenet121, resmasking_dropout1 +from tools.draw_chinese import cv2ImgAddText +# from models import densenet121, resmasking_dropout1 +from .version import __version__ + +if torch.cuda.is_available(): + device = torch.device('cuda') +else: + device = torch.device('cpu') + +def show(img, name="disp", width=1000): + """ + name: name of window, should be name of img + img: source of img, should in type ndarray + """ + cv2.namedWindow(name, cv2.WINDOW_GUI_NORMAL) + cv2.resizeWindow(name, width, 1000) + cv2.imshow(name, img) + cv2.waitKey(0) + cv2.destroyAllWindows() + + +checkpoint_url = "https://github.com/phamquiluan/ResidualMaskingNetwork/releases/download/v0.0.1/Z_resmasking_dropout1_rot30_2019Nov30_13.32" +local_checkpoint_path = "algorithm/detect_emotion/pretrained_ckpt" + +prototxt_url = "https://github.com/phamquiluan/ResidualMaskingNetwork/releases/download/v0.0.1/deploy.prototxt.txt" +local_prototxt_path = "algorithm/detect_emotion/deploy.prototxt.txt" + + +def download_checkpoint(remote_url, local_path): + from tqdm import tqdm + import requests + + response = requests.get(remote_url, stream=True) + total_size_in_bytes = int(response.headers.get("content-length", 0)) + block_size = 1024 # 1 Kibibyte + + progress_bar = tqdm( + desc=f"Downloading {local_path}..", + total=total_size_in_bytes, + unit="iB", + unit_scale=True, + ) + + with open(local_path, "wb") as ref: + for data in response.iter_content(block_size): + progress_bar.update(len(data)) + ref.write(data) + + progress_bar.close() + if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes: + print("ERROR, something went wrong") + + +for remote_path, local_path in [ + (checkpoint_url, local_checkpoint_path), + (prototxt_url, local_prototxt_path), +]: + if not os.path.exists(local_path): + print(f"{local_path} does not exists!") + # print(local_path) + download_checkpoint(remote_url=remote_path, local_path=local_path) + + +def ensure_color(image): + if len(image.shape) == 2: + return np.dstack([image] * 3) + elif image.shape[2] == 1: + return np.dstack([image] * 3) + return image + + +def ensure_gray(image): + try: + image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + except cv2.error: + pass + return image + + + +transform = transforms.Compose( + transforms=[transforms.ToPILImage(), transforms.ToTensor()] +) + +FER_2013_EMO_DICT = { + 0: "生气", + 1: "厌恶", + 2: "恐惧", + 3: "开心", + 4: "难过", + 5: "惊喜", + 6: "中性", +} + +is_cuda = torch.cuda.is_available() + + +# load configs and set random seed +package_root_dir = os.path.dirname(__file__) +config_path = os.path.join(package_root_dir, "configs/fer2013_config.json") +with open(config_path) as ref: + configs = json.load(ref) + +image_size = (configs["image_size"], configs["image_size"]) + + +def get_emo_model(): + emo_model = resmasking_dropout1(in_channels=3, num_classes=7) + state = torch.load(local_checkpoint_path, map_location=device) + emo_model.load_state_dict(state["net"]) + emo_model.to(device) + emo_model.eval() + return emo_model + + +def convert_to_square(xmin, ymin, xmax, ymax): + # convert to square location + center_x = (xmin + xmax) // 2 + center_y = (ymin + ymax) // 2 + + square_length = ((xmax - xmin) + (ymax - ymin)) // 2 // 2 + square_length *= 1.1 + + xmin = int(center_x - square_length) + ymin = int(center_y - square_length) + xmax = int(center_x + square_length) + ymax = int(center_y + square_length) + return xmin, ymin, xmax, ymax + + +class RMN: + def __init__(self,face_detector): + + self.face_detector = face_detector + self.emo_model = get_emo_model() + # print(self.emo_model) + + @torch.no_grad() + def detect_emotion_for_single_face_image(self, face_image): + """ + Params: + ----------- + face_image : np.ndarray + a cropped face image + + Return: + ----------- + emo_label : str + dominant emotion label + + emo_proba : float + dominant emotion proba + + proba_list : list + all emotion label and their proba + """ + assert isinstance(face_image, np.ndarray) + face_image = ensure_color(face_image) + face_image = cv2.resize(face_image, image_size) + + face_image = transform(face_image) + if is_cuda: + face_image = face_image.cuda(0) + + face_image = torch.unsqueeze(face_image, dim=0) + # print(face_image.shape) + + output = torch.squeeze(self.emo_model(face_image), 0) + # print(output) + proba = torch.softmax(output, 0) + + # get dominant emotion + emo_proba, emo_idx = torch.max(proba, dim=0) + emo_idx = emo_idx.item() + emo_proba = emo_proba.item() + emo_label = FER_2013_EMO_DICT[emo_idx] + + # get proba for each emotion + proba = proba.tolist() + proba_list = [] + for emo_idx, emo_name in FER_2013_EMO_DICT.items(): + proba_list.append({emo_name: proba[emo_idx]}) + + # print(proba_list) + + return emo_label, emo_proba, proba_list + + @staticmethod + def draw(frame, results): + """ + Params: + --------- + frame : np.ndarray + + results : list of dict.keys('xmin', 'xmax', 'ymin', 'ymax', 'emo_label', 'emo_proba') + + Returns: + --------- + frame : np.ndarray + """ + for r in results: + xmin = r["xmin"] + xmax = r["xmax"] + ymin = r["ymin"] + ymax = r["ymax"] + emo_label = r["emo_label"] + emo_proba = r["emo_proba"] + + label_size, base_line = cv2.getTextSize( + f"{emo_label}: 000", cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2 + ) + + # draw face + cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (179, 255, 179), 2) + + cv2.rectangle( + frame, + (xmax, ymin + 1 - label_size[1]), + (xmax + label_size[0] , ymin + 1 + base_line), + (223, 128, 255), + cv2.FILLED, + ) + frame = cv2ImgAddText(frame, + f"{emo_label} {int(emo_proba * 50)}", + xmax + 20, + ymin - 10, + (0, 0, 0), + 20,) + # cv2.putText( + # frame, + # f"{emo_label} {int(emo_proba * 100)}", + # (xmax, ymin + 1), + # cv2.FONT_HERSHEY_SIMPLEX, + # 0.8, + # (0, 0, 0), + # 2, + # ) + + return frame + + def detect_faces(self, frame): #该部分使用yolo重构 + + + results = self.face_detector(frame) + face_results = [] + + + for obj in results.xyxy[0]: + + if obj[-1] == 0: # 0 is the class ID for 'person' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + xmin, ymin, xmax, ymax = convert_to_square(xmin, ymin, xmax, ymax) + if xmax <= xmin or ymax <= ymin: + continue + + face_results.append({ + "xmin": xmin, + "ymin": ymin, + "xmax": xmax, + "ymax": ymax, + }) + # print(face_results) + return face_results + + @torch.no_grad() + def detect_emotion_for_single_frame(self, frame): + gray = ensure_gray(frame) + + results = [] + face_results = self.detect_faces(frame) + # print(f"num faces: {len(face_results)}") + + for face in face_results: + xmin = face["xmin"] + ymin = face["ymin"] + xmax = face["xmax"] + ymax = face["ymax"] + + face_image = gray[ymin:ymax, xmin:xmax] + # print(face_image.shape) + + if face_image.shape[0] < 10 or face_image.shape[1] < 10: + continue + emo_label, emo_proba, proba_list = self.detect_emotion_for_single_face_image(face_image) + + results.append({ + "xmin": xmin, + "ymin": ymin, + "xmax": xmax, + "ymax": ymax, + "emo_label": emo_label, + "emo_proba": emo_proba, + "proba_list": proba_list + }) + return results + + diff --git a/algorithm/detect_emotion/rmn/__pycache__/__init__.cpython-38.pyc b/algorithm/detect_emotion/rmn/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..06edb2a Binary files /dev/null and b/algorithm/detect_emotion/rmn/__pycache__/__init__.cpython-38.pyc differ diff --git a/algorithm/detect_emotion/rmn/__pycache__/__init__.cpython-39.pyc b/algorithm/detect_emotion/rmn/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..2ddc417 Binary files /dev/null and b/algorithm/detect_emotion/rmn/__pycache__/__init__.cpython-39.pyc differ diff --git a/algorithm/detect_emotion/rmn/__pycache__/version.cpython-38.pyc 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"batch_size": 48, + "num_workers": 8, + "device": "cuda:0", + "max_epoch_num": 50, + "max_plateau_count": 8, + "plateau_patience": 2, + "steplr": 50, + "log_dir": "saved/logs", + "checkpoint_dir": "saved/checkpoints", + "model_name": "_n" +} diff --git a/algorithm/detect_emotion/rmn/models/__init__.py b/algorithm/detect_emotion/rmn/models/__init__.py new file mode 100644 index 0000000..303b2ab --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/__init__.py @@ -0,0 +1,72 @@ +from .vgg import * +from .resnet import * +from .resnet112 import resnet18x112 +from .resnet50_scratch_dims_2048 import resnet50_pretrained_vgg +from .centerloss_resnet import resnet18_centerloss +from .resatt import * +from .alexnet import * +from .densenet import * +from .googlenet import * +from .inception import * +from .inception_resnet_v1 import * +from .residual_attention_network import * +from .fer2013_models import * +from .res_dense_gle import * +from .masking import masking +from .resmasking import ( + resmasking, + resmasking_dropout1, + resmasking_dropout2, + resmasking50_dropout1, +) +from .resmasking_naive import resmasking_naive_dropout1 +from .brain_humor import * +from .runet import * +from pytorchcv.model_provider import get_model as ptcv_get_model + + +def resattnet56(in_channels, num_classes, pretrained=True): + model = ptcv_get_model("resattnet56", pretrained=False) + model.output = nn.Linear(2048, 7) + return model + + +def cbam_resnet50(in_channels, num_classes, pretrained=True): + model = ptcv_get_model("cbam_resnet50", pretrained=True) + model.output = nn.Linear(2048, 7) + return model + + +def bam_resnet50(in_channels, num_classes, pretrained=True): + model = ptcv_get_model("bam_resnet50", pretrained=True) + model.output = nn.Linear(2048, 7) + return model + + +def efficientnet_b7b(in_channels, num_classes, pretrained=True): + model = ptcv_get_model("efficientnet_b7b", pretrained=True) + model.output = nn.Sequential(nn.Dropout(p=0.5, inplace=False), nn.Linear(2560, 7)) + return model + + +def efficientnet_b3b(in_channels, num_classes, pretrained=True): + model = ptcv_get_model("efficientnet_b3b", pretrained=True) + model.output = nn.Sequential(nn.Dropout(p=0.3, inplace=False), nn.Linear(1536, 7)) + return model + + +def efficientnet_b2b(in_channels, num_classes, pretrained=True): + model = ptcv_get_model("efficientnet_b2b", pretrained=True) + model.output = nn.Sequential( + nn.Dropout(p=0.3, inplace=False), nn.Linear(1408, 7, bias=True) + ) + return model + + +def efficientnet_b1b(in_channels, num_classes, pretrained=True): + model = ptcv_get_model("efficientnet_b1b", pretrained=True) + print(model) + model.output = nn.Sequential( + nn.Dropout(p=0.3, inplace=False), nn.Linear(1280, 7, bias=True) + ) + return model diff --git a/algorithm/detect_emotion/rmn/models/__pycache__/__init__.cpython-38.pyc b/algorithm/detect_emotion/rmn/models/__pycache__/__init__.cpython-38.pyc new file mode 100644 index 0000000..18c9d19 Binary files /dev/null and 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from a model + + It has a strong assumption that the modules have been registered + into the model in the same order as they are used. + This means that one should **not** reuse the same nn.Module + twice in the forward if you want this to work. + + Additionally, it is only able to query submodules that are directly + assigned to the model. So if `model` is passed, `model.feature1` can + be returned, but not `model.feature1.layer2`. + + Arguments: + model (nn.Module): model on which we will extract the features + return_layers (Dict[name, new_name]): a dict containing the names + of the modules for which the activations will be returned as + the key of the dict, and the value of the dict is the name + of the returned activation (which the user can specify). + + Examples:: + + >>> m = torchvision.models.resnet18(pretrained=True) + >>> # extract layer1 and layer3, giving as names `feat1` and feat2` + >>> new_m = torchvision.models._utils.IntermediateLayerGetter(m, + >>> {'layer1': 'feat1', 'layer3': 'feat2'}) + >>> out = new_m(torch.rand(1, 3, 224, 224)) + >>> print([(k, v.shape) for k, v in out.items()]) + >>> [('feat1', torch.Size([1, 64, 56, 56])), + >>> ('feat2', torch.Size([1, 256, 14, 14]))] + """ + + def __init__(self, model, return_layers): + if not set(return_layers).issubset( + [name for name, _ in model.named_children()] + ): + raise ValueError("return_layers are not present in model") + + orig_return_layers = return_layers + return_layers = {k: v for k, v in return_layers.items()} + layers = OrderedDict() + for name, module in model.named_children(): + layers[name] = module + if name in return_layers: + del return_layers[name] + if not return_layers: + break + + super(IntermediateLayerGetter, self).__init__(layers) + self.return_layers = orig_return_layers + + def forward(self, x): + out = OrderedDict() + for name, module in self.named_children(): + x = module(x) + if name in self.return_layers: + out_name = self.return_layers[name] + out[out_name] = x + return out diff --git a/algorithm/detect_emotion/rmn/models/alexnet.py b/algorithm/detect_emotion/rmn/models/alexnet.py new file mode 100644 index 0000000..c70a07b --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/alexnet.py @@ -0,0 +1,68 @@ +import torch +import torch.nn as nn +from .utils import load_state_dict_from_url + + +__all__ = ["AlexNet", "alexnet"] + + +model_urls = { + "alexnet": "https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth", +} + + +class AlexNet(nn.Module): + def __init__(self, in_channels=3, num_classes=1000): + super(AlexNet, self).__init__() + self.features = nn.Sequential( + nn.Conv2d(in_channels, 64, kernel_size=11, stride=4, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.Conv2d(64, 192, kernel_size=5, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.Conv2d(192, 384, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(384, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + ) + self.avgpool = nn.AdaptiveAvgPool2d((6, 6)) + self.classifier = nn.Sequential( + nn.Dropout(), + nn.Linear(256 * 6 * 6, 4096), + nn.ReLU(inplace=True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(inplace=True), + # TODO: strictly set to 1000 to load pretrained + # nn.Linear(4096, num_classes), + nn.Linear(4096, 1000), + ) + + def forward(self, x): + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + +def alexnet(pretrained=True, progress=True, **kwargs): + r"""AlexNet model architecture from the + `"One weird trick..." `_ paper. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + model = AlexNet(**kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls["alexnet"], progress=progress) + model.load_state_dict(state_dict) + + # change to adapt fer + model.classifier[-1] = nn.Linear(4096, 7) + return model diff --git a/algorithm/detect_emotion/rmn/models/attention.py b/algorithm/detect_emotion/rmn/models/attention.py new file mode 100644 index 0000000..2d0dc77 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/attention.py @@ -0,0 +1,203 @@ +import traceback +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .resnet import conv1x1, conv3x3, BasicBlock, Bottleneck + + +def transpose(in_channels, out_channels, kernel_size=2, stride=2): + return nn.Sequential( + nn.ConvTranspose2d( + in_channels, out_channels, kernel_size=kernel_size, stride=stride + ), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ) + + +def downsample(in_channels, out_channels): + return nn.Sequential( + conv1x1(in_channels, out_channels), + nn.BatchNorm2d(num_features(out_channels)), + nn.ReLU(inplace=True), + ) + + +class Attention0(nn.Module): + def __init__(self, channels, block): + super().__init__() + self._trunk1 = block(channels, channels) + self._trunk2 = block(channels, channels) + + self._enc = block(channels, channels) + self._dec = block(channels, channels) + + self._conv1x1 = nn.Sequential( + conv1x1(2 * channels, channels), + nn.BatchNorm2d(num_features=channels), + nn.ReLU(inplace=True), + ) + + self._mp = nn.MaxPool2d(3, 2, 1) + self._relu = nn.ReLU(inplace=True) + + def enc(self, x): + return self._enc(x) + + def dec(self, x): + return self._dec(x) + + def trunking(self, x): + return self._trunk2(self._trunk1(x)) + + def masking(self, x): + x = self.enc(x) + x = self.dec(x) + return torch.sigmoid(x) + + def forward(self, x): + trunk = self.trunking(x) + mask = self.masking(x) + return (1 + mask) * trunk + + +class Attention1(nn.Module): + def __init__(self, channels, block): + super().__init__() + self._trunk1 = block(channels, channels) + self._trunk2 = block(channels, channels) + + self._enc1 = block(channels, channels) + self._enc2 = block(channels, channels) + + self._dec = block(channels, channels) + self._conv1x1 = nn.Sequential( + conv1x1(2 * channels, channels), + nn.BatchNorm2d(num_features=channels), + nn.ReLU(inplace=True), + ) + + self._trans = nn.Sequential( + nn.ConvTranspose2d(channels, channels, kernel_size=2, stride=2), + nn.BatchNorm2d(num_features=channels), + nn.ReLU(inplace=True), + ) + + self._mp = nn.MaxPool2d(3, 2, 1) + self._relu = nn.ReLU(inplace=True) + + def enc(self, x): + x1 = self._enc1(x) + x2 = self._enc2(self._mp(x1)) + return [x1, x2] + + def dec(self, x): + x1, x2 = x + x2 = self._trans(x2) + x = torch.cat([x1, x2], dim=1) + x = self._conv1x1(x) + return self._dec(x) + + def trunking(self, x): + return self._trunk2(self._trunk1(x)) + + def masking(self, x): + x = self.enc(x) + x = self.dec(x) + return torch.sigmoid(x) + + def forward(self, x): + trunk = self.trunking(x) + mask = self.masking(x) + return (1 + mask) * trunk + + +class Attention2(nn.Module): + def __init__(self, channels, block): + super().__init__() + self._trunk1 = block(channels, channels) + self._trunk2 = block(channels, channels) + + self._enc1 = block(channels, channels) + self._enc2 = block(channels, channels) + self._enc3 = nn.Sequential(block(channels, channels), block(channels, channels)) + + self._dec1 = nn.Sequential( + conv1x1(2 * channels, channels), + nn.BatchNorm2d(num_features=channels), + nn.ReLU(inplace=True), + block(channels, channels), + ) + self._dec2 = nn.Sequential( + conv1x1(2 * channels, channels), + nn.BatchNorm2d(num_features=channels), + nn.ReLU(inplace=True), + block(channels, channels), + ) + + self._trans = nn.Sequential( + nn.ConvTranspose2d(channels, channels, kernel_size=2, stride=2), + nn.BatchNorm2d(num_features=channels), + nn.ReLU(inplace=True), + ) + + self._mp = nn.MaxPool2d(3, 2, 1) + self._relu = nn.ReLU(inplace=True) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # ''' try to open this line and see the change of acc + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + # ''' + + def enc(self, x): + x1 = self._enc1(x) + x2 = self._enc2(self._mp(x1)) + x3 = self._enc3(self._mp(x2)) + return [x1, x2, x3] + + def dec(self, x): + x1, x2, x3 = x + + x2 = torch.cat([x2, self._trans(x3)], dim=1) + x2 = self._dec1(x2) + + x3 = torch.cat([x1, self._trans(x2)], dim=1) + x3 = self._dec1(x3) + + return x3 + + def trunking(self, x): + return self._trunk2(self._trunk1(x)) + + def masking(self, x): + x = self.enc(x) + x = self.dec(x) + return torch.sigmoid(x) + + def forward(self, x): + trunk = self.trunking(x) + mask = self.masking(x) + return (1 + mask) * trunk + + +def attention(channels, block=BasicBlock, depth=-1): + if depth == 0: + return Attention0(channels, block) + elif depth == 1: + return Attention1(channels, block) + elif depth == 2: + return Attention2(channels, block) + else: + traceback.print_exc() + raise Exception("depth must be specified") diff --git a/algorithm/detect_emotion/rmn/models/attention_module.py b/algorithm/detect_emotion/rmn/models/attention_module.py new file mode 100644 index 0000000..8274fbb --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/attention_module.py @@ -0,0 +1,79 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools +from torch.autograd import Variable +import numpy as np + +from .basic_layers import ResidualBlock + + +class AttentionModule(nn.Module): + def __init__(self, in_channels, out_channels, size1, size2, size3): + super(AttentionModule, self).__init__() + self.first_residual_blocks = ResidualBlock(in_channels, out_channels) + + self.trunk_branches = nn.Sequential( + ResidualBlock(in_channels, out_channels), + ResidualBlock(out_channels, out_channels), + ) + self.mpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self.softmax1_blocks = ResidualBlock(in_channels, out_channels) + + self.skip1_connection_residual_block = ResidualBlock(in_channels, out_channels) + + self.softmax2_blocks = ResidualBlock(in_channels, out_channels) + + self.skip2_connection_residual_block = ResidualBlock(in_channels, out_channels) + + self.softmax3_blocks = nn.Sequential( + ResidualBlock(in_channels, out_channels), + ResidualBlock(in_channels, out_channels), + ) + + self.interpolation3 = nn.UpsamplingBilinear2d(size=size3) + + self.softmax4_blocks = ResidualBlock(in_channels, out_channels) + + self.interpolation2 = nn.UpsamplingBilinear2d(size=size2) + + self.softmax5_blocks = ResidualBlock(in_channels, out_channels) + + self.interpolation1 = nn.UpsamplingBilinear2d(size=size1) + + self.softmax6_blocks = nn.Sequential( + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, bias=False), + nn.Sigmoid(), + ) + + self.last_blocks = ResidualBlock(in_channels, out_channels) + + def forward(self, x): + x = self.first_residual_blocks(x) + out_trunk = self.trunk_branches(x) + out_mpool1 = self.mpool1(x) + out_softmax1 = self.softmax1_blocks(out_mpool1) + out_skip1_connection = self.skip1_connection_residual_block(out_softmax1) + out_mpool2 = self.mpool2(out_softmax1) + out_softmax2 = self.softmax2_blocks(out_mpool2) + out_skip2_connection = self.skip2_connection_residual_block(out_softmax2) + out_mpool3 = self.mpool3(out_softmax2) + out_softmax3 = self.softmax3_blocks(out_mpool3) + out_interp3 = self.interpolation3(out_softmax3) + out = out_interp3 + out_skip2_connection + out_softmax4 = self.softmax4_blocks(out) + out_interp2 = self.interpolation2(out_softmax4) + out = out_interp2 + out_skip1_connection + out_softmax5 = self.softmax5_blocks(out) + out_interp1 = self.interpolation1(out_softmax5) + out_softmax6 = self.softmax6_blocks(out_interp1) + + out = (1 + out_softmax6) * out_trunk + + return self.last_blocks(out) diff --git a/algorithm/detect_emotion/rmn/models/basic_layers.py b/algorithm/detect_emotion/rmn/models/basic_layers.py new file mode 100644 index 0000000..5077ab8 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/basic_layers.py @@ -0,0 +1,51 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools +from torch.autograd import Variable +import numpy as np + + +class ResidualBlock(nn.Module): + def __init__(self, input_channels, output_channels, stride=1): + super(ResidualBlock, self).__init__() + self.input_channels = input_channels + self.output_channels = output_channels + self.stride = stride + self.bn1 = nn.BatchNorm2d(input_channels) + self.relu = nn.ReLU(inplace=True) + self.conv1 = nn.Conv2d( + input_channels, int(output_channels / 4), 1, 1, bias=False + ) + self.bn2 = nn.BatchNorm2d(int(output_channels / 4)) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d( + int(output_channels / 4), + int(output_channels / 4), + 3, + stride, + padding=1, + bias=False, + ) + self.bn3 = nn.BatchNorm2d(int(output_channels / 4)) + self.relu = nn.ReLU(inplace=True) + self.conv3 = nn.Conv2d( + int(output_channels / 4), output_channels, 1, 1, bias=False + ) + self.conv4 = nn.Conv2d(input_channels, output_channels, 1, stride, bias=False) + + def forward(self, x): + residual = x + out = self.bn1(x) + out1 = self.relu(out) + out = self.conv1(out1) + out = self.bn2(out) + out = self.relu(out) + out = self.conv2(out) + out = self.bn3(out) + out = self.relu(out) + out = self.conv3(out) + if (self.input_channels != self.output_channels) or (self.stride != 1): + residual = self.conv4(out1) + out += residual + return out diff --git a/algorithm/detect_emotion/rmn/models/brain_humor.py b/algorithm/detect_emotion/rmn/models/brain_humor.py new file mode 100644 index 0000000..baebb3e --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/brain_humor.py @@ -0,0 +1,260 @@ +import torch +import torch.nn as nn + + +class PreActivateDoubleConv(nn.Module): + def __init__(self, in_channels, out_channels): + super(PreActivateDoubleConv, self).__init__() + self.double_conv = nn.Sequential( + nn.BatchNorm2d(in_channels), + nn.ReLU(inplace=True), + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + ) + + def forward(self, x): + return self.double_conv(x) + + +class PreActivateResUpBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(PreActivateResUpBlock, self).__init__() + self.ch_avg = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(out_channels), + ) + self.up_sample = nn.Upsample( + scale_factor=2, mode="bilinear", align_corners=True + ) + self.ch_avg = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(out_channels), + ) + self.double_conv = PreActivateDoubleConv(in_channels, out_channels) + + def forward(self, down_input, skip_input): + x = self.up_sample(down_input) + x = torch.cat([x, skip_input], dim=1) + return self.double_conv(x) + self.ch_avg(x) + + +class PreActivateResBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(PreActivateResBlock, self).__init__() + self.ch_avg = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(out_channels), + ) + + self.double_conv = PreActivateDoubleConv(in_channels, out_channels) + self.down_sample = nn.MaxPool2d(2) + + def forward(self, x): + identity = self.ch_avg(x) + out = self.double_conv(x) + out = out + identity + return self.down_sample(out), out + + +class DoubleConv(nn.Module): + def __init__(self, in_channels, out_channels): + super(DoubleConv, self).__init__() + self.double_conv = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ) + + def forward(self, x): + return self.double_conv(x) + + +class ResBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(ResBlock, self).__init__() + self.downsample = nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False), + nn.BatchNorm2d(out_channels), + ) + self.double_conv = DoubleConv(in_channels, out_channels) + self.down_sample = nn.MaxPool2d(2) + self.relu = nn.ReLU() + + def forward(self, x): + identity = self.downsample(x) + out = self.double_conv(x) + out = self.relu(out + identity) + return self.down_sample(out), out + + +class DownBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(DownBlock, self).__init__() + self.double_conv = DoubleConv(in_channels, out_channels) + self.down_sample = nn.MaxPool2d(2) + + def forward(self, x): + skip_out = self.double_conv(x) + down_out = self.down_sample(skip_out) + return (down_out, skip_out) + + +class UpBlock(nn.Module): + def __init__(self, in_channels, out_channels): + super(UpBlock, self).__init__() + self.up_sample = nn.Upsample( + scale_factor=2, mode="bilinear", align_corners=True + ) + self.double_conv = DoubleConv(in_channels, out_channels) + + def forward(self, down_input, skip_input): + x = self.up_sample(down_input) + x = torch.cat([x, skip_input], dim=1) + return self.double_conv(x) + + +class UNet(nn.Module): + def __init__(self, out_classes=1): + super(UNet, self).__init__() + + self.down_conv1 = DownBlock(1, 64) + self.down_conv2 = DownBlock(64, 128) + self.down_conv3 = DownBlock(128, 256) + self.down_conv4 = DownBlock(256, 512) + + self.double_conv = DoubleConv(512, 1024) + + self.up_conv4 = UpBlock(512 + 1024, 512) + self.up_conv3 = UpBlock(256 + 512, 256) + self.up_conv2 = UpBlock(128 + 256, 128) + self.up_conv1 = UpBlock(128 + 64, 64) + + self.conv_last = nn.Conv2d(64, out_classes, kernel_size=1) + + def forward(self, x): + x, skip1_out = self.down_conv1(x) + x, skip2_out = self.down_conv2(x) + x, skip3_out = self.down_conv3(x) + x, skip4_out = self.down_conv4(x) + x = self.double_conv(x) + x = self.up_conv4(x, skip4_out) + x = self.up_conv3(x, skip3_out) + x = self.up_conv2(x, skip2_out) + x = self.up_conv1(x, skip1_out) + x = self.conv_last(x) + return x + + +class DeepResUNet(nn.Module): + def __init__(self, in_channels=3, num_classes=1): + super(DeepResUNet, self).__init__() + + self.down_conv1 = PreActivateResBlock(in_channels, 64) + self.down_conv2 = PreActivateResBlock(64, 128) + self.down_conv3 = PreActivateResBlock(128, 256) + self.down_conv4 = PreActivateResBlock(256, 512) + + self.double_conv = PreActivateDoubleConv(512, 1024) + + self.up_conv4 = PreActivateResUpBlock(512 + 1024, 512) + self.up_conv3 = PreActivateResUpBlock(256 + 512, 256) + self.up_conv2 = PreActivateResUpBlock(128 + 256, 128) + self.up_conv1 = PreActivateResUpBlock(128 + 64, 64) + + self.conv_last = nn.Conv2d(64, num_classes, kernel_size=1) + + def forward(self, x): + x, skip1_out = self.down_conv1(x) + x, skip2_out = self.down_conv2(x) + x, skip3_out = self.down_conv3(x) + x, skip4_out = self.down_conv4(x) + x = self.double_conv(x) + x = self.up_conv4(x, skip4_out) + x = self.up_conv3(x, skip3_out) + x = self.up_conv2(x, skip2_out) + x = self.up_conv1(x, skip1_out) + x = self.conv_last(x) + x = torch.softmax(x, dim=1) + return x + + +class ResUNet(nn.Module): + """ + Hybrid solution of resnet blocks and double conv blocks + """ + + def __init__(self, out_classes=1): + super(ResUNet, self).__init__() + + self.down_conv1 = ResBlock(1, 64) + self.down_conv2 = ResBlock(64, 128) + self.down_conv3 = ResBlock(128, 256) + self.down_conv4 = ResBlock(256, 512) + + self.double_conv = DoubleConv(512, 1024) + + self.up_conv4 = UpBlock(512 + 1024, 512) + self.up_conv3 = UpBlock(256 + 512, 256) + self.up_conv2 = UpBlock(128 + 256, 128) + self.up_conv1 = UpBlock(128 + 64, 64) + + self.conv_last = nn.Conv2d(64, out_classes, kernel_size=1) + + def forward(self, x): + x, skip1_out = self.down_conv1(x) + x, skip2_out = self.down_conv2(x) + x, skip3_out = self.down_conv3(x) + x, skip4_out = self.down_conv4(x) + x = self.double_conv(x) + x = self.up_conv4(x, skip4_out) + x = self.up_conv3(x, skip3_out) + x = self.up_conv2(x, skip2_out) + x = self.up_conv1(x, skip1_out) + x = self.conv_last(x) + return x + + +class ONet(nn.Module): + def __init__(self, alpha=470, beta=40, out_classes=1): + super(ONet, self).__init__() + self.alpha = alpha + self.beta = beta + self.down_conv1 = ResBlock(1, 64) + self.down_conv2 = ResBlock(64, 128) + self.down_conv3 = ResBlock(128, 256) + self.down_conv4 = ResBlock(256, 512) + + self.double_conv = DoubleConv(512, 1024) + + self.up_conv4 = UpBlock(512 + 1024, 512) + self.up_conv3 = UpBlock(256 + 512, 256) + self.up_conv2 = UpBlock(128 + 256, 128) + self.up_conv1 = UpBlock(128 + 64, 64) + + self.conv_last = nn.Conv2d(64, 1, kernel_size=1) + self.input_output_conv = nn.Conv2d(2, 1, kernel_size=1) + + def forward(self, inputs): + input_tensor, bounding = inputs + x, skip1_out = self.down_conv1(input_tensor + (bounding * self.alpha)) + x, skip2_out = self.down_conv2(x) + x, skip3_out = self.down_conv3(x) + x, skip4_out = self.down_conv4(x) + x = self.double_conv(x) + x = self.up_conv4(x, skip4_out) + x = self.up_conv3(x, skip3_out) + x = self.up_conv2(x, skip2_out) + x = self.up_conv1(x, skip1_out) + x = self.conv_last(x) + input_output = torch.cat([x, bounding * self.beta], dim=1) + x = self.input_output_conv(input_output) + return x + + +def deepresunet(in_channels=3, num_classes=2): + return DeepResUNet(in_channels, num_classes) diff --git a/algorithm/detect_emotion/rmn/models/centerloss_resnet.py b/algorithm/detect_emotion/rmn/models/centerloss_resnet.py new file mode 100644 index 0000000..15bd8ee --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/centerloss_resnet.py @@ -0,0 +1,59 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .utils import load_state_dict_from_url + +from .resnet import ResNet, BasicBlock + + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", + "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth", +} + + +class ResNetCenterLoss(ResNet): + def __init__(self, block=BasicBlock, layers=[2, 2, 2, 2]): + super(ResNetCenterLoss, self).__init__( + block=BasicBlock, layers=layers, in_channels=3, num_classes=1000 + ) + state_dict = load_state_dict_from_url(model_urls["resnet18"]) + self.load_state_dict(state_dict) + + # for center loss + self.center_loss_fc = nn.Linear(512, 2) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + features = self.relu(self.center_loss_fc(x)) + outputs = self.fc(x) + # return outputs, features + return outputs + + +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def resnet18_centerloss(pretrained=True, progress=True, **kwargs): + model = ResNetCenterLoss() + model.fc = nn.Linear(512, 7) + return model diff --git a/algorithm/detect_emotion/rmn/models/densenet.py b/algorithm/detect_emotion/rmn/models/densenet.py new file mode 100644 index 0000000..07b4322 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/densenet.py @@ -0,0 +1,316 @@ +import re +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from collections import OrderedDict +from .utils import load_state_dict_from_url + + +__all__ = ["DenseNet", "densenet121", "densenet169", "densenet201", "densenet161"] + +model_urls = { + "densenet121": "https://download.pytorch.org/models/densenet121-a639ec97.pth", + "densenet169": "https://download.pytorch.org/models/densenet169-b2777c0a.pth", + "densenet201": "https://download.pytorch.org/models/densenet201-c1103571.pth", + "densenet161": "https://download.pytorch.org/models/densenet161-8d451a50.pth", +} + + +def _bn_function_factory(norm, relu, conv): + def bn_function(*inputs): + concated_features = torch.cat(inputs, 1) + bottleneck_output = conv(relu(norm(concated_features))) + return bottleneck_output + + return bn_function + + +class _DenseLayer(nn.Sequential): + def __init__( + self, + num_input_features, + growth_rate, + bn_size, + drop_rate, + memory_efficient=False, + ): + super(_DenseLayer, self).__init__() + self.add_module("norm1", nn.BatchNorm2d(num_input_features)), + self.add_module("relu1", nn.ReLU(inplace=True)), + self.add_module( + "conv1", + nn.Conv2d( + num_input_features, + bn_size * growth_rate, + kernel_size=1, + stride=1, + bias=False, + ), + ), + self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate)), + self.add_module("relu2", nn.ReLU(inplace=True)), + self.add_module( + "conv2", + nn.Conv2d( + bn_size * growth_rate, + growth_rate, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ), + ), + self.drop_rate = drop_rate + self.memory_efficient = memory_efficient + + def forward(self, *prev_features): + bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1) + if self.memory_efficient and any( + prev_feature.requires_grad for prev_feature in prev_features + ): + bottleneck_output = cp.checkpoint(bn_function, *prev_features) + else: + bottleneck_output = bn_function(*prev_features) + new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) + if self.drop_rate > 0: + new_features = F.dropout( + new_features, p=self.drop_rate, training=self.training + ) + return new_features + + +class _DenseBlock(nn.Module): + def __init__( + self, + num_layers, + num_input_features, + bn_size, + growth_rate, + drop_rate, + memory_efficient=False, + ): + super(_DenseBlock, self).__init__() + for i in range(num_layers): + layer = _DenseLayer( + num_input_features + i * growth_rate, + growth_rate=growth_rate, + bn_size=bn_size, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + ) + self.add_module("denselayer%d" % (i + 1), layer) + + def forward(self, init_features): + features = [init_features] + for name, layer in self.named_children(): + new_features = layer(*features) + features.append(new_features) + return torch.cat(features, 1) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features, num_output_features): + super(_Transition, self).__init__() + self.add_module("norm", nn.BatchNorm2d(num_input_features)) + self.add_module("relu", nn.ReLU(inplace=True)) + self.add_module( + "conv", + nn.Conv2d( + num_input_features, + num_output_features, + kernel_size=1, + stride=1, + bias=False, + ), + ) + self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2)) + + +class DenseNet(nn.Module): + r"""Densenet-BC model class, based on + `"Densely Connected Convolutional Networks" `_ + + Args: + growth_rate (int) - how many filters to add each layer (`k` in paper) + block_config (list of 4 ints) - how many layers in each pooling block + num_init_features (int) - the number of filters to learn in the first convolution layer + bn_size (int) - multiplicative factor for number of bottle neck layers + (i.e. bn_size * k features in the bottleneck layer) + drop_rate (float) - dropout rate after each dense layer + num_classes (int) - number of classification classes + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + + def __init__( + self, + growth_rate=32, + block_config=(6, 12, 24, 16), + num_init_features=64, + bn_size=4, + drop_rate=0, + num_classes=1000, + memory_efficient=False, + in_channels=3, + ): + + super(DenseNet, self).__init__() + + # First convolution + self.features = nn.Sequential( + OrderedDict( + [ + ( + "conv0", + nn.Conv2d( + 3, + num_init_features, + kernel_size=7, + stride=2, + padding=3, + bias=False, + ), + ), + ("norm0", nn.BatchNorm2d(num_init_features)), + ("relu0", nn.ReLU(inplace=True)), + ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ] + ) + ) + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock( + num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + ) + self.features.add_module("denseblock%d" % (i + 1), block) + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition( + num_input_features=num_features, + num_output_features=num_features // 2, + ) + self.features.add_module("transition%d" % (i + 1), trans) + num_features = num_features // 2 + + # Final batch norm + self.features.add_module("norm5", nn.BatchNorm2d(num_features)) + + # Linear layer + # NOTE: strictly set to 1000 to load pretrained model + # self.classifier = nn.Linear(num_features, num_classes) + self.classifier = nn.Linear(num_features, 1000) + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.constant_(m.bias, 0) + + def forward(self, x): + features = self.features(x) + out = F.relu(features, inplace=True) + out = F.adaptive_avg_pool2d(out, (1, 1)) + out = torch.flatten(out, 1) + out = self.classifier(out) + return out + + +def _load_state_dict(model, model_url, progress): + # '.'s are no longer allowed in module names, but previous _DenseLayer + # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. + # They are also in the checkpoints in model_urls. This pattern is used + # to find such keys. + pattern = re.compile( + r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$" + ) + + state_dict = load_state_dict_from_url(model_url, progress=progress) + for key in list(state_dict.keys()): + res = pattern.match(key) + if res: + new_key = res.group(1) + res.group(2) + state_dict[new_key] = state_dict[key] + del state_dict[key] + model.load_state_dict(state_dict) + + +def _densenet( + arch, growth_rate, block_config, num_init_features, pretrained, progress, **kwargs +): + model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) + if pretrained: + _load_state_dict(model, model_urls[arch], progress) + + model.classifier = nn.Linear(1024, 7) + return model + + +def densenet121(pretrained=False, progress=True, **kwargs): + r"""Densenet-121 model from `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + return _densenet( + "densenet121", 32, (6, 12, 24, 16), 64, pretrained, progress, **kwargs + ) + + +def densenet161(pretrained=False, progress=True, **kwargs): + r"""Densenet-161 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + return _densenet( + "densenet161", 48, (6, 12, 36, 24), 96, pretrained, progress, **kwargs + ) + + +def densenet169(pretrained=False, progress=True, **kwargs): + r"""Densenet-169 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + return _densenet( + "densenet169", 32, (6, 12, 32, 32), 64, pretrained, progress, **kwargs + ) + + +def densenet201(pretrained=False, progress=True, **kwargs): + r"""Densenet-201 model from + `"Densely Connected Convolutional Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, + but slower. Default: *False*. See `"paper" `_ + """ + return _densenet( + "densenet201", 32, (6, 12, 48, 32), 64, pretrained, progress, **kwargs + ) diff --git a/algorithm/detect_emotion/rmn/models/fer2013_models.py b/algorithm/detect_emotion/rmn/models/fer2013_models.py new file mode 100644 index 0000000..1993bd8 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/fer2013_models.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def conv3x3(in_channels, out_channels, stride=1, groups=1, dilation=1): + return nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv1x1(in_channels, out_channels, stride=1): + """1x1 convolution""" + return nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=stride, + bias=False, + ) + + +class ResidualUnit(nn.Module): + def __init__(self, in_channels, out_channels): + super(ResidualUnit, self).__init__() + width = int(out_channels / 4) + + self.conv1 = conv1x1(in_channels, width) + self.bn1 = nn.BatchNorm2d(width) + + self.conv2 = conv3x3(width, width) + self.bn2 = nn.BatchNorm2d(width) + + self.conv3 = conv1x1(width, out_channels) + self.bn3 = nn.BatchNorm2d(out_channels) + + self.relu = nn.ReLU(inplace=True) + + # for downsample + self._downsample = nn.Sequential( + conv1x1(in_channels, out_channels, 1), nn.BatchNorm2d(out_channels) + ) + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + out += self._downsample(identity) + out = self.relu(out) + + return out + + +class BasicBlock(nn.Module): + def __init__(self, in_channels, out_channels): + pass + + def forward(self, x): + pass + + +class BaseNet(nn.Module): + """basenet for fer2013""" + + def __init__(self, in_channels=1, num_classes=7): + super(BaseNet, self).__init__() + norm_layer = nn.BatchNorm2d + + self.conv1 = nn.Conv2d( + in_channels=1, + out_channels=64, + kernel_size=7, + stride=1, + padding=3, + bias=False, + ) + self.bn1 = nn.BatchNorm2d(num_features=64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self.residual_1 = ResidualUnit(in_channels=64, out_channels=256) + self.residual_2 = ResidualUnit(in_channels=256, out_channels=512) + self.residual_3 = ResidualUnit(in_channels=512, out_channels=1024) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(1024, 7) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + + x = self.residual_1(x) + x = self.residual_2(x) + x = self.residual_3(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + return x + + +def basenet(in_channels=1, num_classes=7): + return BaseNet(in_channels, num_classes) + + +if __name__ == "__main__": + net = BaseNet().cuda() + from torchsummary import summary + + print(summary(net, input_size=(1, 48, 48))) diff --git a/algorithm/detect_emotion/rmn/models/googlenet.py b/algorithm/detect_emotion/rmn/models/googlenet.py new file mode 100644 index 0000000..b695536 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/googlenet.py @@ -0,0 +1,249 @@ +import warnings +from collections import namedtuple +import torch +import torch.nn as nn +import torch.nn.functional as F +from .utils import load_state_dict_from_url + +__all__ = ["GoogLeNet", "googlenet"] + +model_urls = { + # GoogLeNet ported from TensorFlow + "googlenet": "https://download.pytorch.org/models/googlenet-1378be20.pth", +} + +_GoogLeNetOutputs = namedtuple( + "GoogLeNetOutputs", ["logits", "aux_logits2", "aux_logits1"] +) + + +def googlenet(pretrained=True, progress=True, **kwargs): + r"""GoogLeNet (Inception v1) model architecture from + `"Going Deeper with Convolutions" `_. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + aux_logits (bool): If True, adds two auxiliary branches that can improve training. + Default: *False* when pretrained is True otherwise *True* + transform_input (bool): If True, preprocesses the input according to the method with which it + was trained on ImageNet. Default: *False* + """ + if pretrained: + if "transform_input" not in kwargs: + kwargs["transform_input"] = True + if "aux_logits" not in kwargs: + kwargs["aux_logits"] = False + if kwargs["aux_logits"]: + warnings.warn( + "auxiliary heads in the pretrained googlenet model are NOT pretrained, " + "so make sure to train them" + ) + original_aux_logits = kwargs["aux_logits"] + kwargs["aux_logits"] = True + kwargs["init_weights"] = False + model = GoogLeNet(**kwargs) + state_dict = load_state_dict_from_url( + model_urls["googlenet"], progress=progress + ) + model.load_state_dict(state_dict) + if not original_aux_logits: + model.aux_logits = False + del model.aux1, model.aux2 + + model.fc = nn.Linear(1024, 7) + return model + + return GoogLeNet(**kwargs) + + +class GoogLeNet(nn.Module): + def __init__( + self, + num_classes=1000, + aux_logits=True, + transform_input=False, + init_weights=True, + in_channels=3, + ): + super(GoogLeNet, self).__init__() + # strict set to 1000 + num_classes = 1000 + + self.aux_logits = aux_logits + self.transform_input = transform_input + + self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3) + self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True) + self.conv2 = BasicConv2d(64, 64, kernel_size=1) + self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1) + self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True) + + self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32) + self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64) + self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True) + + self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64) + self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64) + self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64) + self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64) + self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128) + self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128) + self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128) + + if aux_logits: + self.aux1 = InceptionAux(512, num_classes) + self.aux2 = InceptionAux(528, num_classes) + + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(0.2) + + # strict set to 1000 + self.fc = nn.Linear(1024, num_classes) + + if init_weights: + self._initialize_weights() + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): + import scipy.stats as stats + + X = stats.truncnorm(-2, 2, scale=0.01) + values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype) + values = values.view(m.weight.size()) + with torch.no_grad(): + m.weight.copy_(values) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + if self.transform_input: + x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 + x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 + x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 + x = torch.cat((x_ch0, x_ch1, x_ch2), 1) + + # N x 3 x 224 x 224 + x = self.conv1(x) + # N x 64 x 112 x 112 + x = self.maxpool1(x) + # N x 64 x 56 x 56 + x = self.conv2(x) + # N x 64 x 56 x 56 + x = self.conv3(x) + # N x 192 x 56 x 56 + x = self.maxpool2(x) + + # N x 192 x 28 x 28 + x = self.inception3a(x) + # N x 256 x 28 x 28 + x = self.inception3b(x) + # N x 480 x 28 x 28 + x = self.maxpool3(x) + # N x 480 x 14 x 14 + x = self.inception4a(x) + # N x 512 x 14 x 14 + if self.training and self.aux_logits: + aux1 = self.aux1(x) + + x = self.inception4b(x) + # N x 512 x 14 x 14 + x = self.inception4c(x) + # N x 512 x 14 x 14 + x = self.inception4d(x) + # N x 528 x 14 x 14 + if self.training and self.aux_logits: + aux2 = self.aux2(x) + + x = self.inception4e(x) + # N x 832 x 14 x 14 + x = self.maxpool4(x) + # N x 832 x 7 x 7 + x = self.inception5a(x) + # N x 832 x 7 x 7 + x = self.inception5b(x) + # N x 1024 x 7 x 7 + + x = self.avgpool(x) + # N x 1024 x 1 x 1 + x = torch.flatten(x, 1) + # N x 1024 + x = self.dropout(x) + x = self.fc(x) + # N x 1000 (num_classes) + if self.training and self.aux_logits: + return _GoogLeNetOutputs(x, aux2, aux1) + return x + + +class Inception(nn.Module): + def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj): + super(Inception, self).__init__() + + self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1) + + self.branch2 = nn.Sequential( + BasicConv2d(in_channels, ch3x3red, kernel_size=1), + BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1), + ) + + self.branch3 = nn.Sequential( + BasicConv2d(in_channels, ch5x5red, kernel_size=1), + BasicConv2d(ch5x5red, ch5x5, kernel_size=3, padding=1), + ) + + self.branch4 = nn.Sequential( + nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True), + BasicConv2d(in_channels, pool_proj, kernel_size=1), + ) + + def forward(self, x): + branch1 = self.branch1(x) + branch2 = self.branch2(x) + branch3 = self.branch3(x) + branch4 = self.branch4(x) + + outputs = [branch1, branch2, branch3, branch4] + return torch.cat(outputs, 1) + + +class InceptionAux(nn.Module): + def __init__(self, in_channels, num_classes): + super(InceptionAux, self).__init__() + self.conv = BasicConv2d(in_channels, 128, kernel_size=1) + + self.fc1 = nn.Linear(2048, 1024) + self.fc2 = nn.Linear(1024, num_classes) + + def forward(self, x): + # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14 + x = F.adaptive_avg_pool2d(x, (4, 4)) + # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4 + x = self.conv(x) + # N x 128 x 4 x 4 + x = torch.flatten(x, 1) + # N x 2048 + x = F.relu(self.fc1(x), inplace=True) + # N x 2048 + x = F.dropout(x, 0.7, training=self.training) + # N x 2048 + x = self.fc2(x) + # N x 1024 + + return x + + +class BasicConv2d(nn.Module): + def __init__(self, in_channels, out_channels, **kwargs): + super(BasicConv2d, self).__init__() + self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) + self.bn = nn.BatchNorm2d(out_channels, eps=0.001) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return F.relu(x, inplace=True) diff --git a/algorithm/detect_emotion/rmn/models/grad_cam_resmaking.py b/algorithm/detect_emotion/rmn/models/grad_cam_resmaking.py new file mode 100644 index 0000000..2f6d47b --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/grad_cam_resmaking.py @@ -0,0 +1,62 @@ +import copy +import torch +import torch.nn as nn + +from .utils import load_state_dict_from_url +from .resnet import BasicBlock, Bottleneck, ResNet, resnet18 + + +model_urls = {"resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth"} + + +from .masking import masking + + +class ResMasking(ResNet): + def __init__(self, weight_path): + super(ResMasking, self).__init__( + block=BasicBlock, layers=[3, 4, 6, 3], in_channels=3, num_classes=1000 + ) + self.fc = nn.Linear(512, 7) + self.mask1 = masking(64, 64, depth=4) + self.mask2 = masking(128, 128, depth=3) + self.mask3 = masking(256, 256, depth=2) + self.mask4 = masking(512, 512, depth=1) + + def forward(self, x): # 224 + x = self.conv1(x) # 112 + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) # 56 + + x = self.layer1(x) # 56 + m = self.mask1(x) + x = x * (1 + m) + + x = self.layer2(x) # 28 + m = self.mask2(x) + x = x * (1 + m) + + x = self.layer3(x) # 14 + m = self.mask3(x) + x = x * (1 + m) + + x = self.layer4(x) # 7 + m = self.mask4(x) + x = x * (1 + m) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.fc(x) + return x + + +def resmasking_dropout1(in_channels=3, num_classes=7, weight_path=""): + model = ResMasking(weight_path) + model.fc = nn.Sequential( + nn.Dropout(0.4), + nn.Linear(512, 7) + # nn.Linear(512, num_classes) + ) + return model diff --git a/algorithm/detect_emotion/rmn/models/inception.py b/algorithm/detect_emotion/rmn/models/inception.py new file mode 100644 index 0000000..e218513 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/inception.py @@ -0,0 +1,362 @@ +from collections import namedtuple +import torch +import torch.nn as nn +import torch.nn.functional as F +#from .utils import load_state_dict_from_url +from torch.hub import load_state_dict_from_url + + + +__all__ = ["Inception3", "inception_v3"] + + +model_urls = { + # Inception v3 ported from TensorFlow + "inception_v3_google": "https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth", +} + +_InceptionOutputs = namedtuple("InceptionOutputs", ["logits", "aux_logits"]) + + +def inception_v3(pretrained=True, progress=True, **kwargs): + r"""Inception v3 model architecture from + `"Rethinking the Inception Architecture for Computer Vision" `_. + + .. note:: + **Important**: In contrast to the other models the inception_v3 expects tensors with a size of + N x 3 x 299 x 299, so ensure your images are sized accordingly. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + aux_logits (bool): If True, add an auxiliary branch that can improve training. + Default: *True* + transform_input (bool): If True, preprocesses the input according to the method with which it + was trained on ImageNet. Default: *False* + """ + if pretrained: + if "transform_input" not in kwargs: + kwargs["transform_input"] = True + if "aux_logits" in kwargs: + original_aux_logits = kwargs["aux_logits"] + kwargs["aux_logits"] = True + else: + original_aux_logits = True + model = Inception3(**kwargs) + state_dict = load_state_dict_from_url( + model_urls["inception_v3_google"], progress=progress + ) + model.load_state_dict(state_dict) + if not original_aux_logits: + model.aux_logits = False + del model.AuxLogits + + model.fc = nn.Linear(2048, 7) + return model + + return Inception3(**kwargs) + + +class Inception3(nn.Module): + def __init__( + self, num_classes=1000, aux_logits=True, transform_input=False, in_channels=3 + ): + super(Inception3, self).__init__() + # strictlt set to 1000 + num_classes = 1000 + self.aux_logits = aux_logits + self.transform_input = transform_input + self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2) + self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3) + self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1) + self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1) + self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3) + self.Mixed_5b = InceptionA(192, pool_features=32) + self.Mixed_5c = InceptionA(256, pool_features=64) + self.Mixed_5d = InceptionA(288, pool_features=64) + self.Mixed_6a = InceptionB(288) + self.Mixed_6b = InceptionC(768, channels_7x7=128) + self.Mixed_6c = InceptionC(768, channels_7x7=160) + self.Mixed_6d = InceptionC(768, channels_7x7=160) + self.Mixed_6e = InceptionC(768, channels_7x7=192) + if aux_logits: + self.AuxLogits = InceptionAux(768, num_classes) + self.Mixed_7a = InceptionD(768) + self.Mixed_7b = InceptionE(1280) + self.Mixed_7c = InceptionE(2048) + + self.fc = nn.Linear(2048, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): + import scipy.stats as stats + + stddev = m.stddev if hasattr(m, "stddev") else 0.1 + X = stats.truncnorm(-2, 2, scale=stddev) + values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype) + values = values.view(m.weight.size()) + with torch.no_grad(): + m.weight.copy_(values) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + if self.transform_input: + x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 + x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 + x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 + x = torch.cat((x_ch0, x_ch1, x_ch2), 1) + + # N x 3 x 299 x 299 + x = self.Conv2d_1a_3x3(x) + # N x 32 x 149 x 149 + x = self.Conv2d_2a_3x3(x) + # N x 32 x 147 x 147 + x = self.Conv2d_2b_3x3(x) + # N x 64 x 147 x 147 + x = F.max_pool2d(x, kernel_size=3, stride=2) + # N x 64 x 73 x 73 + x = self.Conv2d_3b_1x1(x) + # N x 80 x 73 x 73 + x = self.Conv2d_4a_3x3(x) + # N x 192 x 71 x 71 + x = F.max_pool2d(x, kernel_size=3, stride=2) + # N x 192 x 35 x 35 + x = self.Mixed_5b(x) + # N x 256 x 35 x 35 + x = self.Mixed_5c(x) + # N x 288 x 35 x 35 + x = self.Mixed_5d(x) + # N x 288 x 35 x 35 + x = self.Mixed_6a(x) + # N x 768 x 17 x 17 + x = self.Mixed_6b(x) + # N x 768 x 17 x 17 + x = self.Mixed_6c(x) + # N x 768 x 17 x 17 + x = self.Mixed_6d(x) + # N x 768 x 17 x 17 + x = self.Mixed_6e(x) + # N x 768 x 17 x 17 + if self.training and self.aux_logits: + aux = self.AuxLogits(x) + # N x 768 x 17 x 17 + x = self.Mixed_7a(x) + # N x 1280 x 8 x 8 + x = self.Mixed_7b(x) + # N x 2048 x 8 x 8 + x = self.Mixed_7c(x) + # N x 2048 x 8 x 8 + # Adaptive average pooling + x = F.adaptive_avg_pool2d(x, (1, 1)) + # N x 2048 x 1 x 1 + x = F.dropout(x, training=self.training) + # N x 2048 x 1 x 1 + x = torch.flatten(x, 1) + # N x 2048 + x = self.fc(x) + # N x 1000 (num_classes) + + """ + if self.training and self.aux_logits: + return _InceptionOutputs(x, aux) + """ + + return x + + +class InceptionA(nn.Module): + def __init__(self, in_channels, pool_features): + super(InceptionA, self).__init__() + self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1) + + self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1) + self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2) + + self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1) + self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1) + self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1) + + self.branch_pool = BasicConv2d(in_channels, pool_features, kernel_size=1) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch5x5 = self.branch5x5_1(x) + branch5x5 = self.branch5x5_2(branch5x5) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class InceptionB(nn.Module): + def __init__(self, in_channels): + super(InceptionB, self).__init__() + self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2) + + self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1) + self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1) + self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, stride=2) + + def forward(self, x): + branch3x3 = self.branch3x3(x) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) + + outputs = [branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class InceptionC(nn.Module): + def __init__(self, in_channels, channels_7x7): + super(InceptionC, self).__init__() + self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1) + + c7 = channels_7x7 + self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1) + self.branch7x7_2 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7_3 = BasicConv2d(c7, 192, kernel_size=(7, 1), padding=(3, 0)) + + self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1) + self.branch7x7dbl_2 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7dbl_3 = BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7dbl_4 = BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7dbl_5 = BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3)) + + self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch7x7 = self.branch7x7_1(x) + branch7x7 = self.branch7x7_2(branch7x7) + branch7x7 = self.branch7x7_3(branch7x7) + + branch7x7dbl = self.branch7x7dbl_1(x) + branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] + return torch.cat(outputs, 1) + + +class InceptionD(nn.Module): + def __init__(self, in_channels): + super(InceptionD, self).__init__() + self.branch3x3_1 = BasicConv2d(in_channels, 192, kernel_size=1) + self.branch3x3_2 = BasicConv2d(192, 320, kernel_size=3, stride=2) + + self.branch7x7x3_1 = BasicConv2d(in_channels, 192, kernel_size=1) + self.branch7x7x3_2 = BasicConv2d(192, 192, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7x3_3 = BasicConv2d(192, 192, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7x3_4 = BasicConv2d(192, 192, kernel_size=3, stride=2) + + def forward(self, x): + branch3x3 = self.branch3x3_1(x) + branch3x3 = self.branch3x3_2(branch3x3) + + branch7x7x3 = self.branch7x7x3_1(x) + branch7x7x3 = self.branch7x7x3_2(branch7x7x3) + branch7x7x3 = self.branch7x7x3_3(branch7x7x3) + branch7x7x3 = self.branch7x7x3_4(branch7x7x3) + + branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) + outputs = [branch3x3, branch7x7x3, branch_pool] + return torch.cat(outputs, 1) + + +class InceptionE(nn.Module): + def __init__(self, in_channels): + super(InceptionE, self).__init__() + self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1) + + self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1) + self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1)) + self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0)) + + self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1) + self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1) + self.branch3x3dbl_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1)) + self.branch3x3dbl_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0)) + + self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1) + + def forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return torch.cat(outputs, 1) + + +class InceptionAux(nn.Module): + def __init__(self, in_channels, num_classes): + super(InceptionAux, self).__init__() + self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1) + self.conv1 = BasicConv2d(128, 768, kernel_size=5) + self.conv1.stddev = 0.01 + self.fc = nn.Linear(768, num_classes) + self.fc.stddev = 0.001 + + def forward(self, x): + # N x 768 x 17 x 17 + x = F.avg_pool2d(x, kernel_size=5, stride=3) + # N x 768 x 5 x 5 + x = self.conv0(x) + # N x 128 x 5 x 5 + x = self.conv1(x) + # N x 768 x 1 x 1 + # Adaptive average pooling + x = F.adaptive_avg_pool2d(x, (1, 1)) + # N x 768 x 1 x 1 + x = torch.flatten(x, 1) + # N x 768 + x = self.fc(x) + # N x 1000 + return x + + +class BasicConv2d(nn.Module): + def __init__(self, in_channels, out_channels, **kwargs): + super(BasicConv2d, self).__init__() + self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) + self.bn = nn.BatchNorm2d(out_channels, eps=0.001) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return F.relu(x, inplace=True) diff --git a/algorithm/detect_emotion/rmn/models/inception_resnet_v1.py b/algorithm/detect_emotion/rmn/models/inception_resnet_v1.py new file mode 100644 index 0000000..7148a53 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/inception_resnet_v1.py @@ -0,0 +1,358 @@ +import torch +from torch import nn +from torch.nn import functional as F +import requests +from requests.adapters import HTTPAdapter +import os + + +class BasicConv2d(nn.Module): + def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): + super().__init__() + self.conv = nn.Conv2d( + in_planes, + out_planes, + kernel_size=kernel_size, + stride=stride, + padding=padding, + bias=False, + ) # verify bias false + self.bn = nn.BatchNorm2d( + out_planes, + eps=0.001, # value found in tensorflow + momentum=0.1, # default pytorch value + affine=True, + ) + self.relu = nn.ReLU(inplace=False) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + x = self.relu(x) + return x + + +class Block35(nn.Module): + def __init__(self, scale=1.0): + super().__init__() + + self.scale = scale + + self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1) + + self.branch1 = nn.Sequential( + BasicConv2d(256, 32, kernel_size=1, stride=1), + BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1), + ) + + self.branch2 = nn.Sequential( + BasicConv2d(256, 32, kernel_size=1, stride=1), + BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1), + BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1), + ) + + self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1) + self.relu = nn.ReLU(inplace=False) + + def forward(self, x): + x0 = self.branch0(x) + x1 = self.branch1(x) + x2 = self.branch2(x) + out = torch.cat((x0, x1, x2), 1) + out = self.conv2d(out) + out = out * self.scale + x + out = self.relu(out) + return out + + +class Block17(nn.Module): + def __init__(self, scale=1.0): + super().__init__() + + self.scale = scale + + self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1) + + self.branch1 = nn.Sequential( + BasicConv2d(896, 128, kernel_size=1, stride=1), + BasicConv2d(128, 128, kernel_size=(1, 7), stride=1, padding=(0, 3)), + BasicConv2d(128, 128, kernel_size=(7, 1), stride=1, padding=(3, 0)), + ) + + self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1) + self.relu = nn.ReLU(inplace=False) + + def forward(self, x): + x0 = self.branch0(x) + x1 = self.branch1(x) + out = torch.cat((x0, x1), 1) + out = self.conv2d(out) + out = out * self.scale + x + out = self.relu(out) + return out + + +class Block8(nn.Module): + def __init__(self, scale=1.0, noReLU=False): + super().__init__() + + self.scale = scale + self.noReLU = noReLU + + self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1) + + self.branch1 = nn.Sequential( + BasicConv2d(1792, 192, kernel_size=1, stride=1), + BasicConv2d(192, 192, kernel_size=(1, 3), stride=1, padding=(0, 1)), + BasicConv2d(192, 192, kernel_size=(3, 1), stride=1, padding=(1, 0)), + ) + + self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1) + if not self.noReLU: + self.relu = nn.ReLU(inplace=False) + + def forward(self, x): + x0 = self.branch0(x) + x1 = self.branch1(x) + out = torch.cat((x0, x1), 1) + out = self.conv2d(out) + out = out * self.scale + x + if not self.noReLU: + out = self.relu(out) + return out + + +class Mixed_6a(nn.Module): + def __init__(self): + super().__init__() + + self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2) + + self.branch1 = nn.Sequential( + BasicConv2d(256, 192, kernel_size=1, stride=1), + BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1), + BasicConv2d(192, 256, kernel_size=3, stride=2), + ) + + self.branch2 = nn.MaxPool2d(3, stride=2) + + def forward(self, x): + x0 = self.branch0(x) + x1 = self.branch1(x) + x2 = self.branch2(x) + out = torch.cat((x0, x1, x2), 1) + return out + + +class Mixed_7a(nn.Module): + def __init__(self): + super().__init__() + + self.branch0 = nn.Sequential( + BasicConv2d(896, 256, kernel_size=1, stride=1), + BasicConv2d(256, 384, kernel_size=3, stride=2), + ) + + self.branch1 = nn.Sequential( + BasicConv2d(896, 256, kernel_size=1, stride=1), + BasicConv2d(256, 256, kernel_size=3, stride=2), + ) + + self.branch2 = nn.Sequential( + BasicConv2d(896, 256, kernel_size=1, stride=1), + BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1), + BasicConv2d(256, 256, kernel_size=3, stride=2), + ) + + self.branch3 = nn.MaxPool2d(3, stride=2) + + def forward(self, x): + x0 = self.branch0(x) + x1 = self.branch1(x) + x2 = self.branch2(x) + x3 = self.branch3(x) + out = torch.cat((x0, x1, x2, x3), 1) + return out + + +class InceptionResnetV1(nn.Module): + """Inception Resnet V1 model with optional loading of pretrained weights. + + Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface + datasets. Pretrained state_dicts are automatically downloaded on model instantiation if + requested and cached in the torch cache. Subsequent instantiations use the cache rather than + redownloading. + + Keyword Arguments: + pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'. + (default: {None}) + classify {bool} -- Whether the model should output classification probabilities or feature + embeddings. (default: {False}) + num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not + equal to that used for the pretrained model, the final linear layer will be randomly + initialized. (default: {None}) + dropout_prob {float} -- Dropout probability. (default: {0.6}) + """ + + def __init__( + self, + pretrained=None, + classify=False, + num_classes=None, + dropout_prob=0.6, + device=None, + ): + super().__init__() + + # Set simple attributes + self.pretrained = pretrained + self.classify = classify + self.num_classes = num_classes + + if pretrained == "vggface2": + tmp_classes = 8631 + elif pretrained == "casia-webface": + tmp_classes = 10575 + elif pretrained is None and self.num_classes is None: + raise Exception( + 'At least one of "pretrained" or "num_classes" must be specified' + ) + else: + tmp_classes = self.num_classes + + # Define layers + self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2) + self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1) + self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1) + self.maxpool_3a = nn.MaxPool2d(3, stride=2) + self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1) + self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1) + self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2) + self.repeat_1 = nn.Sequential( + Block35(scale=0.17), + Block35(scale=0.17), + Block35(scale=0.17), + Block35(scale=0.17), + Block35(scale=0.17), + ) + self.mixed_6a = Mixed_6a() + self.repeat_2 = nn.Sequential( + Block17(scale=0.10), + Block17(scale=0.10), + Block17(scale=0.10), + Block17(scale=0.10), + Block17(scale=0.10), + Block17(scale=0.10), + Block17(scale=0.10), + Block17(scale=0.10), + Block17(scale=0.10), + Block17(scale=0.10), + ) + self.mixed_7a = Mixed_7a() + self.repeat_3 = nn.Sequential( + Block8(scale=0.20), + Block8(scale=0.20), + Block8(scale=0.20), + Block8(scale=0.20), + Block8(scale=0.20), + ) + self.block8 = Block8(noReLU=True) + self.avgpool_1a = nn.AdaptiveAvgPool2d(1) + self.dropout = nn.Dropout(dropout_prob) + self.last_linear = nn.Linear(1792, 512, bias=False) + self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True) + self.logits = nn.Linear(512, tmp_classes) + + if pretrained is not None: + load_weights(self, pretrained) + + if self.num_classes is not None: + self.logits = nn.Linear(512, self.num_classes) + + self.device = torch.device("cpu") + if device is not None: + self.device = device + self.to(device) + + def forward(self, x): + """Calculate embeddings or probabilities given a batch of input image tensors. + + Arguments: + x {torch.tensor} -- Batch of image tensors representing faces. + + Returns: + torch.tensor -- Batch of embeddings or softmax probabilities. + """ + x = self.conv2d_1a(x) + x = self.conv2d_2a(x) + x = self.conv2d_2b(x) + x = self.maxpool_3a(x) + x = self.conv2d_3b(x) + x = self.conv2d_4a(x) + x = self.conv2d_4b(x) + x = self.repeat_1(x) + x = self.mixed_6a(x) + x = self.repeat_2(x) + x = self.mixed_7a(x) + x = self.repeat_3(x) + x = self.block8(x) + x = self.avgpool_1a(x) + x = self.dropout(x) + x = self.last_linear(x.view(x.shape[0], -1)) + x = self.last_bn(x) + x = F.normalize(x, p=2, dim=1) + if self.classify: + x = self.logits(x) + return x + + +def load_weights(mdl, name): + """Download pretrained state_dict and load into model. + + Arguments: + mdl {torch.nn.Module} -- Pytorch model. + name {str} -- Name of dataset that was used to generate pretrained state_dict. + + Raises: + ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'. + """ + if name == "vggface2": + features_path = "https://drive.google.com/uc?export=download&id=1cWLH_hPns8kSfMz9kKl9PsG5aNV2VSMn" + logits_path = "https://drive.google.com/uc?export=download&id=1mAie3nzZeno9UIzFXvmVZrDG3kwML46X" + elif name == "casia-webface": + features_path = "https://drive.google.com/uc?export=download&id=1LSHHee_IQj5W3vjBcRyVaALv4py1XaGy" + logits_path = "https://drive.google.com/uc?export=download&id=1QrhPgn1bGlDxAil2uc07ctunCQoDnCzT" + else: + raise ValueError( + 'Pretrained models only exist for "vggface2" and "casia-webface"' + ) + + model_dir = os.path.join(get_torch_home(), "checkpoints") + os.makedirs(model_dir, exist_ok=True) + + state_dict = {} + for i, path in enumerate([features_path, logits_path]): + cached_file = os.path.join(model_dir, "{}_{}.pt".format(name, path[-10:])) + if not os.path.exists(cached_file): + print("Downloading parameters ({}/2)".format(i + 1)) + s = requests.Session() + s.mount("https://", HTTPAdapter(max_retries=10)) + r = s.get(path, allow_redirects=True) + with open(cached_file, "wb") as f: + f.write(r.content) + state_dict.update(torch.load(cached_file)) + + mdl.load_state_dict(state_dict) + + +def inception_resnet_v1(pretrained=True, progress=True, **kwargs): + return InceptionResnetV1(classify=True, pretrained="vggface2", num_classes=7) + + +def get_torch_home(): + torch_home = os.path.expanduser( + os.getenv( + "TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch") + ) + ) + return torch_home diff --git a/algorithm/detect_emotion/rmn/models/masking.py b/algorithm/detect_emotion/rmn/models/masking.py new file mode 100644 index 0000000..4120656 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/masking.py @@ -0,0 +1,383 @@ +import traceback +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .resnet import conv1x1, conv3x3, BasicBlock, Bottleneck + + +def up_pooling(in_channels, out_channels, kernel_size=2, stride=2): + return nn.Sequential( + nn.ConvTranspose2d( + in_channels, out_channels, kernel_size=kernel_size, stride=stride + ), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ) + + +class Masking4(nn.Module): + def __init__(self, in_channels, out_channels, block=BasicBlock): + assert in_channels == out_channels + super(Masking4, self).__init__() + filters = [ + in_channels, + in_channels * 2, + in_channels * 4, + in_channels * 8, + in_channels * 16, + ] + + self.downsample1 = nn.Sequential( + conv1x1(filters[0], filters[1], 1), + nn.BatchNorm2d(filters[1]), + ) + + self.downsample2 = nn.Sequential( + conv1x1(filters[1], filters[2], 1), + nn.BatchNorm2d(filters[2]), + ) + + self.downsample3 = nn.Sequential( + conv1x1(filters[2], filters[3], 1), + nn.BatchNorm2d(filters[3]), + ) + + self.downsample4 = nn.Sequential( + conv1x1(filters[3], filters[4], 1), + nn.BatchNorm2d(filters[4]), + ) + + """ + self.conv1 = block(filters[0], filters[1], downsample=conv1x1(filters[0], filters[1], 1)) + self.conv2 = block(filters[1], filters[2], downsample=conv1x1(filters[1], filters[2], 1)) + self.conv3 = block(filters[2], filters[3], downsample=conv1x1(filters[2], filters[3], 1)) + """ + + self.conv1 = block(filters[0], filters[1], downsample=self.downsample1) + self.conv2 = block(filters[1], filters[2], downsample=self.downsample2) + self.conv3 = block(filters[2], filters[3], downsample=self.downsample3) + self.conv4 = block(filters[3], filters[4], downsample=self.downsample4) + + self.down_pooling = nn.MaxPool2d(kernel_size=2) + + self.downsample5 = nn.Sequential( + conv1x1(filters[4], filters[3], 1), + nn.BatchNorm2d(filters[3]), + ) + + self.downsample6 = nn.Sequential( + conv1x1(filters[3], filters[2], 1), + nn.BatchNorm2d(filters[2]), + ) + + self.downsample7 = nn.Sequential( + conv1x1(filters[2], filters[1], 1), + nn.BatchNorm2d(filters[1]), + ) + + self.downsample8 = nn.Sequential( + conv1x1(filters[1], filters[0], 1), + nn.BatchNorm2d(filters[0]), + ) + + """ + self.up_pool4 = up_pooling(filters[3], filters[2]) + self.conv4 = block(filters[3], filters[2], downsample=conv1x1(filters[3], filters[2], 1)) + self.up_pool5 = up_pooling(filters[2], filters[1]) + self.conv5 = block(filters[2], filters[1], downsample=conv1x1(filters[2], filters[1], 1)) + + self.conv6 = block(filters[1], filters[0], downsample=conv1x1(filters[1], filters[0], 1)) + """ + + self.up_pool5 = up_pooling(filters[4], filters[3]) + self.conv5 = block(filters[4], filters[3], downsample=self.downsample5) + self.up_pool6 = up_pooling(filters[3], filters[2]) + self.conv6 = block(filters[3], filters[2], downsample=self.downsample6) + self.up_pool7 = up_pooling(filters[2], filters[1]) + self.conv7 = block(filters[2], filters[1], downsample=self.downsample7) + self.conv8 = block(filters[1], filters[0], downsample=self.downsample8) + + # init weight + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def forward(self, x): + x1 = self.conv1(x) + p1 = self.down_pooling(x1) + x2 = self.conv2(p1) + p2 = self.down_pooling(x2) + x3 = self.conv3(p2) + p3 = self.down_pooling(x3) + x4 = self.conv4(p3) + + x5 = self.up_pool5(x4) + x5 = torch.cat([x5, x3], dim=1) + x5 = self.conv5(x5) + + x6 = self.up_pool6(x5) + x6 = torch.cat([x6, x2], dim=1) + x6 = self.conv6(x6) + + x7 = self.up_pool7(x6) + x7 = torch.cat([x7, x1], dim=1) + x7 = self.conv7(x7) + + x8 = self.conv8(x7) + + output = torch.softmax(x8, dim=1) + # output = torch.sigmoid(x8) + return output + + +class Masking3(nn.Module): + def __init__(self, in_channels, out_channels, block=BasicBlock): + assert in_channels == out_channels + super(Masking3, self).__init__() + filters = [in_channels, in_channels * 2, in_channels * 4, in_channels * 8] + + self.downsample1 = nn.Sequential( + conv1x1(filters[0], filters[1], 1), + nn.BatchNorm2d(filters[1]), + ) + + self.downsample2 = nn.Sequential( + conv1x1(filters[1], filters[2], 1), + nn.BatchNorm2d(filters[2]), + ) + + self.downsample3 = nn.Sequential( + conv1x1(filters[2], filters[3], 1), + nn.BatchNorm2d(filters[3]), + ) + + """ + self.conv1 = block(filters[0], filters[1], downsample=conv1x1(filters[0], filters[1], 1)) + self.conv2 = block(filters[1], filters[2], downsample=conv1x1(filters[1], filters[2], 1)) + self.conv3 = block(filters[2], filters[3], downsample=conv1x1(filters[2], filters[3], 1)) + """ + + self.conv1 = block(filters[0], filters[1], downsample=self.downsample1) + self.conv2 = block(filters[1], filters[2], downsample=self.downsample2) + self.conv3 = block(filters[2], filters[3], downsample=self.downsample3) + + self.down_pooling = nn.MaxPool2d(kernel_size=2) + + self.downsample4 = nn.Sequential( + conv1x1(filters[3], filters[2], 1), + nn.BatchNorm2d(filters[2]), + ) + + self.downsample5 = nn.Sequential( + conv1x1(filters[2], filters[1], 1), + nn.BatchNorm2d(filters[1]), + ) + + self.downsample6 = nn.Sequential( + conv1x1(filters[1], filters[0], 1), + nn.BatchNorm2d(filters[0]), + ) + + """ + self.up_pool4 = up_pooling(filters[3], filters[2]) + self.conv4 = block(filters[3], filters[2], downsample=conv1x1(filters[3], filters[2], 1)) + self.up_pool5 = up_pooling(filters[2], filters[1]) + self.conv5 = block(filters[2], filters[1], downsample=conv1x1(filters[2], filters[1], 1)) + + self.conv6 = block(filters[1], filters[0], downsample=conv1x1(filters[1], filters[0], 1)) + """ + + self.up_pool4 = up_pooling(filters[3], filters[2]) + self.conv4 = block(filters[3], filters[2], downsample=self.downsample4) + self.up_pool5 = up_pooling(filters[2], filters[1]) + self.conv5 = block(filters[2], filters[1], downsample=self.downsample5) + + self.conv6 = block(filters[1], filters[0], downsample=self.downsample6) + + # init weight + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def forward(self, x): + x1 = self.conv1(x) + p1 = self.down_pooling(x1) + x2 = self.conv2(p1) + p2 = self.down_pooling(x2) + x3 = self.conv3(p2) + + x4 = self.up_pool4(x3) + x4 = torch.cat([x4, x2], dim=1) + + x4 = self.conv4(x4) + + x5 = self.up_pool5(x4) + x5 = torch.cat([x5, x1], dim=1) + x5 = self.conv5(x5) + + x6 = self.conv6(x5) + + output = torch.softmax(x6, dim=1) + # output = torch.sigmoid(x6) + return output + + +class Masking2(nn.Module): + def __init__(self, in_channels, out_channels, block=BasicBlock): + assert in_channels == out_channels + super(Masking2, self).__init__() + filters = [in_channels, in_channels * 2, in_channels * 4, in_channels * 8] + + self.downsample1 = nn.Sequential( + conv1x1(filters[0], filters[1], 1), + nn.BatchNorm2d(filters[1]), + ) + + self.downsample2 = nn.Sequential( + conv1x1(filters[1], filters[2], 1), + nn.BatchNorm2d(filters[2]), + ) + + """ + self.conv1 = block(filters[0], filters[1], downsample=conv1x1(filters[0], filters[1], 1)) + self.conv2 = block(filters[1], filters[2], downsample=conv1x1(filters[1], filters[2], 1)) + """ + self.conv1 = block(filters[0], filters[1], downsample=self.downsample1) + self.conv2 = block(filters[1], filters[2], downsample=self.downsample2) + + self.down_pooling = nn.MaxPool2d(kernel_size=2) + + self.downsample3 = nn.Sequential( + conv1x1(filters[2], filters[1], 1), + nn.BatchNorm2d(filters[1]), + ) + + self.downsample4 = nn.Sequential( + conv1x1(filters[1], filters[0], 1), + nn.BatchNorm2d(filters[0]), + ) + + """ + self.up_pool3 = up_pooling(filters[2], filters[1]) + self.conv3 = block(filters[2], filters[1], downsample=conv1x1(filters[2], filters[1], 1)) + self.conv4 = block(filters[1], filters[0], downsample=conv1x1(filters[1], filters[0], 1)) + """ + self.up_pool3 = up_pooling(filters[2], filters[1]) + self.conv3 = block(filters[2], filters[1], downsample=self.downsample3) + self.conv4 = block(filters[1], filters[0], downsample=self.downsample4) + + # init weight + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def forward(self, x): + x1 = self.conv1(x) + p1 = self.down_pooling(x1) + x2 = self.conv2(p1) + + x3 = self.up_pool3(x2) + x3 = torch.cat([x3, x1], dim=1) + x3 = self.conv3(x3) + + x4 = self.conv4(x3) + + output = torch.softmax(x4, dim=1) + # output = torch.sigmoid(x4) + return output + + +class Masking1(nn.Module): + def __init__(self, in_channels, out_channels, block=BasicBlock): + assert in_channels == out_channels + super(Masking1, self).__init__() + filters = [in_channels, in_channels * 2, in_channels * 4, in_channels * 8] + + self.downsample1 = nn.Sequential( + conv1x1(filters[0], filters[1], 1), + nn.BatchNorm2d(filters[1]), + ) + + self.conv1 = block(filters[0], filters[1], downsample=self.downsample1) + + self.downsample2 = nn.Sequential( + conv1x1(filters[1], filters[0], 1), + nn.BatchNorm2d(filters[0]), + ) + + self.conv2 = block(filters[1], filters[0], downsample=self.downsample2) + + # init weight + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def forward(self, x): + x1 = self.conv1(x) + x2 = self.conv2(x1) + output = torch.softmax(x2, dim=1) + # output = torch.sigmoid(x2) + return output + + +def masking(in_channels, out_channels, depth, block=BasicBlock): + if depth == 1: + return Masking1(in_channels, out_channels, block) + elif depth == 2: + return Masking2(in_channels, out_channels, block) + elif depth == 3: + return Masking3(in_channels, out_channels, block) + elif depth == 4: + return Masking4(in_channels, out_channels, block) + else: + traceback.print_exc() + raise Exception("depth need to be from 0-3") diff --git a/algorithm/detect_emotion/rmn/models/res_dense_gle.py b/algorithm/detect_emotion/rmn/models/res_dense_gle.py new file mode 100644 index 0000000..343ec74 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/res_dense_gle.py @@ -0,0 +1,65 @@ +import copy +import torch +import torch.nn as nn +import torch.nn.functional as F +from .utils import load_state_dict_from_url +from .resnet import resnet18 +from .densenet import densenet121 +from .googlenet import googlenet + + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", + "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth", + "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth", + "resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth", + "resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth", + "wide_resnet50_2": "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth", + "wide_resnet101_2": "https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth", +} + + +class ResDenseGle(nn.Module): + def __init__(self, in_channels=3, num_classes=7): + super(ResDenseGle, self).__init__() + + self.resnet = resnet18(in_channels, num_classes) + # self.densenet = densenet121(in_channels, num_classes, pretrained=False) + self.densenet = densenet121(in_channels, num_classes) + # self.googlenet = googlenet(in_channels, num_classes, pretrained=False) + self.googlenet = googlenet(in_channels, num_classes) + + # change fc to identity + self.resnet.fc = nn.Identity() + # self.densenet.fc = nn.Identity() + self.densenet.classifier = nn.Identity() + self.googlenet.fc = nn.Identity() + + # create new fc + # self.fc = nn.Linear(512 * 3, 7) + # avoid change fc inside trainer + self._fc = nn.Linear(2536, 7) + # self._fc = nn.Linear(2560, 7) + + # another options for fc + self.fc1 = nn.Linear(2536, 512) + self.fc2 = nn.Linear(512, 7) + + def forward(self, x): + x1 = self.resnet(x) + x2 = self.densenet(x) + x3 = self.googlenet(x) + + x = torch.cat([x1, x2, x3], dim=1) + x = self._fc(x) + # x = self.fc1(x) + # x = self.fc2(x) + return x + + +def rdg(pretrained=False, progress=True, **kwargs): + model = ResDenseGle(kwargs["in_channels"], kwargs["num_classes"]) + + return model diff --git a/algorithm/detect_emotion/rmn/models/resatt.py b/algorithm/detect_emotion/rmn/models/resatt.py new file mode 100644 index 0000000..6df107d --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/resatt.py @@ -0,0 +1,79 @@ +import copy +import torch +import torch.nn as nn +import torch.nn.functional as F +from .utils import load_state_dict_from_url +from .attention import attention +from .resnet import BasicBlock, Bottleneck, ResNet, resnet18 + + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", +} + + +class ResAtt(ResNet): + + # def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, + # groups=1, width_per_group=64, replace_stride_with_dilation=None, + # norm_layer=None, in_channels=3): + def __init__(self): + super(ResAtt, self).__init__( + block=BasicBlock, layers=[2, 2, 2, 2], in_channels=3, num_classes=1000 + ) + # state_dict = load_state_dict_from_url(model_urls['resnet18']) + # self.load_state_dict(state_dict) + + self.att12 = attention(channels=64, block=BasicBlock, depth=2) + self.att23 = attention(channels=128, block=BasicBlock, depth=1) + self.att34 = attention(channels=256, block=BasicBlock, depth=0) + # self.fc = nn.Linear(512, 7) + + # self.init_att() + # self.init_mask() + + def init_att(self): + self.att12._trunk1 = copy.deepcopy(self.layer1[1]) + self.att12._trunk2 = copy.deepcopy(self.layer1[1]) + + self.att23._trunk1 = copy.deepcopy(self.layer2[1]) + self.att23._trunk2 = copy.deepcopy(self.layer2[1]) + + self.att34._trunk1 = copy.deepcopy(self.layer3[1]) + self.att34._trunk2 = copy.deepcopy(self.layer3[1]) + + def init_mask(self): + self.att12._enc = copy.deepcopy(self.layer1[1]) + self.att12._dec = copy.deepcopy(self.layer1[1]) + + self.att23._enc1 = copy.deepcopy(self.layer2[1]) + self.att23._enc2 = copy.deepcopy(self.layer2[1]) + self.att23._dec = copy.deepcopy(self.layer2[1]) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.att12(x) + x = self.layer2(x) + x = self.att23(x) + x = self.layer3(x) + x = self.att34(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.fc(x) + return x + + +def resatt18(pretrained=True, progress=True, **kwargs): + model = ResAtt() + model.fc = nn.Linear(512, 7) + return model diff --git a/algorithm/detect_emotion/rmn/models/residual_attention_network.py b/algorithm/detect_emotion/rmn/models/residual_attention_network.py new file mode 100644 index 0000000..f75a499 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/residual_attention_network.py @@ -0,0 +1,58 @@ +import torch +import torch.nn as nn +from torch.nn import init +import functools +from torch.autograd import Variable +import numpy as np +from .basic_layers import ResidualBlock +from .attention_module import AttentionModule + + +class ResidualAttentionModel(nn.Module): + def __init__(self, in_channels=3, num_classes=1000): + super(ResidualAttentionModel, self).__init__() + self.conv1 = nn.Sequential( + nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + ) + self.mpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.residual_block1 = ResidualBlock(64, 256) + self.attention_module1 = AttentionModule(256, 256, (56, 56), (28, 28), (14, 14)) + self.residual_block2 = ResidualBlock(256, 512, 2) + self.attention_module2 = AttentionModule(512, 512, (28, 28), (14, 14), (7, 7)) + self.residual_block3 = ResidualBlock(512, 1024, 2) + self.attention_module3 = AttentionModule(1024, 1024, (14, 14), (7, 7), (4, 4)) + self.residual_block4 = ResidualBlock(1024, 2048, 2) + self.residual_block5 = ResidualBlock(2048, 2048) + self.residual_block6 = ResidualBlock(2048, 2048) + self.mpool2 = nn.Sequential( + nn.BatchNorm2d(2048), + nn.ReLU(inplace=True), + nn.AvgPool2d(kernel_size=7, stride=1), + ) + self.fc = nn.Linear(2048, num_classes) + + def forward(self, x): + out = self.conv1(x) + out = self.mpool1(out) + # print(out.data) + out = self.residual_block1(out) + out = self.attention_module1(out) + out = self.residual_block2(out) + out = self.attention_module2(out) + out = self.residual_block3(out) + # print(out.data) + out = self.attention_module3(out) + out = self.residual_block4(out) + out = self.residual_block5(out) + out = self.residual_block6(out) + out = self.mpool2(out) + out = out.view(out.size(0), -1) + out = self.fc(out) + + return out + + +def res_attention(in_channels=3, num_classes=1000): + return ResidualAttentionModel(in_channels, num_classes) diff --git a/algorithm/detect_emotion/rmn/models/resmasking.py b/algorithm/detect_emotion/rmn/models/resmasking.py new file mode 100644 index 0000000..ad505dc --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/resmasking.py @@ -0,0 +1,188 @@ +import copy +import torch +import torch.nn as nn + +from .utils import load_state_dict_from_url +from .resnet import BasicBlock, Bottleneck, ResNet, resnet18 + + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", +} + + +from .masking import masking + + +class ResMasking(ResNet): + def __init__(self, weight_path): + super(ResMasking, self).__init__( + block=BasicBlock, layers=[3, 4, 6, 3], in_channels=3, num_classes=1000 + ) + # state_dict = torch.load('saved/checkpoints/resnet18_rot30_2019Nov05_17.44')['net'] + # state_dict = load_state_dict_from_url(model_urls['resnet34'], progress=True) + # self.load_state_dict(state_dict) + + self.fc = nn.Linear(512, 7) + + """ + # freeze all net + for m in self.parameters(): + m.requires_grad = False + """ + + self.mask1 = masking(64, 64, depth=4) + self.mask2 = masking(128, 128, depth=3) + self.mask3 = masking(256, 256, depth=2) + self.mask4 = masking(512, 512, depth=1) + + def forward(self, x): # 224 + x = self.conv1(x) # 112 + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) # 56 + + x = self.layer1(x) # 56 + m = self.mask1(x) + x = x * (1 + m) + # x = x * m + + x = self.layer2(x) # 28 + m = self.mask2(x) + x = x * (1 + m) + # x = x * m + + x = self.layer3(x) # 14 + m = self.mask3(x) + x = x * (1 + m) + # x = x * m + + x = self.layer4(x) # 7 + m = self.mask4(x) + x = x * (1 + m) + # x = x * m + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.fc(x) + return x + + +class ResMasking50(ResNet): + def __init__(self, weight_path): + super(ResMasking50, self).__init__( + block=Bottleneck, layers=[3, 4, 6, 3], in_channels=3, num_classes=1000 + ) + # state_dict = torch.load(weight_path)['net'] + state_dict = load_state_dict_from_url(model_urls["resnet50"], progress=True) + self.load_state_dict(state_dict) + + self.fc = nn.Linear(2048, 7) + + """ + # freeze all net + for m in self.parameters(): + m.requires_grad = False + """ + + self.mask1 = masking(256, 256, depth=4) + self.mask2 = masking(512, 512, depth=3) + self.mask3 = masking(1024, 1024, depth=2) + self.mask4 = masking(2048, 2048, depth=1) + + def forward(self, x): # 224 + x = self.conv1(x) # 112 + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) # 56 + + x = self.layer1(x) # 56 + m = self.mask1(x) + x = x * (1 + m) + + x = self.layer2(x) # 28 + m = self.mask2(x) + x = x * (1 + m) + + x = self.layer3(x) # 14 + m = self.mask3(x) + x = x * (1 + m) + + x = self.layer4(x) # 7 + m = self.mask4(x) + x = x * (1 + m) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.fc(x) + return x + + +# def resmasking(in_channels, num_classes, weight_path='saved/checkpoints/resnet18_rot30_2019Nov05_17.44'): +# return ResMasking(weight_path) + + +def resmasking(in_channels, num_classes, weight_path=""): + return ResMasking(weight_path) + + +def resmasking50_dropout1(in_channels, num_classes, weight_path=""): + model = ResMasking50(weight_path) + model.fc = nn.Sequential(nn.Dropout(0.4), nn.Linear(2048, num_classes)) + return model + + +def resmasking_dropout1(in_channels=3, num_classes=7, weight_path=""): + model = ResMasking(weight_path) + model.fc = nn.Sequential( + nn.Dropout(0.4), + nn.Linear(512, 7) + # nn.Linear(512, num_classes) + ) + return model + + +def resmasking_dropout2(in_channels, num_classes, weight_path=""): + model = ResMasking(weight_path) + + model.fc = nn.Sequential( + nn.Linear(512, 128), + nn.ReLU(), + nn.Dropout(p=0.5), + nn.Linear(128, 7), + ) + return model + + +def resmasking_dropout3(in_channels, num_classes, weight_path=""): + model = ResMasking(weight_path) + + model.fc = nn.Sequential( + nn.Linear(512, 512), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(512, 128), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(128, 7), + ) + return model + + +def resmasking_dropout4(in_channels, num_classes, weight_path=""): + model = ResMasking(weight_path) + + model.fc = nn.Sequential( + nn.Linear(512, 128), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(128, 128), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(128, 7), + ) + return model diff --git a/algorithm/detect_emotion/rmn/models/resmasking_naive.py b/algorithm/detect_emotion/rmn/models/resmasking_naive.py new file mode 100644 index 0000000..0aa71b8 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/resmasking_naive.py @@ -0,0 +1,77 @@ +import copy +import torch +import torch.nn as nn + +from .utils import load_state_dict_from_url +from .resnet import BasicBlock, Bottleneck, ResNet, resnet18 + + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", +} + + +from .masking import masking + + +class ResMaskingNaive(ResNet): + def __init__(self, weight_path): + super(ResMaskingNaive, self).__init__( + block=BasicBlock, layers=[3, 4, 6, 3], in_channels=3, num_classes=1000 + ) + # state_dict = torch.load('saved/checkpoints/resnet18_rot30_2019Nov05_17.44')['net'] + state_dict = load_state_dict_from_url(model_urls["resnet34"], progress=True) + self.load_state_dict(state_dict) + + self.fc = nn.Linear(512, 7) + + """ + # freeze all net + for m in self.parameters(): + m.requires_grad = False + """ + + self.mask1 = masking(64, 64, depth=4) + self.mask2 = masking(128, 128, depth=3) + self.mask3 = masking(256, 256, depth=2) + self.mask4 = masking(512, 512, depth=1) + + def forward(self, x): # 224 + x = self.conv1(x) # 112 + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) # 56 + + x = self.layer1(x) # 56 + # m = self.mask1(x) + # x = x * m + + x = self.layer2(x) # 28 + # m = self.mask2(x) + # x = x * m + + x = self.layer3(x) # 14 + # m = self.mask3(x) + # x = x * m + + x = self.layer4(x) # 7 + # m = self.mask4(x) + # x = x * m + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.fc(x) + return x + + +def resmasking_naive_dropout1(in_channels=3, num_classes=7, weight_path=""): + model = ResMaskingNaive(weight_path) + model.fc = nn.Sequential( + nn.Dropout(0.4), + nn.Linear(512, 7) + # nn.Linear(512, num_classes) + ) + return model diff --git a/algorithm/detect_emotion/rmn/models/resnet.py b/algorithm/detect_emotion/rmn/models/resnet.py new file mode 100644 index 0000000..61ede89 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/resnet.py @@ -0,0 +1,438 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .utils import load_state_dict_from_url + + +__all__ = [ + "ResNet", + "resnet18", + "resnet34", + "resnet50", + "resnet101", + "resnet152", + "resnext50_32x4d", + "resnext101_32x8d", + "wide_resnet50_2", + "wide_resnet101_2", +] + + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", + "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth", + "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth", + "resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth", + "resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth", + "wide_resnet50_2": "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth", + "wide_resnet101_2": "https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth", +} + + +def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): + """3x3 convolution with padding""" + return nn.Conv2d( + in_planes, + out_planes, + kernel_size=3, + stride=stride, + padding=dilation, + groups=groups, + bias=False, + dilation=dilation, + ) + + +def conv1x1(in_planes, out_planes, stride=1): + """1x1 convolution""" + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + __constants__ = ["downsample"] + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + base_width=64, + dilation=1, + norm_layer=None, + ): + super(BasicBlock, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + if groups != 1 or base_width != 64: + raise ValueError("BasicBlock only supports groups=1 and base_width=64") + if dilation > 1: + raise NotImplementedError("Dilation > 1 not supported in BasicBlock") + # Both self.conv1 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = norm_layer(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = norm_layer(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + __constants__ = ["downsample"] + + def __init__( + self, + inplanes, + planes, + stride=1, + downsample=None, + groups=1, + base_width=64, + dilation=1, + norm_layer=None, + ): + super(Bottleneck, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + width = int(planes * (base_width / 64.0)) * groups + # Both self.conv2 and self.downsample layers downsample the input when stride != 1 + self.conv1 = conv1x1(inplanes, width) + self.bn1 = norm_layer(width) + self.conv2 = conv3x3(width, width, stride, groups, dilation) + self.bn2 = norm_layer(width) + self.conv3 = conv1x1(width, planes * self.expansion) + self.bn3 = norm_layer(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + identity = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__( + self, + block, + layers, + num_classes=1000, + zero_init_residual=False, + groups=1, + width_per_group=64, + replace_stride_with_dilation=None, + norm_layer=None, + in_channels=3, + ): + super(ResNet, self).__init__() + if norm_layer is None: + norm_layer = nn.BatchNorm2d + self._norm_layer = norm_layer + + self.inplanes = 64 + self.dilation = 1 + if replace_stride_with_dilation is None: + # each element in the tuple indicates if we should replace + # the 2x2 stride with a dilated convolution instead + replace_stride_with_dilation = [False, False, False] + if len(replace_stride_with_dilation) != 3: + raise ValueError( + "replace_stride_with_dilation should be None " + "or a 3-element tuple, got {}".format(replace_stride_with_dilation) + ) + self.groups = groups + self.base_width = width_per_group + + # NOTE: strictly set the in_channels = 3 to load the pretrained model + self.conv1 = nn.Conv2d( + 3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False + ) + # self.conv1 = nn.Conv2d(in_channels, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = norm_layer(self.inplanes) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer( + block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0] + ) + self.layer3 = self._make_layer( + block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1] + ) + self.layer4 = self._make_layer( + block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2] + ) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + + # NOTE: strictly set the num_classes = 1000 to load the pretrained model + self.fc = nn.Linear(512 * block.expansion, 1000) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + # Zero-initialize the last BN in each residual branch, + # so that the residual branch starts with zeros, and each residual block behaves like an identity. + # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 + if zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + nn.init.constant_(m.bn3.weight, 0) + elif isinstance(m, BasicBlock): + nn.init.constant_(m.bn2.weight, 0) + + def _make_layer(self, block, planes, blocks, stride=1, dilate=False): + norm_layer = self._norm_layer + downsample = None + previous_dilation = self.dilation + if dilate: + self.dilation *= stride + stride = 1 + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + conv1x1(self.inplanes, planes * block.expansion, stride), + norm_layer(planes * block.expansion), + ) + + layers = [] + layers.append( + block( + self.inplanes, + planes, + stride, + downsample, + self.groups, + self.base_width, + previous_dilation, + norm_layer, + ) + ) + self.inplanes = planes * block.expansion + for _ in range(1, blocks): + layers.append( + block( + self.inplanes, + planes, + groups=self.groups, + base_width=self.base_width, + dilation=self.dilation, + norm_layer=norm_layer, + ) + ) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.fc(x) + + return x + + +def _resnet(arch, block, layers, pretrained, progress, **kwargs): + model = ResNet(block, layers, **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def resnet18(pretrained=False, progress=True, **kwargs): + r"""ResNet-18 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + model = _resnet( + "resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs + ) + + # model.fc = nn.Linear(512, kwargs['num_classes']) + model.fc = nn.Linear(512, 7) + return model + + +def resnet34(pretrained=False, progress=True, **kwargs): + r"""ResNet-34 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + model = _resnet( + "resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs + ) + model.fc = nn.Linear(512, kwargs["num_classes"]) + return model + + +def resnet50(pretrained=False, progress=True, **kwargs): + r"""ResNet-50 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + model = _resnet( + "resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs + ) + model.fc = nn.Linear(2048, kwargs["num_classes"]) + return model + + +def resnet101(pretrained=False, progress=True, **kwargs): + r"""ResNet-101 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + model = _resnet( + "resnet101", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs + ) + model.fc = nn.Linear(2048, kwargs["num_classes"]) + return model + + +def resnet152(pretrained=False, progress=True, **kwargs): + r"""ResNet-152 model from + `"Deep Residual Learning for Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + model = _resnet( + "resnet152", Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs + ) + + model.fc = nn.Linear(2048, kwargs["num_classes"]) + return model + + +def resnext50_32x4d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-50 32x4d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["groups"] = 32 + kwargs["width_per_group"] = 4 + return _resnet( + "resnext50_32x4d", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs + ) + + +def resnext101_32x8d(pretrained=False, progress=True, **kwargs): + r"""ResNeXt-101 32x8d model from + `"Aggregated Residual Transformation for Deep Neural Networks" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["groups"] = 32 + kwargs["width_per_group"] = 8 + return _resnet( + "resnext101_32x8d", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs + ) + + +def wide_resnet50_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-50-2 model from + `"Wide Residual Networks" `_ + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["width_per_group"] = 64 * 2 + return _resnet( + "wide_resnet50_2", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs + ) + + +def wide_resnet101_2(pretrained=False, progress=True, **kwargs): + r"""Wide ResNet-101-2 model from + `"Wide Residual Networks" `_ + + The model is the same as ResNet except for the bottleneck number of channels + which is twice larger in every block. The number of channels in outer 1x1 + convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 + channels, and in Wide ResNet-50-2 has 2048-1024-2048. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + kwargs["width_per_group"] = 64 * 2 + return _resnet( + "wide_resnet101_2", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs + ) diff --git a/algorithm/detect_emotion/rmn/models/resnet112.py b/algorithm/detect_emotion/rmn/models/resnet112.py new file mode 100644 index 0000000..9185798 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/resnet112.py @@ -0,0 +1,52 @@ +import torch +import torch.nn + +from .utils import load_state_dict_from_url +from .resnet import ResNet, BasicBlock + +model_urls = { + "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", + "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", + "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", + "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth", +} + + +class ResNet112(ResNet): + def __init__(self, block, layers): + super(ResNet112, self).__init__( + block=block, layers=layers, in_channels=3, num_classes=1000 + ) + + # state_dict = load_state_dict_from_url(model_urls['resnet18']) + # self.load_state_dict(state_dict) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + + x = self.avgpool(x) + x = torch.flatten(x, 1) + + x = self.fc(x) + return x + + +def resnet18x112(pretrained=True, progress=True, **kwargs): + model = ResNet112(block=BasicBlock, layers=[2, 2, 2, 2]) + state_dict = load_state_dict_from_url(model_urls["resnet18"]) + model.load_state_dict(state_dict) + return model + + +def resnet34x112(pretrained=True, progress=True, **kwargs): + model = ResNet112(block=BasicBlock, layers=[3, 4, 6, 3]) + state_dict = load_state_dict_from_url(model_urls["resnet34"]) + model.load_state_dict(state_dict) + return model diff --git a/algorithm/detect_emotion/rmn/models/resnet50_scratch_dims_2048.py b/algorithm/detect_emotion/rmn/models/resnet50_scratch_dims_2048.py new file mode 100644 index 0000000..5a4957b --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/resnet50_scratch_dims_2048.py @@ -0,0 +1,765 @@ +import torch +import torch.nn as nn + + +class Resnet50_scratch(nn.Module): + def __init__(self): + super(Resnet50_scratch, self).__init__() + self.meta = { + "mean": [131.0912, 103.8827, 91.4953], + "std": [1, 1, 1], + "imageSize": [224, 224, 3], + } + self.conv1_7x7_s2 = nn.Conv2d( + 3, 64, kernel_size=[7, 7], stride=(2, 2), padding=(3, 3), bias=False + ) + self.conv1_7x7_s2_bn = nn.BatchNorm2d( + 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv1_relu_7x7_s2 = nn.ReLU() + self.pool1_3x3_s2 = nn.MaxPool2d( + kernel_size=[3, 3], + stride=[2, 2], + padding=(0, 0), + dilation=1, + ceil_mode=True, + ) + self.conv2_1_1x1_reduce = nn.Conv2d( + 64, 64, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv2_1_1x1_reduce_bn = nn.BatchNorm2d( + 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_1_1x1_reduce_relu = nn.ReLU() + self.conv2_1_3x3 = nn.Conv2d( + 64, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv2_1_3x3_bn = nn.BatchNorm2d( + 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_1_3x3_relu = nn.ReLU() + self.conv2_1_1x1_increase = nn.Conv2d( + 64, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv2_1_1x1_increase_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_1_1x1_proj = nn.Conv2d( + 64, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv2_1_1x1_proj_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_1_relu = nn.ReLU() + self.conv2_2_1x1_reduce = nn.Conv2d( + 256, 64, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv2_2_1x1_reduce_bn = nn.BatchNorm2d( + 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_2_1x1_reduce_relu = nn.ReLU() + self.conv2_2_3x3 = nn.Conv2d( + 64, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv2_2_3x3_bn = nn.BatchNorm2d( + 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_2_3x3_relu = nn.ReLU() + self.conv2_2_1x1_increase = nn.Conv2d( + 64, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv2_2_1x1_increase_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_2_relu = nn.ReLU() + self.conv2_3_1x1_reduce = nn.Conv2d( + 256, 64, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv2_3_1x1_reduce_bn = nn.BatchNorm2d( + 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_3_1x1_reduce_relu = nn.ReLU() + self.conv2_3_3x3 = nn.Conv2d( + 64, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv2_3_3x3_bn = nn.BatchNorm2d( + 64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_3_3x3_relu = nn.ReLU() + self.conv2_3_1x1_increase = nn.Conv2d( + 64, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv2_3_1x1_increase_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv2_3_relu = nn.ReLU() + self.conv3_1_1x1_reduce = nn.Conv2d( + 256, 128, kernel_size=[1, 1], stride=(2, 2), bias=False + ) + self.conv3_1_1x1_reduce_bn = nn.BatchNorm2d( + 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_1_1x1_reduce_relu = nn.ReLU() + self.conv3_1_3x3 = nn.Conv2d( + 128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv3_1_3x3_bn = nn.BatchNorm2d( + 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_1_3x3_relu = nn.ReLU() + self.conv3_1_1x1_increase = nn.Conv2d( + 128, 512, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv3_1_1x1_increase_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_1_1x1_proj = nn.Conv2d( + 256, 512, kernel_size=[1, 1], stride=(2, 2), bias=False + ) + self.conv3_1_1x1_proj_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_1_relu = nn.ReLU() + self.conv3_2_1x1_reduce = nn.Conv2d( + 512, 128, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv3_2_1x1_reduce_bn = nn.BatchNorm2d( + 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_2_1x1_reduce_relu = nn.ReLU() + self.conv3_2_3x3 = nn.Conv2d( + 128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv3_2_3x3_bn = nn.BatchNorm2d( + 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_2_3x3_relu = nn.ReLU() + self.conv3_2_1x1_increase = nn.Conv2d( + 128, 512, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv3_2_1x1_increase_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_2_relu = nn.ReLU() + self.conv3_3_1x1_reduce = nn.Conv2d( + 512, 128, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv3_3_1x1_reduce_bn = nn.BatchNorm2d( + 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_3_1x1_reduce_relu = nn.ReLU() + self.conv3_3_3x3 = nn.Conv2d( + 128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv3_3_3x3_bn = nn.BatchNorm2d( + 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_3_3x3_relu = nn.ReLU() + self.conv3_3_1x1_increase = nn.Conv2d( + 128, 512, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv3_3_1x1_increase_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_3_relu = nn.ReLU() + self.conv3_4_1x1_reduce = nn.Conv2d( + 512, 128, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv3_4_1x1_reduce_bn = nn.BatchNorm2d( + 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_4_1x1_reduce_relu = nn.ReLU() + self.conv3_4_3x3 = nn.Conv2d( + 128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv3_4_3x3_bn = nn.BatchNorm2d( + 128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_4_3x3_relu = nn.ReLU() + self.conv3_4_1x1_increase = nn.Conv2d( + 128, 512, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv3_4_1x1_increase_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv3_4_relu = nn.ReLU() + self.conv4_1_1x1_reduce = nn.Conv2d( + 512, 256, kernel_size=[1, 1], stride=(2, 2), bias=False + ) + self.conv4_1_1x1_reduce_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_1_1x1_reduce_relu = nn.ReLU() + self.conv4_1_3x3 = nn.Conv2d( + 256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv4_1_3x3_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_1_3x3_relu = nn.ReLU() + self.conv4_1_1x1_increase = nn.Conv2d( + 256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_1_1x1_increase_bn = nn.BatchNorm2d( + 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_1_1x1_proj = nn.Conv2d( + 512, 1024, kernel_size=[1, 1], stride=(2, 2), bias=False + ) + self.conv4_1_1x1_proj_bn = nn.BatchNorm2d( + 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_1_relu = nn.ReLU() + self.conv4_2_1x1_reduce = nn.Conv2d( + 1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_2_1x1_reduce_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_2_1x1_reduce_relu = nn.ReLU() + self.conv4_2_3x3 = nn.Conv2d( + 256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv4_2_3x3_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_2_3x3_relu = nn.ReLU() + self.conv4_2_1x1_increase = nn.Conv2d( + 256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_2_1x1_increase_bn = nn.BatchNorm2d( + 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_2_relu = nn.ReLU() + self.conv4_3_1x1_reduce = nn.Conv2d( + 1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_3_1x1_reduce_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_3_1x1_reduce_relu = nn.ReLU() + self.conv4_3_3x3 = nn.Conv2d( + 256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv4_3_3x3_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_3_3x3_relu = nn.ReLU() + self.conv4_3_1x1_increase = nn.Conv2d( + 256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_3_1x1_increase_bn = nn.BatchNorm2d( + 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_3_relu = nn.ReLU() + self.conv4_4_1x1_reduce = nn.Conv2d( + 1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_4_1x1_reduce_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_4_1x1_reduce_relu = nn.ReLU() + self.conv4_4_3x3 = nn.Conv2d( + 256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv4_4_3x3_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_4_3x3_relu = nn.ReLU() + self.conv4_4_1x1_increase = nn.Conv2d( + 256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_4_1x1_increase_bn = nn.BatchNorm2d( + 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_4_relu = nn.ReLU() + self.conv4_5_1x1_reduce = nn.Conv2d( + 1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_5_1x1_reduce_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_5_1x1_reduce_relu = nn.ReLU() + self.conv4_5_3x3 = nn.Conv2d( + 256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv4_5_3x3_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_5_3x3_relu = nn.ReLU() + self.conv4_5_1x1_increase = nn.Conv2d( + 256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_5_1x1_increase_bn = nn.BatchNorm2d( + 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_5_relu = nn.ReLU() + self.conv4_6_1x1_reduce = nn.Conv2d( + 1024, 256, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_6_1x1_reduce_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_6_1x1_reduce_relu = nn.ReLU() + self.conv4_6_3x3 = nn.Conv2d( + 256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv4_6_3x3_bn = nn.BatchNorm2d( + 256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_6_3x3_relu = nn.ReLU() + self.conv4_6_1x1_increase = nn.Conv2d( + 256, 1024, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv4_6_1x1_increase_bn = nn.BatchNorm2d( + 1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv4_6_relu = nn.ReLU() + self.conv5_1_1x1_reduce = nn.Conv2d( + 1024, 512, kernel_size=[1, 1], stride=(2, 2), bias=False + ) + self.conv5_1_1x1_reduce_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_1_1x1_reduce_relu = nn.ReLU() + self.conv5_1_3x3 = nn.Conv2d( + 512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv5_1_3x3_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_1_3x3_relu = nn.ReLU() + self.conv5_1_1x1_increase = nn.Conv2d( + 512, 2048, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv5_1_1x1_increase_bn = nn.BatchNorm2d( + 2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_1_1x1_proj = nn.Conv2d( + 1024, 2048, kernel_size=[1, 1], stride=(2, 2), bias=False + ) + self.conv5_1_1x1_proj_bn = nn.BatchNorm2d( + 2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_1_relu = nn.ReLU() + self.conv5_2_1x1_reduce = nn.Conv2d( + 2048, 512, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv5_2_1x1_reduce_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_2_1x1_reduce_relu = nn.ReLU() + self.conv5_2_3x3 = nn.Conv2d( + 512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv5_2_3x3_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_2_3x3_relu = nn.ReLU() + self.conv5_2_1x1_increase = nn.Conv2d( + 512, 2048, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv5_2_1x1_increase_bn = nn.BatchNorm2d( + 2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_2_relu = nn.ReLU() + self.conv5_3_1x1_reduce = nn.Conv2d( + 2048, 512, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv5_3_1x1_reduce_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_3_1x1_reduce_relu = nn.ReLU() + self.conv5_3_3x3 = nn.Conv2d( + 512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1), bias=False + ) + self.conv5_3_3x3_bn = nn.BatchNorm2d( + 512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_3_3x3_relu = nn.ReLU() + self.conv5_3_1x1_increase = nn.Conv2d( + 512, 2048, kernel_size=[1, 1], stride=(1, 1), bias=False + ) + self.conv5_3_1x1_increase_bn = nn.BatchNorm2d( + 2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True + ) + self.conv5_3_relu = nn.ReLU() + self.pool5_7x7_s1 = nn.AvgPool2d(kernel_size=[7, 7], stride=[1, 1], padding=0) + self.classifier = nn.Conv2d(2048, 8631, kernel_size=[1, 1], stride=(1, 1)) + + def forward(self, data): + conv1_7x7_s2 = self.conv1_7x7_s2(data) + conv1_7x7_s2_bn = self.conv1_7x7_s2_bn(conv1_7x7_s2) + conv1_7x7_s2_bnxx = self.conv1_relu_7x7_s2(conv1_7x7_s2_bn) + pool1_3x3_s2 = self.pool1_3x3_s2(conv1_7x7_s2_bnxx) + conv2_1_1x1_reduce = self.conv2_1_1x1_reduce(pool1_3x3_s2) + conv2_1_1x1_reduce_bn = self.conv2_1_1x1_reduce_bn(conv2_1_1x1_reduce) + conv2_1_1x1_reduce_bnxx = self.conv2_1_1x1_reduce_relu(conv2_1_1x1_reduce_bn) + conv2_1_3x3 = self.conv2_1_3x3(conv2_1_1x1_reduce_bnxx) + conv2_1_3x3_bn = self.conv2_1_3x3_bn(conv2_1_3x3) + conv2_1_3x3_bnxx = self.conv2_1_3x3_relu(conv2_1_3x3_bn) + conv2_1_1x1_increase = self.conv2_1_1x1_increase(conv2_1_3x3_bnxx) + conv2_1_1x1_increase_bn = self.conv2_1_1x1_increase_bn(conv2_1_1x1_increase) + conv2_1_1x1_proj = self.conv2_1_1x1_proj(pool1_3x3_s2) + conv2_1_1x1_proj_bn = self.conv2_1_1x1_proj_bn(conv2_1_1x1_proj) + conv2_1 = torch.add(conv2_1_1x1_proj_bn, 1, conv2_1_1x1_increase_bn) + conv2_1x = self.conv2_1_relu(conv2_1) + conv2_2_1x1_reduce = self.conv2_2_1x1_reduce(conv2_1x) + conv2_2_1x1_reduce_bn = self.conv2_2_1x1_reduce_bn(conv2_2_1x1_reduce) + conv2_2_1x1_reduce_bnxx = self.conv2_2_1x1_reduce_relu(conv2_2_1x1_reduce_bn) + conv2_2_3x3 = self.conv2_2_3x3(conv2_2_1x1_reduce_bnxx) + conv2_2_3x3_bn = self.conv2_2_3x3_bn(conv2_2_3x3) + conv2_2_3x3_bnxx = self.conv2_2_3x3_relu(conv2_2_3x3_bn) + conv2_2_1x1_increase = self.conv2_2_1x1_increase(conv2_2_3x3_bnxx) + conv2_2_1x1_increase_bn = self.conv2_2_1x1_increase_bn(conv2_2_1x1_increase) + conv2_2 = torch.add(conv2_1x, 1, conv2_2_1x1_increase_bn) + conv2_2x = self.conv2_2_relu(conv2_2) + conv2_3_1x1_reduce = self.conv2_3_1x1_reduce(conv2_2x) + conv2_3_1x1_reduce_bn = self.conv2_3_1x1_reduce_bn(conv2_3_1x1_reduce) + conv2_3_1x1_reduce_bnxx = self.conv2_3_1x1_reduce_relu(conv2_3_1x1_reduce_bn) + conv2_3_3x3 = self.conv2_3_3x3(conv2_3_1x1_reduce_bnxx) + conv2_3_3x3_bn = self.conv2_3_3x3_bn(conv2_3_3x3) + conv2_3_3x3_bnxx = self.conv2_3_3x3_relu(conv2_3_3x3_bn) + conv2_3_1x1_increase = self.conv2_3_1x1_increase(conv2_3_3x3_bnxx) + conv2_3_1x1_increase_bn = self.conv2_3_1x1_increase_bn(conv2_3_1x1_increase) + conv2_3 = torch.add(conv2_2x, 1, conv2_3_1x1_increase_bn) + conv2_3x = self.conv2_3_relu(conv2_3) + conv3_1_1x1_reduce = self.conv3_1_1x1_reduce(conv2_3x) + conv3_1_1x1_reduce_bn = self.conv3_1_1x1_reduce_bn(conv3_1_1x1_reduce) + conv3_1_1x1_reduce_bnxx = self.conv3_1_1x1_reduce_relu(conv3_1_1x1_reduce_bn) + conv3_1_3x3 = self.conv3_1_3x3(conv3_1_1x1_reduce_bnxx) + conv3_1_3x3_bn = self.conv3_1_3x3_bn(conv3_1_3x3) + conv3_1_3x3_bnxx = self.conv3_1_3x3_relu(conv3_1_3x3_bn) + conv3_1_1x1_increase = self.conv3_1_1x1_increase(conv3_1_3x3_bnxx) + conv3_1_1x1_increase_bn = self.conv3_1_1x1_increase_bn(conv3_1_1x1_increase) + conv3_1_1x1_proj = self.conv3_1_1x1_proj(conv2_3x) + conv3_1_1x1_proj_bn = self.conv3_1_1x1_proj_bn(conv3_1_1x1_proj) + conv3_1 = torch.add(conv3_1_1x1_proj_bn, 1, conv3_1_1x1_increase_bn) + conv3_1x = self.conv3_1_relu(conv3_1) + conv3_2_1x1_reduce = self.conv3_2_1x1_reduce(conv3_1x) + conv3_2_1x1_reduce_bn = self.conv3_2_1x1_reduce_bn(conv3_2_1x1_reduce) + conv3_2_1x1_reduce_bnxx = self.conv3_2_1x1_reduce_relu(conv3_2_1x1_reduce_bn) + conv3_2_3x3 = self.conv3_2_3x3(conv3_2_1x1_reduce_bnxx) + conv3_2_3x3_bn = self.conv3_2_3x3_bn(conv3_2_3x3) + conv3_2_3x3_bnxx = self.conv3_2_3x3_relu(conv3_2_3x3_bn) + conv3_2_1x1_increase = self.conv3_2_1x1_increase(conv3_2_3x3_bnxx) + conv3_2_1x1_increase_bn = self.conv3_2_1x1_increase_bn(conv3_2_1x1_increase) + conv3_2 = torch.add(conv3_1x, 1, conv3_2_1x1_increase_bn) + conv3_2x = self.conv3_2_relu(conv3_2) + conv3_3_1x1_reduce = self.conv3_3_1x1_reduce(conv3_2x) + conv3_3_1x1_reduce_bn = self.conv3_3_1x1_reduce_bn(conv3_3_1x1_reduce) + conv3_3_1x1_reduce_bnxx = self.conv3_3_1x1_reduce_relu(conv3_3_1x1_reduce_bn) + conv3_3_3x3 = self.conv3_3_3x3(conv3_3_1x1_reduce_bnxx) + conv3_3_3x3_bn = self.conv3_3_3x3_bn(conv3_3_3x3) + conv3_3_3x3_bnxx = self.conv3_3_3x3_relu(conv3_3_3x3_bn) + conv3_3_1x1_increase = self.conv3_3_1x1_increase(conv3_3_3x3_bnxx) + conv3_3_1x1_increase_bn = self.conv3_3_1x1_increase_bn(conv3_3_1x1_increase) + conv3_3 = torch.add(conv3_2x, 1, conv3_3_1x1_increase_bn) + conv3_3x = self.conv3_3_relu(conv3_3) + conv3_4_1x1_reduce = self.conv3_4_1x1_reduce(conv3_3x) + conv3_4_1x1_reduce_bn = self.conv3_4_1x1_reduce_bn(conv3_4_1x1_reduce) + conv3_4_1x1_reduce_bnxx = self.conv3_4_1x1_reduce_relu(conv3_4_1x1_reduce_bn) + conv3_4_3x3 = self.conv3_4_3x3(conv3_4_1x1_reduce_bnxx) + conv3_4_3x3_bn = self.conv3_4_3x3_bn(conv3_4_3x3) + conv3_4_3x3_bnxx = self.conv3_4_3x3_relu(conv3_4_3x3_bn) + conv3_4_1x1_increase = self.conv3_4_1x1_increase(conv3_4_3x3_bnxx) + conv3_4_1x1_increase_bn = self.conv3_4_1x1_increase_bn(conv3_4_1x1_increase) + conv3_4 = torch.add(conv3_3x, 1, conv3_4_1x1_increase_bn) + conv3_4x = self.conv3_4_relu(conv3_4) + conv4_1_1x1_reduce = self.conv4_1_1x1_reduce(conv3_4x) + conv4_1_1x1_reduce_bn = self.conv4_1_1x1_reduce_bn(conv4_1_1x1_reduce) + conv4_1_1x1_reduce_bnxx = self.conv4_1_1x1_reduce_relu(conv4_1_1x1_reduce_bn) + conv4_1_3x3 = self.conv4_1_3x3(conv4_1_1x1_reduce_bnxx) + conv4_1_3x3_bn = self.conv4_1_3x3_bn(conv4_1_3x3) + conv4_1_3x3_bnxx = self.conv4_1_3x3_relu(conv4_1_3x3_bn) + conv4_1_1x1_increase = self.conv4_1_1x1_increase(conv4_1_3x3_bnxx) + conv4_1_1x1_increase_bn = self.conv4_1_1x1_increase_bn(conv4_1_1x1_increase) + conv4_1_1x1_proj = self.conv4_1_1x1_proj(conv3_4x) + conv4_1_1x1_proj_bn = self.conv4_1_1x1_proj_bn(conv4_1_1x1_proj) + conv4_1 = torch.add(conv4_1_1x1_proj_bn, 1, conv4_1_1x1_increase_bn) + conv4_1x = self.conv4_1_relu(conv4_1) + conv4_2_1x1_reduce = self.conv4_2_1x1_reduce(conv4_1x) + conv4_2_1x1_reduce_bn = self.conv4_2_1x1_reduce_bn(conv4_2_1x1_reduce) + conv4_2_1x1_reduce_bnxx = self.conv4_2_1x1_reduce_relu(conv4_2_1x1_reduce_bn) + conv4_2_3x3 = self.conv4_2_3x3(conv4_2_1x1_reduce_bnxx) + conv4_2_3x3_bn = self.conv4_2_3x3_bn(conv4_2_3x3) + conv4_2_3x3_bnxx = self.conv4_2_3x3_relu(conv4_2_3x3_bn) + conv4_2_1x1_increase = self.conv4_2_1x1_increase(conv4_2_3x3_bnxx) + conv4_2_1x1_increase_bn = self.conv4_2_1x1_increase_bn(conv4_2_1x1_increase) + conv4_2 = torch.add(conv4_1x, 1, conv4_2_1x1_increase_bn) + conv4_2x = self.conv4_2_relu(conv4_2) + conv4_3_1x1_reduce = self.conv4_3_1x1_reduce(conv4_2x) + conv4_3_1x1_reduce_bn = self.conv4_3_1x1_reduce_bn(conv4_3_1x1_reduce) + conv4_3_1x1_reduce_bnxx = self.conv4_3_1x1_reduce_relu(conv4_3_1x1_reduce_bn) + conv4_3_3x3 = self.conv4_3_3x3(conv4_3_1x1_reduce_bnxx) + conv4_3_3x3_bn = self.conv4_3_3x3_bn(conv4_3_3x3) + conv4_3_3x3_bnxx = self.conv4_3_3x3_relu(conv4_3_3x3_bn) + conv4_3_1x1_increase = self.conv4_3_1x1_increase(conv4_3_3x3_bnxx) + conv4_3_1x1_increase_bn = self.conv4_3_1x1_increase_bn(conv4_3_1x1_increase) + conv4_3 = torch.add(conv4_2x, 1, conv4_3_1x1_increase_bn) + conv4_3x = self.conv4_3_relu(conv4_3) + conv4_4_1x1_reduce = self.conv4_4_1x1_reduce(conv4_3x) + conv4_4_1x1_reduce_bn = self.conv4_4_1x1_reduce_bn(conv4_4_1x1_reduce) + conv4_4_1x1_reduce_bnxx = self.conv4_4_1x1_reduce_relu(conv4_4_1x1_reduce_bn) + conv4_4_3x3 = self.conv4_4_3x3(conv4_4_1x1_reduce_bnxx) + conv4_4_3x3_bn = self.conv4_4_3x3_bn(conv4_4_3x3) + conv4_4_3x3_bnxx = self.conv4_4_3x3_relu(conv4_4_3x3_bn) + conv4_4_1x1_increase = self.conv4_4_1x1_increase(conv4_4_3x3_bnxx) + conv4_4_1x1_increase_bn = self.conv4_4_1x1_increase_bn(conv4_4_1x1_increase) + conv4_4 = torch.add(conv4_3x, 1, conv4_4_1x1_increase_bn) + conv4_4x = self.conv4_4_relu(conv4_4) + conv4_5_1x1_reduce = self.conv4_5_1x1_reduce(conv4_4x) + conv4_5_1x1_reduce_bn = self.conv4_5_1x1_reduce_bn(conv4_5_1x1_reduce) + conv4_5_1x1_reduce_bnxx = self.conv4_5_1x1_reduce_relu(conv4_5_1x1_reduce_bn) + conv4_5_3x3 = self.conv4_5_3x3(conv4_5_1x1_reduce_bnxx) + conv4_5_3x3_bn = self.conv4_5_3x3_bn(conv4_5_3x3) + conv4_5_3x3_bnxx = self.conv4_5_3x3_relu(conv4_5_3x3_bn) + conv4_5_1x1_increase = self.conv4_5_1x1_increase(conv4_5_3x3_bnxx) + conv4_5_1x1_increase_bn = self.conv4_5_1x1_increase_bn(conv4_5_1x1_increase) + conv4_5 = torch.add(conv4_4x, 1, conv4_5_1x1_increase_bn) + conv4_5x = self.conv4_5_relu(conv4_5) + conv4_6_1x1_reduce = self.conv4_6_1x1_reduce(conv4_5x) + conv4_6_1x1_reduce_bn = self.conv4_6_1x1_reduce_bn(conv4_6_1x1_reduce) + conv4_6_1x1_reduce_bnxx = self.conv4_6_1x1_reduce_relu(conv4_6_1x1_reduce_bn) + conv4_6_3x3 = self.conv4_6_3x3(conv4_6_1x1_reduce_bnxx) + conv4_6_3x3_bn = self.conv4_6_3x3_bn(conv4_6_3x3) + conv4_6_3x3_bnxx = self.conv4_6_3x3_relu(conv4_6_3x3_bn) + conv4_6_1x1_increase = self.conv4_6_1x1_increase(conv4_6_3x3_bnxx) + conv4_6_1x1_increase_bn = self.conv4_6_1x1_increase_bn(conv4_6_1x1_increase) + conv4_6 = torch.add(conv4_5x, 1, conv4_6_1x1_increase_bn) + conv4_6x = self.conv4_6_relu(conv4_6) + conv5_1_1x1_reduce = self.conv5_1_1x1_reduce(conv4_6x) + conv5_1_1x1_reduce_bn = self.conv5_1_1x1_reduce_bn(conv5_1_1x1_reduce) + conv5_1_1x1_reduce_bnxx = self.conv5_1_1x1_reduce_relu(conv5_1_1x1_reduce_bn) + conv5_1_3x3 = self.conv5_1_3x3(conv5_1_1x1_reduce_bnxx) + conv5_1_3x3_bn = self.conv5_1_3x3_bn(conv5_1_3x3) + conv5_1_3x3_bnxx = self.conv5_1_3x3_relu(conv5_1_3x3_bn) + conv5_1_1x1_increase = self.conv5_1_1x1_increase(conv5_1_3x3_bnxx) + conv5_1_1x1_increase_bn = self.conv5_1_1x1_increase_bn(conv5_1_1x1_increase) + conv5_1_1x1_proj = self.conv5_1_1x1_proj(conv4_6x) + conv5_1_1x1_proj_bn = self.conv5_1_1x1_proj_bn(conv5_1_1x1_proj) + conv5_1 = torch.add(conv5_1_1x1_proj_bn, 1, conv5_1_1x1_increase_bn) + conv5_1x = self.conv5_1_relu(conv5_1) + conv5_2_1x1_reduce = self.conv5_2_1x1_reduce(conv5_1x) + conv5_2_1x1_reduce_bn = self.conv5_2_1x1_reduce_bn(conv5_2_1x1_reduce) + conv5_2_1x1_reduce_bnxx = self.conv5_2_1x1_reduce_relu(conv5_2_1x1_reduce_bn) + conv5_2_3x3 = self.conv5_2_3x3(conv5_2_1x1_reduce_bnxx) + conv5_2_3x3_bn = self.conv5_2_3x3_bn(conv5_2_3x3) + conv5_2_3x3_bnxx = self.conv5_2_3x3_relu(conv5_2_3x3_bn) + conv5_2_1x1_increase = self.conv5_2_1x1_increase(conv5_2_3x3_bnxx) + conv5_2_1x1_increase_bn = self.conv5_2_1x1_increase_bn(conv5_2_1x1_increase) + conv5_2 = torch.add(conv5_1x, 1, conv5_2_1x1_increase_bn) + conv5_2x = self.conv5_2_relu(conv5_2) + conv5_3_1x1_reduce = self.conv5_3_1x1_reduce(conv5_2x) + conv5_3_1x1_reduce_bn = self.conv5_3_1x1_reduce_bn(conv5_3_1x1_reduce) + conv5_3_1x1_reduce_bnxx = self.conv5_3_1x1_reduce_relu(conv5_3_1x1_reduce_bn) + conv5_3_3x3 = self.conv5_3_3x3(conv5_3_1x1_reduce_bnxx) + conv5_3_3x3_bn = self.conv5_3_3x3_bn(conv5_3_3x3) + conv5_3_3x3_bnxx = self.conv5_3_3x3_relu(conv5_3_3x3_bn) + conv5_3_1x1_increase = self.conv5_3_1x1_increase(conv5_3_3x3_bnxx) + conv5_3_1x1_increase_bn = self.conv5_3_1x1_increase_bn(conv5_3_1x1_increase) + conv5_3 = torch.add(conv5_2x, 1, conv5_3_1x1_increase_bn) + conv5_3x = self.conv5_3_relu(conv5_3) + pool5_7x7_s1 = self.pool5_7x7_s1(conv5_3x) + classifier_preflatten = self.classifier(pool5_7x7_s1) + classifier = classifier_preflatten.view(classifier_preflatten.size(0), -1) + return classifier, pool5_7x7_s1 + + +class Resnet50_pretrained_vgg(Resnet50_scratch): + def __init__(self): + super(Resnet50_pretrained_vgg, self).__init__() + # state_dict = torch.load('./saved/pretrained/resnet50_scratch_dims_2048.pth') + # self.load_state_dict(state_dict) + + self.classifier = nn.Conv2d(2048, 7, kernel_size=[1, 1], stride=(1, 1)) + + def forward(self, data): + conv1_7x7_s2 = self.conv1_7x7_s2(data) + conv1_7x7_s2_bn = self.conv1_7x7_s2_bn(conv1_7x7_s2) + conv1_7x7_s2_bnxx = self.conv1_relu_7x7_s2(conv1_7x7_s2_bn) + pool1_3x3_s2 = self.pool1_3x3_s2(conv1_7x7_s2_bnxx) + conv2_1_1x1_reduce = self.conv2_1_1x1_reduce(pool1_3x3_s2) + conv2_1_1x1_reduce_bn = self.conv2_1_1x1_reduce_bn(conv2_1_1x1_reduce) + conv2_1_1x1_reduce_bnxx = self.conv2_1_1x1_reduce_relu(conv2_1_1x1_reduce_bn) + conv2_1_3x3 = self.conv2_1_3x3(conv2_1_1x1_reduce_bnxx) + conv2_1_3x3_bn = self.conv2_1_3x3_bn(conv2_1_3x3) + conv2_1_3x3_bnxx = self.conv2_1_3x3_relu(conv2_1_3x3_bn) + conv2_1_1x1_increase = self.conv2_1_1x1_increase(conv2_1_3x3_bnxx) + conv2_1_1x1_increase_bn = self.conv2_1_1x1_increase_bn(conv2_1_1x1_increase) + conv2_1_1x1_proj = self.conv2_1_1x1_proj(pool1_3x3_s2) + conv2_1_1x1_proj_bn = self.conv2_1_1x1_proj_bn(conv2_1_1x1_proj) + conv2_1 = torch.add(conv2_1_1x1_proj_bn, 1, conv2_1_1x1_increase_bn) + conv2_1x = self.conv2_1_relu(conv2_1) + conv2_2_1x1_reduce = self.conv2_2_1x1_reduce(conv2_1x) + conv2_2_1x1_reduce_bn = self.conv2_2_1x1_reduce_bn(conv2_2_1x1_reduce) + conv2_2_1x1_reduce_bnxx = self.conv2_2_1x1_reduce_relu(conv2_2_1x1_reduce_bn) + conv2_2_3x3 = self.conv2_2_3x3(conv2_2_1x1_reduce_bnxx) + conv2_2_3x3_bn = self.conv2_2_3x3_bn(conv2_2_3x3) + conv2_2_3x3_bnxx = self.conv2_2_3x3_relu(conv2_2_3x3_bn) + conv2_2_1x1_increase = self.conv2_2_1x1_increase(conv2_2_3x3_bnxx) + conv2_2_1x1_increase_bn = self.conv2_2_1x1_increase_bn(conv2_2_1x1_increase) + conv2_2 = torch.add(conv2_1x, 1, conv2_2_1x1_increase_bn) + conv2_2x = self.conv2_2_relu(conv2_2) + conv2_3_1x1_reduce = self.conv2_3_1x1_reduce(conv2_2x) + conv2_3_1x1_reduce_bn = self.conv2_3_1x1_reduce_bn(conv2_3_1x1_reduce) + conv2_3_1x1_reduce_bnxx = self.conv2_3_1x1_reduce_relu(conv2_3_1x1_reduce_bn) + conv2_3_3x3 = self.conv2_3_3x3(conv2_3_1x1_reduce_bnxx) + conv2_3_3x3_bn = self.conv2_3_3x3_bn(conv2_3_3x3) + conv2_3_3x3_bnxx = self.conv2_3_3x3_relu(conv2_3_3x3_bn) + conv2_3_1x1_increase = self.conv2_3_1x1_increase(conv2_3_3x3_bnxx) + conv2_3_1x1_increase_bn = self.conv2_3_1x1_increase_bn(conv2_3_1x1_increase) + conv2_3 = torch.add(conv2_2x, 1, conv2_3_1x1_increase_bn) + conv2_3x = self.conv2_3_relu(conv2_3) + conv3_1_1x1_reduce = self.conv3_1_1x1_reduce(conv2_3x) + conv3_1_1x1_reduce_bn = self.conv3_1_1x1_reduce_bn(conv3_1_1x1_reduce) + conv3_1_1x1_reduce_bnxx = self.conv3_1_1x1_reduce_relu(conv3_1_1x1_reduce_bn) + conv3_1_3x3 = self.conv3_1_3x3(conv3_1_1x1_reduce_bnxx) + conv3_1_3x3_bn = self.conv3_1_3x3_bn(conv3_1_3x3) + conv3_1_3x3_bnxx = self.conv3_1_3x3_relu(conv3_1_3x3_bn) + conv3_1_1x1_increase = self.conv3_1_1x1_increase(conv3_1_3x3_bnxx) + conv3_1_1x1_increase_bn = self.conv3_1_1x1_increase_bn(conv3_1_1x1_increase) + conv3_1_1x1_proj = self.conv3_1_1x1_proj(conv2_3x) + conv3_1_1x1_proj_bn = self.conv3_1_1x1_proj_bn(conv3_1_1x1_proj) + conv3_1 = torch.add(conv3_1_1x1_proj_bn, 1, conv3_1_1x1_increase_bn) + conv3_1x = self.conv3_1_relu(conv3_1) + conv3_2_1x1_reduce = self.conv3_2_1x1_reduce(conv3_1x) + conv3_2_1x1_reduce_bn = self.conv3_2_1x1_reduce_bn(conv3_2_1x1_reduce) + conv3_2_1x1_reduce_bnxx = self.conv3_2_1x1_reduce_relu(conv3_2_1x1_reduce_bn) + conv3_2_3x3 = self.conv3_2_3x3(conv3_2_1x1_reduce_bnxx) + conv3_2_3x3_bn = self.conv3_2_3x3_bn(conv3_2_3x3) + conv3_2_3x3_bnxx = self.conv3_2_3x3_relu(conv3_2_3x3_bn) + conv3_2_1x1_increase = self.conv3_2_1x1_increase(conv3_2_3x3_bnxx) + conv3_2_1x1_increase_bn = self.conv3_2_1x1_increase_bn(conv3_2_1x1_increase) + conv3_2 = torch.add(conv3_1x, 1, conv3_2_1x1_increase_bn) + conv3_2x = self.conv3_2_relu(conv3_2) + conv3_3_1x1_reduce = self.conv3_3_1x1_reduce(conv3_2x) + conv3_3_1x1_reduce_bn = self.conv3_3_1x1_reduce_bn(conv3_3_1x1_reduce) + conv3_3_1x1_reduce_bnxx = self.conv3_3_1x1_reduce_relu(conv3_3_1x1_reduce_bn) + conv3_3_3x3 = self.conv3_3_3x3(conv3_3_1x1_reduce_bnxx) + conv3_3_3x3_bn = self.conv3_3_3x3_bn(conv3_3_3x3) + conv3_3_3x3_bnxx = self.conv3_3_3x3_relu(conv3_3_3x3_bn) + conv3_3_1x1_increase = self.conv3_3_1x1_increase(conv3_3_3x3_bnxx) + conv3_3_1x1_increase_bn = self.conv3_3_1x1_increase_bn(conv3_3_1x1_increase) + conv3_3 = torch.add(conv3_2x, 1, conv3_3_1x1_increase_bn) + conv3_3x = self.conv3_3_relu(conv3_3) + conv3_4_1x1_reduce = self.conv3_4_1x1_reduce(conv3_3x) + conv3_4_1x1_reduce_bn = self.conv3_4_1x1_reduce_bn(conv3_4_1x1_reduce) + conv3_4_1x1_reduce_bnxx = self.conv3_4_1x1_reduce_relu(conv3_4_1x1_reduce_bn) + conv3_4_3x3 = self.conv3_4_3x3(conv3_4_1x1_reduce_bnxx) + conv3_4_3x3_bn = self.conv3_4_3x3_bn(conv3_4_3x3) + conv3_4_3x3_bnxx = self.conv3_4_3x3_relu(conv3_4_3x3_bn) + conv3_4_1x1_increase = self.conv3_4_1x1_increase(conv3_4_3x3_bnxx) + conv3_4_1x1_increase_bn = self.conv3_4_1x1_increase_bn(conv3_4_1x1_increase) + conv3_4 = torch.add(conv3_3x, 1, conv3_4_1x1_increase_bn) + conv3_4x = self.conv3_4_relu(conv3_4) + conv4_1_1x1_reduce = self.conv4_1_1x1_reduce(conv3_4x) + conv4_1_1x1_reduce_bn = self.conv4_1_1x1_reduce_bn(conv4_1_1x1_reduce) + conv4_1_1x1_reduce_bnxx = self.conv4_1_1x1_reduce_relu(conv4_1_1x1_reduce_bn) + conv4_1_3x3 = self.conv4_1_3x3(conv4_1_1x1_reduce_bnxx) + conv4_1_3x3_bn = self.conv4_1_3x3_bn(conv4_1_3x3) + conv4_1_3x3_bnxx = self.conv4_1_3x3_relu(conv4_1_3x3_bn) + conv4_1_1x1_increase = self.conv4_1_1x1_increase(conv4_1_3x3_bnxx) + conv4_1_1x1_increase_bn = self.conv4_1_1x1_increase_bn(conv4_1_1x1_increase) + conv4_1_1x1_proj = self.conv4_1_1x1_proj(conv3_4x) + conv4_1_1x1_proj_bn = self.conv4_1_1x1_proj_bn(conv4_1_1x1_proj) + conv4_1 = torch.add(conv4_1_1x1_proj_bn, 1, conv4_1_1x1_increase_bn) + conv4_1x = self.conv4_1_relu(conv4_1) + conv4_2_1x1_reduce = self.conv4_2_1x1_reduce(conv4_1x) + conv4_2_1x1_reduce_bn = self.conv4_2_1x1_reduce_bn(conv4_2_1x1_reduce) + conv4_2_1x1_reduce_bnxx = self.conv4_2_1x1_reduce_relu(conv4_2_1x1_reduce_bn) + conv4_2_3x3 = self.conv4_2_3x3(conv4_2_1x1_reduce_bnxx) + conv4_2_3x3_bn = self.conv4_2_3x3_bn(conv4_2_3x3) + conv4_2_3x3_bnxx = self.conv4_2_3x3_relu(conv4_2_3x3_bn) + conv4_2_1x1_increase = self.conv4_2_1x1_increase(conv4_2_3x3_bnxx) + conv4_2_1x1_increase_bn = self.conv4_2_1x1_increase_bn(conv4_2_1x1_increase) + conv4_2 = torch.add(conv4_1x, 1, conv4_2_1x1_increase_bn) + conv4_2x = self.conv4_2_relu(conv4_2) + conv4_3_1x1_reduce = self.conv4_3_1x1_reduce(conv4_2x) + conv4_3_1x1_reduce_bn = self.conv4_3_1x1_reduce_bn(conv4_3_1x1_reduce) + conv4_3_1x1_reduce_bnxx = self.conv4_3_1x1_reduce_relu(conv4_3_1x1_reduce_bn) + conv4_3_3x3 = self.conv4_3_3x3(conv4_3_1x1_reduce_bnxx) + conv4_3_3x3_bn = self.conv4_3_3x3_bn(conv4_3_3x3) + conv4_3_3x3_bnxx = self.conv4_3_3x3_relu(conv4_3_3x3_bn) + conv4_3_1x1_increase = self.conv4_3_1x1_increase(conv4_3_3x3_bnxx) + conv4_3_1x1_increase_bn = self.conv4_3_1x1_increase_bn(conv4_3_1x1_increase) + conv4_3 = torch.add(conv4_2x, 1, conv4_3_1x1_increase_bn) + conv4_3x = self.conv4_3_relu(conv4_3) + conv4_4_1x1_reduce = self.conv4_4_1x1_reduce(conv4_3x) + conv4_4_1x1_reduce_bn = self.conv4_4_1x1_reduce_bn(conv4_4_1x1_reduce) + conv4_4_1x1_reduce_bnxx = self.conv4_4_1x1_reduce_relu(conv4_4_1x1_reduce_bn) + conv4_4_3x3 = self.conv4_4_3x3(conv4_4_1x1_reduce_bnxx) + conv4_4_3x3_bn = self.conv4_4_3x3_bn(conv4_4_3x3) + conv4_4_3x3_bnxx = self.conv4_4_3x3_relu(conv4_4_3x3_bn) + conv4_4_1x1_increase = self.conv4_4_1x1_increase(conv4_4_3x3_bnxx) + conv4_4_1x1_increase_bn = self.conv4_4_1x1_increase_bn(conv4_4_1x1_increase) + conv4_4 = torch.add(conv4_3x, 1, conv4_4_1x1_increase_bn) + conv4_4x = self.conv4_4_relu(conv4_4) + conv4_5_1x1_reduce = self.conv4_5_1x1_reduce(conv4_4x) + conv4_5_1x1_reduce_bn = self.conv4_5_1x1_reduce_bn(conv4_5_1x1_reduce) + conv4_5_1x1_reduce_bnxx = self.conv4_5_1x1_reduce_relu(conv4_5_1x1_reduce_bn) + conv4_5_3x3 = self.conv4_5_3x3(conv4_5_1x1_reduce_bnxx) + conv4_5_3x3_bn = self.conv4_5_3x3_bn(conv4_5_3x3) + conv4_5_3x3_bnxx = self.conv4_5_3x3_relu(conv4_5_3x3_bn) + conv4_5_1x1_increase = self.conv4_5_1x1_increase(conv4_5_3x3_bnxx) + conv4_5_1x1_increase_bn = self.conv4_5_1x1_increase_bn(conv4_5_1x1_increase) + conv4_5 = torch.add(conv4_4x, 1, conv4_5_1x1_increase_bn) + conv4_5x = self.conv4_5_relu(conv4_5) + conv4_6_1x1_reduce = self.conv4_6_1x1_reduce(conv4_5x) + conv4_6_1x1_reduce_bn = self.conv4_6_1x1_reduce_bn(conv4_6_1x1_reduce) + conv4_6_1x1_reduce_bnxx = self.conv4_6_1x1_reduce_relu(conv4_6_1x1_reduce_bn) + conv4_6_3x3 = self.conv4_6_3x3(conv4_6_1x1_reduce_bnxx) + conv4_6_3x3_bn = self.conv4_6_3x3_bn(conv4_6_3x3) + conv4_6_3x3_bnxx = self.conv4_6_3x3_relu(conv4_6_3x3_bn) + conv4_6_1x1_increase = self.conv4_6_1x1_increase(conv4_6_3x3_bnxx) + conv4_6_1x1_increase_bn = self.conv4_6_1x1_increase_bn(conv4_6_1x1_increase) + conv4_6 = torch.add(conv4_5x, 1, conv4_6_1x1_increase_bn) + conv4_6x = self.conv4_6_relu(conv4_6) + conv5_1_1x1_reduce = self.conv5_1_1x1_reduce(conv4_6x) + conv5_1_1x1_reduce_bn = self.conv5_1_1x1_reduce_bn(conv5_1_1x1_reduce) + conv5_1_1x1_reduce_bnxx = self.conv5_1_1x1_reduce_relu(conv5_1_1x1_reduce_bn) + conv5_1_3x3 = self.conv5_1_3x3(conv5_1_1x1_reduce_bnxx) + conv5_1_3x3_bn = self.conv5_1_3x3_bn(conv5_1_3x3) + conv5_1_3x3_bnxx = self.conv5_1_3x3_relu(conv5_1_3x3_bn) + conv5_1_1x1_increase = self.conv5_1_1x1_increase(conv5_1_3x3_bnxx) + conv5_1_1x1_increase_bn = self.conv5_1_1x1_increase_bn(conv5_1_1x1_increase) + conv5_1_1x1_proj = self.conv5_1_1x1_proj(conv4_6x) + conv5_1_1x1_proj_bn = self.conv5_1_1x1_proj_bn(conv5_1_1x1_proj) + conv5_1 = torch.add(conv5_1_1x1_proj_bn, 1, conv5_1_1x1_increase_bn) + conv5_1x = self.conv5_1_relu(conv5_1) + conv5_2_1x1_reduce = self.conv5_2_1x1_reduce(conv5_1x) + conv5_2_1x1_reduce_bn = self.conv5_2_1x1_reduce_bn(conv5_2_1x1_reduce) + conv5_2_1x1_reduce_bnxx = self.conv5_2_1x1_reduce_relu(conv5_2_1x1_reduce_bn) + conv5_2_3x3 = self.conv5_2_3x3(conv5_2_1x1_reduce_bnxx) + conv5_2_3x3_bn = self.conv5_2_3x3_bn(conv5_2_3x3) + conv5_2_3x3_bnxx = self.conv5_2_3x3_relu(conv5_2_3x3_bn) + conv5_2_1x1_increase = self.conv5_2_1x1_increase(conv5_2_3x3_bnxx) + conv5_2_1x1_increase_bn = self.conv5_2_1x1_increase_bn(conv5_2_1x1_increase) + conv5_2 = torch.add(conv5_1x, 1, conv5_2_1x1_increase_bn) + conv5_2x = self.conv5_2_relu(conv5_2) + conv5_3_1x1_reduce = self.conv5_3_1x1_reduce(conv5_2x) + conv5_3_1x1_reduce_bn = self.conv5_3_1x1_reduce_bn(conv5_3_1x1_reduce) + conv5_3_1x1_reduce_bnxx = self.conv5_3_1x1_reduce_relu(conv5_3_1x1_reduce_bn) + conv5_3_3x3 = self.conv5_3_3x3(conv5_3_1x1_reduce_bnxx) + conv5_3_3x3_bn = self.conv5_3_3x3_bn(conv5_3_3x3) + conv5_3_3x3_bnxx = self.conv5_3_3x3_relu(conv5_3_3x3_bn) + conv5_3_1x1_increase = self.conv5_3_1x1_increase(conv5_3_3x3_bnxx) + conv5_3_1x1_increase_bn = self.conv5_3_1x1_increase_bn(conv5_3_1x1_increase) + conv5_3 = torch.add(conv5_2x, 1, conv5_3_1x1_increase_bn) + conv5_3x = self.conv5_3_relu(conv5_3) + pool5_7x7_s1 = self.pool5_7x7_s1(conv5_3x) + classifier_preflatten = self.classifier(pool5_7x7_s1) + classifier = classifier_preflatten.view(classifier_preflatten.size(0), -1) + + # return classifier, pool5_7x7_s1 + return classifier + + +def resnet50_pretrained_vgg(pretrained=True, progress=True, **kwargs): + """ + load imported model instance + + Args: + weights_path (str): If set, loads model weights from the given path + """ + return Resnet50_pretrained_vgg() diff --git a/algorithm/detect_emotion/rmn/models/runet.py b/algorithm/detect_emotion/rmn/models/runet.py new file mode 100644 index 0000000..da487a2 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/runet.py @@ -0,0 +1,868 @@ +from __future__ import print_function, division +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.data +import torch + + +class conv_block(nn.Module): + """ + Convolution Block + """ + + def __init__(self, in_ch, out_ch): + super(conv_block, self).__init__() + + self.conv = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + ) + + def forward(self, x): + + x = self.conv(x) + return x + + +class up_conv(nn.Module): + """ + Up Convolution Block + """ + + def __init__(self, in_ch, out_ch): + super(up_conv, self).__init__() + self.up = nn.Sequential( + nn.Upsample(scale_factor=2), + nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + ) + + def forward(self, x): + x = self.up(x) + return x + + +class U_Net(nn.Module): + """ + UNet - Basic Implementation + Paper : https://arxiv.org/abs/1505.04597 + """ + + def __init__(self, in_ch=3, out_ch=1): + super(U_Net, self).__init__() + + n1 = 64 + filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] + + self.Maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2) + + self.Conv1 = conv_block(in_ch, filters[0]) + self.Conv2 = conv_block(filters[0], filters[1]) + self.Conv3 = conv_block(filters[1], filters[2]) + self.Conv4 = conv_block(filters[2], filters[3]) + self.Conv5 = conv_block(filters[3], filters[4]) + + self.Up5 = up_conv(filters[4], filters[3]) + self.Up_conv5 = conv_block(filters[4], filters[3]) + + self.Up4 = up_conv(filters[3], filters[2]) + self.Up_conv4 = conv_block(filters[3], filters[2]) + + self.Up3 = up_conv(filters[2], filters[1]) + self.Up_conv3 = conv_block(filters[2], filters[1]) + + self.Up2 = up_conv(filters[1], filters[0]) + self.Up_conv2 = conv_block(filters[1], filters[0]) + + self.Conv = nn.Conv2d(filters[0], out_ch, kernel_size=1, stride=1, padding=0) + + # self.active = torch.nn.Sigmoid() + + def forward(self, x): + + e1 = self.Conv1(x) + + e2 = self.Maxpool1(e1) + e2 = self.Conv2(e2) + + e3 = self.Maxpool2(e2) + e3 = self.Conv3(e3) + + e4 = self.Maxpool3(e3) + e4 = self.Conv4(e4) + + e5 = self.Maxpool4(e4) + e5 = self.Conv5(e5) + + d5 = self.Up5(e5) + d5 = torch.cat((e4, d5), dim=1) + + d5 = self.Up_conv5(d5) + + d4 = self.Up4(d5) + d4 = torch.cat((e3, d4), dim=1) + d4 = self.Up_conv4(d4) + + d3 = self.Up3(d4) + d3 = torch.cat((e2, d3), dim=1) + d3 = self.Up_conv3(d3) + + d2 = self.Up2(d3) + d2 = torch.cat((e1, d2), dim=1) + d2 = self.Up_conv2(d2) + + out = self.Conv(d2) + + # d1 = self.active(out) + + return out + + +class Recurrent_block(nn.Module): + """ + Recurrent Block for R2Unet_CNN + """ + + def __init__(self, out_ch, t=2): + super(Recurrent_block, self).__init__() + + self.t = t + self.out_ch = out_ch + self.conv = nn.Sequential( + nn.Conv2d(out_ch, out_ch, kernel_size=3, stride=1, padding=1, bias=True), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + ) + + def forward(self, x): + for i in range(self.t): + if i == 0: + x = self.conv(x) + out = self.conv(x + x) + return out + + +class RRCNN_block(nn.Module): + """ + Recurrent Residual Convolutional Neural Network Block + """ + + def __init__(self, in_ch, out_ch, t=2): + super(RRCNN_block, self).__init__() + + self.RCNN = nn.Sequential( + Recurrent_block(out_ch, t=t), Recurrent_block(out_ch, t=t) + ) + self.Conv = nn.Conv2d(in_ch, out_ch, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + x1 = self.Conv(x) + x2 = self.RCNN(x1) + out = x1 + x2 + return out + + +class R2U_Net(nn.Module): + """ + R2U-Unet implementation + Paper: https://arxiv.org/abs/1802.06955 + """ + + def __init__(self, in_channels=3, num_classes=2, t=2): + super(R2U_Net, self).__init__() + + n1 = 64 + filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] + + self.Maxpool = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2) + + self.Upsample = nn.Upsample(scale_factor=2) + + self.RRCNN1 = RRCNN_block(in_channels, filters[0], t=t) + + self.RRCNN2 = RRCNN_block(filters[0], filters[1], t=t) + + self.RRCNN3 = RRCNN_block(filters[1], filters[2], t=t) + + self.RRCNN4 = RRCNN_block(filters[2], filters[3], t=t) + + self.RRCNN5 = RRCNN_block(filters[3], filters[4], t=t) + + self.Up5 = up_conv(filters[4], filters[3]) + self.Up_RRCNN5 = RRCNN_block(filters[4], filters[3], t=t) + + self.Up4 = up_conv(filters[3], filters[2]) + self.Up_RRCNN4 = RRCNN_block(filters[3], filters[2], t=t) + + self.Up3 = up_conv(filters[2], filters[1]) + self.Up_RRCNN3 = RRCNN_block(filters[2], filters[1], t=t) + + self.Up2 = up_conv(filters[1], filters[0]) + self.Up_RRCNN2 = RRCNN_block(filters[1], filters[0], t=t) + + self.Conv = nn.Conv2d( + filters[0], num_classes, kernel_size=1, stride=1, padding=0 + ) + + # self.active = torch.nn.Sigmoid() + + def forward(self, x): + + e1 = self.RRCNN1(x) + + e2 = self.Maxpool(e1) + e2 = self.RRCNN2(e2) + + e3 = self.Maxpool1(e2) + e3 = self.RRCNN3(e3) + + e4 = self.Maxpool2(e3) + e4 = self.RRCNN4(e4) + + e5 = self.Maxpool3(e4) + e5 = self.RRCNN5(e5) + + d5 = self.Up5(e5) + d5 = torch.cat((e4, d5), dim=1) + d5 = self.Up_RRCNN5(d5) + + d4 = self.Up4(d5) + d4 = torch.cat((e3, d4), dim=1) + d4 = self.Up_RRCNN4(d4) + + d3 = self.Up3(d4) + d3 = torch.cat((e2, d3), dim=1) + d3 = self.Up_RRCNN3(d3) + + d2 = self.Up2(d3) + d2 = torch.cat((e1, d2), dim=1) + d2 = self.Up_RRCNN2(d2) + + out = self.Conv(d2) + + # out = self.active(out) + return out + + +class Attention_block(nn.Module): + """ + Attention Block + """ + + def __init__(self, F_g, F_l, F_int): + super(Attention_block, self).__init__() + + self.W_g = nn.Sequential( + nn.Conv2d(F_l, F_int, kernel_size=1, stride=1, padding=0, bias=True), + nn.BatchNorm2d(F_int), + ) + + self.W_x = nn.Sequential( + nn.Conv2d(F_g, F_int, kernel_size=1, stride=1, padding=0, bias=True), + nn.BatchNorm2d(F_int), + ) + + self.psi = nn.Sequential( + nn.Conv2d(F_int, 1, kernel_size=1, stride=1, padding=0, bias=True), + nn.BatchNorm2d(1), + nn.Sigmoid(), + ) + + self.relu = nn.ReLU(inplace=True) + + def forward(self, g, x): + g1 = self.W_g(g) + x1 = self.W_x(x) + psi = self.relu(g1 + x1) + psi = self.psi(psi) + out = x * psi + return out + + +class AttU_Net(nn.Module): + """ + Attention Unet implementation + Paper: https://arxiv.org/abs/1804.03999 + """ + + def __init__(self, in_channels=3, num_classes=1): + super(AttU_Net, self).__init__() + + n1 = 64 + filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] + + self.Maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2) + + self.Conv1 = conv_block(in_channels, filters[0]) + self.Conv2 = conv_block(filters[0], filters[1]) + self.Conv3 = conv_block(filters[1], filters[2]) + self.Conv4 = conv_block(filters[2], filters[3]) + self.Conv5 = conv_block(filters[3], filters[4]) + + self.Up5 = up_conv(filters[4], filters[3]) + self.Att5 = Attention_block(F_g=filters[3], F_l=filters[3], F_int=filters[2]) + self.Up_conv5 = conv_block(filters[4], filters[3]) + + self.Up4 = up_conv(filters[3], filters[2]) + self.Att4 = Attention_block(F_g=filters[2], F_l=filters[2], F_int=filters[1]) + self.Up_conv4 = conv_block(filters[3], filters[2]) + + self.Up3 = up_conv(filters[2], filters[1]) + self.Att3 = Attention_block(F_g=filters[1], F_l=filters[1], F_int=filters[0]) + self.Up_conv3 = conv_block(filters[2], filters[1]) + + self.Up2 = up_conv(filters[1], filters[0]) + self.Att2 = Attention_block(F_g=filters[0], F_l=filters[0], F_int=32) + self.Up_conv2 = conv_block(filters[1], filters[0]) + + self.Conv = nn.Conv2d( + filters[0], num_classes, kernel_size=1, stride=1, padding=0 + ) + + # self.active = torch.nn.Sigmoid() + + def forward(self, x): + + e1 = self.Conv1(x) + + e2 = self.Maxpool1(e1) + e2 = self.Conv2(e2) + + e3 = self.Maxpool2(e2) + e3 = self.Conv3(e3) + + e4 = self.Maxpool3(e3) + e4 = self.Conv4(e4) + + e5 = self.Maxpool4(e4) + e5 = self.Conv5(e5) + + # print(x5.shape) + d5 = self.Up5(e5) + # print(d5.shape) + x4 = self.Att5(g=d5, x=e4) + d5 = torch.cat((x4, d5), dim=1) + d5 = self.Up_conv5(d5) + + d4 = self.Up4(d5) + x3 = self.Att4(g=d4, x=e3) + d4 = torch.cat((x3, d4), dim=1) + d4 = self.Up_conv4(d4) + + d3 = self.Up3(d4) + x2 = self.Att3(g=d3, x=e2) + d3 = torch.cat((x2, d3), dim=1) + d3 = self.Up_conv3(d3) + + d2 = self.Up2(d3) + x1 = self.Att2(g=d2, x=e1) + d2 = torch.cat((x1, d2), dim=1) + d2 = self.Up_conv2(d2) + + out = self.Conv(d2) + + # out = self.active(out) + + out = torch.softmax(out, dim=1) + return out + + +class R2AttU_Net(nn.Module): + """ + Residual Recuurent Block with attention Unet + Implementation : https://github.com/LeeJunHyun/Image_Segmentation + """ + + def __init__(self, in_ch=3, out_ch=1, t=2): + super(R2AttU_Net, self).__init__() + + n1 = 64 + filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] + + self.Maxpool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool3 = nn.MaxPool2d(kernel_size=2, stride=2) + self.Maxpool4 = nn.MaxPool2d(kernel_size=2, stride=2) + + self.RRCNN1 = RRCNN_block(in_ch, filters[0], t=t) + self.RRCNN2 = RRCNN_block(filters[0], filters[1], t=t) + self.RRCNN3 = RRCNN_block(filters[1], filters[2], t=t) + self.RRCNN4 = RRCNN_block(filters[2], filters[3], t=t) + self.RRCNN5 = RRCNN_block(filters[3], filters[4], t=t) + + self.Up5 = up_conv(filters[4], filters[3]) + self.Att5 = Attention_block(F_g=filters[3], F_l=filters[3], F_int=filters[2]) + self.Up_RRCNN5 = RRCNN_block(filters[4], filters[3], t=t) + + self.Up4 = up_conv(filters[3], filters[2]) + self.Att4 = Attention_block(F_g=filters[2], F_l=filters[2], F_int=filters[1]) + self.Up_RRCNN4 = RRCNN_block(filters[3], filters[2], t=t) + + self.Up3 = up_conv(filters[2], filters[1]) + self.Att3 = Attention_block(F_g=filters[1], F_l=filters[1], F_int=filters[0]) + self.Up_RRCNN3 = RRCNN_block(filters[2], filters[1], t=t) + + self.Up2 = up_conv(filters[1], filters[0]) + self.Att2 = Attention_block(F_g=filters[0], F_l=filters[0], F_int=32) + self.Up_RRCNN2 = RRCNN_block(filters[1], filters[0], t=t) + + self.Conv = nn.Conv2d(filters[0], out_ch, kernel_size=1, stride=1, padding=0) + + # self.active = torch.nn.Sigmoid() + + def forward(self, x): + + e1 = self.RRCNN1(x) + + e2 = self.Maxpool1(e1) + e2 = self.RRCNN2(e2) + + e3 = self.Maxpool2(e2) + e3 = self.RRCNN3(e3) + + e4 = self.Maxpool3(e3) + e4 = self.RRCNN4(e4) + + e5 = self.Maxpool4(e4) + e5 = self.RRCNN5(e5) + + d5 = self.Up5(e5) + e4 = self.Att5(g=d5, x=e4) + d5 = torch.cat((e4, d5), dim=1) + d5 = self.Up_RRCNN5(d5) + + d4 = self.Up4(d5) + e3 = self.Att4(g=d4, x=e3) + d4 = torch.cat((e3, d4), dim=1) + d4 = self.Up_RRCNN4(d4) + + d3 = self.Up3(d4) + e2 = self.Att3(g=d3, x=e2) + d3 = torch.cat((e2, d3), dim=1) + d3 = self.Up_RRCNN3(d3) + + d2 = self.Up2(d3) + e1 = self.Att2(g=d2, x=e1) + d2 = torch.cat((e1, d2), dim=1) + d2 = self.Up_RRCNN2(d2) + + out = self.Conv(d2) + + # out = self.active(out) + + return out + + +# For nested 3 channels are required + + +class conv_block_nested(nn.Module): + def __init__(self, in_ch, mid_ch, out_ch): + super(conv_block_nested, self).__init__() + self.activation = nn.ReLU(inplace=True) + self.conv1 = nn.Conv2d(in_ch, mid_ch, kernel_size=3, padding=1, bias=True) + self.bn1 = nn.BatchNorm2d(mid_ch) + self.conv2 = nn.Conv2d(mid_ch, out_ch, kernel_size=3, padding=1, bias=True) + self.bn2 = nn.BatchNorm2d(out_ch) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.activation(x) + + x = self.conv2(x) + x = self.bn2(x) + output = self.activation(x) + + return output + + +# Nested Unet + + +class NestedUNet(nn.Module): + """ + Implementation of this paper: + https://arxiv.org/pdf/1807.10165.pdf + """ + + def __init__(self, in_ch=3, out_ch=1): + super(NestedUNet, self).__init__() + + n1 = 64 + filters = [n1, n1 * 2, n1 * 4, n1 * 8, n1 * 16] + + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + self.Up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True) + + self.conv0_0 = conv_block_nested(in_ch, filters[0], filters[0]) + self.conv1_0 = conv_block_nested(filters[0], filters[1], filters[1]) + self.conv2_0 = conv_block_nested(filters[1], filters[2], filters[2]) + self.conv3_0 = conv_block_nested(filters[2], filters[3], filters[3]) + self.conv4_0 = conv_block_nested(filters[3], filters[4], filters[4]) + + self.conv0_1 = conv_block_nested( + filters[0] + filters[1], filters[0], filters[0] + ) + self.conv1_1 = conv_block_nested( + filters[1] + filters[2], filters[1], filters[1] + ) + self.conv2_1 = conv_block_nested( + filters[2] + filters[3], filters[2], filters[2] + ) + self.conv3_1 = conv_block_nested( + filters[3] + filters[4], filters[3], filters[3] + ) + + self.conv0_2 = conv_block_nested( + filters[0] * 2 + filters[1], filters[0], filters[0] + ) + self.conv1_2 = conv_block_nested( + filters[1] * 2 + filters[2], filters[1], filters[1] + ) + self.conv2_2 = conv_block_nested( + filters[2] * 2 + filters[3], filters[2], filters[2] + ) + + self.conv0_3 = conv_block_nested( + filters[0] * 3 + filters[1], filters[0], filters[0] + ) + self.conv1_3 = conv_block_nested( + filters[1] * 3 + filters[2], filters[1], filters[1] + ) + + self.conv0_4 = conv_block_nested( + filters[0] * 4 + filters[1], filters[0], filters[0] + ) + + self.final = nn.Conv2d(filters[0], out_ch, kernel_size=1) + + def forward(self, x): + + x0_0 = self.conv0_0(x) + x1_0 = self.conv1_0(self.pool(x0_0)) + x0_1 = self.conv0_1(torch.cat([x0_0, self.Up(x1_0)], 1)) + + x2_0 = self.conv2_0(self.pool(x1_0)) + x1_1 = self.conv1_1(torch.cat([x1_0, self.Up(x2_0)], 1)) + x0_2 = self.conv0_2(torch.cat([x0_0, x0_1, self.Up(x1_1)], 1)) + + x3_0 = self.conv3_0(self.pool(x2_0)) + x2_1 = self.conv2_1(torch.cat([x2_0, self.Up(x3_0)], 1)) + x1_2 = self.conv1_2(torch.cat([x1_0, x1_1, self.Up(x2_1)], 1)) + x0_3 = self.conv0_3(torch.cat([x0_0, x0_1, x0_2, self.Up(x1_2)], 1)) + + x4_0 = self.conv4_0(self.pool(x3_0)) + x3_1 = self.conv3_1(torch.cat([x3_0, self.Up(x4_0)], 1)) + x2_2 = self.conv2_2(torch.cat([x2_0, x2_1, self.Up(x3_1)], 1)) + x1_3 = self.conv1_3(torch.cat([x1_0, x1_1, x1_2, self.Up(x2_2)], 1)) + x0_4 = self.conv0_4(torch.cat([x0_0, x0_1, x0_2, x0_3, self.Up(x1_3)], 1)) + + output = self.final(x0_4) + return output + + +# Dictioary Unet +# if required for getting the filters and model parameters for each step + + +class ConvolutionBlock(nn.Module): + """Convolution block""" + + def __init__( + self, in_filters, out_filters, kernel_size=3, batchnorm=True, last_active=F.relu + ): + super(ConvolutionBlock, self).__init__() + + self.bn = batchnorm + self.last_active = last_active + self.c1 = nn.Conv2d(in_filters, out_filters, kernel_size, padding=1) + self.b1 = nn.BatchNorm2d(out_filters) + self.c2 = nn.Conv2d(out_filters, out_filters, kernel_size, padding=1) + self.b2 = nn.BatchNorm2d(out_filters) + + def forward(self, x): + x = self.c1(x) + if self.bn: + x = self.b1(x) + x = F.relu(x) + x = self.c2(x) + if self.bn: + x = self.b2(x) + x = self.last_active(x) + return x + + +class ContractiveBlock(nn.Module): + """Deconvuling Block""" + + def __init__( + self, + in_filters, + out_filters, + conv_kern=3, + pool_kern=2, + dropout=0.5, + batchnorm=True, + ): + super(ContractiveBlock, self).__init__() + self.c1 = ConvolutionBlock( + in_filters=in_filters, + out_filters=out_filters, + kernel_size=conv_kern, + batchnorm=batchnorm, + ) + self.p1 = nn.MaxPool2d(kernel_size=pool_kern, ceil_mode=True) + self.d1 = nn.Dropout2d(dropout) + + def forward(self, x): + c = self.c1(x) + return c, self.d1(self.p1(c)) + + +class ExpansiveBlock(nn.Module): + """Upconvole Block""" + + def __init__( + self, + in_filters1, + in_filters2, + out_filters, + tr_kern=3, + conv_kern=3, + stride=2, + dropout=0.5, + ): + super(ExpansiveBlock, self).__init__() + self.t1 = nn.ConvTranspose2d( + in_filters1, out_filters, tr_kern, stride=2, padding=1, output_padding=1 + ) + self.d1 = nn.Dropout(dropout) + self.c1 = ConvolutionBlock(out_filters + in_filters2, out_filters, conv_kern) + + def forward(self, x, contractive_x): + x_ups = self.t1(x) + x_concat = torch.cat([x_ups, contractive_x], 1) + x_fin = self.c1(self.d1(x_concat)) + return x_fin + + +class Unet_dict(nn.Module): + """Unet which operates with filters dictionary values""" + + def __init__(self, n_labels, n_filters=32, p_dropout=0.5, batchnorm=True): + super(Unet_dict, self).__init__() + filters_dict = {} + filt_pair = [3, n_filters] + + for i in range(4): + self.add_module( + "contractive_" + str(i), + ContractiveBlock(filt_pair[0], filt_pair[1], batchnorm=batchnorm), + ) + filters_dict["contractive_" + str(i)] = (filt_pair[0], filt_pair[1]) + filt_pair[0] = filt_pair[1] + filt_pair[1] = filt_pair[1] * 2 + + self.bottleneck = ConvolutionBlock( + filt_pair[0], filt_pair[1], batchnorm=batchnorm + ) + filters_dict["bottleneck"] = (filt_pair[0], filt_pair[1]) + + for i in reversed(range(4)): + self.add_module( + "expansive_" + str(i), + ExpansiveBlock( + filt_pair[1], filters_dict["contractive_" + str(i)][1], filt_pair[0] + ), + ) + filters_dict["expansive_" + str(i)] = (filt_pair[1], filt_pair[0]) + filt_pair[1] = filt_pair[0] + filt_pair[0] = filt_pair[0] // 2 + + self.output = nn.Conv2d(filt_pair[1], n_labels, kernel_size=1) + filters_dict["output"] = (filt_pair[1], n_labels) + self.filters_dict = filters_dict + + # final_forward + def forward(self, x): + c00, c0 = self.contractive_0(x) + c11, c1 = self.contractive_1(c0) + c22, c2 = self.contractive_2(c1) + c33, c3 = self.contractive_3(c2) + bottle = self.bottleneck(c3) + u3 = F.relu(self.expansive_3(bottle, c33)) + u2 = F.relu(self.expansive_2(u3, c22)) + u1 = F.relu(self.expansive_1(u2, c11)) + u0 = F.relu(self.expansive_0(u1, c00)) + return F.softmax(self.output(u0), dim=1) + + +# Need to check why this Unet is not workin properly +# +# class Convolution2(nn.Module): +# """Convolution Block using 2 Conv2D +# Args: +# in_channels = Input Channels +# out_channels = Output Channels +# kernal_size = 3 +# activation = Relu +# batchnorm = True +# +# Output: +# Sequential Relu output """ +# +# def __init__(self, in_channels, out_channels, kernal_size=3, activation='Relu', batchnorm=True): +# super(Convolution2, self).__init__() +# +# self.in_channels = in_channels +# self.out_channels = out_channels +# self.kernal_size = kernal_size +# self.batchnorm1 = batchnorm +# +# self.batchnorm2 = batchnorm +# self.activation = activation +# +# self.conv1 = nn.Conv2d(self.in_channels, self.out_channels, self.kernal_size, padding=1, bias=True) +# self.conv2 = nn.Conv2d(self.out_channels, self.out_channels, self.kernal_size, padding=1, bias=True) +# +# self.b1 = nn.BatchNorm2d(out_channels) +# self.b2 = nn.BatchNorm2d(out_channels) +# +# if self.activation == 'LRelu': +# self.a1 = nn.LeakyReLU(inplace=True) +# if self.activation == 'Relu': +# self.a1 = nn.ReLU(inplace=True) +# +# if self.activation == 'LRelu': +# self.a2 = nn.LeakyReLU(inplace=True) +# if self.activation == 'Relu': +# self.a2 = nn.ReLU(inplace=True) +# +# def forward(self, x): +# x1 = self.conv1(x) +# +# if self.batchnorm1: +# x1 = self.b1(x1) +# +# x1 = self.a1(x1) +# +# x1 = self.conv2(x1) +# +# if self.batchnorm2: +# x1 = self.b1(x1) +# +# x = self.a2(x1) +# +# return x +# +# +# class UNet(nn.Module): +# """Implementation of U-Net: Convolutional Networks for Biomedical Image Segmentation (Ronneberger et al., 2015) +# https://arxiv.org/abs/1505.04597 +# Args: +# n_class = no. of classes""" +# +# def __init__(self, n_class, dropout=0.4): +# super(UNet, self).__init__() +# +# in_ch = 3 +# n1 = 64 +# n2 = n1*2 +# n3 = n2*2 +# n4 = n3*2 +# n5 = n4*2 +# +# self.dconv_down1 = Convolution2(in_ch, n1) +# self.dconv_down2 = Convolution2(n1, n2) +# self.dconv_down3 = Convolution2(n2, n3) +# self.dconv_down4 = Convolution2(n3, n4) +# self.dconv_down5 = Convolution2(n4, n5) +# +# self.maxpool1 = nn.MaxPool2d(2) +# self.maxpool2 = nn.MaxPool2d(2) +# self.maxpool3 = nn.MaxPool2d(2) +# self.maxpool4 = nn.MaxPool2d(2) +# +# self.upsample1 = nn.Upsample(scale_factor=2)#, mode='bilinear', align_corners=True) +# self.upsample2 = nn.Upsample(scale_factor=2)#, mode='bilinear', align_corners=True) +# self.upsample3 = nn.Upsample(scale_factor=2)#, mode='bilinear', align_corners=True) +# self.upsample4 = nn.Upsample(scale_factor=2)#, mode='bilinear', align_corners=True) +# +# self.dropout1 = nn.Dropout(dropout) +# self.dropout2 = nn.Dropout(dropout) +# self.dropout3 = nn.Dropout(dropout) +# self.dropout4 = nn.Dropout(dropout) +# self.dropout5 = nn.Dropout(dropout) +# self.dropout6 = nn.Dropout(dropout) +# self.dropout7 = nn.Dropout(dropout) +# self.dropout8 = nn.Dropout(dropout) +# +# self.dconv_up4 = Convolution2(n4 + n5, n4) +# self.dconv_up3 = Convolution2(n3 + n4, n3) +# self.dconv_up2 = Convolution2(n2 + n3, n2) +# self.dconv_up1 = Convolution2(n1 + n2, n1) +# +# self.conv_last = nn.Conv2d(n1, n_class, kernel_size=1, stride=1, padding=0) +# # self.active = torch.nn.Sigmoid() +# +# +# +# def forward(self, x): +# conv1 = self.dconv_down1(x) +# x = self.maxpool1(conv1) +# # x = self.dropout1(x) +# +# conv2 = self.dconv_down2(x) +# x = self.maxpool2(conv2) +# # x = self.dropout2(x) +# +# conv3 = self.dconv_down3(x) +# x = self.maxpool3(conv3) +# # x = self.dropout3(x) +# +# conv4 = self.dconv_down4(x) +# x = self.maxpool4(conv4) +# #x = self.dropout4(x) +# +# x = self.dconv_down5(x) +# +# x = self.upsample4(x) +# x = torch.cat((x, conv4), dim=1) +# #x = self.dropout5(x) +# +# x = self.dconv_up4(x) +# x = self.upsample3(x) +# x = torch.cat((x, conv3), dim=1) +# # x = self.dropout6(x) +# +# x = self.dconv_up3(x) +# x = self.upsample2(x) +# x = torch.cat((x, conv2), dim=1) +# #x = self.dropout7(x) +# +# x = self.dconv_up2(x) +# x = self.upsample1(x) +# x = torch.cat((x, conv1), dim=1) +# #x = self.dropout8(x) +# +# x = self.dconv_up1(x) +# +# x = self.conv_last(x) +# # out = self.active(x) +# +# return x diff --git a/algorithm/detect_emotion/rmn/models/segmentation/__init__.py b/algorithm/detect_emotion/rmn/models/segmentation/__init__.py new file mode 100644 index 0000000..ac40ae8 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/segmentation/__init__.py @@ -0,0 +1,4 @@ +from .segmentation import * +from .fcn import * +from .deeplabv3 import * +from .unet_basic import * diff --git a/algorithm/detect_emotion/rmn/models/segmentation/_utils.py b/algorithm/detect_emotion/rmn/models/segmentation/_utils.py new file mode 100644 index 0000000..acc60bb --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/segmentation/_utils.py @@ -0,0 +1,32 @@ +from collections import OrderedDict + +import torch +from torch import nn +from torch.nn import functional as F + + +class _SimpleSegmentationModel(nn.Module): + def __init__(self, backbone, classifier, aux_classifier=None): + super(_SimpleSegmentationModel, self).__init__() + self.backbone = backbone + self.classifier = classifier + self.aux_classifier = aux_classifier + + def forward(self, x): + input_shape = x.shape[-2:] + # contract: features is a dict of tensors + features = self.backbone(x) + + result = OrderedDict() + x = features["out"] + x = self.classifier(x) + x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False) + result["out"] = x + + if self.aux_classifier is not None: + x = features["aux"] + x = self.aux_classifier(x) + x = F.interpolate(x, size=input_shape, mode="bilinear", align_corners=False) + result["aux"] = x + + return result diff --git a/algorithm/detect_emotion/rmn/models/segmentation/deeplabv3.py b/algorithm/detect_emotion/rmn/models/segmentation/deeplabv3.py new file mode 100644 index 0000000..3473d62 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/segmentation/deeplabv3.py @@ -0,0 +1,106 @@ +import torch +from torch import nn +from torch.nn import functional as F + +from ._utils import _SimpleSegmentationModel + + +__all__ = ["DeepLabV3"] + + +class DeepLabV3(_SimpleSegmentationModel): + """ + Implements DeepLabV3 model from + `"Rethinking Atrous Convolution for Semantic Image Segmentation" + `_. + + Arguments: + backbone (nn.Module): the network used to compute the features for the model. + The backbone should return an OrderedDict[Tensor], with the key being + "out" for the last feature map used, and "aux" if an auxiliary classifier + is used. + classifier (nn.Module): module that takes the "out" element returned from + the backbone and returns a dense prediction. + aux_classifier (nn.Module, optional): auxiliary classifier used during training + """ + + pass + + +class DeepLabHead(nn.Sequential): + def __init__(self, in_channels, num_classes): + super(DeepLabHead, self).__init__( + ASPP(in_channels, [12, 24, 36]), + nn.Conv2d(256, 256, 3, padding=1, bias=False), + nn.BatchNorm2d(256), + nn.ReLU(), + nn.Conv2d(256, num_classes, 1), + ) + + +class ASPPConv(nn.Sequential): + def __init__(self, in_channels, out_channels, dilation): + modules = [ + nn.Conv2d( + in_channels, + out_channels, + 3, + padding=dilation, + dilation=dilation, + bias=False, + ), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + ] + super(ASPPConv, self).__init__(*modules) + + +class ASPPPooling(nn.Sequential): + def __init__(self, in_channels, out_channels): + super(ASPPPooling, self).__init__( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(in_channels, out_channels, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + ) + + def forward(self, x): + size = x.shape[-2:] + x = super(ASPPPooling, self).forward(x) + return F.interpolate(x, size=size, mode="bilinear", align_corners=False) + + +class ASPP(nn.Module): + def __init__(self, in_channels, atrous_rates): + super(ASPP, self).__init__() + out_channels = 256 + modules = [] + modules.append( + nn.Sequential( + nn.Conv2d(in_channels, out_channels, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + ) + ) + + rate1, rate2, rate3 = tuple(atrous_rates) + modules.append(ASPPConv(in_channels, out_channels, rate1)) + modules.append(ASPPConv(in_channels, out_channels, rate2)) + modules.append(ASPPConv(in_channels, out_channels, rate3)) + modules.append(ASPPPooling(in_channels, out_channels)) + + self.convs = nn.ModuleList(modules) + + self.project = nn.Sequential( + nn.Conv2d(5 * out_channels, out_channels, 1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(), + nn.Dropout(0.5), + ) + + def forward(self, x): + res = [] + for conv in self.convs: + res.append(conv(x)) + res = torch.cat(res, dim=1) + return self.project(res) diff --git a/algorithm/detect_emotion/rmn/models/segmentation/fcn.py b/algorithm/detect_emotion/rmn/models/segmentation/fcn.py new file mode 100644 index 0000000..5fce0b8 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/segmentation/fcn.py @@ -0,0 +1,37 @@ +from torch import nn + +from ._utils import _SimpleSegmentationModel + + +__all__ = ["FCN"] + + +class FCN(_SimpleSegmentationModel): + """ + Implements a Fully-Convolutional Network for semantic segmentation. + + Arguments: + backbone (nn.Module): the network used to compute the features for the model. + The backbone should return an OrderedDict[Tensor], with the key being + "out" for the last feature map used, and "aux" if an auxiliary classifier + is used. + classifier (nn.Module): module that takes the "out" element returned from + the backbone and returns a dense prediction. + aux_classifier (nn.Module, optional): auxiliary classifier used during training + """ + + pass + + +class FCNHead(nn.Sequential): + def __init__(self, in_channels, channels): + inter_channels = in_channels // 4 + layers = [ + nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), + nn.BatchNorm2d(inter_channels), + nn.ReLU(), + nn.Dropout(0.1), + nn.Conv2d(inter_channels, channels, 1), + ] + + super(FCNHead, self).__init__(*layers) diff --git a/algorithm/detect_emotion/rmn/models/segmentation/segmentation.py b/algorithm/detect_emotion/rmn/models/segmentation/segmentation.py new file mode 100644 index 0000000..d09f27c --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/segmentation/segmentation.py @@ -0,0 +1,127 @@ +from .._utils import IntermediateLayerGetter +from ..utils import load_state_dict_from_url +from .. import resnet +from .deeplabv3 import DeepLabHead, DeepLabV3 +from .fcn import FCN, FCNHead + + +__all__ = ["fcn_resnet50", "fcn_resnet101", "deeplabv3_resnet50", "deeplabv3_resnet101"] + + +model_urls = { + "fcn_resnet50_coco": None, + "fcn_resnet101_coco": "https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth", + "deeplabv3_resnet50_coco": None, + "deeplabv3_resnet101_coco": "https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth", +} + + +def _segm_resnet(name, backbone_name, num_classes, aux, pretrained_backbone=True): + backbone = resnet.__dict__[backbone_name]( + pretrained=pretrained_backbone, replace_stride_with_dilation=[False, True, True] + ) + + return_layers = {"layer4": "out"} + if aux: + return_layers["layer3"] = "aux" + backbone = IntermediateLayerGetter(backbone, return_layers=return_layers) + + aux_classifier = None + if aux: + inplanes = 1024 + aux_classifier = FCNHead(inplanes, num_classes) + + model_map = { + "deeplabv3": (DeepLabHead, DeepLabV3), + "fcn": (FCNHead, FCN), + } + inplanes = 2048 + classifier = model_map[name][0](inplanes, num_classes) + base_model = model_map[name][1] + + model = base_model(backbone, classifier, aux_classifier) + return model + + +def _load_model( + arch_type, backbone, pretrained, progress, num_classes, aux_loss, **kwargs +): + if pretrained: + aux_loss = True + model = _segm_resnet(arch_type, backbone, num_classes, aux_loss, **kwargs) + if pretrained: + arch = arch_type + "_" + backbone + "_coco" + model_url = model_urls[arch] + if model_url is None: + raise NotImplementedError( + "pretrained {} is not supported as of now".format(arch) + ) + else: + state_dict = load_state_dict_from_url(model_url, progress=progress) + model.load_state_dict(state_dict) + return model + + +def fcn_resnet50( + pretrained=False, progress=True, num_classes=21, aux_loss=None, **kwargs +): + """Constructs a Fully-Convolutional Network model with a ResNet-50 backbone. + + Args: + pretrained (bool): If True, returns a model pre-trained on COCO train2017 which + contains the same classes as Pascal VOC + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _load_model( + "fcn", "resnet50", pretrained, progress, num_classes, aux_loss, **kwargs + ) + + +def fcn_resnet101( + pretrained=False, progress=True, num_classes=21, aux_loss=None, **kwargs +): + """Constructs a Fully-Convolutional Network model with a ResNet-101 backbone. + + Args: + pretrained (bool): If True, returns a model pre-trained on COCO train2017 which + contains the same classes as Pascal VOC + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _load_model( + "fcn", "resnet101", pretrained, progress, num_classes, aux_loss, **kwargs + ) + + +def deeplabv3_resnet50( + in_channels=3, + pretrained=False, + progress=True, + num_classes=21, + aux_loss=None, + **kwargs +): + """Constructs a DeepLabV3 model with a ResNet-50 backbone. + + Args: + pretrained (bool): If True, returns a model pre-trained on COCO train2017 which + contains the same classes as Pascal VOC + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _load_model( + "deeplabv3", "resnet50", pretrained, progress, num_classes, aux_loss, **kwargs + ) + + +def deeplabv3_resnet101( + pretrained=False, progress=True, num_classes=21, aux_loss=None, **kwargs +): + """Constructs a DeepLabV3 model with a ResNet-101 backbone. + + Args: + pretrained (bool): If True, returns a model pre-trained on COCO train2017 which + contains the same classes as Pascal VOC + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _load_model( + "deeplabv3", "resnet101", pretrained, progress, num_classes, aux_loss, **kwargs + ) diff --git a/algorithm/detect_emotion/rmn/models/segmentation/unet_basic.py b/algorithm/detect_emotion/rmn/models/segmentation/unet_basic.py new file mode 100644 index 0000000..e6bcbd1 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/segmentation/unet_basic.py @@ -0,0 +1,105 @@ +"""no need residual :)""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def block(in_channels, out_channels, kernel_size=3, stride=1, padding=1): + return nn.Sequential( + nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + ), + nn.BatchNorm2d(num_features=out_channels), + nn.ReLU(inplace=True), + nn.Conv2d( + out_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + ), + nn.BatchNorm2d(num_features=out_channels), + nn.ReLU(inplace=True), + ) + + +def up_pooling(in_channels, out_channels, kernel_size=2, stride=2): + return nn.Sequential( + nn.ConvTranspose2d( + in_channels, out_channels, kernel_size=kernel_size, stride=stride + ), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ) + + +class Unet(nn.Module): + def __init__(self, in_channels, num_classes): + super().__init__() + + _filters = [64, 128, 256, 512, 1024] + filters = [32, 64, 128, 256, 512] + _filters = [16, 32, 64, 128, 256] + + self.conv1 = block(in_channels, filters[0]) + self.conv2 = block(filters[0], filters[1]) + self.conv3 = block(filters[1], filters[2]) + self.conv4 = block(filters[2], filters[3]) + self.conv5 = block(filters[3], filters[4]) + self.down_pooling = nn.MaxPool2d(2) + + self.up_pool6 = up_pooling(filters[4], filters[3]) + self.conv6 = block(filters[4], filters[3]) + self.up_pool7 = up_pooling(filters[3], filters[2]) + self.conv7 = block(filters[3], filters[2]) + self.up_pool8 = up_pooling(filters[2], filters[1]) + self.conv8 = block(filters[2], filters[1]) + self.up_pool9 = up_pooling(filters[1], filters[0]) + self.conv9 = block(filters[1], filters[0]) + + self.conv10 = nn.Conv2d(filters[0], num_classes, 1) + + # default xavier init + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.Linear)): + nn.init.xavier_uniform(m.weight) + + def forward(self, x): + x1 = self.conv1(x) + p1 = self.down_pooling(x1) + x2 = self.conv2(p1) + p2 = self.down_pooling(x2) + x3 = self.conv3(p2) + p3 = self.down_pooling(x3) + x4 = self.conv4(p3) + p4 = self.down_pooling(x4) + x5 = self.conv5(p4) + + # go up + p6 = self.up_pool6(x5) + x6 = torch.cat([p6, x4], dim=1) + x6 = self.conv6(x6) + + p7 = self.up_pool7(x6) + x7 = torch.cat([p7, x3], dim=1) + x7 = self.conv7(x7) + + p8 = self.up_pool8(x7) + x8 = torch.cat([p8, x2], dim=1) + x8 = self.conv8(x8) + + p9 = self.up_pool9(x8) + x9 = torch.cat([p9, x1], dim=1) + x9 = self.conv9(x9) + + output = self.conv10(x9) + output = torch.softmax(output, dim=1) + return output + + +def basic_unet(in_channels, num_classes): + return Unet(in_channels, num_classes) diff --git a/algorithm/detect_emotion/rmn/models/tmp.py b/algorithm/detect_emotion/rmn/models/tmp.py new file mode 100644 index 0000000..5b37669 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/tmp.py @@ -0,0 +1,19 @@ +import numpy as np +import torch +from torchvision import transforms + + +image = np.random.rand(48, 48, 1) +image = image * 255 +image = image.astype(np.uint8) + +transform = transforms.Compose([transforms.ToPILImage(), transforms.ToTensor()]) + +tensor = transform(image) +tensor = torch.unsqueeze(tensor, 0) + + +from fer2013_models import BaseNet + +model = BaseNet() +print(model(tensor)) diff --git a/algorithm/detect_emotion/rmn/models/utils.py b/algorithm/detect_emotion/rmn/models/utils.py new file mode 100644 index 0000000..638ef07 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/utils.py @@ -0,0 +1,4 @@ +try: + from torch.hub import load_state_dict_from_url +except ImportError: + from torch.utils.model_zoo import load_url as load_state_dict_from_url diff --git a/algorithm/detect_emotion/rmn/models/vgg.py b/algorithm/detect_emotion/rmn/models/vgg.py new file mode 100644 index 0000000..df2ad39 --- /dev/null +++ b/algorithm/detect_emotion/rmn/models/vgg.py @@ -0,0 +1,251 @@ +import torch +import torch.nn as nn +from .utils import load_state_dict_from_url + + +__all__ = [ + "VGG", + "vgg11", + "vgg11_bn", + "vgg13", + "vgg13_bn", + "vgg16", + "vgg16_bn", + "vgg19_bn", + "vgg19", +] + + +model_urls = { + "vgg11": "https://download.pytorch.org/models/vgg11-bbd30ac9.pth", + "vgg13": "https://download.pytorch.org/models/vgg13-c768596a.pth", + "vgg16": "https://download.pytorch.org/models/vgg16-397923af.pth", + "vgg19": "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth", + "vgg11_bn": "https://download.pytorch.org/models/vgg11_bn-6002323d.pth", + "vgg13_bn": "https://download.pytorch.org/models/vgg13_bn-abd245e5.pth", + "vgg16_bn": "https://download.pytorch.org/models/vgg16_bn-6c64b313.pth", + "vgg19_bn": "https://download.pytorch.org/models/vgg19_bn-c79401a0.pth", +} + + +class VGG(nn.Module): + def __init__(self, features, in_channels=3, num_classes=1000, init_weights=True): + super(VGG, self).__init__() + self.features = features + self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) + self.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 1000), + ) + if init_weights: + self._initialize_weights() + + def forward(self, x): + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + +def make_layers(cfg, batch_norm=False, **kwargs): + layers = [] + # in_channels = 3 + in_channels = kwargs["in_channels"] + for v in cfg: + if v == "M": + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) + if batch_norm: + layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] + else: + layers += [conv2d, nn.ReLU(inplace=True)] + in_channels = v + return nn.Sequential(*layers) + + +cfgs = { + "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], + "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], + "D": [ + 64, + 64, + "M", + 128, + 128, + "M", + 256, + 256, + 256, + "M", + 512, + 512, + 512, + "M", + 512, + 512, + 512, + "M", + ], + "E": [ + 64, + 64, + "M", + 128, + 128, + "M", + 256, + 256, + 256, + 256, + "M", + 512, + 512, + 512, + 512, + "M", + 512, + 512, + 512, + 512, + "M", + ], +} + + +def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs): + if pretrained: + kwargs["init_weights"] = False + model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm, **kwargs), **kwargs) + if pretrained: + state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) + model.load_state_dict(state_dict) + return model + + +def vgg11(pretrained=False, progress=True, **kwargs): + r"""VGG 11-layer model (configuration "A") from + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg("vgg11", "A", False, pretrained, progress, **kwargs) + + +def vgg11_bn(pretrained=False, progress=True, **kwargs): + r"""VGG 11-layer model (configuration "A") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg("vgg11_bn", "A", True, pretrained, progress, **kwargs) + + +def vgg13(pretrained=False, progress=True, **kwargs): + r"""VGG 13-layer model (configuration "B") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg("vgg13", "B", False, pretrained, progress, **kwargs) + + +def vgg13_bn(pretrained=False, progress=True, **kwargs): + r"""VGG 13-layer model (configuration "B") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg("vgg13_bn", "B", True, pretrained, progress, **kwargs) + + +def vgg16(pretrained=False, progress=True, **kwargs): + r"""VGG 16-layer model (configuration "D") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg("vgg16", "D", False, pretrained, progress, **kwargs) + + +def vgg16_bn(pretrained=False, progress=True, **kwargs): + r"""VGG 16-layer model (configuration "D") with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + return _vgg("vgg16_bn", "D", True, pretrained, progress, **kwargs) + + +def vgg19(pretrained=True, progress=True, **kwargs): + r"""VGG 19-layer model (configuration "E") + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + model = _vgg("vgg19", "E", False, pretrained, progress, **kwargs) + model.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 7), + ) + + return model + + +def vgg19_bn(pretrained=True, progress=True, **kwargs): + r"""VGG 19-layer model (configuration 'E') with batch normalization + `"Very Deep Convolutional Networks For Large-Scale Image Recognition" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + """ + model = _vgg("vgg19_bn", "E", True, pretrained, progress, **kwargs) + model.classifier = nn.Sequential( + nn.Linear(512 * 7 * 7, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(True), + nn.Dropout(), + nn.Linear(4096, 7), + ) + return model diff --git a/algorithm/detect_emotion/rmn/version.py b/algorithm/detect_emotion/rmn/version.py new file mode 100644 index 0000000..9efc5c6 --- /dev/null +++ b/algorithm/detect_emotion/rmn/version.py @@ -0,0 +1 @@ +__version__ = '3.0.10' diff --git a/algorithm/drowsy_detection.py b/algorithm/drowsy_detection.py new file mode 100644 index 0000000..7d81800 --- /dev/null +++ b/algorithm/drowsy_detection.py @@ -0,0 +1,87 @@ +import time +from pathlib import Path +import datetime + +import cv2 +import numpy as np +import torch +import torch.backends.cudnn as cudnn + + +from read_data import LoadImages, LoadStreams + + +class DrowsyDetection(): + + + def __init__(self, video_path=None, model=None): + self.model = model + self.classes = self.model.names + self.imgsz = 640 + self.stride = self.model.stride + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.dataset = LoadImages(self.video_name, img_size=self.imgsz, stride = self.stride) + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + self.source = source + cudnn.benchmark = True + # self.dataset = LoadStreams(source, img_size=self.imgsz) + self.dataset = LoadStreams(source) + + def class_to_label(self, x): + return self.classes[int(x)] + + def get_frame(self): + + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + results = self.model(img, size=640) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + accuracy = 0 + num_people = 0 + bgr = (0, 255, 0) + + + for obj in results.xyxy[0]: + + xmin, ymin, xmax, ymax = map(int, obj[:4]) + + accuracy = obj[4] + + c = int(obj[-1]) + + + if self.classes[c] == 'normal': + color = (255, 200, 90) + elif self.classes[c] == 'drowsy': + color = (0, 0, 255) + elif self.classes[c] == 'drowsy#2': + color = (0, 0, 255) + elif self.classes[c] == 'yawning': + color = (51, 255, 255) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2) + cv2.putText(img, f"{self.classes[c]}, {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + ret, jpeg = cv2.imencode(".jpg", img) + # print(num_people) + return jpeg.tobytes(), '' diff --git a/algorithm/easyocr.py b/algorithm/easyocr.py new file mode 100644 index 0000000..ce675bd --- /dev/null +++ b/algorithm/easyocr.py @@ -0,0 +1,82 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn +import easyocr +from tools.draw_chinese import cv2ImgAddText + +class OCR(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = easyocr.Reader(['ch_sim','en'], gpu=True, model_storage_directory="weight/ocr/",download_enabled=False) # this needs to run only once to load the model into memory + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + result = self.model.readtext(img, detail = 0) + + + img = cv2ImgAddText(img, + f'识别结果: {result}', + 10, + 10, + (0, 250, 0), + 20,) + + # cv2.putText(img, f'识别结果: {result}', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + txt = f'{result}' + + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + + return jpeg.tobytes(), txt + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() \ No newline at end of file diff --git a/algorithm/electromobile_detection.py b/algorithm/electromobile_detection.py new file mode 100644 index 0000000..c690c30 --- /dev/null +++ b/algorithm/electromobile_detection.py @@ -0,0 +1,99 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class ElectromobileDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/electromobile.pt', force_reload=True) + self.classes = ["电动车", "摩托车"] + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + results = self.model(img, size=640) + # print(results) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + num_people = 0 + bgr = (0, 255, 0) + + txt = "" + objs = results.xyxy[0] + for c in objs[:,-1].unique(): + n = (objs[:,-1] == c).sum() # detections per class + txt += f"{n} {self.classes[int(c)]}{'s' * (n > 1)}, " # add to string + + + for obj in objs: + if obj[-1] == 0: # 1 is the class ID for '未戴头盔' + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + elif obj[-1] == 1: # 1 is the class ID for '未戴头盔' + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + return jpeg.tobytes(), txt + \ No newline at end of file diff --git a/algorithm/face_detection.py b/algorithm/face_detection.py new file mode 100644 index 0000000..1ec8430 --- /dev/null +++ b/algorithm/face_detection.py @@ -0,0 +1,99 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class FaceDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + global num_people + num_people = 100 + global accuracy + accuracy = 1 + + def __init__(self,video_path=None, model=None): + + self.model = model + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + results = self.model(img, size=640) + + num_people = 0 + bgr = (0, 255, 0) + + for obj in results.xyxy[0]: + + + if obj[-1] == 0: # 0 is the class ID for 'person' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + global accuracy + accuracy = obj[4] + if (accuracy > 0.5): + num_people += 1 + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + #cv2.putText(img, f'FPS: {int(self.cap.get(cv2.CAP_PROP_FPS))}', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + # cv2.putText(img, f'People: {num_people}', (10, 80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + # cv2.putText(img, f'Processed FPS: {VideoPeopleDetection.processed_fps}', (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) + + + # Draw the number of people on the frame and display it + + + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + + return jpeg.tobytes() + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() \ No newline at end of file diff --git a/algorithm/face_recognition/20220711163101.jpg b/algorithm/face_recognition/20220711163101.jpg new file mode 100644 index 0000000..ace7a99 Binary files /dev/null and b/algorithm/face_recognition/20220711163101.jpg differ diff --git a/algorithm/face_recognition/666666.png b/algorithm/face_recognition/666666.png new file mode 100644 index 0000000..ca904f3 Binary files /dev/null and b/algorithm/face_recognition/666666.png differ diff --git a/algorithm/face_recognition/LICENSE b/algorithm/face_recognition/LICENSE new file mode 100644 index 0000000..4dc5353 --- /dev/null +++ b/algorithm/face_recognition/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2018-2021 coneypo + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/algorithm/face_recognition/README.rst b/algorithm/face_recognition/README.rst new file mode 100644 index 0000000..ce36a39 --- /dev/null +++ b/algorithm/face_recognition/README.rst @@ -0,0 +1,269 @@ +Face recognition from camera with Dlib +###################################### + +Introduction +************ + +调用摄像头进行人脸识别, 支持多张人脸同时识别 / Detect and recognize single or multi faces from camera; + +#. Tkinter 人脸录入界面, 支持录入时设置 (中文) 姓名 / Face register GUI with Tkinter, support setting (chinese) name when registering + + .. image:: introduction/face_register_tkinter_GUI.png + :width: 1000 + :align: center + +#. 简单的 OpenCV 摄像头人脸录入界面 / Simple face register GUI with OpenCV, tkinter not needed and cannot set name + + .. image:: introduction/face_register.png + :width: 1000 + :align: center + + 离摄像头过近, 人脸超出摄像头范围时, 会有 "OUT OF RANGE" 提醒 / + Too close to the camera, or face ROI out of camera area, will have "OUT OF RANGE" warning; + + .. image:: introduction/face_register_warning.png + :width: 1000 + :align: center + +#. 提取特征建立人脸数据库 / Generate face database from images captured +#. 利用摄像头进行人脸识别 / Face recognizer + + face_reco_from_camera.py, 对于每一帧都做检测识别 / Do detection and recognition for every frame: + + .. image:: introduction/face_reco.png + :width: 1000 + :align: center + + face_reco_from_camera_single_face.py, 对于人脸<=1, 只有新人脸出现才进行再识别来提高 FPS / + Do re-reco only for new single face: + + .. image:: introduction/face_reco_single.png + :width: 1000 + :align: center + + face_reco_from_camera_ot.py, 利用 OT 来实现再识别提高 FPS / Use OT to instead of re-reco for every frame to improve FPS: + + .. image:: introduction/face_reco_ot.png + :width: 1000 + :align: center + + 定制显示名字, 可以写中文 / Show chinese name: + + .. image:: introduction/face_reco_chinese_name.png + :width: 1000 + :align: center + + +** 关于精度 / About accuracy: + +* When using a distance threshold of ``0.6``, the dlib model obtains an accuracy of ``99.38%`` on the standard LFW face recognition benchmark. + +** 关于算法 / About algorithm + +* 基于 Residual Neural Network / 残差网络的 CNN 模型; + +* This model is a ResNet network with 29 conv layers. +It's essentially a version of the ResNet-34 network from the paper Deep Residual Learning for Image Recognition +by He, Zhang, Ren, and Sun with a few layers removed and the number of filters per layer reduced by half. + +Overview +******** + +此项目中人脸识别的实现流程 (no OT, 每一帧都进行检测+识别) / +Design of this repo, do detection and recognization for every frame: + +.. image:: introduction/overview.png + :width: 1000 + :align: center + +实现流程 (with OT, 初始帧进行检测+识别, 后续帧检测+质心跟踪) / OT used: + +.. image:: introduction/overview_with_ot.png + :width: 1000 + :align: center + +如果利用 OT 来跟踪, 可以大大提高 FPS, 因为做识别时候需要提取特征描述子的耗时很多 / +Use OT can save the time for face descriptor computation to improve FPS; + +Steps +***** + +#. 下载源码 / Git clone source code + + .. code-block:: bash + + git clone https://github.com/coneypo/Dlib_face_recognition_from_camera + +#. 安装依赖库 / Install some python packages needed + + .. code-block:: bash + + pip install -r requirements.txt + +#. 进行人脸信息采集录入, Tkinter GUI / Register faces with Tkinter GUI + + .. code-block:: bash + + # Install Tkinter + sudo apt-get install python3-tk python3-pil python3-pil.imagetk + + python3 get_faces_from_camera_tkinter.py + +#. 进行人脸信息采集录入, OpenCV GUI / Register faces with OpenCV GUI, same with above step + + .. code-block:: bash + + python3 get_face_from_camera.py + +#. 提取所有录入人脸数据存入 ``features_all.csv`` / Features extraction and save into ``features_all.csv`` + + .. code-block:: bash + + python3 features_extraction_to_csv.py + +#. 调用摄像头进行实时人脸识别 / Real-time face recognition + + .. code-block:: bash + + python3 face_reco_from_camera.py + +#. 对于人脸数<=1, 调用摄像头进行实时人脸识别 / Real-time face recognition (Better FPS compared with ``face_reco_from_camera.py``) + + .. code-block:: bash + + python3 face_reco_from_camera_single_face.py + +#. 利用 OT 算法, 调用摄像头进行实时人脸识别 / Real-time face recognition with OT (Better FPS) + + .. code-block:: bash + + python3 face_reco_from_camera_ot.py + +About Source Code +***************** + +代码结构 / Code structure: + +:: + + . + ├── get_faces_from_camera.py # Step 1. Face register GUI with OpenCV + ├── get_faces_from_camera_tkinter.py # Step 1. Face register GUI with Tkinter + ├── features_extraction_to_csv.py # Step 2. Feature extraction + ├── face_reco_from_camera.py # Step 3. Face recognizer + ├── face_reco_from_camera_single_face.py # Step 3. Face recognizer for single person + ├── face_reco_from_camera_ot.py # Step 3. Face recognizer with OT + ├── face_descriptor_from_camera.py # Face descriptor computation + ├── how_to_use_camera.py # Use the default camera by opencv + ├── data + │   ├── data_dlib # Dlib's model + │   │   ├── dlib_face_recognition_resnet_model_v1.dat + │   │   └── shape_predictor_68_face_landmarks.dat + │   ├── data_faces_from_camera # Face images captured from camera (will generate after step 1) + │   │   ├── person_1 + │   │   │   ├── img_face_1.jpg + │   │   │   └── img_face_2.jpg + │   │   └── person_2 + │   │   └── img_face_1.jpg + │   │   └── img_face_2.jpg + │   └── features_all.csv # CSV to save all the features of known faces (will generate after step 2) + ├── README.rst + └── requirements.txt # Some python packages needed + +用到的 Dlib 相关模型函数 / Dlib related functions used in this repo: + +#. Dlib 正向人脸检测器 (based on HOG), output: ```` / Dlib frontal face detector + + + .. code-block:: python + + detector = dlib.get_frontal_face_detector() + faces = detector(img_gray, 0) + +#. Dlib 人脸 landmark 特征点检测器, output: ```` / Dlib face landmark predictor, will use ``shape_predictor_68_face_landmarks.dat`` + + .. code-block:: python + + # This is trained on the ibug 300-W dataset (https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/) + # Also note that this model file is designed for use with dlib's HOG face detector. + # That is, it expects the bounding boxes from the face detector to be aligned a certain way, + the way dlib's HOG face detector does it. + # It won't work as well when used with a face detector that produces differently aligned boxes, + # such as the CNN based mmod_human_face_detector.dat face detector. + + predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_68_face_landmarks.dat") + shape = predictor(img_rd, faces[i]) + + +#. Dlib 特征描述子 / Face recognition model, the object maps human faces into 128D vectors + + + .. code-block:: python + + face_rec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") + + +Python 源码介绍如下 / Source code: + +#. ``get_face_from_camera.py``: + + 人脸信息采集录入 / Face register with OpenCV GUI + + * 请注意存储人脸图片时, 矩形框不要超出摄像头范围, 要不然无法保存到本地; + * 超出会有 "out of range" 的提醒; + + +#. ``get_faces_from_camera_tkinter.py``: + + 进行人脸信息采集录入 Tkinter GUI / Face register with Tkinter GUI + +#. ``features_extraction_to_csv.py``: + + 从上一步存下来的图像文件中, 提取人脸数据存入 CSV / Extract features from face images saved in step 1; + + * 会生成一个存储所有特征人脸数据的 ``features_all.csv`` + * Size: ``n*129`` , n means n faces you registered and 129 means face name + 128D features of this face + +#. ``face_reco_from_camera.py``: + + 这一步将调用摄像头进行实时人脸识别; / This part will implement real-time face recognition; + + * 将捕获到的人脸数据和之前存的人脸数据进行对比计算欧式距离, 由此判断是否是同一个人; + + * Compare the faces captured from camera with the faces you have registered which are saved in ``features_all.csv``; + +#. ``face_reco_from_camera_single_face.py``: + + 针对于人脸数 <=1 的场景, 区别于 ``face_reco_from_camera.py`` (对每一帧都进行检测+识别), 只有人脸出现的时候进行识别; + +#. ``face_reco_from_camera_ot.py``: + + 只会对初始帧做检测+识别, 对后续帧做检测+质心跟踪; + +#. (optional) ``face_descriptor_from_camera.py`` + + 调用摄像头进行实时特征描述子计算; / Real-time face descriptor computation; + +More +**** + +#. 如果希望详细了解 dlib 的用法, 请参考 Dlib 官方 Python api 的网站 / You can refer to this link for more information of how to use dlib: http://dlib.net/python/index.html + +#. Modify log level to ``logging.basicConfig(level=logging.DEBUG)`` to print info for every frame if needed (Default is ``logging.INFO``) + +#. 代码最好不要有中文路径 / No chinese characters in your code directory + +#. 人脸录入的时候先建文件夹再保存图片, 先 ``N`` 再 ``S`` / Press ``N`` before ``S`` + +#. 关于 ``face_reco_from_camera.py`` 人脸识别卡顿 FPS 低问题, 原因是特征描述子提取很费时间; 光跑 ``face_descriptor_from_camera.py`` 中 ``face_reco_model.compute_face_descriptor`` 在我的机器上得到的平均 FPS 在 5 左右 (检测在 ``0.03s`` , 特征描述子提取在 ``0.158s`` , 和已知人脸进行遍历对比在 ``0.003s`` 左右); 所以主要提取特征时候耗资源, 可以用 OT 去做追踪 (使用 ``face_reco_from_camera_ot.py`` ), 而不是对每一帧都做检测+识别, 识别的性能从 20 FPS -> 200 FPS + +可以访问我的博客获取本项目的更详细介绍, 如有问题可以邮件联系我 / +For more details, please visit my blog (in chinese) or send mail to coneypo@foxmail.com: + +* Blog: https://www.cnblogs.com/AdaminXie/p/9010298.html + +* 关于 OT 部分的更新在 Blog: https://www.cnblogs.com/AdaminXie/p/13566269.html + +* Feel free to create issue or contribute PR for it:) + +Thanks for your support. diff --git 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diff --git a/algorithm/face_recognition/face_4.jpg b/algorithm/face_recognition/face_4.jpg new file mode 100644 index 0000000..859d915 Binary files /dev/null and b/algorithm/face_recognition/face_4.jpg differ diff --git a/algorithm/face_recognition/face_5.jpg b/algorithm/face_recognition/face_5.jpg new file mode 100644 index 0000000..abb941f Binary files /dev/null and b/algorithm/face_recognition/face_5.jpg differ diff --git a/algorithm/face_recognition/face_recognition copy.py b/algorithm/face_recognition/face_recognition copy.py new file mode 100644 index 0000000..2ff1659 --- /dev/null +++ b/algorithm/face_recognition/face_recognition copy.py @@ -0,0 +1,250 @@ +# Copyright (C) 2018-2021 coneypo +# SPDX-License-Identifier: MIT + +# Author: coneypo +# Blog: http://www.cnblogs.com/AdaminXie +# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera +# Mail: coneypo@foxmail.com + +# 摄像头实时人脸识别 / Real-time face detection and recognition + +import dlib +import numpy as np +import cv2 +import pandas as pd +import os +import time +import logging +from PIL import Image, ImageDraw, ImageFont +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + + + +# Dlib 人脸 landmark 特征点检测器 / Get face landmarks +predictor = dlib.shape_predictor('algorithm/face_recognition/data/data_dlib/shape_predictor_68_face_landmarks.dat') + +# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor +face_reco_model = dlib.face_recognition_model_v1("algorithm/face_recognition/data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") + + +class Face_Recognizer(): + def __init__(self, video_path = None, model=None): + self.face_feature_known_list = [] # 用来存放所有录入人脸特征的数组 / Save the features of faces in database + self.face_name_known_list = [] # 存储录入人脸名字 / Save the name of faces in database + + self.current_frame_face_cnt = 0 # 存储当前摄像头中捕获到的人脸数 / Counter for faces in current frame + self.current_frame_face_feature_list = [] # 存储当前摄像头中捕获到的人脸特征 / Features of faces in current frame + self.current_frame_face_name_list = [] # 存储当前摄像头中捕获到的所有人脸的名字 / Names of faces in current frame + self.current_frame_face_name_position_list = [] # 存储当前摄像头中捕获到的所有人脸的名字坐标 / Positions of faces in current frame + + # Update FPS + self.fps = 0 # FPS of current frame + self.fps_show = 0 # FPS per second + self.frame_start_time = 0 + self.frame_cnt = 0 + self.start_time = time.time() + + self.font = cv2.FONT_ITALIC + self.font_chinese = ImageFont.truetype("simsun.ttc", 30) + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.dataset = LoadImages(self.video_name) + self.face_detector = model + + self.get_face_database() + + + def use_webcam(self, source): + + source = source + cudnn.benchmark = True + self.dataset = LoadStreams(source) + + + # 从 "features_all.csv" 读取录入人脸特征 / Read known faces from "features_all.csv" + def get_face_database(self): + if os.path.exists("algorithm/face_recognition/data/features_all.csv"): + path_features_known_csv = "algorithm/face_recognition/data/features_all.csv" + csv_rd = pd.read_csv(path_features_known_csv, header=None) + for i in range(csv_rd.shape[0]): + features_someone_arr = [] + self.face_name_known_list.append(csv_rd.iloc[i][0]) + for j in range(1, 129): + if csv_rd.iloc[i][j] == '': + features_someone_arr.append('0') + else: + features_someone_arr.append(csv_rd.iloc[i][j]) + self.face_feature_known_list.append(features_someone_arr) + logging.info("Faces in Database：%d", len(self.face_feature_known_list)) + return 1 + else: + logging.warning("'features_all.csv' not found!") + logging.warning("Please run 'get_faces_from_camera.py' " + "and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'") + return 0 + + # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features + @staticmethod + def return_euclidean_distance(feature_1, feature_2): + feature_1 = np.array(feature_1) + feature_2 = np.array(feature_2) + dist = np.sqrt(np.sum(np.square(feature_1 - feature_2))) + return dist + + def update_fps(self): + now = time.time() + # 每秒刷新 fps / Refresh fps per second + if str(self.start_time).split(".")[0] != str(now).split(".")[0]: + self.fps_show = self.fps + self.start_time = now + self.frame_time = now - self.frame_start_time + self.fps = 1.0 / self.frame_time + self.frame_start_time = now + + # 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window + def draw_note(self, img_rd): + cv2.putText(img_rd, "Face Recognizer", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) + cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1, + cv2.LINE_AA) + cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1, + cv2.LINE_AA) + cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1, + cv2.LINE_AA) + + def draw_name(self, img_rd): + # 在人脸框下面写人脸名字 / Write names under rectangle + img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)) + draw = ImageDraw.Draw(img) + for i in range(self.current_frame_face_cnt): + # cv2.putText(img_rd, self.current_frame_face_name_list[i], self.current_frame_face_name_position_list[i], self.font, 0.8, (0, 255, 255), 1, cv2.LINE_AA) + draw.text(xy=self.current_frame_face_name_position_list[i], text=str(self.current_frame_face_name_list[i]), font=self.font_chinese, + fill=(255, 255, 0)) + img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) + return img_rd + + # 修改显示人名 / Show names in chinese + def show_chinese_name(self): + # Default known name: person_1, person_2, person_3 + if self.current_frame_face_cnt >= 1: + # 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name + self.face_name_known_list[0] = '张三'.encode('utf-8').decode() + # self.face_name_known_list[1] = '张四'.encode('utf-8').decode() + + def detect_faces(self, img): + + results = self.face_detector(img) + + face_results = dlib.rectangles() + + for obj in results.xyxy[0]: + + if obj[-1] == 0: # 0 is the class ID for 'person' + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + xmin, ymin, xmax, ymax = convert_to_square(xmin, ymin, xmax, ymax) + if xmax <= xmin or ymax <= ymin: + continue + rectangle = dlib.rectangle(xmin, ymin, xmax, ymax) + + face_results.append(rectangle) + + return face_results + + # 处理获取的视频流，进行人脸识别 / Face detection and recognition from input video stream + def get_frame(self): + # 1. 读取存放所有人脸特征的 csv / Read known faces from "features.all.csv" + if self.get_face_database(): + self.frame_cnt += 1 + logging.debug("Frame %d starts", self.frame_cnt) + + + for im0s in self.dataset: + + if self.dataset.mode == 'stream': + img_rd = im0s[0].copy() + else: + img_rd = im0s.copy() + + faces = self.detect_faces(img_rd) + + self.draw_note(img_rd) + + self.current_frame_face_feature_list = [] + self.current_frame_face_cnt = 0 + self.current_frame_face_name_position_list = [] + self.current_frame_face_name_list = [] + # 2. 检测到人脸 / Face detected in current frame + if len(faces) != 0: + # 3. 获取当前捕获到的图像的所有人脸的特征 / Compute the face descriptors for faces in current frame + for i in range(len(faces)): + shape = predictor(img_rd, faces[i]) + print(face_reco_model.compute_face_descriptor(img_rd, shape)) + self.current_frame_face_feature_list.append(face_reco_model.compute_face_descriptor(img_rd, shape)) + # 4. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database + for k in range(len(faces)): + logging.info("For face %d in camera:", k+1) + # 先默认所有人不认识，是 unknown / Set the default names of faces with "unknown" + self.current_frame_face_name_list.append("unknown") + # 每个捕获人脸的名字坐标 / Positions of faces captured + self.current_frame_face_name_position_list.append(tuple( + [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) + # 5. 对于某张人脸，遍历所有存储的人脸特征 + # For every faces detected, compare the faces in the database + current_frame_e_distance_list = [] + for i in range(len(self.face_feature_known_list)): + # 如果 person_X 数据不为空 + if str(self.face_feature_known_list[i][0]) != '0.0': + e_distance_tmp = self.return_euclidean_distance(self.current_frame_face_feature_list[k], + self.face_feature_known_list[i]) + logging.info(" With person %s, the e-distance is %f", str(i + 1), e_distance_tmp) + current_frame_e_distance_list.append(e_distance_tmp) + else: + # 空数据 person_X + current_frame_e_distance_list.append(999999999) + # 6. 寻找出最小的欧式距离匹配 / Find the one with minimum e-distance + similar_person_num = current_frame_e_distance_list.index(min(current_frame_e_distance_list)) + logging.info("Minimum e-distance with %s: %f", self.face_name_known_list[similar_person_num], min(current_frame_e_distance_list)) + if min(current_frame_e_distance_list) < 0.4: + self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num] + logging.info("Face recognition result: %s", self.face_name_known_list[similar_person_num]) + else: + logging.info("Face recognition result: Unknown person") + logging.info("\n") + # 矩形框 / Draw rectangle + for kk, d in enumerate(faces): + # 绘制矩形框 + # print(kk,d) + cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), + (0, 255, 0), 2) + + self.update_fps() + self.current_frame_face_cnt = len(faces) + img_rd = self.draw_name(img_rd) + + + else: + img_with_name = img_rd + + + ret, jpeg = cv2.imencode(".jpg", img_rd) + return jpeg.tobytes() + + +def convert_to_square(xmin, ymin, xmax, ymax): + # convert to square location + center_x = (xmin + xmax) // 2 + center_y = (ymin + ymax) // 2 + + square_length = ((xmax - xmin) + (ymax - ymin)) // 2 // 2 + square_length *= 1.1 + + xmin = int(center_x - square_length) + ymin = int(center_y - square_length) + xmax = int(center_x + square_length) + ymax = int(center_y + square_length) + return xmin, ymin, xmax, ymax diff --git a/algorithm/face_recognition/face_recognition.py b/algorithm/face_recognition/face_recognition.py new file mode 100644 index 0000000..828f2aa --- /dev/null +++ b/algorithm/face_recognition/face_recognition.py @@ -0,0 +1,230 @@ +# Copyright (C) 2018-2021 coneypo +# SPDX-License-Identifier: MIT + +# Author: coneypo +# Blog: http://www.cnblogs.com/AdaminXie +# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera +# Mail: coneypo@foxmail.com + +# 摄像头实时人脸识别 / Real-time face detection and recognition + +import dlib +import numpy as np +import cv2 +import pandas as pd +import os +import time +import logging +from PIL import Image, ImageDraw, ImageFont +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + + + +# Dlib 人脸 landmark 特征点检测器 / Get face landmarks +predictor = dlib.shape_predictor('algorithm/face_recognition/data/data_dlib/shape_predictor_68_face_landmarks.dat') + +# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor +face_reco_model = dlib.face_recognition_model_v1("algorithm/face_recognition/data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") + + +class Face_Recognizer(): + def __init__(self, video_path = None, model=None): + self.face_feature_known_list = [] # 用来存放所有录入人脸特征的数组 / Save the features of faces in database + self.face_name_known_list = [] # 存储录入人脸名字 / Save the name of faces in database + + self.current_frame_face_cnt = 0 # 存储当前摄像头中捕获到的人脸数 / Counter for faces in current frame + self.current_frame_face_feature_list = [] # 存储当前摄像头中捕获到的人脸特征 / Features of faces in current frame + self.current_frame_face_name_list = [] # 存储当前摄像头中捕获到的所有人脸的名字 / Names of faces in current frame + self.current_frame_face_name_position_list = [] # 存储当前摄像头中捕获到的所有人脸的名字坐标 / Positions of faces in current frame + + # Update FPS + self.fps = 0 # FPS of current frame + self.fps_show = 0 # FPS per second + self.frame_start_time = 0 + self.frame_cnt = 0 + self.start_time = time.time() + + self.font = cv2.FONT_ITALIC + self.font_chinese = ImageFont.truetype("simsun.ttc", 30) + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.dataset = LoadImages(self.video_name) + self.face_detector = model + + self.get_face_database() + + + def use_webcam(self, source): + + source = source + cudnn.benchmark = True + self.dataset = LoadStreams(source) + + + # 从 "features_all.csv" 读取录入人脸特征 / Read known faces from "features_all.csv" + def get_face_database(self): + if os.path.exists("algorithm/face_recognition/data/features_all.csv"): + path_features_known_csv = "algorithm/face_recognition/data/features_all.csv" + csv_rd = pd.read_csv(path_features_known_csv, header=None) + for i in range(csv_rd.shape[0]): + features_someone_arr = [] + self.face_name_known_list.append(csv_rd.iloc[i][0]) + for j in range(1, 129): + if csv_rd.iloc[i][j] == '': + features_someone_arr.append('0') + else: + features_someone_arr.append(csv_rd.iloc[i][j]) + self.face_feature_known_list.append(features_someone_arr) + logging.info("Faces in Database：%d", len(self.face_feature_known_list)) + return 1 + else: + logging.warning("'features_all.csv' not found!") + logging.warning("Please run 'get_faces_from_camera.py' " + "and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'") + return 0 + + # 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features + @staticmethod + def return_euclidean_distance(feature_1, feature_2): + feature_1 = np.array(feature_1) + feature_2 = np.array(feature_2) + dist = np.sqrt(np.sum(np.square(feature_1 - feature_2))) + return dist + + def draw_name(self, img_rd): + # 在人脸框下面写人脸名字 / Write names under rectangle + img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)) + draw = ImageDraw.Draw(img) + name = [] + for i in range(self.current_frame_face_cnt): + name.append(self.current_frame_face_name_list[i]) + + # cv2.putText(img_rd, self.current_frame_face_name_list[i], self.current_frame_face_name_position_list[i], self.font, 0.8, (0, 255, 255), 1, cv2.LINE_AA) + draw.text(xy=self.current_frame_face_name_position_list[i], text=str(self.current_frame_face_name_list[i]), font=self.font_chinese, + fill=(255, 255, 0)) + img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR) + return img_rd, name + + + def detect_faces(self, img): + + results = self.face_detector(img) + face_results = dlib.rectangles() + + for obj in results.xyxy[0]: + + if obj[-1] == 0: # 0 is the class ID for 'person' + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + xmin, ymin, xmax, ymax = convert_to_square(xmin, ymin, xmax, ymax) + if xmax <= xmin or ymax <= ymin: + continue + rectangle = dlib.rectangle(xmin, ymin, xmax, ymax) + + face_results.append(rectangle) + + return face_results + + # 处理获取的视频流，进行人脸识别 / Face detection and recognition from input video stream + def get_frame(self): + # 1. 读取存放所有人脸特征的 csv / Read known faces from "features.all.csv" + # logging.debug("Frame %d starts", self.frame_cnt) + + for im0s in self.dataset: + self.frame_cnt += 1 + if self.dataset.mode == 'stream': + img_rd = im0s[0].copy() + else: + img_rd = im0s.copy() + + faces = self.detect_faces(img_rd) + + # if self.frame_cnt % 1 == 0: + + # self.draw_note(img_rd) + + self.current_frame_face_feature_list = [] + self.current_frame_face_cnt = 0 + self.current_frame_face_name_position_list = [] + self.current_frame_face_name_list = [] + # 2. 检测到人脸 / Face detected in current frame + if len(faces) != 0: + # 3. 获取当前捕获到的图像的所有人脸的特征 / Compute the face descriptors for faces in current frame + for i in range(len(faces)): + shape = predictor(img_rd, faces[i]) + self.current_frame_face_feature_list.append(face_reco_model.compute_face_descriptor(img_rd, shape)) + # 4. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database + for k in range(len(faces)): + logging.info("For face %d in camera:", k+1) + # 先默认所有人不认识，是 unknown / Set the default names of faces with "unknown" + self.current_frame_face_name_list.append("unknown") + # 每个捕获人脸的名字坐标 / Positions of faces captured + self.current_frame_face_name_position_list.append(tuple( + [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) + # 5. 对于某张人脸，遍历所有存储的人脸特征 + # For every faces detected, compare the faces in the database + current_frame_e_distance_list = [] + for i in range(len(self.face_feature_known_list)): + # 如果 person_X 数据不为空 + if str(self.face_feature_known_list[i][0]) != '0.0': + e_distance_tmp = self.return_euclidean_distance(self.current_frame_face_feature_list[k], + self.face_feature_known_list[i]) + logging.info(" With person %s, the e-distance is %f", str(i + 1), e_distance_tmp) + current_frame_e_distance_list.append(e_distance_tmp) + else: + # 空数据 person_X + current_frame_e_distance_list.append(999999999) + # 6. 寻找出最小的欧式距离匹配 / Find the one with minimum e-distance + similar_person_num = current_frame_e_distance_list.index(min(current_frame_e_distance_list)) + logging.info("Minimum e-distance with %s: %f", self.face_name_known_list[similar_person_num], min(current_frame_e_distance_list)) + if min(current_frame_e_distance_list) < 0.4: + self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num] + logging.info("Face recognition result: %s", self.face_name_known_list[similar_person_num]) + else: + logging.info("Face recognition result: Unknown person") + logging.info("\n") + # 矩形框 / Draw rectangle + for kk, d in enumerate(faces): + # # 绘制矩形框 + # print(kk,d) + cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), + (0, 255, 0), 2) + + # self.update_fps() + self.current_frame_face_cnt = len(faces) + img_rd, name = self.draw_name(img_rd) + + + else: + img_with_name = img_rd + + + ret, jpeg = cv2.imencode(".jpg", img_rd) + + resText=f'人脸识别到了{name}' + + return jpeg.tobytes(), resText + # else: + # ret, jpeg = cv2.imencode(".jpg", img_rd) + # return jpeg.tobytes() + + + +def convert_to_square(xmin, ymin, xmax, ymax): + # convert to square location + center_x = (xmin + xmax) // 2 + center_y = (ymin + ymax) // 2 + + square_length = ((xmax - xmin) + (ymax - ymin)) // 2 // 2 + square_length *= 1.1 + + xmin = int(center_x - square_length) + ymin = int(center_y - square_length) + xmax = int(center_x + square_length) + ymax = int(center_y + square_length) + return xmin, ymin, xmax, ymax diff --git a/algorithm/face_recognition/features_extraction_to_csv.py b/algorithm/face_recognition/features_extraction_to_csv.py new file mode 100644 index 0000000..5671b47 --- /dev/null +++ b/algorithm/face_recognition/features_extraction_to_csv.py @@ -0,0 +1,107 @@ +# Copyright (C) 2018-2021 coneypo +# SPDX-License-Identifier: MIT + +# Author: coneypo +# Blog: http://www.cnblogs.com/AdaminXie +# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera +# Mail: coneypo@foxmail.com + +# 从人脸图像文件中提取人脸特征存入 "features_all.csv" / Extract features from images and save into "features_all.csv" + +import os +import dlib +import csv +import numpy as np +import logging +import cv2 + +# 要读取人脸图像文件的路径 / Path of cropped faces +path_images_from_camera = (os.getcwd()) + "/data/data_faces_from_camera/" + +# Dlib 正向人脸检测器 / Use frontal face detector of Dlib +detector = dlib.get_frontal_face_detector() + +# Dlib 人脸 landmark 特征点检测器 / Get face landmarks +predictor = dlib.shape_predictor((os.getcwd()) + '/data/data_dlib/shape_predictor_68_face_landmarks.dat') + +# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor +face_reco_model = dlib.face_recognition_model_v1((os.getcwd()) +"/data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") + + +# 返回单张图像的 128D 特征 / Return 128D features for single image +# Input: path_img +# Output: face_descriptor +def return_128d_features(path_img): + img_rd = cv2.imread(path_img) + faces = detector(img_rd, 1) + + logging.info("%-40s %-20s", "检测到人脸的图像 / Image with faces detected:", path_img) + + # 因为有可能截下来的人脸再去检测，检测不出来人脸了, 所以要确保是 检测到人脸的人脸图像拿去算特征 + # For photos of faces saved, we need to make sure that we can detect faces from the cropped images + if len(faces) != 0: + shape = predictor(img_rd, faces[0]) + face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape) + else: + face_descriptor = 0 + logging.warning("no face") + return face_descriptor + + +# 返回 personX 的 128D 特征均值 / Return the mean value of 128D face descriptor for person X +# Input: path_face_personX +# Output: features_mean_personX +def return_features_mean_personX(path_face_personX): + features_list_personX = [] + photos_list = os.listdir(path_face_personX) + if photos_list: + for i in range(len(photos_list)): + # 调用 return_128d_features() 得到 128D 特征 / Get 128D features for single image of personX + logging.info("%-40s %-20s", "正在读的人脸图像 / Reading image:", path_face_personX + "/" + photos_list[i]) + features_128d = return_128d_features(path_face_personX + "/" + photos_list[i]) + # 遇到没有检测出人脸的图片跳过 / Jump if no face detected from image + if features_128d == 0: + i += 1 + else: + features_list_personX.append(features_128d) + else: + logging.warning("文件夹内图像文件为空 / Warning: No images in%s/", path_face_personX) + + # 计算 128D 特征的均值 / Compute the mean + # personX 的 N 张图像 x 128D -> 1 x 128D + if features_list_personX: + features_mean_personX = np.array(features_list_personX, dtype=object).mean(axis=0) + else: + features_mean_personX = np.zeros(128, dtype=object, order='C') + return features_mean_personX + + +def main(): + logging.basicConfig(level=logging.INFO) + # 获取已录入的最后一个人脸序号 / Get the order of latest person + person_list = os.listdir("data/data_faces_from_camera/") + person_list.sort() + + with open("data/features_all.csv", "w", newline="") as csvfile: + writer = csv.writer(csvfile) + for person in person_list: + print(person_list) + # Get the mean/average features of face/personX, it will be a list with a length of 128D + logging.info("%sperson_%s", path_images_from_camera, person) + features_mean_personX = return_features_mean_personX(path_images_from_camera + person) + + if len(person.split('_', 2)) == 2: + # "person_x" + person_name = person + else: + # "person_x_tom" + person_name = person.split('_', 2)[-1] + features_mean_personX = np.insert(features_mean_personX, 0, person_name, axis=0) + # features_mean_personX will be 129D, person name + 128 features + writer.writerow(features_mean_personX) + logging.info('\n') + logging.info("所有录入人脸数据存入 / Save all the features of faces registered into: data/features_all.csv") + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/algorithm/face_recognition/get_faces_from_photo.py b/algorithm/face_recognition/get_faces_from_photo.py new file mode 100644 index 0000000..9475413 --- /dev/null +++ b/algorithm/face_recognition/get_faces_from_photo.py @@ -0,0 +1,40 @@ +import os +from PIL import Image +import cv2 +import matplotlib.pyplot as plt # plt 用于显示图片 +import torch + +def convert_to_square(xmin, ymin, xmax, ymax): + # convert to square location + center_x = (xmin + xmax) // 2 + center_y = (ymin + ymax) // 2 + + square_length = ((xmax - xmin) + (ymax - ymin)) // 2 // 2 + square_length *= 1.1 + + xmin = int(center_x - square_length) + ymin = int(center_y - square_length) + xmax = int(center_x + square_length) + ymax = int(center_y + square_length) + return xmin, ymin, xmax, ymax + + +face_model = torch.hub.load("algorithm/yolov5", 'custom', source='local', path='./weight/face.pt', force_reload=True) +image = cv2.imread("/home/ykn/algorithm_system/flask_web/algorithm/face_recognition/20220711163101.jpg") +assert image is not None +results = face_model(image) +print(results) + + +for obj in results.xyxy[0]: + if obj[-1] == 0: # 0 is the class ID for 'person' + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + xmin, ymin, xmax, ymax = convert_to_square(xmin, ymin, xmax, ymax) + + # 截取人脸部分 + face_image = image[ymin:ymax, xmin:xmax] + id = input("请输入名称：") + # 保存截取的部分 + cv2.imwrite('face_{}.jpg'.format(id), face_image) # 这里假设每个位置信息都有一个唯一的'id' + diff --git a/algorithm/face_recognition/introduction/Dlib_Face_recognition_by_coneypo.pptx b/algorithm/face_recognition/introduction/Dlib_Face_recognition_by_coneypo.pptx new file mode 100644 index 0000000..4df6ec6 Binary files /dev/null and b/algorithm/face_recognition/introduction/Dlib_Face_recognition_by_coneypo.pptx differ diff --git a/algorithm/face_recognition/introduction/face_reco.png b/algorithm/face_recognition/introduction/face_reco.png new file mode 100644 index 0000000..cf7e435 Binary files /dev/null and b/algorithm/face_recognition/introduction/face_reco.png differ diff --git a/algorithm/face_recognition/introduction/face_reco_chinese_name.png b/algorithm/face_recognition/introduction/face_reco_chinese_name.png new file mode 100644 index 0000000..81b815c Binary files /dev/null and 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b/algorithm/face_recognition/introduction/overview_with_ot.png differ diff --git a/algorithm/face_recognition/requirements.txt b/algorithm/face_recognition/requirements.txt new file mode 100644 index 0000000..b2736bf --- /dev/null +++ b/algorithm/face_recognition/requirements.txt @@ -0,0 +1,5 @@ +dlib==19.17.0 +numpy==1.22.0 +scikit-image==0.18.3 +pandas==1.3.4 +opencv-python==4.5.4.58 \ No newline at end of file diff --git a/algorithm/face_recognition/simsun.ttc b/algorithm/face_recognition/simsun.ttc new file mode 100644 index 0000000..40e9693 Binary files /dev/null and b/algorithm/face_recognition/simsun.ttc differ diff --git a/algorithm/fire_detection.py b/algorithm/fire_detection.py new file mode 100644 index 0000000..5a9d969 --- /dev/null +++ b/algorithm/fire_detection.py @@ -0,0 +1,102 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class FireDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None, model=None): + + self.model = model + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + results = self.model(img, size=640) + # print(results) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + + # Loop through each detected object and count the people + num_people = 0 + bgr = (0, 255, 0) + + + for obj in results.xyxy[0]: + # xmin, ymin, xmax, ymax = map(int, obj[:4]) + # accuracy = obj[4] + # if (accuracy > 0.5): + + # cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + # cv2.putText(img, f" {round(float(accuracy), 2), self.classes[obj[-1].item()]}", (xmin, ymin), + # cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + + if obj[-1] == 0: # 0 is the class ID for 'person' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + num_people += 1 + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + + + # Draw the number of people on the frame and display it + + + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + + return jpeg.tobytes(), '' + \ No newline at end of file diff --git a/algorithm/glove_detection.py b/algorithm/glove_detection.py new file mode 100644 index 0000000..559db65 --- /dev/null +++ b/algorithm/glove_detection.py @@ -0,0 +1,103 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class GloveDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/glove.pt', force_reload=True) + self.classes = self.model.names + # {0: 'glove', 1: 'hand'} + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + results = self.model(img, size=640) + # print(results) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + + bgr = (0, 255, 0) + + txt = "" + objs = results.xyxy[0] + for c in objs[:,-1].unique(): + n = (objs[:,-1] == c).sum() # detections per class + txt += f"{n} {self.classes[int(c)]}{'s' * (n > 1)}, " # add to string + + + for obj in objs: + if obj[-1] == 0: # 1 is the class ID for '未戴头盔' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.3): + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + elif obj[-1] == 1: # 1 is the class ID for '不戴手套' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.3): + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + return jpeg.tobytes(), txt + \ No newline at end of file diff --git a/algorithm/helmet_detection.py b/algorithm/helmet_detection.py new file mode 100644 index 0000000..f694a28 --- /dev/null +++ b/algorithm/helmet_detection.py @@ -0,0 +1,93 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class HelmetDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/helmet.pt', force_reload=True) + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + results = self.model(img, size=640) + # print(results) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + num_people = 0 + bgr = (0, 255, 0) + + txt = "" + objs = results.xyxy[0] + for c in objs[:,-1].unique(): + n = (objs[:,-1] == c).sum() # detections per class + txt += f"{n} {self.classes[int(c)]}{'s' * (n > 1)}, " # add to string + + + for obj in objs: + if obj[-1] == 1: # 1 is the class ID for '未戴头盔' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + num_people += 1 + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + return jpeg.tobytes(), txt + \ No newline at end of file diff --git a/algorithm/image_segmentation.py b/algorithm/image_segmentation.py new file mode 100644 index 0000000..6b7d35f --- /dev/null +++ b/algorithm/image_segmentation.py @@ -0,0 +1,82 @@ +import torch +import argparse +import cv2 +import os +import numpy as np +import matplotlib.pyplot as plt +from torch.utils.data import DataLoader +from torch import nn, optim +from torchvision.transforms import transforms +from algorithm.Unetliversegmaster.unet import Unet +from algorithm.Unetliversegmaster.dataset import LiverDataset +from algorithm.Unetliversegmaster.common_tools import transform_invert +import PIL.Image as Image +from datetime import datetime +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn +import argparse + + +class ImageSegmentation(): + def __init__(self,video_path=None): + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.x_transforms = transforms.ToTensor() + self.model = Unet(1, 1) + self.model.load_state_dict(torch.load('/home/shared/wy/flask_web/algorithm/Unetliversegmaster/model/weights_100.pth', map_location='cuda')) + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + # self.dataset = LoadImages(self.video_name) + self.dataset = cv2.imread(self.video_name, cv2.IMREAD_GRAYSCALE) + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + + return model + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + + # print(self.dataset.mode) + # print(self.dataset) + + img = self.dataset + + pil_image = Image.fromarray(img) + img_x = pil_image.convert('L') + + img_x = self.x_transforms(img_x) + self.model.eval() + with torch.no_grad(): + + + img_x = img_x.unsqueeze(0) + + x = img_x.type(torch.FloatTensor) + + y = self.model(x) + + img_y = torch.squeeze(y).detach().numpy() + cv2.imwrite('test111111111111.png', img_y * 255) + + ret, jpeg = cv2.imencode(".png", img_y* 255) + # save_path = os.path.join(save_root, "predict_%s_.png" % time_str) + # cv2.imwrite(save_path, img_y * 255) + + return jpeg.tobytes(), "" + diff --git a/algorithm/lane_detection.py b/algorithm/lane_detection.py new file mode 100644 index 0000000..4124fb9 --- /dev/null +++ b/algorithm/lane_detection.py @@ -0,0 +1,471 @@ +import argparse +import os +import platform +import sys +from pathlib import Path +import cv2 +import numpy as np +import time +import torchvision +import torch +from read_data import LoadImages, LoadStreams + +# from models.common import DetectMultiBackend + +from PIL import Image, ImageDraw, ImageFont + + +class LaneDetection(): + counter_frame = 0 + processed_fps = 0 + + def __init__(self, video_path=None): + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/lane.pt', force_reload=True) + # self.model = torch.load('weight/traffic/lane.pt', map_location=self.device)['model'].float().fuse() + self.classes = self.model.names + + self.frame = [None] + self.imgsz = (640, 640) + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + self.dataset = LoadImages(self.video_name, img_size=self.imgsz) + + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + # return model + + def class_to_label(self, x): + return self.classes[int(x)] + + + def get_frame(self): + + red_thres = 120, + green_thres = 160, + blue_thres = 120, + scale = 0.6 + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + image = im0s[0].copy() + else: + image = im0s.copy() + + img = image[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + + img0 = img.copy() + + img = torch.tensor(img0) + + img = img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + img = img.to(self.device) + + pred = self.model(img) + pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000) + # print(pred) + + for i, det in enumerate(pred): # per image + im0 = im0s.copy() + annotator = Annotator(im0, line_width=3, example=str(self.classes)) + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], im0.shape).round() + + im0 = annotator.result() + + + color_im0 = color_select(im0, red_thres, green_thres, blue_thres) + edg_im0 = canny_edg_(color_im0) + im0 = Hough_transform(edg_im0, im0, scale) + + + ret, jpeg = cv2.imencode(".jpg", im0) + + accuracy = 0 + num_people = 0 + + return jpeg.tobytes(), '' + + + + + + + +class Annotator: + # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations + def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): + assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' + non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic + self.pil = pil or non_ascii + if self.pil: # use PIL + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + self.font = 'Arial.Unicode.ttf' + else: # use cv2 + self.im = im + self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width + + + def masks(self, masks, colors, im_gpu, alpha=0.5): + """Plot masks at once. + Args: + masks (tensor): predicted masks on cuda, shape: [n, h, w] + colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n] + im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1] + alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque + """ + if self.pil: + # convert to numpy first + self.im = np.asarray(self.im).copy() + if im_gpu is None: + # Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...) + if len(masks) == 0: + return + if isinstance(masks, torch.Tensor): + masks = torch.as_tensor(masks, dtype=torch.uint8) + masks = masks.permute(1, 2, 0).contiguous() + masks = masks.cpu().numpy() + # masks = np.ascontiguousarray(masks.transpose(1, 2, 0)) + masks = scale_image(masks.shape[:2], masks, self.im.shape) + masks = np.asarray(masks, dtype=np.float32) + colors = np.asarray(colors, dtype=np.float32) # shape(n,3) + s = masks.sum(2, keepdims=True).clip(0, 1) # add all masks together + masks = (masks @ colors).clip(0, 255) # (h,w,n) @ (n,3) = (h,w,3) + self.im[:] = masks * alpha + self.im * (1 - s * alpha) + else: + if len(masks) == 0: + self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255 + colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0 + colors = colors[:, None, None] # shape(n,1,1,3) + masks = masks.unsqueeze(3) # shape(n,h,w,1) + masks_color = masks * (colors * alpha) # shape(n,h,w,3) + + inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1) + mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3) + + im_gpu = im_gpu.flip(dims=[0]) # flip channel + im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3) + im_gpu = im_gpu * inv_alph_masks[-1] + mcs + im_mask = (im_gpu * 255).byte().cpu().numpy() + # print(type(im_gpu), type(im_mask), type(self.im.shape)) + self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape) + if self.pil: + # convert im back to PIL and update draw + self.fromarray(self.im) + + + def rectangle(self, xy, fill=None, outline=None, width=1): + # Add rectangle to image (PIL-only) + self.draw.rectangle(xy, fill, outline, width) + + def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'): + # Add text to image (PIL-only) + if anchor == 'bottom': # start y from font bottom + w, h = self.font.getsize(text) # text width, height + xy[1] += 1 - h + self.draw.text(xy, text, fill=txt_color, font=self.font) + + def fromarray(self, im): + # Update self.im from a numpy array + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + + def result(self): + # Return annotated image as array + return np.asarray(self.im) + + + +def canny_edg_(img): + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转为灰度图像 + kernel_size = 5 + blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0) # 高斯滤波 + low_thres = 160 + high_thres = 240 + edg_img = cv2.Canny(blur_gray, low_thres, high_thres) + return edg_img + + +def color_select(img, red_thres=120, green_thres=160, blue_thres=120): + # h, w = img.shape[:2] + color_select = np.copy(img) + bgr_thre = [blue_thres, green_thres, red_thres] + thresholds = (img[:, :, 0] < bgr_thre[0]) | (img[:, :, 1] < bgr_thre[1]) | (img[:, :, 2] < bgr_thre[2]) + color_select[thresholds] = [0, 0, 0] # 小于阈值的像素设置为0 + return color_select + +def Hough_transform(edg_img, img, mask_scale=0.6): + # img是原始图像 + + mask_img = get_mask(edg_img, mask_scale) # 掩膜图像 + # -----------------霍夫曼变换----------------------- + # 定义Hough 变换的参数 + rho = 1 + theta = np.pi/180 + threshold = 2 + min_line_length = 4 # 组成一条线的最小像素 + max_line_length = 5 # 可连接线段之间的最大像素距离 + + lines = cv2.HoughLinesP(mask_img, rho, theta, threshold, np.array([]), + min_line_length, max_line_length) + + left_line = [] + right_line = [] + for line in lines: + for x1, y1, x2, y2 in line: + if x1 == x2: + pass + else: + # 求直线方程斜率判断左右车道 + m = (y2 - y1) / (x2 - x1) + c = y1 - m * x1 + if m < 0: # 左车道 + left_line.append((m, c)) + elif m >= 0: # 右车道 + right_line.append((m, c)) + cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 5) + return img + +def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): + """ + img1_shape: model input shape, [h, w] + img0_shape: origin pic shape, [h, w, 3] + masks: [h, w, num] + """ + # Rescale coordinates (xyxy) from im1_shape to im0_shape + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + # masks = masks.permute(2, 0, 1).contiguous() + # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] + # masks = masks.permute(1, 2, 0).contiguous() + + masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) + + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0, # number of masks +): + """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) + prediction = prediction[0] # select only inference output + + device = prediction.device + mps = 'mps' in device.type # Apple MPS + if mps: # MPS not fully supported yet, convert tensors to CPU labelme_dataset NMS + prediction = prediction.cpu() + bs = prediction.shape[0] # batch size + nc = prediction.shape[2] - nm - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Checks + assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + + # Settings + # min_wh = 2 # (pixels) minimum box width and height + max_wh = 7680 # (pixels) maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 0.5 + 0.05 * bs # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + mi = 5 + nc # mask start index + output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + lb = labels[xi] + v = torch.zeros((len(lb), nc + nm + 5), device=x.device) + v[:, :4] = lb[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box/Mask + box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) + mask = x[:, mi:] # zero columns if no masks + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) + else: # best class only + conf, j = x[:, 5:mi].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + else: + x = x[x[:, 4].argsort(descending=True)] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if mps: + output[xi] = output[xi].to(device) + + + return output + + +def box_iou(box1, box2, eps=1e-7): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) + +def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): + # Rescale boxes (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + clip_boxes(boxes, img0_shape) + return boxes + +def is_ascii(s=''): + # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) + s = str(s) # convert list, tuple, None, etc. to str + return len(s.encode().decode('ascii', 'ignore')) == len(s) + +def clip_boxes(boxes, shape): + # Clip boxes (xyxy) to image shape (height, width) + if isinstance(boxes, torch.Tensor): # faster individually + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 + else: # np.array (faster grouped) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + +def get_mask(edg_img, mask_scale=0.6): + # ----------------检测区域的选择--------------------- + mask = np.zeros_like(edg_img) # 全黑的图像 + ignore_mask_color = 255 + # get image size + imgshape = edg_img.shape + # 设置mask shape [1,4,2] 一般车道位置大概占据画面的1/3的位置 + ret = np.array([[(1, imgshape[0]), (1, int(imgshape[0] * mask_scale)), (imgshape[1] - 1, int(imgshape[0] * mask_scale)), + (imgshape[1] - 1, imgshape[0] - 1)]], dtype=np.int32) + # 多边形填充，mask是需要填充的图像，ret是多边形顶点, 将需要保留的区域填充为白色矩形 + cv2.fillPoly(mask, ret, ignore_mask_color) # mask下面部分变成白色 + # 图像与运算，保留掩膜图像 + mask_img = cv2.bitwise_and(edg_img, mask) + # ------------------------------------------------ + return mask_img + + diff --git a/algorithm/mask_detection.py b/algorithm/mask_detection.py new file mode 100644 index 0000000..7fe1431 --- /dev/null +++ b/algorithm/mask_detection.py @@ -0,0 +1,103 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class MaskDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/mask.pt', force_reload=True) + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + results = self.model(img, size=640) + # print(results) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + num_people = 0 + bgr = (0, 255, 0) + + txt = "" + objs = results.xyxy[0] + for c in objs[:,-1].unique(): + n = (objs[:,-1] == c).sum() # detections per class + txt += f"{n} {self.classes[int(c)]}{'s' * (n > 1)}, " # add to string + + + for obj in objs: + if obj[-1] == 0: # 1 is the class ID for '未戴头盔' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + num_people += 1 + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) + if obj[-1] == 1: # 1 is the class ID for '未戴头盔' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + num_people += 1 + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + return jpeg.tobytes(), txt + \ No newline at end of file diff --git a/algorithm/pcb_detection.py b/algorithm/pcb_detection.py new file mode 100644 index 0000000..d47ccde --- /dev/null +++ b/algorithm/pcb_detection.py @@ -0,0 +1,108 @@ +import time +from pathlib import Path +import datetime + +import cv2 +import numpy as np +import torch +import torch.backends.cudnn as cudnn +import os + +from read_data import LoadImages, LoadStreams +from tools.draw_chinese import cv2ImgAddText + +class PCBDetection(): + + + def __init__(self, video_path=None): + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/pcb.pt', force_reload=True) + self.classes = self.model.names + self.imgsz = 640 + self.stride = self.model.stride + # print( self.stride) + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.dataset = LoadImages(self.video_name, img_size=self.imgsz, stride = self.stride) + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + self.source = source + cudnn.benchmark = True + # self.dataset = LoadStreams(source, img_size=self.imgsz) + self.dataset = LoadStreams(source) + + def class_to_label(self, x): + return self.classes[int(x)] + + def get_frame(self): + + chinese_name = ['漏孔', '鼠牙洞', '开路', '短路', '毛刺', '杂铜'] + i = 0 + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + results = self.model(img, size=640) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + accuracy = 0 + num_problem = len(results.xyxy[0]) + bgr = (0, 255, 0) + + + for obj in results.xyxy[0]: + + xmin, ymin, xmax, ymax = map(int, obj[:4]) + + accuracy = obj[4] + + c = int(obj[-1]) + + + if self.classes[c] == 'missing_hole': + color = (255, 200, 90) + elif self.classes[c] == 'mouse_bite': + color = (0, 0, 255) + elif self.classes[c] == 'caopen_circuitr': + color = (0, 255, 0) + elif self.classes[c] == 'short': + color = (50, 50, 50) + elif self.classes[c] == 'spur': + color = (255, 0, 0) + elif self.classes[c] == 'spurious_copper': + color = (0, 0, 0) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2) + + + img = cv2ImgAddText(img, + f'{chinese_name[c]}', + xmax + 2, + ymin - 1, + (0, 250, 0), + 20,) + # cv2.putText(img, f"{self.classes[c]}, {round(float(accuracy), 2)}", (xmin, ymin), + # cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + + ret, jpeg = cv2.imencode(".jpg", img) + + + resText=f'PCB检测到{num_problem}个缺陷' + # print(num_people) + i = i+1 + return jpeg.tobytes(), resText + diff --git a/algorithm/people_detection.py b/algorithm/people_detection.py new file mode 100644 index 0000000..6ca8108 --- /dev/null +++ b/algorithm/people_detection.py @@ -0,0 +1,102 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class VideoPeopleDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + + def __init__(self,video_path=None, model=None): + + self.model = model + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + + def class_to_label(self, x): + return self.classes[int(x)] + # def transpic(self, img): + # return cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + results = self.model(img, size=640) + + + # Loop through each detected object and count the people + accuracy = 0 + num_people = 0 + bgr = (0, 255, 0) + + + for obj in results.xyxy[0]: + # xmin, ymin, xmax, ymax = map(int, obj[:4]) + # accuracy = obj[4] + # if (accuracy > 0.5): + + # cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + # cv2.putText(img, f" {round(float(accuracy), 2), self.classes[obj[-1].item()]}", (xmin, ymin), + # cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + if obj[-1] == 0: # 0 is the class ID for 'person' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + + accuracy = obj[4] + if (accuracy > 0.5): + num_people += 1 + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + + + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", img) + resText=f'检测到{num_people}人' + # print(num_people) + return jpeg.tobytes(), resText + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() \ No newline at end of file diff --git a/algorithm/people_detection_test.py b/algorithm/people_detection_test.py new file mode 100644 index 0000000..21d6d05 --- /dev/null +++ b/algorithm/people_detection_test.py @@ -0,0 +1,73 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + + +def use_webcam(source, model): + + source = source + imgsz = 640 + cudnn.benchmark = True + dataset = LoadStreams(source, img_size=imgsz) + + + for im0s in dataset: + # print(self.dataset.mode) + # print(self.dataset) + if dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + results = model(img, size=640) + + + # Loop through each detected object and count the people + num_people = 0 + bgr = (0, 255, 0) + + + for obj in results.xyxy[0]: + # xmin, ymin, xmax, ymax = map(int, obj[:4]) + # accuracy = obj[4] + # if (accuracy > 0.5): + + # cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + # cv2.putText(img, f" {round(float(accuracy), 2), self.classes[obj[-1].item()]}", (xmin, ymin), + # cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + if obj[-1] == 0: # 0 is the class ID for 'person' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.5): + num_people += 1 + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + + # Draw the number of people on the frame and display it + + + ret, jpeg = cv2.imencode(".jpg", img) + + + return jpeg.tobytes() + + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + diff --git a/algorithm/phone_detection.py b/algorithm/phone_detection.py new file mode 100644 index 0000000..b5d471e --- /dev/null +++ b/algorithm/phone_detection.py @@ -0,0 +1,102 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class PhoneDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/phone.pt', force_reload=True) + self.classes = ["phone"] + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + results = self.model(img, size=640) + # print(results) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + + bgr = (0, 255, 0) + + txt = "" + objs = results.xyxy[0] + for c in objs[:,-1].unique(): + n = (objs[:,-1] == c).sum() # detections per class + txt += f"{n} {self.classes[int(c)]}{'s' * (n > 1)}, " # add to string + + + for obj in objs: + if obj[-1] == 0: # 1 is the class ID for '未戴头盔' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + elif obj[-1] == 1: # 1 is the class ID for '不戴手套' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + return jpeg.tobytes(), txt + \ No newline at end of file diff --git a/algorithm/pose_detection.py b/algorithm/pose_detection.py new file mode 100644 index 0000000..e040238 --- /dev/null +++ b/algorithm/pose_detection.py @@ -0,0 +1,748 @@ +import cv2 +import copy +import math +import numpy as np +from collections import OrderedDict +from scipy.ndimage.filters import gaussian_filter + +import torch +import torch.nn as nn + +import matplotlib +from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas +from matplotlib.figure import Figure +import matplotlib.pyplot as plt +from skimage.measure import label +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class PoseDetection(): + def __init__(self, video_path=None): + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.body_estimation = Body('weight/pose/body_pose_model.pth') + self.hand_estimation = Hand('weight/pose/hand_pose_model.pth') + self.source = video_path + self.classes = 'pose_detection' + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + oriImg = im0s[0].copy() + else: + oriImg = im0s.copy() + # oriImg = torch.from_numpy(oriImg) + # oriImg = oriImg.to(self.device) + candidate, subset = self.body_estimation(oriImg) + # print(candidate) + canvas = copy.deepcopy(oriImg) + canvas = draw_bodypose(canvas, candidate, subset) + + + # # detect hand + # hands_list = handDetect(candidate, subset, oriImg) + # all_hand_peaks = [] + # for x, y, w, is_left in hands_list: + # peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]) + # peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], peaks[:, 0]+x) + # peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y) + + # all_hand_peaks.append(peaks) + + # canvas = draw_handpose(canvas, all_hand_peaks) + + + img = np.array(canvas[:, :, [2, 1, 0]]) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + accuracy = 0 + num_people = 0 + + ret, jpeg = cv2.imencode(".jpg", img) + return jpeg.tobytes(), '' + + +def make_layers(block, no_relu_layers): + layers = [] + for layer_name, v in block.items(): + if 'pool' in layer_name: + layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], + padding=v[2]) + layers.append((layer_name, layer)) + else: + conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], + kernel_size=v[2], stride=v[3], + padding=v[4]) + layers.append((layer_name, conv2d)) + if layer_name not in no_relu_layers: + layers.append(('relu_'+layer_name, nn.ReLU(inplace=True))) + + return nn.Sequential(OrderedDict(layers)) + +class bodypose_model(nn.Module): + def __init__(self): + super(bodypose_model, self).__init__() + + # these layers have no relu layer + no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\ + 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\ + 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\ + 'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1'] + blocks = {} + block0 = OrderedDict([ + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3_CPM', [512, 256, 3, 1, 1]), + ('conv4_4_CPM', [256, 128, 3, 1, 1]) + ]) + + + # Stage 1 + block1_1 = OrderedDict([ + ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L1', [512, 38, 1, 1, 0]) + ]) + + block1_2 = OrderedDict([ + ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L2', [512, 19, 1, 1, 0]) + ]) + blocks['block1_1'] = block1_1 + blocks['block1_2'] = block1_2 + + self.model0 = make_layers(block0, no_relu_layers) + + # Stages 2 - 6 + for i in range(2, 7): + blocks['block%d_1' % i] = OrderedDict([ + ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0]) + ]) + + blocks['block%d_2' % i] = OrderedDict([ + ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0]) + ]) + + for k in blocks.keys(): + blocks[k] = make_layers(blocks[k], no_relu_layers) + + self.model1_1 = blocks['block1_1'] + self.model2_1 = blocks['block2_1'] + self.model3_1 = blocks['block3_1'] + self.model4_1 = blocks['block4_1'] + self.model5_1 = blocks['block5_1'] + self.model6_1 = blocks['block6_1'] + + self.model1_2 = blocks['block1_2'] + self.model2_2 = blocks['block2_2'] + self.model3_2 = blocks['block3_2'] + self.model4_2 = blocks['block4_2'] + self.model5_2 = blocks['block5_2'] + self.model6_2 = blocks['block6_2'] + + + def forward(self, x): + + out1 = self.model0(x) + + out1_1 = self.model1_1(out1) + out1_2 = self.model1_2(out1) + out2 = torch.cat([out1_1, out1_2, out1], 1) + + out2_1 = self.model2_1(out2) + out2_2 = self.model2_2(out2) + out3 = torch.cat([out2_1, out2_2, out1], 1) + + out3_1 = self.model3_1(out3) + out3_2 = self.model3_2(out3) + out4 = torch.cat([out3_1, out3_2, out1], 1) + + out4_1 = self.model4_1(out4) + out4_2 = self.model4_2(out4) + out5 = torch.cat([out4_1, out4_2, out1], 1) + + out5_1 = self.model5_1(out5) + out5_2 = self.model5_2(out5) + out6 = torch.cat([out5_1, out5_2, out1], 1) + + out6_1 = self.model6_1(out6) + out6_2 = self.model6_2(out6) + + return out6_1, out6_2 + +class handpose_model(nn.Module): + def __init__(self): + super(handpose_model, self).__init__() + + # these layers have no relu layer + no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\ + 'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6'] + # stage 1 + block1_0 = OrderedDict([ + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3', [512, 512, 3, 1, 1]), + ('conv4_4', [512, 512, 3, 1, 1]), + ('conv5_1', [512, 512, 3, 1, 1]), + ('conv5_2', [512, 512, 3, 1, 1]), + ('conv5_3_CPM', [512, 128, 3, 1, 1]) + ]) + + block1_1 = OrderedDict([ + ('conv6_1_CPM', [128, 512, 1, 1, 0]), + ('conv6_2_CPM', [512, 22, 1, 1, 0]) + ]) + + blocks = {} + blocks['block1_0'] = block1_0 + blocks['block1_1'] = block1_1 + + # stage 2-6 + for i in range(2, 7): + blocks['block%d' % i] = OrderedDict([ + ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]), + ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0]) + ]) + + for k in blocks.keys(): + blocks[k] = make_layers(blocks[k], no_relu_layers) + + self.model1_0 = blocks['block1_0'] + self.model1_1 = blocks['block1_1'] + self.model2 = blocks['block2'] + self.model3 = blocks['block3'] + self.model4 = blocks['block4'] + self.model5 = blocks['block5'] + self.model6 = blocks['block6'] + + def forward(self, x): + out1_0 = self.model1_0(x) + out1_1 = self.model1_1(out1_0) + concat_stage2 = torch.cat([out1_1, out1_0], 1) + out_stage2 = self.model2(concat_stage2) + concat_stage3 = torch.cat([out_stage2, out1_0], 1) + out_stage3 = self.model3(concat_stage3) + concat_stage4 = torch.cat([out_stage3, out1_0], 1) + out_stage4 = self.model4(concat_stage4) + concat_stage5 = torch.cat([out_stage4, out1_0], 1) + out_stage5 = self.model5(concat_stage5) + concat_stage6 = torch.cat([out_stage5, out1_0], 1) + out_stage6 = self.model6(concat_stage6) + return out_stage6 + + +class Body(object): + def __init__(self, model_path): + self.model = bodypose_model() + if torch.cuda.is_available(): + self.model = self.model.cuda() + model_dict = transfer(self.model, torch.load(model_path)) + self.model.load_state_dict(model_dict) + self.model.eval() + + def __call__(self, oriImg): + # scale_search = [0.5, 1.0, 1.5, 2.0] + scale_search = [0.5] + boxsize = 368 + stride = 8 + padValue = 128 + thre1 = 0.1 + thre2 = 0.05 + multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search] + heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19)) + paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38)) + + for m in range(len(multiplier)): + scale = multiplier[m] + imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) + imageToTest_padded, pad = padRightDownCorner(imageToTest, stride, padValue) + im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 + im = np.ascontiguousarray(im) + + data = torch.from_numpy(im).float() + if torch.cuda.is_available(): + data = data.cuda() + # data = data.permute([2, 0, 1]).unsqueeze(0).float() + with torch.no_grad(): + Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data) + Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy() + Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy() + + # extract outputs, resize, and remove padding + # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps + heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) # output 1 is heatmaps + heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) + heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) + + # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs + paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs + paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) + paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) + + heatmap_avg += heatmap_avg + heatmap / len(multiplier) + paf_avg += + paf / len(multiplier) + + all_peaks = [] + peak_counter = 0 + + for part in range(18): + map_ori = heatmap_avg[:, :, part] + one_heatmap = gaussian_filter(map_ori, sigma=3) + + map_left = np.zeros(one_heatmap.shape) + map_left[1:, :] = one_heatmap[:-1, :] + map_right = np.zeros(one_heatmap.shape) + map_right[:-1, :] = one_heatmap[1:, :] + map_up = np.zeros(one_heatmap.shape) + map_up[:, 1:] = one_heatmap[:, :-1] + map_down = np.zeros(one_heatmap.shape) + map_down[:, :-1] = one_heatmap[:, 1:] + + peaks_binary = np.logical_and.reduce( + (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1)) + peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse + peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks] + peak_id = range(peak_counter, peak_counter + len(peaks)) + peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))] + + all_peaks.append(peaks_with_score_and_id) + peak_counter += len(peaks) + + # find connection in the specified sequence, center 29 is in the position 15 + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + [1, 16], [16, 18], [3, 17], [6, 18]] + # the middle joints heatmap correpondence + mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \ + [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \ + [55, 56], [37, 38], [45, 46]] + + connection_all = [] + special_k = [] + mid_num = 10 + + for k in range(len(mapIdx)): + score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]] + candA = all_peaks[limbSeq[k][0] - 1] + candB = all_peaks[limbSeq[k][1] - 1] + nA = len(candA) + nB = len(candB) + indexA, indexB = limbSeq[k] + if (nA != 0 and nB != 0): + connection_candidate = [] + for i in range(nA): + for j in range(nB): + vec = np.subtract(candB[j][:2], candA[i][:2]) + norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1]) + norm = max(0.001, norm) + vec = np.divide(vec, norm) + + startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \ + np.linspace(candA[i][1], candB[j][1], num=mid_num))) + + vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \ + for I in range(len(startend))]) + vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \ + for I in range(len(startend))]) + + score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1]) + score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min( + 0.5 * oriImg.shape[0] / norm - 1, 0) + criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts) + criterion2 = score_with_dist_prior > 0 + if criterion1 and criterion2: + connection_candidate.append( + [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]]) + + connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) + connection = np.zeros((0, 5)) + for c in range(len(connection_candidate)): + i, j, s = connection_candidate[c][0:3] + if (i not in connection[:, 3] and j not in connection[:, 4]): + connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]]) + if (len(connection) >= min(nA, nB)): + break + + connection_all.append(connection) + else: + special_k.append(k) + connection_all.append([]) + + # last number in each row is the total parts number of that person + # the second last number in each row is the score of the overall configuration + subset = -1 * np.ones((0, 20)) + candidate = np.array([item for sublist in all_peaks for item in sublist]) + + for k in range(len(mapIdx)): + if k not in special_k: + partAs = connection_all[k][:, 0] + partBs = connection_all[k][:, 1] + indexA, indexB = np.array(limbSeq[k]) - 1 + + for i in range(len(connection_all[k])): # = 1:size(temp,1) + found = 0 + subset_idx = [-1, -1] + for j in range(len(subset)): # 1:size(subset,1): + if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]: + subset_idx[found] = j + found += 1 + + if found == 1: + j = subset_idx[0] + if subset[j][indexB] != partBs[i]: + subset[j][indexB] = partBs[i] + subset[j][-1] += 1 + subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + elif found == 2: # if found 2 and disjoint, merge them + j1, j2 = subset_idx + membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2] + if len(np.nonzero(membership == 2)[0]) == 0: # merge + subset[j1][:-2] += (subset[j2][:-2] + 1) + subset[j1][-2:] += subset[j2][-2:] + subset[j1][-2] += connection_all[k][i][2] + subset = np.delete(subset, j2, 0) + else: # as like found == 1 + subset[j1][indexB] = partBs[i] + subset[j1][-1] += 1 + subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + + # if find no partA in the subset, create a new subset + elif not found and k < 17: + row = -1 * np.ones(20) + row[indexA] = partAs[i] + row[indexB] = partBs[i] + row[-1] = 2 + row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2] + subset = np.vstack([subset, row]) + # delete some rows of subset which has few parts occur + deleteIdx = [] + for i in range(len(subset)): + if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4: + deleteIdx.append(i) + subset = np.delete(subset, deleteIdx, axis=0) + + # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts + # candidate: x, y, score, id + return candidate, subset + + + +class Hand(object): + def __init__(self, model_path): + self.model = handpose_model() + if torch.cuda.is_available(): + self.model = self.model.cuda() + model_dict = transfer(self.model, torch.load(model_path)) + self.model.load_state_dict(model_dict) + self.model.eval() + + def __call__(self, oriImg): + scale_search = [0.5, 1.0, 1.5, 2.0] + # scale_search = [0.5] + boxsize = 368 + stride = 8 + padValue = 128 + thre = 0.05 + multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search] + heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 22)) + # paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38)) + + for m in range(len(multiplier)): + scale = multiplier[m] + imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) + imageToTest_padded, pad = padRightDownCorner(imageToTest, stride, padValue) + im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 + im = np.ascontiguousarray(im) + + data = torch.from_numpy(im).float() + if torch.cuda.is_available(): + data = data.cuda() + # data = data.permute([2, 0, 1]).unsqueeze(0).float() + with torch.no_grad(): + output = self.model(data).cpu().numpy() + # output = self.model(data).numpy()q + + # extract outputs, resize, and remove padding + heatmap = np.transpose(np.squeeze(output), (1, 2, 0)) # output 1 is heatmaps + heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) + heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) + + heatmap_avg += heatmap / len(multiplier) + + all_peaks = [] + for part in range(21): + map_ori = heatmap_avg[:, :, part] + one_heatmap = gaussian_filter(map_ori, sigma=3) + binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8) + # 全部小于阈值 + if np.sum(binary) == 0: + all_peaks.append([0, 0]) + continue + label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim) + max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1 + label_img[label_img != max_index] = 0 + map_ori[label_img == 0] = 0 + + y, x = npmax(map_ori) + all_peaks.append([x, y]) + return np.array(all_peaks) + + + + +def padRightDownCorner(img, stride, padValue): + h = img.shape[0] + w = img.shape[1] + + pad = 4 * [None] + pad[0] = 0 # up + pad[1] = 0 # left + pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down + pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right + + img_padded = img + pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) + img_padded = np.concatenate((pad_up, img_padded), axis=0) + pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1)) + img_padded = np.concatenate((pad_left, img_padded), axis=1) + pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1)) + img_padded = np.concatenate((img_padded, pad_down), axis=0) + pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1)) + img_padded = np.concatenate((img_padded, pad_right), axis=1) + + return img_padded, pad + +# transfer caffe model to pytorch which will match the layer name +def transfer(model, model_weights): + transfered_model_weights = {} + for weights_name in model.state_dict().keys(): + transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] + return transfered_model_weights + +# draw the body keypoint and lims +def draw_bodypose(canvas, candidate, subset): + stickwidth = 4 + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + [1, 16], [16, 18], [3, 17], [6, 18]] + + colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ + [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ + [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] + for i in range(18): + for n in range(len(subset)): + index = int(subset[n][i]) + if index == -1: + continue + x, y = candidate[index][0:2] + cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) + for i in range(17): + for n in range(len(subset)): + index = subset[n][np.array(limbSeq[i]) - 1] + if -1 in index: + continue + cur_canvas = canvas.copy() + Y = candidate[index.astype(int), 0] + X = candidate[index.astype(int), 1] + mX = np.mean(X) + mY = np.mean(Y) + length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 + angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) + cv2.fillConvexPoly(cur_canvas, polygon, colors[i]) + canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0) + # plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]]) + # plt.imshow(canvas[:, :, [2, 1, 0]]) + return canvas + +def draw_handpose(canvas, all_hand_peaks, show_number=False): + edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ + [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] + fig = Figure(figsize=plt.figaspect(canvas)) + + fig.subplots_adjust(0, 0, 1, 1) + fig.subplots_adjust(bottom=0, top=1, left=0, right=1) + bg = FigureCanvas(fig) + ax = fig.subplots() + ax.axis('off') + ax.imshow(canvas) + + width, height = ax.figure.get_size_inches() * ax.figure.get_dpi() + + for peaks in all_hand_peaks: + for ie, e in enumerate(edges): + if np.sum(np.all(peaks[e], axis=1)==0)==0: + x1, y1 = peaks[e[0]] + x2, y2 = peaks[e[1]] + ax.plot([x1, x2], [y1, y2], color=matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])) + + for i, keyponit in enumerate(peaks): + x, y = keyponit + ax.plot(x, y, 'r.') + if show_number: + ax.text(x, y, str(i)) + bg.draw() + canvas = np.fromstring(bg.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3) + return canvas + +# image drawed by opencv is not good. +def draw_handpose_by_opencv(canvas, peaks, show_number=False): + edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ + [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] + # cv2.rectangle(canvas, (x, y), (x+w, y+w), (0, 255, 0), 2, lineType=cv2.LINE_AA) + # cv2.putText(canvas, 'left' if is_left else 'right', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) + for ie, e in enumerate(edges): + if np.sum(np.all(peaks[e], axis=1)==0)==0: + x1, y1 = peaks[e[0]] + x2, y2 = peaks[e[1]] + cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])*255, thickness=2) + + for i, keyponit in enumerate(peaks): + x, y = keyponit + cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1) + if show_number: + cv2.putText(canvas, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA) + return canvas + +# detect hand according to body pose keypoints +# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp +def handDetect(candidate, subset, oriImg): + # right hand: wrist 4, elbow 3, shoulder 2 + # left hand: wrist 7, elbow 6, shoulder 5 + ratioWristElbow = 0.33 + detect_result = [] + image_height, image_width = oriImg.shape[0:2] + for person in subset.astype(int): + # if any of three not detected + has_left = np.sum(person[[5, 6, 7]] == -1) == 0 + has_right = np.sum(person[[2, 3, 4]] == -1) == 0 + if not (has_left or has_right): + continue + hands = [] + #left hand + if has_left: + left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]] + x1, y1 = candidate[left_shoulder_index][:2] + x2, y2 = candidate[left_elbow_index][:2] + x3, y3 = candidate[left_wrist_index][:2] + hands.append([x1, y1, x2, y2, x3, y3, True]) + # right hand + if has_right: + right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]] + x1, y1 = candidate[right_shoulder_index][:2] + x2, y2 = candidate[right_elbow_index][:2] + x3, y3 = candidate[right_wrist_index][:2] + hands.append([x1, y1, x2, y2, x3, y3, False]) + + for x1, y1, x2, y2, x3, y3, is_left in hands: + # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox + # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]); + # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]); + # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow); + # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder); + # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder); + x = x3 + ratioWristElbow * (x3 - x2) + y = y3 + ratioWristElbow * (y3 - y2) + distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2) + distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder) + # x-y refers to the center --> offset to topLeft point + # handRectangle.x -= handRectangle.width / 2.f; + # handRectangle.y -= handRectangle.height / 2.f; + x -= width / 2 + y -= width / 2 # width = height + # overflow the image + if x < 0: x = 0 + if y < 0: y = 0 + width1 = width + width2 = width + if x + width > image_width: width1 = image_width - x + if y + width > image_height: width2 = image_height - y + width = min(width1, width2) + # the max hand box value is 20 pixels + if width >= 20: + detect_result.append([int(x), int(y), int(width), is_left]) + + ''' + return value: [[x, y, w, True if left hand else False]]. + width=height since the network require squared input. + x, y is the coordinate of top left + ''' + return detect_result + +# get max index of 2d array +def npmax(array): + arrayindex = array.argmax(1) + arrayvalue = array.max(1) + i = arrayvalue.argmax() + j = arrayindex[i] + return i, j diff --git a/algorithm/reflective_detection.py b/algorithm/reflective_detection.py new file mode 100644 index 0000000..db72120 --- /dev/null +++ b/algorithm/reflective_detection.py @@ -0,0 +1,103 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class ReflectiveDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/reflective.pt', force_reload=True) + self.classes = self.model.names + # {0: 'reflective_clothes', 1: 'other_clothes'} + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + results = self.model(img, size=640) + # print(results) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + + bgr = (0, 255, 0) + + txt = "" + objs = results.xyxy[0] + for c in objs[:,-1].unique(): + n = (objs[:,-1] == c).sum() # detections per class + txt += f"{n} {self.classes[int(c)]}{'s' * (n > 1)}, " # add to string + + + for obj in objs: + if obj[-1] == 0: # 1 is the class ID for '未戴头盔' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + elif obj[-1] == 1: # 1 is the class ID for '不戴手套' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + return jpeg.tobytes(), txt + \ No newline at end of file diff --git a/algorithm/safe_detection.py b/algorithm/safe_detection.py new file mode 100644 index 0000000..f8bb1c2 --- /dev/null +++ b/algorithm/safe_detection.py @@ -0,0 +1,115 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn +from tools.draw_chinese import cv2ImgAddText + + +class SafeDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/safe_guard.pt', force_reload=True) + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + self.source = source + cudnn.benchmark = True + # self.dataset = LoadStreams(source, img_size=self.imgsz) + self.dataset = LoadStreams(source) + + def class_to_label(self, x): + return self.classes[int(x)] + + def get_frame(self): + + i = 0 + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + results = self.model(img, size=640) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + accuracy = 0 + num_problem = len(results.xyxy[0]) + bgr = (0, 255, 0) + + + for obj in results.xyxy[0]: + + xmin, ymin, xmax, ymax = map(int, obj[:4]) + + accuracy = obj[4] + + c = int(obj[-1]) + + + if self.classes[c] == 'glove': + color = (255, 200, 90) + elif self.classes[c] == 'goggles': + color = (0, 0, 255) + elif self.classes[c] == 'helmet': + color = (0, 255, 0) + elif self.classes[c] == 'mask': + color = (50, 50, 50) + elif self.classes[c] == 'no_glove': + color = (255, 0, 0) + elif self.classes[c] == 'no_goggles': + color = (10, 20, 30) + elif self.classes[c] == 'no_mask': + color = (100, 0, 120) + elif self.classes[c] == 'no_shoes': + color = (100, 100, 0) + elif self.classes[c] == 'shoes': + color = (0, 0, 0) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2) + + + img = cv2ImgAddText(img, + f'{self.classes[c]}', + xmax + 2, + ymin - 1, + (0, 250, 0), + 20,) + # cv2.putText(img, f"{self.classes[c]}, {round(float(accuracy), 2)}", (xmin, ymin), + # cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + + ret, jpeg = cv2.imencode(".jpg", img) + + + resText=f'正在进行生产环境安全检测' + # print(num_people) + i = i+1 + return jpeg.tobytes(), resText + diff --git a/algorithm/smog_detection.py b/algorithm/smog_detection.py new file mode 100644 index 0000000..c9f12dc --- /dev/null +++ b/algorithm/smog_detection.py @@ -0,0 +1,105 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn + +class SmogDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None, model=None): + + self.model = model + self.classes = self.model.names + + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + #self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + def class_to_label(self, x): + return self.classes[int(x)] + + + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + results = self.model(img, size=640) + # print(results) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + + # Loop through each detected object and count the people + num_people = 0 + bgr = (0, 255, 0) + txt = "" + + for obj in results.xyxy[0]: + # xmin, ymin, xmax, ymax = map(int, obj[:4]) + # accuracy = obj[4] + # if (accuracy > 0.5): + + # cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + # cv2.putText(img, f" {round(float(accuracy), 2), self.classes[obj[-1].item()]}", (xmin, ymin), + # cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + + + if obj[-1] == 0: # 0 is the class ID for 'person' + + # Draw bounding boxes around people + xmin, ymin, xmax, ymax = map(int, obj[:4]) + accuracy = obj[4] + if (accuracy > 0.2): + num_people += 1 + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + cv2.putText(img, f" {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + txt = "Smog Warning" + + + # Draw the number of people on the frame and display it + + + ret, jpeg = cv2.imencode(".jpg", img) + # print(jpeg.shape) + + + return jpeg.tobytes(), txt + \ No newline at end of file diff --git a/algorithm/traffic_detection.py b/algorithm/traffic_detection.py new file mode 100644 index 0000000..66c21a3 --- /dev/null +++ b/algorithm/traffic_detection.py @@ -0,0 +1,82 @@ +import argparse +import os +import platform +import sys +from pathlib import Path + +import cv2 +import torch +import torch.backends.cudnn as cudnn + +from read_data import LoadImages, LoadStreams + +class TrafficDetection(): + def __init__(self, video_path=None, model=None): + self.model = model + self.classes = self.model.names + self.imgsz = 640 + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.dataset = LoadImages(self.video_name, img_size=self.imgsz) + self.flag = 0 + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + self.source = source + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + + def class_to_label(self, x): + return self.classes[int(x)] + + def get_frame(self): + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + results = self.model(img, size=640) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + accuracy = 0 + num_people = 0 + + color = (255, 200, 90) + + for obj in results.xyxy[0]: + # xmin, ymin, xmax, ymax = map(int, obj[:4]) + # accuracy = obj[4] + # if (accuracy > 0.5): + + # cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2) + # cv2.putText(img, f" {round(float(accuracy), 2), self.classes[obj[-1].item()]}", (xmin, ymin), + # cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + xmin, ymin, xmax, ymax = map(int, obj[:4]) + + accuracy = obj[4] + + c = int(obj[-1]) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2) + cv2.putText(img, f"{self.classes[c]}, {round(float(accuracy), 2)}", (xmin, ymin), + cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", img) + # print(num_people) + return jpeg.tobytes(), num_people\ \ No newline at end of file diff --git a/algorithm/traffic_logo_detection.py b/algorithm/traffic_logo_detection.py new file mode 100644 index 0000000..0ac27b8 --- /dev/null +++ b/algorithm/traffic_logo_detection.py @@ -0,0 +1,96 @@ +import argparse +import os +import platform +import sys +from pathlib import Path + +import cv2 +import torch +import torch.backends.cudnn as cudnn +import datetime +from tools.draw_chinese import cv2ImgAddText +from read_data import LoadImages, LoadStreams + +class TrafficLogoDetection(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/traffic_logo.pt', force_reload=True) + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + self.flag = 0 + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + self.source = source + cudnn.benchmark = True + # self.dataset = LoadStreams(source, img_size=self.imgsz) + self.dataset = LoadStreams(source) + + def class_to_label(self, x): + return self.classes[int(x)] + + def get_frame(self): + + i = 0 + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + img = im0s[0].copy() + else: + img = im0s.copy() + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + results = self.model(img, size=640) + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + # Loop through each detected object and count the people + accuracy = 0 + num_problem = len(results.xyxy[0]) + bgr = (0, 255, 0) + + + for obj in results.xyxy[0]: + + xmin, ymin, xmax, ymax = map(int, obj[:4]) + + accuracy = obj[4] + + c = int(obj[-1]) + + color = (255, 200, 90) + + cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color, 2) + + + img = cv2ImgAddText(img, + f'{self.classes[c]}', + xmax + 2, + ymin - 1, + (0, 250, 0), + 20,) + # cv2.putText(img, f"{self.classes[c]}, {round(float(accuracy), 2)}", (xmin, ymin), + # cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1) + + ret, jpeg = cv2.imencode(".jpg", img) + + + resText=f'正在进行车标检测' + # print(num_people) + i = i+1 + return jpeg.tobytes(), resText + diff --git a/algorithm/trafiic_lights.py b/algorithm/trafiic_lights.py new file mode 100644 index 0000000..117abf1 --- /dev/null +++ b/algorithm/trafiic_lights.py @@ -0,0 +1,471 @@ +import argparse +import os +import platform +import sys +from pathlib import Path +import cv2 +import numpy as np +import time +import torchvision +import torch +from read_data import LoadImages, LoadStreams + +# from models.common import DetectMultiBackend + +from PIL import Image, ImageDraw, ImageFont + + +class TrafficLightsDetection(): + counter_frame = 0 + processed_fps = 0 + + def __init__(self, video_path=None): + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/plate_rec_color.pth', force_reload=True) + # self.model = torch.load('weight/traffic/lane.pt', map_location=self.device)['model'].float().fuse() + self.classes = self.model.names + + self.frame = [None] + self.imgsz = (640, 640) + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + self.dataset = LoadImages(self.video_name, img_size=self.imgsz) + + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + + self.dataset = LoadStreams(source, img_size=self.imgsz) + self.flag = 1 + + # return model + + def class_to_label(self, x): + return self.classes[int(x)] + + + def get_frame(self): + + red_thres = 120, + green_thres = 160, + blue_thres = 120, + scale = 0.6 + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + image = im0s[0].copy() + else: + image = im0s.copy() + + img = image[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + + img0 = img.copy() + + img = torch.tensor(img0) + + img = img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + img = img.to(self.device) + + pred = self.model(img) + pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000) + # print(pred) + + for i, det in enumerate(pred): # per image + im0 = im0s.copy() + annotator = Annotator(im0, line_width=3, example=str(self.classes)) + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], im0.shape).round() + + im0 = annotator.result() + + + color_im0 = color_select(im0, red_thres, green_thres, blue_thres) + edg_im0 = canny_edg_(color_im0) + im0 = Hough_transform(edg_im0, im0, scale) + + + ret, jpeg = cv2.imencode(".jpg", im0) + + accuracy = 0 + num_people = 0 + + return jpeg.tobytes(), '' + + + + + + + +class Annotator: + # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations + def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): + assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' + non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic + self.pil = pil or non_ascii + if self.pil: # use PIL + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + self.font = 'Arial.Unicode.ttf' + else: # use cv2 + self.im = im + self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width + + + def masks(self, masks, colors, im_gpu, alpha=0.5): + """Plot masks at once. + Args: + masks (tensor): predicted masks on cuda, shape: [n, h, w] + colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n] + im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1] + alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque + """ + if self.pil: + # convert to numpy first + self.im = np.asarray(self.im).copy() + if im_gpu is None: + # Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...) + if len(masks) == 0: + return + if isinstance(masks, torch.Tensor): + masks = torch.as_tensor(masks, dtype=torch.uint8) + masks = masks.permute(1, 2, 0).contiguous() + masks = masks.cpu().numpy() + # masks = np.ascontiguousarray(masks.transpose(1, 2, 0)) + masks = scale_image(masks.shape[:2], masks, self.im.shape) + masks = np.asarray(masks, dtype=np.float32) + colors = np.asarray(colors, dtype=np.float32) # shape(n,3) + s = masks.sum(2, keepdims=True).clip(0, 1) # add all masks together + masks = (masks @ colors).clip(0, 255) # (h,w,n) @ (n,3) = (h,w,3) + self.im[:] = masks * alpha + self.im * (1 - s * alpha) + else: + if len(masks) == 0: + self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255 + colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0 + colors = colors[:, None, None] # shape(n,1,1,3) + masks = masks.unsqueeze(3) # shape(n,h,w,1) + masks_color = masks * (colors * alpha) # shape(n,h,w,3) + + inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1) + mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3) + + im_gpu = im_gpu.flip(dims=[0]) # flip channel + im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3) + im_gpu = im_gpu * inv_alph_masks[-1] + mcs + im_mask = (im_gpu * 255).byte().cpu().numpy() + # print(type(im_gpu), type(im_mask), type(self.im.shape)) + self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape) + if self.pil: + # convert im back to PIL and update draw + self.fromarray(self.im) + + + def rectangle(self, xy, fill=None, outline=None, width=1): + # Add rectangle to image (PIL-only) + self.draw.rectangle(xy, fill, outline, width) + + def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'): + # Add text to image (PIL-only) + if anchor == 'bottom': # start y from font bottom + w, h = self.font.getsize(text) # text width, height + xy[1] += 1 - h + self.draw.text(xy, text, fill=txt_color, font=self.font) + + def fromarray(self, im): + # Update self.im from a numpy array + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + + def result(self): + # Return annotated image as array + return np.asarray(self.im) + + + +def canny_edg_(img): + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转为灰度图像 + kernel_size = 5 + blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0) # 高斯滤波 + low_thres = 160 + high_thres = 240 + edg_img = cv2.Canny(blur_gray, low_thres, high_thres) + return edg_img + + +def color_select(img, red_thres=120, green_thres=160, blue_thres=120): + # h, w = img.shape[:2] + color_select = np.copy(img) + bgr_thre = [blue_thres, green_thres, red_thres] + thresholds = (img[:, :, 0] < bgr_thre[0]) | (img[:, :, 1] < bgr_thre[1]) | (img[:, :, 2] < bgr_thre[2]) + color_select[thresholds] = [0, 0, 0] # 小于阈值的像素设置为0 + return color_select + +def Hough_transform(edg_img, img, mask_scale=0.6): + # img是原始图像 + + mask_img = get_mask(edg_img, mask_scale) # 掩膜图像 + # -----------------霍夫曼变换----------------------- + # 定义Hough 变换的参数 + rho = 1 + theta = np.pi/180 + threshold = 2 + min_line_length = 4 # 组成一条线的最小像素 + max_line_length = 5 # 可连接线段之间的最大像素距离 + + lines = cv2.HoughLinesP(mask_img, rho, theta, threshold, np.array([]), + min_line_length, max_line_length) + + left_line = [] + right_line = [] + for line in lines: + for x1, y1, x2, y2 in line: + if x1 == x2: + pass + else: + # 求直线方程斜率判断左右车道 + m = (y2 - y1) / (x2 - x1) + c = y1 - m * x1 + if m < 0: # 左车道 + left_line.append((m, c)) + elif m >= 0: # 右车道 + right_line.append((m, c)) + cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 5) + return img + +def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): + """ + img1_shape: model input shape, [h, w] + img0_shape: origin pic shape, [h, w, 3] + masks: [h, w, num] + """ + # Rescale coordinates (xyxy) from im1_shape to im0_shape + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + # masks = masks.permute(2, 0, 1).contiguous() + # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] + # masks = masks.permute(1, 2, 0).contiguous() + + masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) + + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0, # number of masks +): + """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) + prediction = prediction[0] # select only inference output + + device = prediction.device + mps = 'mps' in device.type # Apple MPS + if mps: # MPS not fully supported yet, convert tensors to CPU labelme_dataset NMS + prediction = prediction.cpu() + bs = prediction.shape[0] # batch size + nc = prediction.shape[2] - nm - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Checks + assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + + # Settings + # min_wh = 2 # (pixels) minimum box width and height + max_wh = 7680 # (pixels) maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 0.5 + 0.05 * bs # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + mi = 5 + nc # mask start index + output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + lb = labels[xi] + v = torch.zeros((len(lb), nc + nm + 5), device=x.device) + v[:, :4] = lb[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box/Mask + box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) + mask = x[:, mi:] # zero columns if no masks + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) + else: # best class only + conf, j = x[:, 5:mi].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + else: + x = x[x[:, 4].argsort(descending=True)] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if mps: + output[xi] = output[xi].to(device) + + + return output + + +def box_iou(box1, box2, eps=1e-7): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) + +def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): + # Rescale boxes (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + clip_boxes(boxes, img0_shape) + return boxes + +def is_ascii(s=''): + # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) + s = str(s) # convert list, tuple, None, etc. to str + return len(s.encode().decode('ascii', 'ignore')) == len(s) + +def clip_boxes(boxes, shape): + # Clip boxes (xyxy) to image shape (height, width) + if isinstance(boxes, torch.Tensor): # faster individually + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 + else: # np.array (faster grouped) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + +def get_mask(edg_img, mask_scale=0.6): + # ----------------检测区域的选择--------------------- + mask = np.zeros_like(edg_img) # 全黑的图像 + ignore_mask_color = 255 + # get image size + imgshape = edg_img.shape + # 设置mask shape [1,4,2] 一般车道位置大概占据画面的1/3的位置 + ret = np.array([[(1, imgshape[0]), (1, int(imgshape[0] * mask_scale)), (imgshape[1] - 1, int(imgshape[0] * mask_scale)), + (imgshape[1] - 1, imgshape[0] - 1)]], dtype=np.int32) + # 多边形填充，mask是需要填充的图像，ret是多边形顶点, 将需要保留的区域填充为白色矩形 + cv2.fillPoly(mask, ret, ignore_mask_color) # mask下面部分变成白色 + # 图像与运算，保留掩膜图像 + mask_img = cv2.bitwise_and(edg_img, mask) + # ------------------------------------------------ + return mask_img + + diff --git a/algorithm/yolo_segment.py b/algorithm/yolo_segment.py new file mode 100644 index 0000000..1b6eed4 --- /dev/null +++ b/algorithm/yolo_segment.py @@ -0,0 +1,517 @@ +import datetime +import os +import time +import ffmpeg +import torch +import cv2 +import numpy as np +from multiprocessing import Process, Manager +from threading import Thread +from read_data import LoadImages, LoadStreams +import torch.backends.cudnn as cudnn +import torch.nn.functional as F +import torchvision + +from PIL import Image, ImageDraw, ImageFont + +class YOLO_Segment(): + time_reference = datetime.datetime.now() + counter_frame = 0 + processed_fps = 0 + + def __init__(self,video_path=None): + + + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + + self.model = torch.load('weight/segment/yolov5s-seg.pt', map_location=self.device)['model'].float().fuse() + self.classes = self.model.names + + self.frame = [None] + + if video_path is not None: + self.video_name = video_path + else: + self.video_name = 'vid2.mp4' # A default video file + + + self.dataset = LoadImages(self.video_name) + + self.names = self.model.names + + def use_webcam(self, source): + # self.dataset.release() # Release any existing video capture + # self.cap = cv2.VideoCapture(0) # Open default webcam + # print('use_webcam') + source = source + self.imgsz = 640 + cudnn.benchmark = True + self.dataset = LoadStreams(source, img_size=self.imgsz) + + def class_to_label(self, x): + return self.classes[int(x)] + + def get_frame(self): + + colors = Colors() + + for im0s in self.dataset: + # print(self.dataset.mode) + # print(self.dataset) + if self.dataset.mode == 'stream': + image = im0s[0].copy() + else: + image = im0s.copy() + img = image[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + + img0 = img.copy() + + img = torch.tensor(img0) + + img = img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + img = img.to(self.device) + self.model.to(self.device) + pred, proto = self.model(img)[:2] + + + pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, max_det=1000, nm=32) + + for i, det in enumerate(pred): # per image + annotator = Annotator(image, line_width=3, example=str(self.names)) + + if len(det): + masks = process_mask(proto[i], det[:, 6:], det[:, :4], img.shape[2:], upsample=True) # HWC + det[:, :4] = scale_boxes(img.shape[2:], det[:, :4], img.shape).round() # rescale boxes to im0 size + segments = reversed(masks2segments(masks)) + segments = [scale_segments(img.shape[2:], x, img.shape, normalize=True) for x in segments] + + # Print results + txt = "" + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + txt += f"{n} {self.classes[int(c)]}{'s' * (n > 1)}, " # add to string + + annotator.masks(masks, + colors=[colors(x, True) for x in det[:, 5]], + im_gpu=img[i]) + + im0 = annotator.result() + + + # Draw the number of people on the frame and display it + ret, jpeg = cv2.imencode(".jpg", im0) + + return jpeg.tobytes(), txt + +class Colors: + # Ultralytics color palette https://ultralytics.com/ + def __init__(self): + # hex = matplotlib.colors.TABLEAU_COLORS.values() + hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', + '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') + self.palette = [self.hex2rgb(f'#{c}') for c in hexs] + self.n = len(self.palette) + + def __call__(self, i, bgr=False): + c = self.palette[int(i) % self.n] + return (c[2], c[1], c[0]) if bgr else c + + @staticmethod + def hex2rgb(h): # rgb order (PIL) + return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + +class Annotator: + # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations + def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): + assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' + non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic + self.pil = pil or non_ascii + if self.pil: # use PIL + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + self.font = 'Arial.Unicode.ttf' + else: # use cv2 + self.im = im + self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width + + + def masks(self, masks, colors, im_gpu, alpha=0.5): + """Plot masks at once. + Args: + masks (tensor): predicted masks on cuda, shape: [n, h, w] + colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n] + im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1] + alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque + """ + if self.pil: + # convert to numpy first + self.im = np.asarray(self.im).copy() + if im_gpu is None: + # Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...) + if len(masks) == 0: + return + if isinstance(masks, torch.Tensor): + masks = torch.as_tensor(masks, dtype=torch.uint8) + masks = masks.permute(1, 2, 0).contiguous() + masks = masks.cpu().numpy() + # masks = np.ascontiguousarray(masks.transpose(1, 2, 0)) + masks = scale_image(masks.shape[:2], masks, self.im.shape) + masks = np.asarray(masks, dtype=np.float32) + colors = np.asarray(colors, dtype=np.float32) # shape(n,3) + s = masks.sum(2, keepdims=True).clip(0, 1) # add all masks together + masks = (masks @ colors).clip(0, 255) # (h,w,n) @ (n,3) = (h,w,3) + self.im[:] = masks * alpha + self.im * (1 - s * alpha) + else: + if len(masks) == 0: + self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255 + colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0 + colors = colors[:, None, None] # shape(n,1,1,3) + masks = masks.unsqueeze(3) # shape(n,h,w,1) + masks_color = masks * (colors * alpha) # shape(n,h,w,3) + + inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1) + mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3) + + im_gpu = im_gpu.flip(dims=[0]) # flip channel + im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3) + im_gpu = im_gpu * inv_alph_masks[-1] + mcs + im_mask = (im_gpu * 255).byte().cpu().numpy() + # print(type(im_gpu), type(im_mask), type(self.im.shape)) + self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape) + if self.pil: + # convert im back to PIL and update draw + self.fromarray(self.im) + + + def rectangle(self, xy, fill=None, outline=None, width=1): + # Add rectangle to image (PIL-only) + self.draw.rectangle(xy, fill, outline, width) + + def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'): + # Add text to image (PIL-only) + if anchor == 'bottom': # start y from font bottom + w, h = self.font.getsize(text) # text width, height + xy[1] += 1 - h + self.draw.text(xy, text, fill=txt_color, font=self.font) + + def fromarray(self, im): + # Update self.im from a numpy array + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + + def result(self): + # Return annotated image as array + return np.asarray(self.im) + + + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + +def is_ascii(s=''): + # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) + s = str(s) # convert list, tuple, None, etc. to str + return len(s.encode().decode('ascii', 'ignore')) == len(s) + +def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): + # Rescale boxes (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + clip_boxes(boxes, img0_shape) + return boxes + +def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + segments[:, 0] -= pad[0] # x padding + segments[:, 1] -= pad[1] # y padding + segments /= gain + clip_segments(segments, img0_shape) + if normalize: + segments[:, 0] /= img0_shape[1] # width + segments[:, 1] /= img0_shape[0] # height + return segments + +def clip_boxes(boxes, shape): + # Clip boxes (xyxy) to image shape (height, width) + if isinstance(boxes, torch.Tensor): # faster individually + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 + else: # np.array (faster grouped) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + + +def clip_segments(segments, shape): + # Clip segments (xy1,xy2,...) to image shape (height, width) + if isinstance(segments, torch.Tensor): # faster individually + segments[:, 0].clamp_(0, shape[1]) # x + segments[:, 1].clamp_(0, shape[0]) # y + else: # np.array (faster grouped) + segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x + segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y + + + + + +def masks2segments(masks, strategy='largest'): + # Convert masks(n,160,160) into segments(n,xy) + segments = [] + for x in masks.int().cpu().numpy().astype('uint8'): + c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + if c: + if strategy == 'concat': # concatenate all segments + c = np.concatenate([x.reshape(-1, 2) for x in c]) + elif strategy == 'largest': # select largest segment + c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) + else: + c = np.zeros((0, 2)) # no segments found + segments.append(c.astype('float32')) + return segments + +def process_mask(protos, masks_in, bboxes, shape, upsample=False): + """ + Crop before upsample. + proto_out: [mask_dim, mask_h, mask_w] + out_masks: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape:input_image_size, (h, w) + + return: h, w, n + """ + + c, mh, mw = protos.shape # CHW + ih, iw = shape + # print(masks_in.shape, protos.shape) + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW + + downsampled_bboxes = bboxes.clone() + downsampled_bboxes[:, 0] *= mw / iw + downsampled_bboxes[:, 2] *= mw / iw + downsampled_bboxes[:, 3] *= mh / ih + downsampled_bboxes[:, 1] *= mh / ih + + masks = crop_mask(masks, downsampled_bboxes) # CHW + if upsample: + masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + return masks.gt_(0.5) + + +def crop_mask(masks, boxes): + """ + "Crop" predicted masks by zeroing out everything not in the predicted bbox. + Vectorized by Chong (thanks Chong). + + Args: + - masks should be a size [h, w, n] tensor of masks + - boxes should be a size [n, 4] tensor of bbox coords in relative point form + """ + + n, h, w = masks.shape + x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n) + r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1) + c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1) + + return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) + + + + + +def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): + """ + img1_shape: model input shape, [h, w] + img0_shape: origin pic shape, [h, w, 3] + masks: [h, w, num] + """ + # Rescale coordinates (xyxy) from im1_shape to im0_shape + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + # masks = masks.permute(2, 0, 1).contiguous() + # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] + # masks = masks.permute(1, 2, 0).contiguous() + + masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) + + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0, # number of masks +): + """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) + prediction = prediction[0] # select only inference output + + device = prediction.device + mps = 'mps' in device.type # Apple MPS + if mps: # MPS not fully supported yet, convert tensors to CPU labelme_dataset NMS + prediction = prediction.cpu() + bs = prediction.shape[0] # batch size + nc = prediction.shape[2] - nm - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Checks + assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + + # Settings + # min_wh = 2 # (pixels) minimum box width and height + max_wh = 7680 # (pixels) maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 0.5 + 0.05 * bs # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + mi = 5 + nc # mask start index + output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + lb = labels[xi] + v = torch.zeros((len(lb), nc + nm + 5), device=x.device) + v[:, :4] = lb[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box/Mask + box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) + mask = x[:, mi:] # zero columns if no masks + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) + else: # best class only + conf, j = x[:, 5:mi].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence + else: + x = x[x[:, 4].argsort(descending=True)] # sort by confidence + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if mps: + output[xi] = output[xi].to(device) + + + return output + + +def box_iou(box1, box2, eps=1e-7): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) \ No newline at end of file diff --git a/algorithm/yolov5-master/.dockerignore b/algorithm/yolov5-master/.dockerignore new file mode 100644 index 0000000..3b66925 --- /dev/null +++ b/algorithm/yolov5-master/.dockerignore @@ -0,0 +1,222 @@ +# Repo-specific DockerIgnore ------------------------------------------------------------------------------------------- +.git +.cache +.idea +runs +output +coco +storage.googleapis.com + +data/samples/* +**/results*.csv +*.jpg + +# Neural Network weights ----------------------------------------------------------------------------------------------- +**/*.pt +**/*.pth +**/*.onnx +**/*.engine +**/*.mlmodel +**/*.torchscript +**/*.torchscript.pt +**/*.tflite +**/*.h5 +**/*.pb +*_saved_model/ +*_web_model/ +*_openvino_model/ + +# Below Copied From .gitignore ----------------------------------------------------------------------------------------- +# Below Copied From .gitignore ----------------------------------------------------------------------------------------- + + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +wandb/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/algorithm/yolov5-master/.gitattributes b/algorithm/yolov5-master/.gitattributes new file mode 100644 index 0000000..dad4239 --- /dev/null +++ b/algorithm/yolov5-master/.gitattributes @@ -0,0 +1,2 @@ +# this drop notebooks from GitHub language stats +*.ipynb linguist-vendored diff --git a/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/bug-report.yml b/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/bug-report.yml new file mode 100644 index 0000000..fcb6413 --- /dev/null +++ b/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/bug-report.yml @@ -0,0 +1,85 @@ +name: 🐛 Bug Report +# title: " " +description: Problems with YOLOv5 +labels: [bug, triage] +body: + - type: markdown + attributes: + value: | + Thank you for submitting a YOLOv5 🐛 Bug Report! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar bug report already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report. + required: true + + - type: dropdown + attributes: + label: YOLOv5 Component + description: | + Please select the part of YOLOv5 where you found the bug. + multiple: true + options: + - "Training" + - "Validation" + - "Detection" + - "Export" + - "PyTorch Hub" + - "Multi-GPU" + - "Evolution" + - "Integrations" + - "Other" + validations: + required: false + + - type: textarea + attributes: + label: Bug + description: Provide console output with error messages and/or screenshots of the bug. + placeholder: | + 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. + validations: + required: true + + - type: textarea + attributes: + label: Environment + description: Please specify the software and hardware you used to produce the bug. + placeholder: | + - YOLO: YOLOv5 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB) + - OS: Ubuntu 20.04 + - Python: 3.9.0 + validations: + required: false + + - type: textarea + attributes: + label: Minimal Reproducible Example + description: > + When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem. + This is referred to by community members as creating a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). + placeholder: | + ``` + # Code to reproduce your issue here + ``` + validations: + required: false + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? + + - type: checkboxes + attributes: + label: Are you willing to submit a PR? + description: > + (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature. + See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started. + options: + - label: Yes I'd like to help by submitting a PR! diff --git a/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/config.yml b/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..4db7cef --- /dev/null +++ b/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,8 @@ +blank_issues_enabled: true +contact_links: + - name: 💬 Forum + url: https://community.ultralytics.com/ + about: Ask on Ultralytics Community Forum + - name: Stack Overflow + url: https://stackoverflow.com/search?q=YOLOv5 + about: Ask on Stack Overflow with 'YOLOv5' tag diff --git a/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/feature-request.yml b/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/feature-request.yml new file mode 100644 index 0000000..68ef985 --- /dev/null +++ b/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/feature-request.yml @@ -0,0 +1,50 @@ +name: 🚀 Feature Request +description: Suggest a YOLOv5 idea +# title: " " +labels: [enhancement] +body: + - type: markdown + attributes: + value: | + Thank you for submitting a YOLOv5 🚀 Feature Request! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar feature request already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar feature requests. + required: true + + - type: textarea + attributes: + label: Description + description: A short description of your feature. + placeholder: | + What new feature would you like to see in YOLOv5? + validations: + required: true + + - type: textarea + attributes: + label: Use case + description: | + Describe the use case of your feature request. It will help us understand and prioritize the feature request. + placeholder: | + How would this feature be used, and who would use it? + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? + + - type: checkboxes + attributes: + label: Are you willing to submit a PR? + description: > + (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature. + See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started. + options: + - label: Yes I'd like to help by submitting a PR! diff --git a/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/question.yml b/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/question.yml new file mode 100644 index 0000000..8e0993c --- /dev/null +++ b/algorithm/yolov5-master/.github/ISSUE_TEMPLATE/question.yml @@ -0,0 +1,33 @@ +name: ❓ Question +description: Ask a YOLOv5 question +# title: " " +labels: [question] +body: + - type: markdown + attributes: + value: | + Thank you for asking a YOLOv5 ❓ Question! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) to see if a similar question already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions. + required: true + + - type: textarea + attributes: + label: Question + description: What is your question? + placeholder: | + 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. + validations: + required: true + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? diff --git a/algorithm/yolov5-master/.github/PULL_REQUEST_TEMPLATE.md b/algorithm/yolov5-master/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..f25b017 --- /dev/null +++ b/algorithm/yolov5-master/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,9 @@ + diff --git a/algorithm/yolov5-master/.github/dependabot.yml b/algorithm/yolov5-master/.github/dependabot.yml new file mode 100644 index 0000000..c1b3d5d --- /dev/null +++ b/algorithm/yolov5-master/.github/dependabot.yml @@ -0,0 +1,23 @@ +version: 2 +updates: + - package-ecosystem: pip + directory: "/" + schedule: + interval: weekly + time: "04:00" + open-pull-requests-limit: 10 + reviewers: + - glenn-jocher + labels: + - dependencies + + - package-ecosystem: github-actions + directory: "/" + schedule: + interval: weekly + time: "04:00" + open-pull-requests-limit: 5 + reviewers: + - glenn-jocher + labels: + - dependencies diff --git a/algorithm/yolov5-master/.github/workflows/ci-testing.yml b/algorithm/yolov5-master/.github/workflows/ci-testing.yml new file mode 100644 index 0000000..a6f47bb --- /dev/null +++ b/algorithm/yolov5-master/.github/workflows/ci-testing.yml @@ -0,0 +1,153 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# YOLOv5 Continuous Integration (CI) GitHub Actions tests + +name: YOLOv5 CI + +on: + push: + branches: [ master ] + pull_request: + branches: [ master ] + schedule: + - cron: '0 0 * * *' # runs at 00:00 UTC every day + +jobs: + Benchmarks: + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest ] + python-version: [ '3.10' ] # requires python<=3.10 + model: [ yolov5n ] + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' # caching pip dependencies + - name: Install requirements + run: | + python -m pip install --upgrade pip wheel + pip install -r requirements.txt coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu + python --version + pip --version + pip list + - name: Benchmark DetectionModel + run: | + python benchmarks.py --data coco128.yaml --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29 + - name: Benchmark SegmentationModel + run: | + python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320 --hard-fail 0.22 + - name: Test predictions + run: | + python export.py --weights ${{ matrix.model }}-cls.pt --include onnx --img 224 + python detect.py --weights ${{ matrix.model }}.onnx --img 320 + python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320 + python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224 + + Tests: + timeout-minutes: 60 + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest, windows-latest ] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049 + python-version: [ '3.10' ] + model: [ yolov5n ] + include: + - os: ubuntu-latest + python-version: '3.7' # '3.6.8' min + model: yolov5n + - os: ubuntu-latest + python-version: '3.8' + model: yolov5n + - os: ubuntu-latest + python-version: '3.9' + model: yolov5n + - os: ubuntu-latest + python-version: '3.8' # torch 1.7.0 requires python >=3.6, <=3.8 + model: yolov5n + torch: '1.7.0' # min torch version CI https://pypi.org/project/torchvision/ + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' # caching pip dependencies + - name: Install requirements + run: | + python -m pip install --upgrade pip wheel + if [ "${{ matrix.torch }}" == "1.7.0" ]; then + pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu + else + pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu + fi + shell: bash # for Windows compatibility + - name: Check environment + run: | + python -c "import utils; utils.notebook_init()" + echo "RUNNER_OS is ${{ runner.os }}" + echo "GITHUB_EVENT_NAME is ${{ github.event_name }}" + echo "GITHUB_WORKFLOW is ${{ github.workflow }}" + echo "GITHUB_ACTOR is ${{ github.actor }}" + echo "GITHUB_REPOSITORY is ${{ github.repository }}" + echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}" + python --version + pip --version + pip list + - name: Test detection + shell: bash # for Windows compatibility + run: | + # export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories + m=${{ matrix.model }} # official weights + b=runs/train/exp/weights/best # best.pt checkpoint + python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train + for d in cpu; do # devices + for w in $m $b; do # weights + python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val + python detect.py --imgsz 64 --weights $w.pt --device $d # detect + done + done + python hubconf.py --model $m # hub + # python models/tf.py --weights $m.pt # build TF model + python models/yolo.py --cfg $m.yaml # build PyTorch model + python export.py --weights $m.pt --img 64 --include torchscript # export + python - <=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). To get started: + ```bash + git clone https://github.com/ultralytics/yolov5 # clone + cd yolov5 + pip install -r requirements.txt # install + ``` + + ## Environments + + YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): + + - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle + - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) + - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart) + - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) Docker Pulls + + ## Status + + YOLOv5 CI + + If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. + + ## Introducing YOLOv8 🚀 + + We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀! + + Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects. + + Check out our [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with: + ```bash + pip install ultralytics + ``` diff --git a/algorithm/yolov5-master/.github/workflows/stale.yml b/algorithm/yolov5-master/.github/workflows/stale.yml new file mode 100644 index 0000000..b21e9c0 --- /dev/null +++ b/algorithm/yolov5-master/.github/workflows/stale.yml @@ -0,0 +1,40 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +name: Close stale issues +on: + schedule: + - cron: '0 0 * * *' # Runs at 00:00 UTC every day + +jobs: + stale: + runs-on: ubuntu-latest + steps: + - uses: actions/stale@v7 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + stale-issue-message: | + 👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs. + + Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources: + - **Wiki** – https://github.com/ultralytics/yolov5/wiki + - **Tutorials** – https://github.com/ultralytics/yolov5#tutorials + - **Docs** – https://docs.ultralytics.com + + Access additional [Ultralytics](https://ultralytics.com) ⚡ resources: + - **Ultralytics HUB** – https://ultralytics.com/hub + - **Vision API** – https://ultralytics.com/yolov5 + - **About Us** – https://ultralytics.com/about + - **Join Our Team** – https://ultralytics.com/work + - **Contact Us** – https://ultralytics.com/contact + + Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed! + + Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐! + + stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.' + days-before-issue-stale: 30 + days-before-issue-close: 10 + days-before-pr-stale: 90 + days-before-pr-close: 30 + exempt-issue-labels: 'documentation,tutorial,TODO' + operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting. diff --git a/algorithm/yolov5-master/.github/workflows/translate-readme.yml b/algorithm/yolov5-master/.github/workflows/translate-readme.yml new file mode 100644 index 0000000..2bb351e --- /dev/null +++ b/algorithm/yolov5-master/.github/workflows/translate-readme.yml @@ -0,0 +1,26 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# README translation action to translate README.md to Chinese as README.zh-CN.md on any change to README.md + +name: Translate README + +on: + push: + branches: + - translate_readme # replace with 'master' to enable action + paths: + - README.md + +jobs: + Translate: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Setup Node.js + uses: actions/setup-node@v3 + with: + node-version: 16 + # ISO Language Codes: https://cloud.google.com/translate/docs/languages + - name: Adding README - Chinese Simplified + uses: dephraiim/translate-readme@main + with: + LANG: zh-CN diff --git a/algorithm/yolov5-master/.gitignore b/algorithm/yolov5-master/.gitignore new file mode 100644 index 0000000..6bcedfa --- /dev/null +++ b/algorithm/yolov5-master/.gitignore @@ -0,0 +1,257 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.jpeg +*.png +*.bmp +*.tif +*.tiff +*.heic +*.JPG +*.JPEG +*.PNG +*.BMP +*.TIF +*.TIFF +*.HEIC +*.mp4 +*.mov +*.MOV +*.avi +*.data +*.json +*.cfg +!setup.cfg +!cfg/yolov3*.cfg + +storage.googleapis.com +runs/* +data/* +data/images/* +!data/*.yaml +!data/hyps +!data/scripts +!data/images +!data/images/zidane.jpg +!data/images/bus.jpg +!data/*.sh + +results*.csv + +# Datasets ------------------------------------------------------------------------------------------------------------- +coco/ +coco128/ +VOC/ + +# MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- +*.m~ +*.mat +!targets*.mat + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.pb +*.onnx +*.engine +*.mlmodel +*.torchscript +*.tflite +*.h5 +*_saved_model/ +*_web_model/ +*_openvino_model/ +*_paddle_model/ +darknet53.conv.74 +yolov3-tiny.conv.15 + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +/wandb/ +.installed.cfg +*.egg + + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/algorithm/yolov5-master/.pre-commit-config.yaml b/algorithm/yolov5-master/.pre-commit-config.yaml new file mode 100644 index 0000000..c516237 --- /dev/null +++ b/algorithm/yolov5-master/.pre-commit-config.yaml @@ -0,0 +1,69 @@ +# Ultralytics YOLO 🚀, GPL-3.0 license +# Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md + +exclude: 'docs/' +# Define bot property if installed via https://github.com/marketplace/pre-commit-ci +ci: + autofix_prs: true + autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions' + autoupdate_schedule: monthly + # submodules: true + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-case-conflict + - id: check-yaml + - id: check-docstring-first + - id: double-quote-string-fixer + - id: detect-private-key + + - repo: https://github.com/asottile/pyupgrade + rev: v3.3.1 + hooks: + - id: pyupgrade + name: Upgrade code + args: [--py37-plus] + + - repo: https://github.com/PyCQA/isort + rev: 5.12.0 + hooks: + - id: isort + name: Sort imports + + - repo: https://github.com/google/yapf + rev: v0.32.0 + hooks: + - id: yapf + name: YAPF formatting + + - repo: https://github.com/executablebooks/mdformat + rev: 0.7.16 + hooks: + - id: mdformat + name: MD formatting + additional_dependencies: + - mdformat-gfm + - mdformat-black + # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md" + + - repo: https://github.com/PyCQA/flake8 + rev: 6.0.0 + hooks: + - id: flake8 + name: PEP8 + + - repo: https://github.com/codespell-project/codespell + rev: v2.2.2 + hooks: + - id: codespell + args: + - --ignore-words-list=crate,nd,strack,dota + + #- repo: https://github.com/asottile/yesqa + # rev: v1.4.0 + # hooks: + # - id: yesqa diff --git a/algorithm/yolov5-master/CITATION.cff b/algorithm/yolov5-master/CITATION.cff new file mode 100644 index 0000000..8e2cf11 --- /dev/null +++ b/algorithm/yolov5-master/CITATION.cff @@ -0,0 +1,14 @@ +cff-version: 1.2.0 +preferred-citation: + type: software + message: If you use YOLOv5, please cite it as below. + authors: + - family-names: Jocher + given-names: Glenn + orcid: "https://orcid.org/0000-0001-5950-6979" + title: "YOLOv5 by Ultralytics" + version: 7.0 + doi: 10.5281/zenodo.3908559 + date-released: 2020-5-29 + license: GPL-3.0 + url: "https://github.com/ultralytics/yolov5" diff --git a/algorithm/yolov5-master/CONTRIBUTING.md b/algorithm/yolov5-master/CONTRIBUTING.md new file mode 100644 index 0000000..71857fa --- /dev/null +++ b/algorithm/yolov5-master/CONTRIBUTING.md @@ -0,0 +1,93 @@ +## Contributing to YOLOv5 🚀 + +We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's: + +- Reporting a bug +- Discussing the current state of the code +- Submitting a fix +- Proposing a new feature +- Becoming a maintainer + +YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be +helping push the frontiers of what's possible in AI 😃! + +## Submitting a Pull Request (PR) 🛠️ + +Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps: + +### 1. Select File to Update + +Select `requirements.txt` to update by clicking on it in GitHub. + +

PR_step1

+ +### 2. Click 'Edit this file' + +The button is in the top-right corner. + +

PR_step2

+ +### 3. Make Changes + +Change the `matplotlib` version from `3.2.2` to `3.3`. + +

PR_step3

+ +### 4. Preview Changes and Submit PR + +Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** +for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose +changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃! + +

PR_step4

+ +### PR recommendations + +To allow your work to be integrated as seamlessly as possible, we advise you to: + +- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update + your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally. + +

Screenshot 2022-08-29 at 22 47 15

+ +- ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**. + +

Screenshot 2022-08-29 at 22 47 03

+ +- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase + but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee + +## Submitting a Bug Report 🐛 + +If you spot a problem with YOLOv5 please submit a Bug Report! + +For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few +short guidelines below to help users provide what we need to get started. + +When asking a question, people will be better able to provide help if you provide **code** that they can easily +understand and use to **reproduce** the problem. This is referred to by community members as creating +a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces +the problem should be: + +- ✅ **Minimal** – Use as little code as possible that still produces the same problem +- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself +- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem + +In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code +should be: + +- ✅ **Current** – Verify that your code is up-to-date with the current + GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new + copy to ensure your problem has not already been resolved by previous commits. +- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this + repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. + +If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 +**Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and provide +a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better +understand and diagnose your problem. + +## License + +By contributing, you agree that your contributions will be licensed under +the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/) diff --git a/algorithm/yolov5-master/LICENSE b/algorithm/yolov5-master/LICENSE new file mode 100644 index 0000000..92b370f --- /dev/null +++ b/algorithm/yolov5-master/LICENSE @@ -0,0 +1,674 @@ +GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/algorithm/yolov5-master/README.md b/algorithm/yolov5-master/README.md new file mode 100644 index 0000000..16dfd9f --- /dev/null +++ b/algorithm/yolov5-master/README.md @@ -0,0 +1,493 @@ +
+

+ + +

+ +[English](README.md) | [简体中文](README.zh-CN.md) +
+ +
+ YOLOv5 CI + YOLOv5 Citation + Docker Pulls +
+ Run on Gradient + Open In Colab + Open In Kaggle +
+
+ +YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. + +To request an Enterprise License please complete the form at Ultralytics Licensing. + +
+ + + + + + + + + + + + + + + + + + + + +
+
+
+ +##
YOLOv8 🚀 NEW
+ +We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model +released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. +YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of +object detection, image segmentation and image classification tasks. + +See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with: + +```commandline +pip install ultralytics +``` + +
+ + +
+ +##
Documentation
+ +See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. See below for quickstart examples. + +
+Install + +Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a +[**Python>=3.7.0**](https://www.python.org/) environment, including +[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). + +```bash +git clone https://github.com/ultralytics/yolov5 # clone +cd yolov5 +pip install -r requirements.txt # install +``` + +
+ +
+Inference + +YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest +YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). + +```python +import torch + +# Model +model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom + +# Images +img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list + +# Inference +results = model(img) + +# Results +results.print() # or .show(), .save(), .crop(), .pandas(), etc. +``` + +
+ +
+Inference with detect.py + +`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from +the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. + +```bash +python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream +``` + +
+ +
+Training + +The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) +results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) +and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest +YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are +1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the +largest `--batch-size` possible, or pass `--batch-size -1` for +YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. + +```bash +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 + yolov5s 64 + yolov5m 40 + yolov5l 24 + yolov5x 16 +``` + + + +
+ +
+Tutorials + +- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 RECOMMENDED +- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️ + RECOMMENDED +- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) +- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 NEW +- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 +- [NVIDIA Jetson Nano Deployment](https://github.com/ultralytics/yolov5/issues/9627) 🌟 NEW +- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) +- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) +- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) +- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) +- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) +- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) 🌟 NEW +- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)  🌟 NEW +- [ClearML Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 NEW +- [YOLOv5 with Neural Magic's Deepsparse](https://bit.ly/yolov5-neuralmagic) 🌟 NEW +- [Comet Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet) 🌟 NEW + +
+ +##
Integrations
+ +
+ + +
+
+ +
+ + + + + + + + + + + +
+ +| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | +| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | +| Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions | Run YOLOv5 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | + +##
Ultralytics HUB
+ +Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now! + + + + +##
Why YOLOv5
+ +YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results. + +

+
+ YOLOv5-P5 640 Figure + +

+
+
+ Figure Notes + +- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536. +- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32. +- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8. +- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` + +
+ +### Pretrained Checkpoints + +| Model | size
(pixels) | mAPval
50-95 | mAPval
50 | Speed
CPU b1
(ms) | Speed
V100 b1
(ms) | Speed
V100 b32
(ms) | params
(M) | FLOPs
@640 (B) | +| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- | +| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | +| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | +| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | +| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | +| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | +| | | | | | | | | | +| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | +| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | +| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | +| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | +| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)
+ [TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | + +
+ Table Notes + +- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). +- **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` +- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.
Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1` +- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` + +
+ +##
Segmentation
+ +Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials. + +
+ Segmentation Checkpoints + +
+ + +
+ +We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility. + +| Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Train time
300 epochs
A100 (hours) | Speed
ONNX CPU
(ms) | Speed
TRT A100
(ms) | params
(M) | FLOPs
@640 (B) | +| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- | +| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | +| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | +| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | +| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | +| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | + +- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official +- **Accuracy** values are for single-model single-scale on COCO dataset.
Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` +- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image).
Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` +- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`.
Reproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` + +
+ +
+ Segmentation Usage Examples  Open In Colab + +### Train + +YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`. + +```bash +# Single-GPU +python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 + +# Multi-GPU DDP +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 +``` + +### Val + +Validate YOLOv5s-seg mask mAP on COCO dataset: + +```bash +bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images) +python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate +``` + +### Predict + +Use pretrained YOLOv5m-seg.pt to predict bus.jpg: + +```bash +python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5m-seg.pt" +) # load from PyTorch Hub (WARNING: inference not yet supported) +``` + +| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | +| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | + +### Export + +Export YOLOv5s-seg model to ONNX and TensorRT: + +```bash +python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 +``` + +
+ +##
Classification
+ +YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials. + +
+ Classification Checkpoints + +
+ +We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility. + +| Model | size
(pixels) | acc
top1 | acc
top5 | Training
90 epochs
4xA100 (hours) | Speed
ONNX CPU
(ms) | Speed
TensorRT V100
(ms) | params
(M) | FLOPs
@224 (B) | +| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- | +| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | +| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | +| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | +| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | +| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | +| | | | | | | | | | +| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | +| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | +| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | +| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | +| | | | | | | | | | +| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | +| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | +| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | +| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 | + +
+ Table Notes (click to expand) + +- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 +- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.
Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224` +- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.
Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` +- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`.
Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` + +
+
+ +
+ Classification Usage Examples  Open In Colab + +### Train + +YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`. + +```bash +# Single-GPU +python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 + +# Multi-GPU DDP +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 +``` + +### Val + +Validate YOLOv5m-cls accuracy on ImageNet-1k dataset: + +```bash +bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) +python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate +``` + +### Predict + +Use pretrained YOLOv5s-cls.pt to predict bus.jpg: + +```bash +python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5s-cls.pt" +) # load from PyTorch Hub +``` + +### Export + +Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT: + +```bash +python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 +``` + +
+ +##
Environments
+ +Get started in seconds with our verified environments. Click each icon below for details. + +
+ + + + + + + + + + + + + + + + + +
+ +##
Contribute
+ +We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors! + + + + + + +##
License
+ +YOLOv5 is available under two different licenses: + +- **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details. +- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license). + +##
Contact
+ +For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues) or the [Ultralytics Community Forum](https://community.ultralytics.com/). + +
+
+ + + + + + + + + + + + + + + + + + + + +
+ +[tta]: https://github.com/ultralytics/yolov5/issues/303 diff --git a/algorithm/yolov5-master/README.zh-CN.md b/algorithm/yolov5-master/README.zh-CN.md new file mode 100644 index 0000000..800a670 --- /dev/null +++ b/algorithm/yolov5-master/README.zh-CN.md @@ -0,0 +1,488 @@ +
+

+ + +

+ +[英文](README.md)|[简体中文](README.zh-CN.md)
+ +
+ YOLOv5 CI + YOLOv5 Citation + Docker Pulls +
+ Run on Gradient + Open In Colab + Open In Kaggle +
+
+ +YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表 Ultralytics 对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。 + +如果要申请企业许可证，请填写表格Ultralytics 许可. + +
+ + + + + + + + + + + + + + + + + + + + +
+
+ +##
YOLOv8 🚀 NEW
+ +We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model +released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. +YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of +object detection, image segmentation and image classification tasks. + +See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with: + +```commandline +pip install ultralytics +``` + +
+ + +
+ +##
文档
+ +有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com)。请参阅下面的快速入门示例。 + +
+安装 + +克隆 repo，并要求在 [**Python>=3.7.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/) 。 + +```bash +git clone https://github.com/ultralytics/yolov5 # clone +cd yolov5 +pip install -r requirements.txt # install +``` + +
+ +
+推理 + +使用 YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 +YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 + +```python +import torch + +# Model +model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom + +# Images +img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list + +# Inference +results = model(img) + +# Results +results.print() # or .show(), .save(), .crop(), .pandas(), etc. +``` + +
+ +
+使用 detect.py 推理 + +`detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 +最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。 + +```bash +python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream +``` + +
+ +
+训练 + +下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 +最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) +将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 +YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://github.com/ultralytics/yolov5/issues/475) 训练速度更快）。 +尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 +YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 + +```bash +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 + yolov5s 64 + yolov5m 40 + yolov5l 24 + yolov5x 16 +``` + + + +
+ +
+教程 + +- [训练自定义数据](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)🚀 推荐 +- [获得最佳训练结果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)☘️ 推荐 +- [多 GPU 训练](https://github.com/ultralytics/yolov5/issues/475) +- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)🌟 新 +- [TFLite、ONNX、CoreML、TensorRT 导出](https://github.com/ultralytics/yolov5/issues/251)🚀 +- [NVIDIA Jetson Nano 部署](https://github.com/ultralytics/yolov5/issues/9627)🌟 新 +- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303) +- [模型集成](https://github.com/ultralytics/yolov5/issues/318) +- [模型修剪/稀疏度](https://github.com/ultralytics/yolov5/issues/304) +- [超参数进化](https://github.com/ultralytics/yolov5/issues/607) +- [使用冻结层进行迁移学习](https://github.com/ultralytics/yolov5/issues/1314) +- [架构总结](https://github.com/ultralytics/yolov5/issues/6998)🌟 新 +- [用于数据集、标签和主动学习的 Roboflow](https://github.com/ultralytics/yolov5/issues/4975)🌟 新 +- [ClearML 记录](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml)🌟 新 +- [Deci 平台](https://github.com/ultralytics/yolov5/wiki/Deci-Platform)🌟 新 +- [Comet Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet)🌟 新 + +
+ +##
模块集成
+ +
+ + +
+
+ +
+ + + + + + + + + + + +
+ +| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 | +| :--------------------------------------------------------------------------------: | :-------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: | +| 将您的自定义数据集进行标注并直接导出到 YOLOv5 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv5 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv5 推理的速度最高可提高6倍 | + +##
Ultralytics HUB
+ +[Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！ + + + + +##
为什么选择 YOLOv5
+ +YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结果。 + +

+
+ YOLOv5-P5 640 图 + +

+
+
+ 图表笔记 + +- **COCO AP val** 表示 mAP@0.5:0.95 指标，在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得， 图像包含 256 到 1536 各种推理大小。 +- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例，batchsize 为 32 。 +- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ， batchsize 为32。 +- **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` + +
+ +### 预训练模型 + +| 模型 | 尺寸
（像素） | mAPval
50-95 | mAPval
50 | 推理速度
CPU b1
（ms） | 推理速度
V100 b1
（ms） | 速度
V100 b32
（ms） | 参数量
(M) | FLOPs
@640 (B) | +| ---------------------------------------------------------------------------------------------- | --------------- | -------------------- | ----------------- | --------------------------- | ---------------------------- | --------------------------- | --------------- | ---------------------- | +| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | +| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | +| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | +| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | +| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | +| | | | | | | | | | +| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | +| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | +| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | +| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | +| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)
+[TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | + +
+ 笔记 + +- 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。 +- \*\*mAPval\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。
复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` +- **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。
复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1` +- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和尺度变换。
复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` + +
+ +##
实例分割模型 ⭐ 新
+ +我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型，击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。 + +
+ 实例分割模型列表 + +
+ +
+ + +
+ +我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现，我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。 + +| 模型 | 尺寸
（像素） | mAPbox
50-95 | mAPmask
50-95 | 训练时长
300 epochs
A100 GPU（小时） | 推理速度
ONNX CPU
（ms） | 推理速度
TRT A100
（ms） | 参数量
(M) | FLOPs
@640 (B) | +| ------------------------------------------------------------------------------------------ | --------------- | -------------------- | --------------------- | --------------------------------------- | ----------------------------- | ----------------------------- | --------------- | ---------------------- | +| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | +| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | +| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | +| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | +| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | + +- 所有模型使用 SGD 优化器训练， 都使用 `lr0=0.01` 和 `weight_decay=5e-5` 参数， 图像大小为 640 。
训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official +- **准确性**结果都在 COCO 数据集上，使用单模型单尺度测试得到。
复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` +- **推理速度**是使用 100 张图像推理时间进行平均得到，测试环境使用 [Colab Pro](https://colab.research.google.com/signup) 上 A100 高 RAM 实例。结果仅表示推理速度（NMS 每张图像增加约 1 毫秒）。
复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` +- **模型转换**到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 `export.py`.
运行命令 `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` + +
+ +
+ 分割模型使用示例  Open In Colab + +### 训练 + +YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集，用户仅需在启动指令中包含 `--data coco128-seg.yaml` 参数。 若要手动下载，使用命令 `bash data/scripts/get_coco.sh --train --val --segments`， 在下载完毕后，使用命令 `python train.py --data coco.yaml` 开启训练。 + +```bash +# 单 GPU +python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 + +# 多 GPU， DDP 模式 +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 +``` + +### 验证 + +在 COCO 数据集上验证 YOLOv5s-seg mask mAP： + +```bash +bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images) +python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证 +``` + +### 预测 + +使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg： + +```bash +python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5m-seg.pt" +) # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持) +``` + +| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | +| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | + +### 模型导出 + +将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT： + +```bash +python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 +``` + +
+ +##
分类网络 ⭐ 新
+ +YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对分类模型训练、验证和部署的支持！详情请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v6.2) 或访问我们的 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) 以快速入门。 + +
+ 分类网络模型 + +
+ +我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现，我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。 + +| 模型 | 尺寸
（像素） | acc
top1 | acc
top5 | 训练时长
90 epochs
4xA100（小时） | 推理速度
ONNX CPU
（ms） | 推理速度
TensorRT V100
（ms） | 参数
(M) | FLOPs
@640 (B) | +| -------------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | ------------------------------------ | ----------------------------- | ---------------------------------- | -------------- | ---------------------- | +| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | +| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | +| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | +| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | +| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | +| | | | | | | | | | +| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | +| [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | +| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | +| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | +| | | | | | | | | | +| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | +| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | +| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | +| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 | + +
+ Table Notes (点击以展开) + +- 所有模型都使用 SGD 优化器训练 90 个 epochs，都使用 `lr0=0.001` 和 `weight_decay=5e-5` 参数， 图像大小为 224 ，且都使用默认设置。
训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 +- **准确性**都在单模型单尺度上计算，数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。
复现命令 `python classify/val.py --data ../datasets/imagenet --img 224` +- **推理速度**是使用 100 个推理图像进行平均得到，测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。
复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` +- **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。
复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` +
+
+ +
+ 分类训练示例  Open In Colab + +### 训练 + +YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集，命令中使用 `--data` 即可。 MNIST 示例 `--data mnist` 。 + +```bash +# 单 GPU +python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 + +# 多 GPU， DDP 模式 +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 +``` + +### 验证 + +在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性： + +```bash +bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) +python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate +``` + +### 预测 + +使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg： + +```bash +python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5s-cls.pt" +) # load from PyTorch Hub +``` + +### 模型导出 + +将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT： + +```bash +python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 +``` + +
+ +##
环境
+ +使用下面我们经过验证的环境，在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。 + +
+ + + + + + + + + + + + + + + + + +
+ +##
贡献
+ +我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 [投稿指南](CONTRIBUTING.md)，并填写 [YOLOv5调查](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！ + + + + + + +##
License
+ +YOLOv5 在两种不同的 License 下可用： + +- **GPL-3.0 License**： 查看 [License](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件的详细信息。 +- **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI 模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。 + +##
联系我们
+ +请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues) 或 [Ultralytics Community Forum](https://community.ultralytis.com) 以报告 YOLOv5 错误和请求功能。 + +
+
+ + + + + + + + + + + + + + + + + + + + +
+ +[tta]: https://github.com/ultralytics/yolov5/issues/303 diff --git a/algorithm/yolov5-master/benchmarks.py b/algorithm/yolov5-master/benchmarks.py new file mode 100644 index 0000000..09108b8 --- /dev/null +++ b/algorithm/yolov5-master/benchmarks.py @@ -0,0 +1,169 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 benchmarks on all supported export formats + +Format | `export.py --include` | Model +--- | --- | --- +PyTorch | - | yolov5s.pt +TorchScript | `torchscript` | yolov5s.torchscript +ONNX | `onnx` | yolov5s.onnx +OpenVINO | `openvino` | yolov5s_openvino_model/ +TensorRT | `engine` | yolov5s.engine +CoreML | `coreml` | yolov5s.mlmodel +TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ +TensorFlow GraphDef | `pb` | yolov5s.pb +TensorFlow Lite | `tflite` | yolov5s.tflite +TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov5s_web_model/ + +Requirements: + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU + $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT + +Usage: + $ python benchmarks.py --weights yolov5s.pt --img 640 +""" + +import argparse +import platform +import sys +import time +from pathlib import Path + +import pandas as pd + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +# ROOT = ROOT.relative_to(Path.cwd()) # relative + +import export +from models.experimental import attempt_load +from models.yolo import SegmentationModel +from segment.val import run as val_seg +from utils import notebook_init +from utils.general import LOGGER, check_yaml, file_size, print_args +from utils.torch_utils import select_device +from val import run as val_det + + +def run( + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure +): + y, t = [], time.time() + device = select_device(device) + model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc. + for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU) + try: + assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported + assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML + if 'cpu' in device.type: + assert cpu, 'inference not supported on CPU' + if 'cuda' in device.type: + assert gpu, 'inference not supported on GPU' + + # Export + if f == '-': + w = weights # PyTorch format + else: + w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # all others + assert suffix in str(w), 'export failed' + + # Validate + if model_type == SegmentationModel: + result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half) + metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls)) + else: # DetectionModel: + result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half) + metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls)) + speed = result[2][1] # times (preprocess, inference, postprocess) + y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference + except Exception as e: + if hard_fail: + assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}' + LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}') + y.append([name, None, None, None]) # mAP, t_inference + if pt_only and i == 0: + break # break after PyTorch + + # Print results + LOGGER.info('\n') + parse_opt() + notebook_init() # print system info + c = ['Format', 'Size (MB)', 'mAP50-95', 'Inference time (ms)'] if map else ['Format', 'Export', '', ''] + py = pd.DataFrame(y, columns=c) + LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)') + LOGGER.info(str(py if map else py.iloc[:, :2])) + if hard_fail and isinstance(hard_fail, str): + metrics = py['mAP50-95'].array # values to compare to floor + floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n + assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: mAP50-95 < floor {floor}' + return py + + +def test( + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure +): + y, t = [], time.time() + device = select_device(device) + for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable) + try: + w = weights if f == '-' else \ + export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights + assert suffix in str(w), 'export failed' + y.append([name, True]) + except Exception: + y.append([name, False]) # mAP, t_inference + + # Print results + LOGGER.info('\n') + parse_opt() + notebook_init() # print system info + py = pd.DataFrame(y, columns=['Format', 'Export']) + LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)') + LOGGER.info(str(py)) + return py + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--test', action='store_true', help='test exports only') + parser.add_argument('--pt-only', action='store_true', help='test PyTorch only') + parser.add_argument('--hard-fail', nargs='?', const=True, default=False, help='Exception on error or < min metric') + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + print_args(vars(opt)) + return opt + + +def main(opt): + test(**vars(opt)) if opt.test else run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/classify/predict.py b/algorithm/yolov5-master/classify/predict.py new file mode 100644 index 0000000..5f0d407 --- /dev/null +++ b/algorithm/yolov5-master/classify/predict.py @@ -0,0 +1,226 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python classify/predict.py --weights yolov5s-cls.pt # PyTorch + yolov5s-cls.torchscript # TorchScript + yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-cls_openvino_model # OpenVINO + yolov5s-cls.engine # TensorRT + yolov5s-cls.mlmodel # CoreML (macOS-only) + yolov5s-cls_saved_model # TensorFlow SavedModel + yolov5s-cls.pb # TensorFlow GraphDef + yolov5s-cls.tflite # TensorFlow Lite + yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-cls_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch +import torch.nn.functional as F + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.augmentations import classify_transforms +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, print_args, strip_optimizer) +from utils.plots import Annotator +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(224, 224), # inference size (height, width) + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + nosave=False, # do not save images/videos + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/predict-cls', # save results to project/name + name='exp', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.Tensor(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + results = model(im) + + # Post-process + with dt[2]: + pred = F.softmax(results, dim=1) # probabilities + + # Process predictions + for i, prob in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + + s += '%gx%g ' % im.shape[2:] # print string + annotator = Annotator(im0, example=str(names), pil=True) + + # Print results + top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices + s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, " + + # Write results + text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i) + if save_img or view_img: # Add bbox to image + annotator.text((32, 32), text, txt_color=(255, 255, 255)) + if save_txt: # Write to file + with open(f'{txt_path}.txt', 'a') as f: + f.write(text + '\n') + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f'{s}{dt[1].dt * 1E3:.1f}ms') + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)') + parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/predict-cls', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/classify/train.py b/algorithm/yolov5-master/classify/train.py new file mode 100644 index 0000000..ae2363c --- /dev/null +++ b/algorithm/yolov5-master/classify/train.py @@ -0,0 +1,333 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Train a YOLOv5 classifier model on a classification dataset + +Usage - Single-GPU training: + $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 + +Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data' +YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt +Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html +""" + +import argparse +import os +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.hub as hub +import torch.optim.lr_scheduler as lr_scheduler +import torchvision +from torch.cuda import amp +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from classify import val as validate +from models.experimental import attempt_load +from models.yolo import ClassificationModel, DetectionModel +from utils.dataloaders import create_classification_dataloader +from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_info, check_git_status, + check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save) +from utils.loggers import GenericLogger +from utils.plots import imshow_cls +from utils.torch_utils import (ModelEMA, de_parallel, model_info, reshape_classifier_output, select_device, smart_DDP, + smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first) + +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +GIT_INFO = check_git_info() + + +def train(opt, device): + init_seeds(opt.seed + 1 + RANK, deterministic=True) + save_dir, data, bs, epochs, nw, imgsz, pretrained = \ + opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), \ + opt.imgsz, str(opt.pretrained).lower() == 'true' + cuda = device.type != 'cpu' + + # Directories + wdir = save_dir / 'weights' + wdir.mkdir(parents=True, exist_ok=True) # make dir + last, best = wdir / 'last.pt', wdir / 'best.pt' + + # Save run settings + yaml_save(save_dir / 'opt.yaml', vars(opt)) + + # Logger + logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None + + # Download Dataset + with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): + data_dir = data if data.is_dir() else (DATASETS_DIR / data) + if not data_dir.is_dir(): + LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...') + t = time.time() + if str(data) == 'imagenet': + subprocess.run(['bash', str(ROOT / 'data/scripts/get_imagenet.sh')], shell=True, check=True) + else: + url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip' + download(url, dir=data_dir.parent) + s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n" + LOGGER.info(s) + + # Dataloaders + nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()]) # number of classes + trainloader = create_classification_dataloader(path=data_dir / 'train', + imgsz=imgsz, + batch_size=bs // WORLD_SIZE, + augment=True, + cache=opt.cache, + rank=LOCAL_RANK, + workers=nw) + + test_dir = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val + if RANK in {-1, 0}: + testloader = create_classification_dataloader(path=test_dir, + imgsz=imgsz, + batch_size=bs // WORLD_SIZE * 2, + augment=False, + cache=opt.cache, + rank=-1, + workers=nw) + + # Model + with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): + if Path(opt.model).is_file() or opt.model.endswith('.pt'): + model = attempt_load(opt.model, device='cpu', fuse=False) + elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0 + model = torchvision.models.__dict__[opt.model](weights='IMAGENET1K_V1' if pretrained else None) + else: + m = hub.list('ultralytics/yolov5') # + hub.list('pytorch/vision') # models + raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m)) + if isinstance(model, DetectionModel): + LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'") + model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model + reshape_classifier_output(model, nc) # update class count + for m in model.modules(): + if not pretrained and hasattr(m, 'reset_parameters'): + m.reset_parameters() + if isinstance(m, torch.nn.Dropout) and opt.dropout is not None: + m.p = opt.dropout # set dropout + for p in model.parameters(): + p.requires_grad = True # for training + model = model.to(device) + + # Info + if RANK in {-1, 0}: + model.names = trainloader.dataset.classes # attach class names + model.transforms = testloader.dataset.torch_transforms # attach inference transforms + model_info(model) + if opt.verbose: + LOGGER.info(model) + images, labels = next(iter(trainloader)) + file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / 'train_images.jpg') + logger.log_images(file, name='Train Examples') + logger.log_graph(model, imgsz) # log model + + # Optimizer + optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay) + + # Scheduler + lrf = 0.01 # final lr (fraction of lr0) + # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine + lf = lambda x: (1 - x / epochs) * (1 - lrf) + lrf # linear + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1, + # final_div_factor=1 / 25 / lrf) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Train + t0 = time.time() + criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function + best_fitness = 0.0 + scaler = amp.GradScaler(enabled=cuda) + val = test_dir.stem # 'val' or 'test' + LOGGER.info(f'Image sizes {imgsz} train, {imgsz} test\n' + f'Using {nw * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n' + f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}") + for epoch in range(epochs): # loop over the dataset multiple times + tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness + model.train() + if RANK != -1: + trainloader.sampler.set_epoch(epoch) + pbar = enumerate(trainloader) + if RANK in {-1, 0}: + pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT) + for i, (images, labels) in pbar: # progress bar + images, labels = images.to(device, non_blocking=True), labels.to(device) + + # Forward + with amp.autocast(enabled=cuda): # stability issues when enabled + loss = criterion(model(images), labels) + + # Backward + scaler.scale(loss).backward() + + # Optimize + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + if RANK in {-1, 0}: + # Print + tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses + mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) + pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + ' ' * 36 + + # Test + if i == len(pbar) - 1: # last batch + top1, top5, vloss = validate.run(model=ema.ema, + dataloader=testloader, + criterion=criterion, + pbar=pbar) # test accuracy, loss + fitness = top1 # define fitness as top1 accuracy + + # Scheduler + scheduler.step() + + # Log metrics + if RANK in {-1, 0}: + # Best fitness + if fitness > best_fitness: + best_fitness = fitness + + # Log + metrics = { + 'train/loss': tloss, + f'{val}/loss': vloss, + 'metrics/accuracy_top1': top1, + 'metrics/accuracy_top5': top5, + 'lr/0': optimizer.param_groups[0]['lr']} # learning rate + logger.log_metrics(metrics, epoch) + + # Save model + final_epoch = epoch + 1 == epochs + if (not opt.nosave) or final_epoch: + ckpt = { + 'epoch': epoch, + 'best_fitness': best_fitness, + 'model': deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(), + 'ema': None, # deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': None, # optimizer.state_dict(), + 'opt': vars(opt), + 'git': GIT_INFO, # {remote, branch, commit} if a git repo + 'date': datetime.now().isoformat()} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fitness: + torch.save(ckpt, best) + del ckpt + + # Train complete + if RANK in {-1, 0} and final_epoch: + LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)' + f"\nResults saved to {colorstr('bold', save_dir)}" + f'\nPredict: python classify/predict.py --weights {best} --source im.jpg' + f'\nValidate: python classify/val.py --weights {best} --data {data_dir}' + f'\nExport: python export.py --weights {best} --include onnx' + f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')" + f'\nVisualize: https://netron.app\n') + + # Plot examples + images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels + pred = torch.max(ema.ema(images.to(device)), 1)[1] + file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / 'test_images.jpg') + + # Log results + meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()} + logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch) + logger.log_model(best, epochs, metadata=meta) + + +def parse_opt(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path') + parser.add_argument('--data', type=str, default='imagenette160', help='cifar10, cifar100, mnist, imagenet, ...') + parser.add_argument('--epochs', type=int, default=10, help='total training epochs') + parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--project', default=ROOT / 'runs/train-cls', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--pretrained', nargs='?', const=True, default=True, help='start from i.e. --pretrained False') + parser.add_argument('--optimizer', choices=['SGD', 'Adam', 'AdamW', 'RMSProp'], default='Adam', help='optimizer') + parser.add_argument('--lr0', type=float, default=0.001, help='initial learning rate') + parser.add_argument('--decay', type=float, default=5e-5, help='weight decay') + parser.add_argument('--label-smoothing', type=float, default=0.1, help='Label smoothing epsilon') + parser.add_argument('--cutoff', type=int, default=None, help='Model layer cutoff index for Classify() head') + parser.add_argument('--dropout', type=float, default=None, help='Dropout (fraction)') + parser.add_argument('--verbose', action='store_true', help='Verbose mode') + parser.add_argument('--seed', type=int, default=0, help='Global training seed') + parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt): + # Checks + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements() + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + assert opt.batch_size != -1, 'AutoBatch is coming soon for classification, please pass a valid --batch-size' + assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' + assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + torch.cuda.set_device(LOCAL_RANK) + device = torch.device('cuda', LOCAL_RANK) + dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + + # Parameters + opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run + + # Train + train(opt, device) + + +def run(**kwargs): + # Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m') + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/classify/tutorial.ipynb b/algorithm/yolov5-master/classify/tutorial.ipynb new file mode 100644 index 0000000..5872360 --- /dev/null +++ b/algorithm/yolov5-master/classify/tutorial.ipynb @@ -0,0 +1,1480 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "
\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wbvMlHd_QwMG", + "outputId": "0806e375-610d-4ec0-c867-763dbb518279" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + ] + } + ], + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Predict\n", + "\n", + "`classify/predict.py` runs YOLOv5 Classification inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict-cls`. Example inference sources are:\n", + "\n", + "```shell\n", + "python classify/predict.py --source 0 # webcam\n", + " img.jpg # image \n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zR9ZbuQCH7FX", + "outputId": "50504ef7-aa3e-4281-a4e3-d0c7df3c0ffe" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['yolov5s-cls.pt'], source=data/images, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt...\n", + "100% 10.5M/10.5M [00:00<00:00, 12.3MB/s]\n", + "\n", + "Fusing layers... \n", + "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 224x224 minibus 0.39, police van 0.24, amphibious vehicle 0.05, recreational vehicle 0.04, trolleybus 0.03, 3.9ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 224x224 suit 0.38, bow tie 0.19, bridegroom 0.18, rugby ball 0.04, stage 0.02, 4.6ms\n", + "Speed: 0.3ms pre-process, 4.3ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224)\n", + "Results saved to \u001b[1mruns/predict-cls/exp\u001b[0m\n" + ] + } + ], + "source": [ + "!python classify/predict.py --weights yolov5s-cls.pt --img 224 --source data/images\n", + "# display.Image(filename='runs/predict-cls/exp/zidane.jpg', width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [Imagenet](https://image-net.org/) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WQPtK1QYVaD_", + "outputId": "20fc0630-141e-4a90-ea06-342cbd7ce496" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n", + "Resolving image-net.org (image-net.org)... 171.64.68.16\n", + "Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6744924160 (6.3G) [application/x-tar]\n", + "Saving to: ‘ILSVRC2012_img_val.tar’\n", + "\n", + "ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s \n", + "\n", + "2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n", + "\n" + ] + } + ], + "source": [ + "# Download Imagenet val (6.3G, 50000 images)\n", + "!bash data/scripts/get_imagenet.sh --val" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X58w8JLpMnjH", + "outputId": "41843132-98e2-4c25-d474-4cd7b246fb8e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=../datasets/imagenet, weights=['yolov5s-cls.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=True, dnn=False\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Fusing layers... \n", + "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n", + "validating: 100% 391/391 [04:57<00:00, 1.31it/s]\n", + " Class Images top1_acc top5_acc\n", + " all 50000 0.715 0.902\n", + " tench 50 0.94 0.98\n", + " goldfish 50 0.88 0.92\n", + " great white shark 50 0.78 0.96\n", + " tiger shark 50 0.68 0.96\n", + " hammerhead shark 50 0.82 0.92\n", + " electric ray 50 0.76 0.9\n", + " stingray 50 0.7 0.9\n", + " cock 50 0.78 0.92\n", + " hen 50 0.84 0.96\n", + " ostrich 50 0.98 1\n", + " brambling 50 0.9 0.96\n", + " goldfinch 50 0.92 0.98\n", + " house finch 50 0.88 0.96\n", + " junco 50 0.94 0.98\n", + " indigo bunting 50 0.86 0.88\n", + " American robin 50 0.9 0.96\n", + " bulbul 50 0.84 0.96\n", + " jay 50 0.9 0.96\n", + " magpie 50 0.84 0.96\n", + " chickadee 50 0.9 1\n", + " American dipper 50 0.82 0.92\n", + " kite 50 0.76 0.94\n", + " bald eagle 50 0.92 1\n", + " vulture 50 0.96 1\n", + " great grey owl 50 0.94 0.98\n", + " fire salamander 50 0.96 0.98\n", + " smooth newt 50 0.58 0.94\n", + " newt 50 0.74 0.9\n", + " spotted salamander 50 0.86 0.94\n", + " axolotl 50 0.86 0.96\n", + " American bullfrog 50 0.78 0.92\n", + " tree frog 50 0.84 0.96\n", + " tailed frog 50 0.48 0.8\n", + " loggerhead sea turtle 50 0.68 0.94\n", + " leatherback sea turtle 50 0.5 0.8\n", + " mud turtle 50 0.64 0.84\n", + " terrapin 50 0.52 0.98\n", + " box turtle 50 0.84 0.98\n", + " banded gecko 50 0.7 0.88\n", + " green iguana 50 0.76 0.94\n", + " Carolina anole 50 0.58 0.96\n", + "desert grassland whiptail lizard 50 0.82 0.94\n", + " agama 50 0.74 0.92\n", + " frilled-necked lizard 50 0.84 0.86\n", + " alligator lizard 50 0.58 0.78\n", + " Gila monster 50 0.72 0.8\n", + " European green lizard 50 0.42 0.9\n", + " chameleon 50 0.76 0.84\n", + " Komodo dragon 50 0.86 0.96\n", + " Nile crocodile 50 0.7 0.84\n", + " American alligator 50 0.76 0.96\n", + " triceratops 50 0.9 0.94\n", + " worm snake 50 0.76 0.88\n", + " ring-necked snake 50 0.8 0.92\n", + " eastern hog-nosed snake 50 0.58 0.88\n", + " smooth green snake 50 0.6 0.94\n", + " kingsnake 50 0.82 0.9\n", + " garter snake 50 0.88 0.94\n", + " water snake 50 0.7 0.94\n", + " vine snake 50 0.66 0.76\n", + " night snake 50 0.34 0.82\n", + " boa constrictor 50 0.8 0.96\n", + " African rock python 50 0.48 0.76\n", + " Indian cobra 50 0.82 0.94\n", + " green mamba 50 0.54 0.86\n", + " sea snake 50 0.62 0.9\n", + " Saharan horned viper 50 0.56 0.86\n", + "eastern diamondback rattlesnake 50 0.6 0.86\n", + " sidewinder 50 0.28 0.86\n", + " trilobite 50 0.98 0.98\n", + " harvestman 50 0.86 0.94\n", + " scorpion 50 0.86 0.94\n", + " yellow garden spider 50 0.92 0.96\n", + " barn spider 50 0.38 0.98\n", + " European garden spider 50 0.62 0.98\n", + " southern black widow 50 0.88 0.94\n", + " tarantula 50 0.94 1\n", + " wolf spider 50 0.82 0.92\n", + " tick 50 0.74 0.84\n", + " centipede 50 0.68 0.82\n", + " black grouse 50 0.88 0.98\n", + " ptarmigan 50 0.78 0.94\n", + " ruffed grouse 50 0.88 1\n", + " prairie grouse 50 0.92 1\n", + " peacock 50 0.88 0.9\n", + " quail 50 0.9 0.94\n", + " partridge 50 0.74 0.96\n", + " grey parrot 50 0.9 0.96\n", + " macaw 50 0.88 0.98\n", + "sulphur-crested cockatoo 50 0.86 0.92\n", + " lorikeet 50 0.96 1\n", + " coucal 50 0.82 0.88\n", + " bee eater 50 0.96 0.98\n", + " hornbill 50 0.9 0.96\n", + " hummingbird 50 0.88 0.96\n", + " jacamar 50 0.92 0.94\n", + " toucan 50 0.84 0.94\n", + " duck 50 0.76 0.94\n", + " red-breasted merganser 50 0.86 0.96\n", + " goose 50 0.74 0.96\n", + " black swan 50 0.94 0.98\n", + " tusker 50 0.54 0.92\n", + " echidna 50 0.98 1\n", + " platypus 50 0.72 0.84\n", + " wallaby 50 0.78 0.88\n", + " koala 50 0.84 0.92\n", + " wombat 50 0.78 0.84\n", + " jellyfish 50 0.88 0.96\n", + " sea anemone 50 0.72 0.9\n", + " brain coral 50 0.88 0.96\n", + " flatworm 50 0.8 0.98\n", + " nematode 50 0.86 0.9\n", + " conch 50 0.74 0.88\n", + " snail 50 0.78 0.88\n", + " slug 50 0.74 0.82\n", + " sea slug 50 0.88 0.98\n", + " chiton 50 0.88 0.98\n", + " chambered nautilus 50 0.88 0.92\n", + " Dungeness crab 50 0.78 0.94\n", + " rock crab 50 0.68 0.86\n", + " fiddler crab 50 0.64 0.86\n", + " red king crab 50 0.76 0.96\n", + " American lobster 50 0.78 0.96\n", + " spiny lobster 50 0.74 0.88\n", + " crayfish 50 0.56 0.86\n", + " hermit crab 50 0.78 0.96\n", + " isopod 50 0.66 0.78\n", + " white stork 50 0.88 0.96\n", + " black stork 50 0.84 0.98\n", + " spoonbill 50 0.96 1\n", + " flamingo 50 0.94 1\n", + " little blue heron 50 0.92 0.98\n", + " great egret 50 0.9 0.96\n", + " bittern 50 0.86 0.94\n", + " crane (bird) 50 0.62 0.9\n", + " limpkin 50 0.98 1\n", + " common gallinule 50 0.92 0.96\n", + " American coot 50 0.9 0.98\n", + " bustard 50 0.92 0.96\n", + " ruddy turnstone 50 0.94 1\n", + " dunlin 50 0.86 0.94\n", + " common redshank 50 0.9 0.96\n", + " dowitcher 50 0.84 0.96\n", + " oystercatcher 50 0.86 0.94\n", + " pelican 50 0.92 0.96\n", + " king penguin 50 0.88 0.96\n", + " albatross 50 0.9 1\n", + " grey whale 50 0.84 0.92\n", + " killer whale 50 0.92 1\n", + " dugong 50 0.84 0.96\n", + " sea lion 50 0.82 0.92\n", + " Chihuahua 50 0.66 0.84\n", + " Japanese Chin 50 0.72 0.98\n", + " Maltese 50 0.76 0.94\n", + " Pekingese 50 0.84 0.94\n", + " Shih Tzu 50 0.74 0.96\n", + " King Charles Spaniel 50 0.88 0.98\n", + " Papillon 50 0.86 0.94\n", + " toy terrier 50 0.48 0.94\n", + " Rhodesian Ridgeback 50 0.76 0.98\n", + " Afghan Hound 50 0.84 1\n", + " Basset Hound 50 0.8 0.92\n", + " Beagle 50 0.82 0.96\n", + " Bloodhound 50 0.48 0.72\n", + " Bluetick Coonhound 50 0.86 0.94\n", + " Black and Tan Coonhound 50 0.54 0.8\n", + "Treeing Walker Coonhound 50 0.66 0.98\n", + " English foxhound 50 0.32 0.84\n", + " Redbone Coonhound 50 0.62 0.94\n", + " borzoi 50 0.92 1\n", + " Irish Wolfhound 50 0.48 0.88\n", + " Italian Greyhound 50 0.76 0.98\n", + " Whippet 50 0.74 0.92\n", + " Ibizan Hound 50 0.6 0.86\n", + " Norwegian Elkhound 50 0.88 0.98\n", + " Otterhound 50 0.62 0.9\n", + " Saluki 50 0.72 0.92\n", + " Scottish Deerhound 50 0.86 0.98\n", + " Weimaraner 50 0.88 0.94\n", + "Staffordshire Bull Terrier 50 0.66 0.98\n", + "American Staffordshire Terrier 50 0.64 0.92\n", + " Bedlington Terrier 50 0.9 0.92\n", + " Border Terrier 50 0.86 0.92\n", + " Kerry Blue Terrier 50 0.78 0.98\n", + " Irish Terrier 50 0.7 0.96\n", + " Norfolk Terrier 50 0.68 0.9\n", + " Norwich Terrier 50 0.72 1\n", + " Yorkshire Terrier 50 0.66 0.9\n", + " Wire Fox Terrier 50 0.64 0.98\n", + " Lakeland Terrier 50 0.74 0.92\n", + " Sealyham Terrier 50 0.76 0.9\n", + " Airedale Terrier 50 0.82 0.92\n", + " Cairn Terrier 50 0.76 0.9\n", + " Australian Terrier 50 0.48 0.84\n", + " Dandie Dinmont Terrier 50 0.82 0.92\n", + " Boston Terrier 50 0.92 1\n", + " Miniature Schnauzer 50 0.68 0.9\n", + " Giant Schnauzer 50 0.72 0.98\n", + " Standard Schnauzer 50 0.74 1\n", + " Scottish Terrier 50 0.76 0.96\n", + " Tibetan Terrier 50 0.48 1\n", + "Australian Silky Terrier 50 0.66 0.96\n", + "Soft-coated Wheaten Terrier 50 0.74 0.96\n", + "West Highland White Terrier 50 0.88 0.96\n", + " Lhasa Apso 50 0.68 0.96\n", + " Flat-Coated Retriever 50 0.72 0.94\n", + " Curly-coated Retriever 50 0.82 0.94\n", + " Golden Retriever 50 0.86 0.94\n", + " Labrador Retriever 50 0.82 0.94\n", + "Chesapeake Bay Retriever 50 0.76 0.96\n", + "German Shorthaired Pointer 50 0.8 0.96\n", + " Vizsla 50 0.68 0.96\n", + " English Setter 50 0.7 1\n", + " Irish Setter 50 0.8 0.9\n", + " Gordon Setter 50 0.84 0.92\n", + " Brittany 50 0.84 0.96\n", + " Clumber Spaniel 50 0.92 0.96\n", + "English Springer Spaniel 50 0.88 1\n", + " Welsh Springer Spaniel 50 0.92 1\n", + " Cocker Spaniels 50 0.7 0.94\n", + " Sussex Spaniel 50 0.72 0.92\n", + " Irish Water Spaniel 50 0.88 0.98\n", + " Kuvasz 50 0.66 0.9\n", + " Schipperke 50 0.9 0.98\n", + " Groenendael 50 0.8 0.94\n", + " Malinois 50 0.86 0.98\n", + " Briard 50 0.52 0.8\n", + " Australian Kelpie 50 0.6 0.88\n", + " Komondor 50 0.88 0.94\n", + " Old English Sheepdog 50 0.94 0.98\n", + " Shetland Sheepdog 50 0.74 0.9\n", + " collie 50 0.6 0.96\n", + " Border Collie 50 0.74 0.96\n", + " Bouvier des Flandres 50 0.78 0.94\n", + " Rottweiler 50 0.88 0.96\n", + " German Shepherd Dog 50 0.8 0.98\n", + " Dobermann 50 0.68 0.96\n", + " Miniature Pinscher 50 0.76 0.88\n", + "Greater Swiss Mountain Dog 50 0.68 0.94\n", + " Bernese Mountain Dog 50 0.96 1\n", + " Appenzeller Sennenhund 50 0.22 1\n", + " Entlebucher Sennenhund 50 0.64 0.98\n", + " Boxer 50 0.7 0.92\n", + " Bullmastiff 50 0.78 0.98\n", + " Tibetan Mastiff 50 0.88 0.96\n", + " French Bulldog 50 0.84 0.94\n", + " Great Dane 50 0.54 0.9\n", + " St. Bernard 50 0.92 1\n", + " husky 50 0.46 0.98\n", + " Alaskan Malamute 50 0.76 0.96\n", + " Siberian Husky 50 0.46 0.98\n", + " Dalmatian 50 0.94 0.98\n", + " Affenpinscher 50 0.78 0.9\n", + " Basenji 50 0.92 0.94\n", + " pug 50 0.94 0.98\n", + " Leonberger 50 1 1\n", + " Newfoundland 50 0.78 0.96\n", + " Pyrenean Mountain Dog 50 0.78 0.96\n", + " Samoyed 50 0.96 1\n", + " Pomeranian 50 0.98 1\n", + " Chow Chow 50 0.9 0.96\n", + " Keeshond 50 0.88 0.94\n", + " Griffon Bruxellois 50 0.84 0.98\n", + " Pembroke Welsh Corgi 50 0.82 0.94\n", + " Cardigan Welsh Corgi 50 0.66 0.98\n", + " Toy Poodle 50 0.52 0.88\n", + " Miniature Poodle 50 0.52 0.92\n", + " Standard Poodle 50 0.8 1\n", + " Mexican hairless dog 50 0.88 0.98\n", + " grey wolf 50 0.82 0.92\n", + " Alaskan tundra wolf 50 0.78 0.98\n", + " red wolf 50 0.48 0.9\n", + " coyote 50 0.64 0.86\n", + " dingo 50 0.76 0.88\n", + " dhole 50 0.9 0.98\n", + " African wild dog 50 0.98 1\n", + " hyena 50 0.88 0.96\n", + " red fox 50 0.54 0.92\n", + " kit fox 50 0.72 0.98\n", + " Arctic fox 50 0.94 1\n", + " grey fox 50 0.7 0.94\n", + " tabby cat 50 0.54 0.92\n", + " tiger cat 50 0.22 0.94\n", + " Persian cat 50 0.9 0.98\n", + " Siamese cat 50 0.96 1\n", + " Egyptian Mau 50 0.54 0.8\n", + " cougar 50 0.9 1\n", + " lynx 50 0.72 0.88\n", + " leopard 50 0.78 0.98\n", + " snow leopard 50 0.9 0.98\n", + " jaguar 50 0.7 0.94\n", + " lion 50 0.9 0.98\n", + " tiger 50 0.92 0.98\n", + " cheetah 50 0.94 0.98\n", + " brown bear 50 0.94 0.98\n", + " American black bear 50 0.8 1\n", + " polar bear 50 0.84 0.96\n", + " sloth bear 50 0.72 0.92\n", + " mongoose 50 0.7 0.92\n", + " meerkat 50 0.82 0.92\n", + " tiger beetle 50 0.92 0.94\n", + " ladybug 50 0.86 0.94\n", + " ground beetle 50 0.64 0.94\n", + " longhorn beetle 50 0.62 0.88\n", + " leaf beetle 50 0.64 0.98\n", + " dung beetle 50 0.86 0.98\n", + " rhinoceros beetle 50 0.86 0.94\n", + " weevil 50 0.9 1\n", + " fly 50 0.78 0.94\n", + " bee 50 0.68 0.94\n", + " ant 50 0.68 0.78\n", + " grasshopper 50 0.5 0.92\n", + " cricket 50 0.64 0.92\n", + " stick insect 50 0.64 0.92\n", + " cockroach 50 0.72 0.8\n", + " mantis 50 0.64 0.86\n", + " cicada 50 0.9 0.96\n", + " leafhopper 50 0.88 0.94\n", + " lacewing 50 0.78 0.92\n", + " dragonfly 50 0.82 0.98\n", + " damselfly 50 0.82 1\n", + " red admiral 50 0.94 0.96\n", + " ringlet 50 0.86 0.98\n", + " monarch butterfly 50 0.9 0.92\n", + " small white 50 0.9 1\n", + " sulphur butterfly 50 0.92 1\n", + "gossamer-winged butterfly 50 0.88 1\n", + " starfish 50 0.88 0.92\n", + " sea urchin 50 0.84 0.94\n", + " sea cucumber 50 0.66 0.84\n", + " cottontail rabbit 50 0.72 0.94\n", + " hare 50 0.84 0.96\n", + " Angora rabbit 50 0.94 0.98\n", + " hamster 50 0.96 1\n", + " porcupine 50 0.88 0.98\n", + " fox squirrel 50 0.76 0.94\n", + " marmot 50 0.92 0.96\n", + " beaver 50 0.78 0.94\n", + " guinea pig 50 0.78 0.94\n", + " common sorrel 50 0.96 0.98\n", + " zebra 50 0.94 0.96\n", + " pig 50 0.5 0.76\n", + " wild boar 50 0.84 0.96\n", + " warthog 50 0.84 0.96\n", + " hippopotamus 50 0.88 0.96\n", + " ox 50 0.48 0.94\n", + " water buffalo 50 0.78 0.94\n", + " bison 50 0.88 0.96\n", + " ram 50 0.58 0.92\n", + " bighorn sheep 50 0.66 1\n", + " Alpine ibex 50 0.92 0.98\n", + " hartebeest 50 0.94 1\n", + " impala 50 0.82 0.96\n", + " gazelle 50 0.7 0.96\n", + " dromedary 50 0.9 1\n", + " llama 50 0.82 0.94\n", + " weasel 50 0.44 0.92\n", + " mink 50 0.78 0.96\n", + " European polecat 50 0.46 0.9\n", + " black-footed ferret 50 0.68 0.96\n", + " otter 50 0.66 0.88\n", + " skunk 50 0.96 0.96\n", + " badger 50 0.86 0.92\n", + " armadillo 50 0.88 0.9\n", + " three-toed sloth 50 0.96 1\n", + " orangutan 50 0.78 0.92\n", + " gorilla 50 0.82 0.94\n", + " chimpanzee 50 0.84 0.94\n", + " gibbon 50 0.76 0.86\n", + " siamang 50 0.68 0.94\n", + " guenon 50 0.8 0.94\n", + " patas monkey 50 0.62 0.82\n", + " baboon 50 0.9 0.98\n", + " macaque 50 0.8 0.86\n", + " langur 50 0.6 0.82\n", + " black-and-white colobus 50 0.86 0.9\n", + " proboscis monkey 50 1 1\n", + " marmoset 50 0.74 0.98\n", + " white-headed capuchin 50 0.72 0.9\n", + " howler monkey 50 0.86 0.94\n", + " titi 50 0.5 0.9\n", + "Geoffroy's spider monkey 50 0.42 0.8\n", + " common squirrel monkey 50 0.76 0.92\n", + " ring-tailed lemur 50 0.72 0.94\n", + " indri 50 0.9 0.96\n", + " Asian elephant 50 0.58 0.92\n", + " African bush elephant 50 0.7 0.98\n", + " red panda 50 0.94 0.94\n", + " giant panda 50 0.94 0.98\n", + " snoek 50 0.74 0.9\n", + " eel 50 0.6 0.84\n", + " coho salmon 50 0.84 0.96\n", + " rock beauty 50 0.88 0.98\n", + " clownfish 50 0.78 0.98\n", + " sturgeon 50 0.68 0.94\n", + " garfish 50 0.62 0.8\n", + " lionfish 50 0.96 0.96\n", + " pufferfish 50 0.88 0.96\n", + " abacus 50 0.74 0.88\n", + " abaya 50 0.84 0.92\n", + " academic gown 50 0.42 0.86\n", + " accordion 50 0.8 0.9\n", + " acoustic guitar 50 0.5 0.76\n", + " aircraft carrier 50 0.8 0.96\n", + " airliner 50 0.92 1\n", + " airship 50 0.76 0.82\n", + " altar 50 0.64 0.98\n", + " ambulance 50 0.88 0.98\n", + " amphibious vehicle 50 0.64 0.94\n", + " analog clock 50 0.52 0.92\n", + " apiary 50 0.82 0.96\n", + " apron 50 0.7 0.84\n", + " waste container 50 0.4 0.8\n", + " assault rifle 50 0.42 0.84\n", + " backpack 50 0.34 0.64\n", + " bakery 50 0.4 0.68\n", + " balance beam 50 0.8 0.98\n", + " balloon 50 0.86 0.96\n", + " ballpoint pen 50 0.52 0.96\n", + " Band-Aid 50 0.7 0.9\n", + " banjo 50 0.84 1\n", + " baluster 50 0.68 0.94\n", + " barbell 50 0.56 0.9\n", + " barber chair 50 0.7 0.92\n", + " barbershop 50 0.54 0.86\n", + " barn 50 0.96 0.96\n", + " barometer 50 0.84 0.98\n", + " barrel 50 0.56 0.88\n", + " wheelbarrow 50 0.66 0.88\n", + " baseball 50 0.74 0.98\n", + " basketball 50 0.88 0.98\n", + " bassinet 50 0.66 0.92\n", + " bassoon 50 0.74 0.98\n", + " swimming cap 50 0.62 0.88\n", + " bath towel 50 0.54 0.78\n", + " bathtub 50 0.4 0.88\n", + " station wagon 50 0.66 0.84\n", + " lighthouse 50 0.78 0.94\n", + " beaker 50 0.52 0.68\n", + " military cap 50 0.84 0.96\n", + " beer bottle 50 0.66 0.88\n", + " beer glass 50 0.6 0.84\n", + " bell-cot 50 0.56 0.96\n", + " bib 50 0.58 0.82\n", + " tandem bicycle 50 0.86 0.96\n", + " bikini 50 0.56 0.88\n", + " ring binder 50 0.64 0.84\n", + " binoculars 50 0.54 0.78\n", + " birdhouse 50 0.86 0.94\n", + " boathouse 50 0.74 0.92\n", + " bobsleigh 50 0.92 0.96\n", + " bolo tie 50 0.8 0.94\n", + " poke bonnet 50 0.64 0.86\n", + " bookcase 50 0.66 0.92\n", + " bookstore 50 0.62 0.88\n", + " bottle cap 50 0.58 0.7\n", + " bow 50 0.72 0.86\n", + " bow tie 50 0.7 0.9\n", + " brass 50 0.92 0.96\n", + " bra 50 0.5 0.7\n", + " breakwater 50 0.62 0.86\n", + " breastplate 50 0.4 0.9\n", + " broom 50 0.6 0.86\n", + " bucket 50 0.66 0.8\n", + " buckle 50 0.5 0.68\n", + " bulletproof vest 50 0.5 0.78\n", + " high-speed train 50 0.94 0.96\n", + " butcher shop 50 0.74 0.94\n", + " taxicab 50 0.64 0.86\n", + " cauldron 50 0.44 0.66\n", + " candle 50 0.48 0.74\n", + " cannon 50 0.88 0.94\n", + " canoe 50 0.94 1\n", + " can opener 50 0.66 0.86\n", + " cardigan 50 0.68 0.8\n", + " car mirror 50 0.94 0.96\n", + " carousel 50 0.94 0.98\n", + " tool kit 50 0.56 0.78\n", + " carton 50 0.42 0.7\n", + " car wheel 50 0.38 0.74\n", + "automated teller machine 50 0.76 0.94\n", + " cassette 50 0.52 0.8\n", + " cassette player 50 0.28 0.9\n", + " castle 50 0.78 0.88\n", + " catamaran 50 0.78 1\n", + " CD player 50 0.52 0.82\n", + " cello 50 0.82 1\n", + " mobile phone 50 0.68 0.86\n", + " chain 50 0.38 0.66\n", + " chain-link fence 50 0.7 0.84\n", + " chain mail 50 0.64 0.9\n", + " chainsaw 50 0.84 0.92\n", + " chest 50 0.68 0.92\n", + " chiffonier 50 0.26 0.64\n", + " chime 50 0.62 0.84\n", + " china cabinet 50 0.82 0.96\n", + " Christmas stocking 50 0.92 0.94\n", + " church 50 0.62 0.9\n", + " movie theater 50 0.58 0.88\n", + " cleaver 50 0.32 0.62\n", + " cliff dwelling 50 0.88 1\n", + " cloak 50 0.32 0.64\n", + " clogs 50 0.58 0.88\n", + " cocktail shaker 50 0.62 0.7\n", + " coffee mug 50 0.44 0.72\n", + " coffeemaker 50 0.64 0.92\n", + " coil 50 0.66 0.84\n", + " combination lock 50 0.64 0.84\n", + " computer keyboard 50 0.7 0.82\n", + " confectionery store 50 0.54 0.86\n", + " container ship 50 0.82 0.98\n", + " convertible 50 0.78 0.98\n", + " corkscrew 50 0.82 0.92\n", + " cornet 50 0.46 0.88\n", + " cowboy boot 50 0.64 0.8\n", + " cowboy hat 50 0.64 0.82\n", + " cradle 50 0.38 0.8\n", + " crane (machine) 50 0.78 0.94\n", + " crash helmet 50 0.92 0.96\n", + " crate 50 0.52 0.82\n", + " infant bed 50 0.74 1\n", + " Crock Pot 50 0.78 0.9\n", + " croquet ball 50 0.9 0.96\n", + " crutch 50 0.46 0.7\n", + " cuirass 50 0.54 0.86\n", + " dam 50 0.74 0.92\n", + " desk 50 0.6 0.86\n", + " desktop computer 50 0.54 0.94\n", + " rotary dial telephone 50 0.88 0.94\n", + " diaper 50 0.68 0.84\n", + " digital clock 50 0.54 0.76\n", + " digital watch 50 0.58 0.86\n", + " dining table 50 0.76 0.9\n", + " dishcloth 50 0.94 1\n", + " dishwasher 50 0.44 0.78\n", + " disc brake 50 0.98 1\n", + " dock 50 0.54 0.94\n", + " dog sled 50 0.84 1\n", + " dome 50 0.72 0.92\n", + " doormat 50 0.56 0.82\n", + " drilling rig 50 0.84 0.96\n", + " drum 50 0.38 0.68\n", + " drumstick 50 0.56 0.72\n", + " dumbbell 50 0.62 0.9\n", + " Dutch oven 50 0.7 0.84\n", + " electric fan 50 0.82 0.86\n", + " electric guitar 50 0.62 0.84\n", + " electric locomotive 50 0.92 0.98\n", + " entertainment center 50 0.9 0.98\n", + " envelope 50 0.44 0.86\n", + " espresso machine 50 0.72 0.94\n", + " face powder 50 0.7 0.92\n", + " feather boa 50 0.7 0.84\n", + " filing cabinet 50 0.88 0.98\n", + " fireboat 50 0.94 0.98\n", + " fire engine 50 0.84 0.9\n", + " fire screen sheet 50 0.62 0.76\n", + " flagpole 50 0.74 0.88\n", + " flute 50 0.36 0.72\n", + " folding chair 50 0.62 0.84\n", + " football helmet 50 0.86 0.94\n", + " forklift 50 0.8 0.92\n", + " fountain 50 0.84 0.94\n", + " fountain pen 50 0.76 0.92\n", + " four-poster bed 50 0.78 0.94\n", + " freight car 50 0.96 1\n", + " French horn 50 0.76 0.92\n", + " frying pan 50 0.36 0.78\n", + " fur coat 50 0.84 0.96\n", + " garbage truck 50 0.9 0.98\n", + " gas mask 50 0.84 0.92\n", + " gas pump 50 0.9 0.98\n", + " goblet 50 0.68 0.82\n", + " go-kart 50 0.9 1\n", + " golf ball 50 0.84 0.9\n", + " golf cart 50 0.78 0.86\n", + " gondola 50 0.98 0.98\n", + " gong 50 0.74 0.92\n", + " gown 50 0.62 0.96\n", + " grand piano 50 0.7 0.96\n", + " greenhouse 50 0.8 0.98\n", + " grille 50 0.72 0.9\n", + " grocery store 50 0.66 0.94\n", + " guillotine 50 0.86 0.92\n", + " barrette 50 0.52 0.66\n", + " hair spray 50 0.5 0.74\n", + " half-track 50 0.78 0.9\n", + " hammer 50 0.56 0.76\n", + " hamper 50 0.64 0.84\n", + " hair dryer 50 0.56 0.74\n", + " hand-held computer 50 0.42 0.86\n", + " handkerchief 50 0.78 0.94\n", + " hard disk drive 50 0.76 0.84\n", + " harmonica 50 0.7 0.88\n", + " harp 50 0.88 0.96\n", + " harvester 50 0.78 1\n", + " hatchet 50 0.54 0.74\n", + " holster 50 0.66 0.84\n", + " home theater 50 0.64 0.94\n", + " honeycomb 50 0.56 0.88\n", + " hook 50 0.3 0.6\n", + " hoop skirt 50 0.64 0.86\n", + " horizontal bar 50 0.68 0.98\n", + " horse-drawn vehicle 50 0.88 0.94\n", + " hourglass 50 0.88 0.96\n", + " iPod 50 0.76 0.94\n", + " clothes iron 50 0.82 0.88\n", + " jack-o'-lantern 50 0.98 0.98\n", + " jeans 50 0.68 0.84\n", + " jeep 50 0.72 0.9\n", + " T-shirt 50 0.72 0.96\n", + " jigsaw puzzle 50 0.84 0.94\n", + " pulled rickshaw 50 0.86 0.94\n", + " joystick 50 0.8 0.9\n", + " kimono 50 0.84 0.96\n", + " knee pad 50 0.62 0.88\n", + " knot 50 0.66 0.8\n", + " lab coat 50 0.8 0.96\n", + " ladle 50 0.36 0.64\n", + " lampshade 50 0.48 0.84\n", + " laptop computer 50 0.26 0.88\n", + " lawn mower 50 0.78 0.96\n", + " lens cap 50 0.46 0.72\n", + " paper knife 50 0.26 0.5\n", + " library 50 0.54 0.9\n", + " lifeboat 50 0.92 0.98\n", + " lighter 50 0.56 0.78\n", + " limousine 50 0.76 0.92\n", + " ocean liner 50 0.88 0.94\n", + " lipstick 50 0.74 0.9\n", + " slip-on shoe 50 0.74 0.92\n", + " lotion 50 0.5 0.86\n", + " speaker 50 0.52 0.68\n", + " loupe 50 0.32 0.52\n", + " sawmill 50 0.72 0.9\n", + " magnetic compass 50 0.52 0.82\n", + " mail bag 50 0.68 0.92\n", + " mailbox 50 0.82 0.92\n", + " tights 50 0.22 0.94\n", + " tank suit 50 0.24 0.9\n", + " manhole cover 50 0.96 0.98\n", + " maraca 50 0.74 0.9\n", + " marimba 50 0.84 0.94\n", + " mask 50 0.44 0.82\n", + " match 50 0.66 0.9\n", + " maypole 50 0.96 1\n", + " maze 50 0.8 0.96\n", + " measuring cup 50 0.54 0.76\n", + " medicine chest 50 0.6 0.84\n", + " megalith 50 0.8 0.92\n", + " microphone 50 0.52 0.7\n", + " microwave oven 50 0.48 0.72\n", + " military uniform 50 0.62 0.84\n", + " milk can 50 0.68 0.82\n", + " minibus 50 0.7 1\n", + " miniskirt 50 0.46 0.76\n", + " minivan 50 0.38 0.8\n", + " missile 50 0.4 0.84\n", + " mitten 50 0.76 0.88\n", + " mixing bowl 50 0.8 0.92\n", + " mobile home 50 0.54 0.78\n", + " Model T 50 0.92 0.96\n", + " modem 50 0.58 0.86\n", + " monastery 50 0.44 0.9\n", + " monitor 50 0.4 0.86\n", + " moped 50 0.56 0.94\n", + " mortar 50 0.68 0.94\n", + " square academic cap 50 0.5 0.84\n", + " mosque 50 0.9 1\n", + " mosquito net 50 0.9 0.98\n", + " scooter 50 0.9 0.98\n", + " mountain bike 50 0.78 0.96\n", + " tent 50 0.88 0.96\n", + " computer mouse 50 0.42 0.82\n", + " mousetrap 50 0.76 0.88\n", + " moving van 50 0.4 0.72\n", + " muzzle 50 0.5 0.72\n", + " nail 50 0.68 0.74\n", + " neck brace 50 0.56 0.68\n", + " necklace 50 0.86 1\n", + " nipple 50 0.7 0.88\n", + " notebook computer 50 0.34 0.84\n", + " obelisk 50 0.8 0.92\n", + " oboe 50 0.6 0.84\n", + " ocarina 50 0.8 0.86\n", + " odometer 50 0.96 1\n", + " oil filter 50 0.58 0.82\n", + " organ 50 0.82 0.9\n", + " oscilloscope 50 0.9 0.96\n", + " overskirt 50 0.2 0.7\n", + " bullock cart 50 0.7 0.94\n", + " oxygen mask 50 0.46 0.84\n", + " packet 50 0.5 0.78\n", + " paddle 50 0.56 0.94\n", + " paddle wheel 50 0.86 0.96\n", + " padlock 50 0.74 0.78\n", + " paintbrush 50 0.62 0.8\n", + " pajamas 50 0.56 0.92\n", + " palace 50 0.64 0.96\n", + " pan flute 50 0.84 0.86\n", + " paper towel 50 0.66 0.84\n", + " parachute 50 0.92 0.94\n", + " parallel bars 50 0.62 0.96\n", + " park bench 50 0.74 0.9\n", + " parking meter 50 0.84 0.92\n", + " passenger car 50 0.5 0.82\n", + " patio 50 0.58 0.84\n", + " payphone 50 0.74 0.92\n", + " pedestal 50 0.52 0.9\n", + " pencil case 50 0.64 0.92\n", + " pencil sharpener 50 0.52 0.78\n", + " perfume 50 0.7 0.9\n", + " Petri dish 50 0.6 0.8\n", + " photocopier 50 0.88 0.98\n", + " plectrum 50 0.7 0.84\n", + " Pickelhaube 50 0.72 0.86\n", + " picket fence 50 0.84 0.94\n", + " pickup truck 50 0.64 0.92\n", + " pier 50 0.52 0.82\n", + " piggy bank 50 0.82 0.94\n", + " pill bottle 50 0.76 0.86\n", + " pillow 50 0.76 0.9\n", + " ping-pong ball 50 0.84 0.88\n", + " pinwheel 50 0.76 0.88\n", + " pirate ship 50 0.76 0.94\n", + " pitcher 50 0.46 0.84\n", + " hand plane 50 0.84 0.94\n", + " planetarium 50 0.88 0.98\n", + " plastic bag 50 0.36 0.62\n", + " plate rack 50 0.52 0.78\n", + " plow 50 0.78 0.88\n", + " plunger 50 0.42 0.7\n", + " Polaroid camera 50 0.84 0.92\n", + " pole 50 0.38 0.74\n", + " police van 50 0.76 0.94\n", + " poncho 50 0.58 0.86\n", + " billiard table 50 0.8 0.88\n", + " soda bottle 50 0.56 0.94\n", + " pot 50 0.78 0.92\n", + " potter's wheel 50 0.9 0.94\n", + " power drill 50 0.42 0.72\n", + " prayer rug 50 0.7 0.86\n", + " printer 50 0.54 0.86\n", + " prison 50 0.7 0.9\n", + " projectile 50 0.28 0.9\n", + " projector 50 0.62 0.84\n", + " hockey puck 50 0.92 0.96\n", + " punching bag 50 0.6 0.68\n", + " purse 50 0.42 0.78\n", + " quill 50 0.68 0.84\n", + " quilt 50 0.64 0.9\n", + " race car 50 0.72 0.92\n", + " racket 50 0.72 0.9\n", + " radiator 50 0.66 0.76\n", + " radio 50 0.64 0.92\n", + " radio telescope 50 0.9 0.96\n", + " rain barrel 50 0.8 0.98\n", + " recreational vehicle 50 0.84 0.94\n", + " reel 50 0.72 0.82\n", + " reflex camera 50 0.72 0.92\n", + " refrigerator 50 0.7 0.9\n", + " remote control 50 0.7 0.88\n", + " restaurant 50 0.5 0.66\n", + " revolver 50 0.82 1\n", + " rifle 50 0.38 0.7\n", + " rocking chair 50 0.62 0.84\n", + " rotisserie 50 0.88 0.92\n", + " eraser 50 0.54 0.76\n", + " rugby ball 50 0.86 0.94\n", + " ruler 50 0.68 0.86\n", + " running shoe 50 0.78 0.94\n", + " safe 50 0.82 0.92\n", + " safety pin 50 0.4 0.62\n", + " salt shaker 50 0.66 0.9\n", + " sandal 50 0.66 0.86\n", + " sarong 50 0.64 0.86\n", + " saxophone 50 0.66 0.88\n", + " scabbard 50 0.76 0.92\n", + " weighing scale 50 0.58 0.78\n", + " school bus 50 0.92 1\n", + " schooner 50 0.84 1\n", + " scoreboard 50 0.9 0.96\n", + " CRT screen 50 0.14 0.7\n", + " screw 50 0.9 0.98\n", + " screwdriver 50 0.3 0.58\n", + " seat belt 50 0.88 0.94\n", + " sewing machine 50 0.76 0.9\n", + " shield 50 0.56 0.82\n", + " shoe store 50 0.78 0.96\n", + " shoji 50 0.8 0.92\n", + " shopping basket 50 0.52 0.88\n", + " shopping cart 50 0.76 0.92\n", + " shovel 50 0.62 0.84\n", + " shower cap 50 0.7 0.84\n", + " shower curtain 50 0.64 0.82\n", + " ski 50 0.74 0.92\n", + " ski mask 50 0.72 0.88\n", + " sleeping bag 50 0.68 0.8\n", + " slide rule 50 0.72 0.88\n", + " sliding door 50 0.44 0.78\n", + " slot machine 50 0.94 0.98\n", + " snorkel 50 0.86 0.98\n", + " snowmobile 50 0.88 1\n", + " snowplow 50 0.84 0.98\n", + " soap dispenser 50 0.56 0.86\n", + " soccer ball 50 0.86 0.96\n", + " sock 50 0.62 0.76\n", + " solar thermal collector 50 0.72 0.96\n", + " sombrero 50 0.6 0.84\n", + " soup bowl 50 0.56 0.94\n", + " space bar 50 0.34 0.88\n", + " space heater 50 0.52 0.74\n", + " space shuttle 50 0.82 0.96\n", + " spatula 50 0.3 0.6\n", + " motorboat 50 0.86 1\n", + " spider web 50 0.7 0.9\n", + " spindle 50 0.86 0.98\n", + " sports car 50 0.6 0.94\n", + " spotlight 50 0.26 0.6\n", + " stage 50 0.68 0.86\n", + " steam locomotive 50 0.94 1\n", + " through arch bridge 50 0.84 0.96\n", + " steel drum 50 0.82 0.9\n", + " stethoscope 50 0.6 0.82\n", + " scarf 50 0.5 0.92\n", + " stone wall 50 0.76 0.9\n", + " stopwatch 50 0.58 0.9\n", + " stove 50 0.46 0.74\n", + " strainer 50 0.64 0.84\n", + " tram 50 0.88 0.96\n", + " stretcher 50 0.6 0.8\n", + " couch 50 0.8 0.96\n", + " stupa 50 0.88 0.88\n", + " submarine 50 0.72 0.92\n", + " suit 50 0.4 0.78\n", + " sundial 50 0.58 0.74\n", + " sunglass 50 0.14 0.58\n", + " sunglasses 50 0.28 0.58\n", + " sunscreen 50 0.32 0.7\n", + " suspension bridge 50 0.6 0.94\n", + " mop 50 0.74 0.92\n", + " sweatshirt 50 0.28 0.66\n", + " swimsuit 50 0.52 0.82\n", + " swing 50 0.76 0.84\n", + " switch 50 0.56 0.76\n", + " syringe 50 0.62 0.82\n", + " table lamp 50 0.6 0.88\n", + " tank 50 0.8 0.96\n", + " tape player 50 0.46 0.76\n", + " teapot 50 0.84 1\n", + " teddy bear 50 0.82 0.94\n", + " television 50 0.6 0.9\n", + " tennis ball 50 0.7 0.94\n", + " thatched roof 50 0.88 0.9\n", + " front curtain 50 0.8 0.92\n", + " thimble 50 0.6 0.8\n", + " threshing machine 50 0.56 0.88\n", + " throne 50 0.72 0.82\n", + " tile roof 50 0.72 0.94\n", + " toaster 50 0.66 0.84\n", + " tobacco shop 50 0.42 0.7\n", + " toilet seat 50 0.62 0.88\n", + " torch 50 0.64 0.84\n", + " totem pole 50 0.92 0.98\n", + " tow truck 50 0.62 0.88\n", + " toy store 50 0.6 0.94\n", + " tractor 50 0.76 0.98\n", + " semi-trailer truck 50 0.78 0.92\n", + " tray 50 0.46 0.64\n", + " trench coat 50 0.54 0.72\n", + " tricycle 50 0.72 0.94\n", + " trimaran 50 0.7 0.98\n", + " tripod 50 0.58 0.86\n", + " triumphal arch 50 0.92 0.98\n", + " trolleybus 50 0.9 1\n", + " trombone 50 0.54 0.88\n", + " tub 50 0.24 0.82\n", + " turnstile 50 0.84 0.94\n", + " typewriter keyboard 50 0.68 0.98\n", + " umbrella 50 0.52 0.7\n", + " unicycle 50 0.74 0.96\n", + " upright piano 50 0.76 0.9\n", + " vacuum cleaner 50 0.62 0.9\n", + " vase 50 0.5 0.78\n", + " vault 50 0.76 0.92\n", + " velvet 50 0.2 0.42\n", + " vending machine 50 0.9 1\n", + " vestment 50 0.54 0.82\n", + " viaduct 50 0.78 0.86\n", + " violin 50 0.68 0.78\n", + " volleyball 50 0.86 1\n", + " waffle iron 50 0.72 0.88\n", + " wall clock 50 0.54 0.88\n", + " wallet 50 0.52 0.9\n", + " wardrobe 50 0.68 0.88\n", + " military aircraft 50 0.9 0.98\n", + " sink 50 0.72 0.96\n", + " washing machine 50 0.78 0.94\n", + " water bottle 50 0.54 0.74\n", + " water jug 50 0.22 0.74\n", + " water tower 50 0.9 0.96\n", + " whiskey jug 50 0.64 0.74\n", + " whistle 50 0.72 0.84\n", + " wig 50 0.84 0.9\n", + " window screen 50 0.68 0.8\n", + " window shade 50 0.52 0.76\n", + " Windsor tie 50 0.22 0.66\n", + " wine bottle 50 0.42 0.82\n", + " wing 50 0.54 0.96\n", + " wok 50 0.46 0.82\n", + " wooden spoon 50 0.58 0.8\n", + " wool 50 0.32 0.82\n", + " split-rail fence 50 0.74 0.9\n", + " shipwreck 50 0.84 0.96\n", + " yawl 50 0.78 0.96\n", + " yurt 50 0.84 1\n", + " website 50 0.98 1\n", + " comic book 50 0.62 0.9\n", + " crossword 50 0.84 0.88\n", + " traffic sign 50 0.78 0.9\n", + " traffic light 50 0.8 0.94\n", + " dust jacket 50 0.72 0.94\n", + " menu 50 0.82 0.96\n", + " plate 50 0.44 0.88\n", + " guacamole 50 0.8 0.92\n", + " consomme 50 0.54 0.88\n", + " hot pot 50 0.86 0.98\n", + " trifle 50 0.92 0.98\n", + " ice cream 50 0.68 0.94\n", + " ice pop 50 0.62 0.84\n", + " baguette 50 0.62 0.88\n", + " bagel 50 0.64 0.92\n", + " pretzel 50 0.72 0.88\n", + " cheeseburger 50 0.9 1\n", + " hot dog 50 0.74 0.94\n", + " mashed potato 50 0.74 0.9\n", + " cabbage 50 0.84 0.96\n", + " broccoli 50 0.9 0.96\n", + " cauliflower 50 0.82 1\n", + " zucchini 50 0.74 0.9\n", + " spaghetti squash 50 0.8 0.96\n", + " acorn squash 50 0.82 0.96\n", + " butternut squash 50 0.7 0.94\n", + " cucumber 50 0.6 0.96\n", + " artichoke 50 0.84 0.94\n", + " bell pepper 50 0.84 0.98\n", + " cardoon 50 0.88 0.94\n", + " mushroom 50 0.38 0.92\n", + " Granny Smith 50 0.9 0.96\n", + " strawberry 50 0.6 0.88\n", + " orange 50 0.7 0.92\n", + " lemon 50 0.78 0.98\n", + " fig 50 0.82 0.96\n", + " pineapple 50 0.86 0.96\n", + " banana 50 0.84 0.96\n", + " jackfruit 50 0.9 0.98\n", + " custard apple 50 0.86 0.96\n", + " pomegranate 50 0.82 0.98\n", + " hay 50 0.8 0.92\n", + " carbonara 50 0.88 0.94\n", + " chocolate syrup 50 0.46 0.84\n", + " dough 50 0.4 0.6\n", + " meatloaf 50 0.58 0.84\n", + " pizza 50 0.84 0.96\n", + " pot pie 50 0.68 0.9\n", + " burrito 50 0.8 0.98\n", + " red wine 50 0.54 0.82\n", + " espresso 50 0.64 0.88\n", + " cup 50 0.38 0.7\n", + " eggnog 50 0.38 0.7\n", + " alp 50 0.54 0.88\n", + " bubble 50 0.8 0.96\n", + " cliff 50 0.64 1\n", + " coral reef 50 0.72 0.96\n", + " geyser 50 0.94 1\n", + " lakeshore 50 0.54 0.88\n", + " promontory 50 0.58 0.94\n", + " shoal 50 0.6 0.96\n", + " seashore 50 0.44 0.78\n", + " valley 50 0.72 0.94\n", + " volcano 50 0.78 0.96\n", + " baseball player 50 0.72 0.94\n", + " bridegroom 50 0.72 0.88\n", + " scuba diver 50 0.8 1\n", + " rapeseed 50 0.94 0.98\n", + " daisy 50 0.96 0.98\n", + " yellow lady's slipper 50 1 1\n", + " corn 50 0.4 0.88\n", + " acorn 50 0.92 0.98\n", + " rose hip 50 0.92 0.98\n", + " horse chestnut seed 50 0.94 0.98\n", + " coral fungus 50 0.96 0.96\n", + " agaric 50 0.82 0.94\n", + " gyromitra 50 0.98 1\n", + " stinkhorn mushroom 50 0.8 0.94\n", + " earth star 50 0.98 1\n", + " hen-of-the-woods 50 0.8 0.96\n", + " bolete 50 0.74 0.94\n", + " ear 50 0.48 0.94\n", + " toilet paper 50 0.36 0.68\n", + "Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\n", + "Results saved to \u001b[1mruns/val-cls/exp\u001b[0m\n" + ] + } + ], + "source": [ + "# Validate YOLOv5s on Imagenet val\n", + "!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZY2VXXXu74w5" + }, + "source": [ + "# 3. Train\n", + "\n", + "

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s Classification model on the [Imagenette](https://image-net.org/) dataset with `--data imagenet`, starting from pretrained `--pretrained yolov5s-cls.pt`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **Training Results** are saved to `runs/train-cls/` with incrementing run directories, i.e. `runs/train-cls/exp2`, `runs/train-cls/exp3` etc.\n", + "

\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Train on Custom Data with Roboflow 🌟 NEW\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", + "\n", + "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-classification-custom-data/](https://blog.roboflow.com/train-yolov5-classification-custom-data/?ref=ultralytics)\n", + "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KZiKUAjtARHAfZCXbJRv14-pOnIsBLPV?usp=sharing)\n", + "
\n", + "\n", + "

Label images lightning fast (including with model-assisted labeling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "outputs": [], + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML']\n", + "\n", + "if logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train\n", + "elif logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'ClearML':\n", + " import clearml; clearml.browser_login()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1NcFxRcFdJ_O", + "outputId": "77c8d487-16db-4073-b3ea-06cabf2e7766" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download...\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenette160.zip to /content/datasets/imagenette160.zip...\n", + "100% 103M/103M [00:00<00:00, 347MB/s] \n", + "Unzipping /content/datasets/imagenette160.zip...\n", + "Dataset download success ✅ (3.3s), saved to \u001b[1m/content/datasets/imagenette160\u001b[0m\n", + "\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n", + "Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\n", + "Image sizes 224 train, 224 test\n", + "Using 1 dataloader workers\n", + "Logging results to \u001b[1mruns/train-cls/exp\u001b[0m\n", + "Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs...\n", + "\n", + " Epoch GPU_mem train_loss val_loss top1_acc top5_acc\n", + " 1/5 1.47G 1.05 0.974 0.828 0.975: 100% 148/148 [00:38<00:00, 3.82it/s]\n", + " 2/5 1.73G 0.895 0.766 0.911 0.994: 100% 148/148 [00:36<00:00, 4.03it/s]\n", + " 3/5 1.73G 0.82 0.704 0.934 0.996: 100% 148/148 [00:35<00:00, 4.20it/s]\n", + " 4/5 1.73G 0.766 0.664 0.951 0.998: 100% 148/148 [00:36<00:00, 4.05it/s]\n", + " 5/5 1.73G 0.724 0.634 0.959 0.997: 100% 148/148 [00:37<00:00, 3.94it/s]\n", + "\n", + "Training complete (0.052 hours)\n", + "Results saved to \u001b[1mruns/train-cls/exp\u001b[0m\n", + "Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\n", + "Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160\n", + "Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx\n", + "PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt')\n", + "Visualize: https://netron.app\n", + "\n" + ] + } + ], + "source": [ + "# Train YOLOv5s Classification on Imagenette160 for 3 epochs\n", + "!python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 --cache" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15glLzbQx5u0" + }, + "source": [ + "# 4. Visualize" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nWOsI5wJR1o3" + }, + "source": [ + "## Comet Logging and Visualization 🌟 NEW\n", + "\n", + "[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n", + "\n", + "Getting started is easy:\n", + "```shell\n", + "pip install comet_ml # 1. install\n", + "export COMET_API_KEY= # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lay2WsTjNJzP" + }, + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "\n", + "\n", + "\"ClearML" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GMusP4OAxFu6" + }, + "outputs": [], + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "YOLOv5 Classification Tutorial", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/algorithm/yolov5-master/classify/val.py b/algorithm/yolov5-master/classify/val.py new file mode 100644 index 0000000..4edd5a1 --- /dev/null +++ b/algorithm/yolov5-master/classify/val.py @@ -0,0 +1,170 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Validate a trained YOLOv5 classification model on a classification dataset + +Usage: + $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) + $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet + +Usage - formats: + $ python classify/val.py --weights yolov5s-cls.pt # PyTorch + yolov5s-cls.torchscript # TorchScript + yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-cls_openvino_model # OpenVINO + yolov5s-cls.engine # TensorRT + yolov5s-cls.mlmodel # CoreML (macOS-only) + yolov5s-cls_saved_model # TensorFlow SavedModel + yolov5s-cls.pb # TensorFlow GraphDef + yolov5s-cls.tflite # TensorFlow Lite + yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-cls_paddle_model # PaddlePaddle +""" + +import argparse +import os +import sys +from pathlib import Path + +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import create_classification_dataloader +from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr, + increment_path, print_args) +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + data=ROOT / '../datasets/mnist', # dataset dir + weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) + batch_size=128, # batch size + imgsz=224, # inference size (pixels) + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + verbose=False, # verbose output + project=ROOT / 'runs/val-cls', # save to project/name + name='exp', # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + criterion=None, + pbar=None, +): + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != 'cpu' # half precision only supported on CUDA + model.half() if half else model.float() + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + save_dir.mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + + # Dataloader + data = Path(data) + test_dir = data / 'test' if (data / 'test').exists() else data / 'val' # data/test or data/val + dataloader = create_classification_dataloader(path=test_dir, + imgsz=imgsz, + batch_size=batch_size, + augment=False, + rank=-1, + workers=workers) + + model.eval() + pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile()) + n = len(dataloader) # number of batches + action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing' + desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}' + bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0) + with torch.cuda.amp.autocast(enabled=device.type != 'cpu'): + for images, labels in bar: + with dt[0]: + images, labels = images.to(device, non_blocking=True), labels.to(device) + + with dt[1]: + y = model(images) + + with dt[2]: + pred.append(y.argsort(1, descending=True)[:, :5]) + targets.append(labels) + if criterion: + loss += criterion(y, labels) + + loss /= n + pred, targets = torch.cat(pred), torch.cat(targets) + correct = (targets[:, None] == pred).float() + acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy + top1, top5 = acc.mean(0).tolist() + + if pbar: + pbar.desc = f'{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}' + if verbose: # all classes + LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}") + LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}") + for i, c in model.names.items(): + acc_i = acc[targets == i] + top1i, top5i = acc_i.mean(0).tolist() + LOGGER.info(f'{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}') + + # Print results + t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image + shape = (1, 3, imgsz, imgsz) + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t) + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") + + return top1, top5, loss + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path') + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)') + parser.add_argument('--batch-size', type=int, default=128, help='batch size') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output') + parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + opt = parser.parse_args() + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/data/Argoverse.yaml b/algorithm/yolov5-master/data/Argoverse.yaml new file mode 100644 index 0000000..558151d --- /dev/null +++ b/algorithm/yolov5-master/data/Argoverse.yaml @@ -0,0 +1,74 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI +# Example usage: python train.py --data Argoverse.yaml +# parent +# ├── yolov5 +# └── datasets +# └── Argoverse ← downloads here (31.3 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/Argoverse # dataset root dir +train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images +val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images +test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: bus + 5: truck + 6: traffic_light + 7: stop_sign + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import json + + from tqdm import tqdm + from utils.general import download, Path + + + def argoverse2yolo(set): + labels = {} + a = json.load(open(set, "rb")) + for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): + img_id = annot['image_id'] + img_name = a['images'][img_id]['name'] + img_label_name = f'{img_name[:-3]}txt' + + cls = annot['category_id'] # instance class id + x_center, y_center, width, height = annot['bbox'] + x_center = (x_center + width / 2) / 1920.0 # offset and scale + y_center = (y_center + height / 2) / 1200.0 # offset and scale + width /= 1920.0 # scale + height /= 1200.0 # scale + + img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] + if not img_dir.exists(): + img_dir.mkdir(parents=True, exist_ok=True) + + k = str(img_dir / img_label_name) + if k not in labels: + labels[k] = [] + labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") + + for k in labels: + with open(k, "w") as f: + f.writelines(labels[k]) + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] + download(urls, dir=dir, delete=False) + + # Convert + annotations_dir = 'Argoverse-HD/annotations/' + (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' + for d in "train.json", "val.json": + argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels diff --git a/algorithm/yolov5-master/data/GlobalWheat2020.yaml b/algorithm/yolov5-master/data/GlobalWheat2020.yaml new file mode 100644 index 0000000..01812d0 --- /dev/null +++ b/algorithm/yolov5-master/data/GlobalWheat2020.yaml @@ -0,0 +1,54 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan +# Example usage: python train.py --data GlobalWheat2020.yaml +# parent +# ├── yolov5 +# └── datasets +# └── GlobalWheat2020 ← downloads here (7.0 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/GlobalWheat2020 # dataset root dir +train: # train images (relative to 'path') 3422 images + - images/arvalis_1 + - images/arvalis_2 + - images/arvalis_3 + - images/ethz_1 + - images/rres_1 + - images/inrae_1 + - images/usask_1 +val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1) + - images/ethz_1 +test: # test images (optional) 1276 images + - images/utokyo_1 + - images/utokyo_2 + - images/nau_1 + - images/uq_1 + +# Classes +names: + 0: wheat_head + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from utils.general import download, Path + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] + download(urls, dir=dir) + + # Make Directories + for p in 'annotations', 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + + # Move + for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ + 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': + (dir / p).rename(dir / 'images' / p) # move to /images + f = (dir / p).with_suffix('.json') # json file + if f.exists(): + f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations diff --git a/algorithm/yolov5-master/data/ImageNet.yaml b/algorithm/yolov5-master/data/ImageNet.yaml new file mode 100644 index 0000000..14f1295 --- /dev/null +++ b/algorithm/yolov5-master/data/ImageNet.yaml @@ -0,0 +1,1022 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet ← downloads here (144 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + 10: brambling + 11: goldfinch + 12: house finch + 13: junco + 14: indigo bunting + 15: American robin + 16: bulbul + 17: jay + 18: magpie + 19: chickadee + 20: American dipper + 21: kite + 22: bald eagle + 23: vulture + 24: great grey owl + 25: fire salamander + 26: smooth newt + 27: newt + 28: spotted salamander + 29: axolotl + 30: American bullfrog + 31: tree frog + 32: tailed frog + 33: loggerhead sea turtle + 34: leatherback sea turtle + 35: mud turtle + 36: terrapin + 37: box turtle + 38: banded gecko + 39: green iguana + 40: Carolina anole + 41: desert grassland whiptail lizard + 42: agama + 43: frilled-necked lizard + 44: alligator lizard + 45: Gila monster + 46: European green lizard + 47: chameleon + 48: Komodo dragon + 49: Nile crocodile + 50: American alligator + 51: triceratops + 52: worm snake + 53: ring-necked snake + 54: eastern hog-nosed snake + 55: smooth green snake + 56: kingsnake + 57: garter snake + 58: water snake + 59: vine snake + 60: night snake + 61: boa constrictor + 62: African rock python + 63: Indian cobra + 64: green mamba + 65: sea snake + 66: Saharan horned viper + 67: eastern diamondback rattlesnake + 68: sidewinder + 69: trilobite + 70: harvestman + 71: scorpion + 72: yellow garden spider + 73: barn spider + 74: European garden spider + 75: southern black widow + 76: tarantula + 77: wolf spider + 78: tick + 79: centipede + 80: black grouse + 81: ptarmigan + 82: ruffed grouse + 83: prairie grouse + 84: peacock + 85: quail + 86: partridge + 87: grey parrot + 88: macaw + 89: sulphur-crested cockatoo + 90: lorikeet + 91: coucal + 92: bee eater + 93: hornbill + 94: hummingbird + 95: jacamar + 96: toucan + 97: duck + 98: red-breasted merganser + 99: goose + 100: black swan + 101: tusker + 102: echidna + 103: platypus + 104: wallaby + 105: koala + 106: wombat + 107: jellyfish + 108: sea anemone + 109: brain coral + 110: flatworm + 111: nematode + 112: conch + 113: snail + 114: slug + 115: sea slug + 116: chiton + 117: chambered nautilus + 118: Dungeness crab + 119: rock crab + 120: fiddler crab + 121: red king crab + 122: American lobster + 123: spiny lobster + 124: crayfish + 125: hermit crab + 126: isopod + 127: white stork + 128: black stork + 129: spoonbill + 130: flamingo + 131: little blue heron + 132: great egret + 133: bittern + 134: crane (bird) + 135: limpkin + 136: common gallinule + 137: American coot + 138: bustard + 139: ruddy turnstone + 140: dunlin + 141: common redshank + 142: dowitcher + 143: oystercatcher + 144: pelican + 145: king penguin + 146: albatross + 147: grey whale + 148: killer whale + 149: dugong + 150: sea lion + 151: Chihuahua + 152: Japanese Chin + 153: Maltese + 154: Pekingese + 155: Shih Tzu + 156: King Charles Spaniel + 157: Papillon + 158: toy terrier + 159: Rhodesian Ridgeback + 160: Afghan Hound + 161: Basset Hound + 162: Beagle + 163: Bloodhound + 164: Bluetick Coonhound + 165: Black and Tan Coonhound + 166: Treeing Walker Coonhound + 167: English foxhound + 168: Redbone Coonhound + 169: borzoi + 170: Irish Wolfhound + 171: Italian Greyhound + 172: Whippet + 173: Ibizan Hound + 174: Norwegian Elkhound + 175: Otterhound + 176: Saluki + 177: Scottish Deerhound + 178: Weimaraner + 179: Staffordshire Bull Terrier + 180: American Staffordshire Terrier + 181: Bedlington Terrier + 182: Border Terrier + 183: Kerry Blue Terrier + 184: Irish Terrier + 185: Norfolk Terrier + 186: Norwich Terrier + 187: Yorkshire Terrier + 188: Wire Fox Terrier + 189: Lakeland Terrier + 190: Sealyham Terrier + 191: Airedale Terrier + 192: Cairn Terrier + 193: Australian Terrier + 194: Dandie Dinmont Terrier + 195: Boston Terrier + 196: Miniature Schnauzer + 197: Giant Schnauzer + 198: Standard Schnauzer + 199: Scottish Terrier + 200: Tibetan Terrier + 201: Australian Silky Terrier + 202: Soft-coated Wheaten Terrier + 203: West Highland White Terrier + 204: Lhasa Apso + 205: Flat-Coated Retriever + 206: Curly-coated Retriever + 207: Golden Retriever + 208: Labrador Retriever + 209: Chesapeake Bay Retriever + 210: German Shorthaired Pointer + 211: Vizsla + 212: English Setter + 213: Irish Setter + 214: Gordon Setter + 215: Brittany + 216: Clumber Spaniel + 217: English Springer Spaniel + 218: Welsh Springer Spaniel + 219: Cocker Spaniels + 220: Sussex Spaniel + 221: Irish Water Spaniel + 222: Kuvasz + 223: Schipperke + 224: Groenendael + 225: Malinois + 226: Briard + 227: Australian Kelpie + 228: Komondor + 229: Old English Sheepdog + 230: Shetland Sheepdog + 231: collie + 232: Border Collie + 233: Bouvier des Flandres + 234: Rottweiler + 235: German Shepherd Dog + 236: Dobermann + 237: Miniature Pinscher + 238: Greater Swiss Mountain Dog + 239: Bernese Mountain Dog + 240: Appenzeller Sennenhund + 241: Entlebucher Sennenhund + 242: Boxer + 243: Bullmastiff + 244: Tibetan Mastiff + 245: French Bulldog + 246: Great Dane + 247: St. Bernard + 248: husky + 249: Alaskan Malamute + 250: Siberian Husky + 251: Dalmatian + 252: Affenpinscher + 253: Basenji + 254: pug + 255: Leonberger + 256: Newfoundland + 257: Pyrenean Mountain Dog + 258: Samoyed + 259: Pomeranian + 260: Chow Chow + 261: Keeshond + 262: Griffon Bruxellois + 263: Pembroke Welsh Corgi + 264: Cardigan Welsh Corgi + 265: Toy Poodle + 266: Miniature Poodle + 267: Standard Poodle + 268: Mexican hairless dog + 269: grey wolf + 270: Alaskan tundra wolf + 271: red wolf + 272: coyote + 273: dingo + 274: dhole + 275: African wild dog + 276: hyena + 277: red fox + 278: kit fox + 279: Arctic fox + 280: grey fox + 281: tabby cat + 282: tiger cat + 283: Persian cat + 284: Siamese cat + 285: Egyptian Mau + 286: cougar + 287: lynx + 288: leopard + 289: snow leopard + 290: jaguar + 291: lion + 292: tiger + 293: cheetah + 294: brown bear + 295: American black bear + 296: polar bear + 297: sloth bear + 298: mongoose + 299: meerkat + 300: tiger beetle + 301: ladybug + 302: ground beetle + 303: longhorn beetle + 304: leaf beetle + 305: dung beetle + 306: rhinoceros beetle + 307: weevil + 308: fly + 309: bee + 310: ant + 311: grasshopper + 312: cricket + 313: stick insect + 314: cockroach + 315: mantis + 316: cicada + 317: leafhopper + 318: lacewing + 319: dragonfly + 320: damselfly + 321: red admiral + 322: ringlet + 323: monarch butterfly + 324: small white + 325: sulphur butterfly + 326: gossamer-winged butterfly + 327: starfish + 328: sea urchin + 329: sea cucumber + 330: cottontail rabbit + 331: hare + 332: Angora rabbit + 333: hamster + 334: porcupine + 335: fox squirrel + 336: marmot + 337: beaver + 338: guinea pig + 339: common sorrel + 340: zebra + 341: pig + 342: wild boar + 343: warthog + 344: hippopotamus + 345: ox + 346: water buffalo + 347: bison + 348: ram + 349: bighorn sheep + 350: Alpine ibex + 351: hartebeest + 352: impala + 353: gazelle + 354: dromedary + 355: llama + 356: weasel + 357: mink + 358: European polecat + 359: black-footed ferret + 360: otter + 361: skunk + 362: badger + 363: armadillo + 364: three-toed sloth + 365: orangutan + 366: gorilla + 367: chimpanzee + 368: gibbon + 369: siamang + 370: guenon + 371: patas monkey + 372: baboon + 373: macaque + 374: langur + 375: black-and-white colobus + 376: proboscis monkey + 377: marmoset + 378: white-headed capuchin + 379: howler monkey + 380: titi + 381: Geoffroy's spider monkey + 382: common squirrel monkey + 383: ring-tailed lemur + 384: indri + 385: Asian elephant + 386: African bush elephant + 387: red panda + 388: giant panda + 389: snoek + 390: eel + 391: coho salmon + 392: rock beauty + 393: clownfish + 394: sturgeon + 395: garfish + 396: lionfish + 397: pufferfish + 398: abacus + 399: abaya + 400: academic gown + 401: accordion + 402: acoustic guitar + 403: aircraft carrier + 404: airliner + 405: airship + 406: altar + 407: ambulance + 408: amphibious vehicle + 409: analog clock + 410: apiary + 411: apron + 412: waste container + 413: assault rifle + 414: backpack + 415: bakery + 416: balance beam + 417: balloon + 418: ballpoint pen + 419: Band-Aid + 420: banjo + 421: baluster + 422: barbell + 423: barber chair + 424: barbershop + 425: barn + 426: barometer + 427: barrel + 428: wheelbarrow + 429: baseball + 430: basketball + 431: bassinet + 432: bassoon + 433: swimming cap + 434: bath towel + 435: bathtub + 436: station wagon + 437: lighthouse + 438: beaker + 439: military cap + 440: beer bottle + 441: beer glass + 442: bell-cot + 443: bib + 444: tandem bicycle + 445: bikini + 446: ring binder + 447: binoculars + 448: birdhouse + 449: boathouse + 450: bobsleigh + 451: bolo tie + 452: poke bonnet + 453: bookcase + 454: bookstore + 455: bottle cap + 456: bow + 457: bow tie + 458: brass + 459: bra + 460: breakwater + 461: breastplate + 462: broom + 463: bucket + 464: buckle + 465: bulletproof vest + 466: high-speed train + 467: butcher shop + 468: taxicab + 469: cauldron + 470: candle + 471: cannon + 472: canoe + 473: can opener + 474: cardigan + 475: car mirror + 476: carousel + 477: tool kit + 478: carton + 479: car wheel + 480: automated teller machine + 481: cassette + 482: cassette player + 483: castle + 484: catamaran + 485: CD player + 486: cello + 487: mobile phone + 488: chain + 489: chain-link fence + 490: chain mail + 491: chainsaw + 492: chest + 493: chiffonier + 494: chime + 495: china cabinet + 496: Christmas stocking + 497: church + 498: movie theater + 499: cleaver + 500: cliff dwelling + 501: cloak + 502: clogs + 503: cocktail shaker + 504: coffee mug + 505: coffeemaker + 506: coil + 507: combination lock + 508: computer keyboard + 509: confectionery store + 510: container ship + 511: convertible + 512: corkscrew + 513: cornet + 514: cowboy boot + 515: cowboy hat + 516: cradle + 517: crane (machine) + 518: crash helmet + 519: crate + 520: infant bed + 521: Crock Pot + 522: croquet ball + 523: crutch + 524: cuirass + 525: dam + 526: desk + 527: desktop computer + 528: rotary dial telephone + 529: diaper + 530: digital clock + 531: digital watch + 532: dining table + 533: dishcloth + 534: dishwasher + 535: disc brake + 536: dock + 537: dog sled + 538: dome + 539: doormat + 540: drilling rig + 541: drum + 542: drumstick + 543: dumbbell + 544: Dutch oven + 545: electric fan + 546: electric guitar + 547: electric locomotive + 548: entertainment center + 549: envelope + 550: espresso machine + 551: face powder + 552: feather boa + 553: filing cabinet + 554: fireboat + 555: fire engine + 556: fire screen sheet + 557: flagpole + 558: flute + 559: folding chair + 560: football helmet + 561: forklift + 562: fountain + 563: fountain pen + 564: four-poster bed + 565: freight car + 566: French horn + 567: frying pan + 568: fur coat + 569: garbage truck + 570: gas mask + 571: gas pump + 572: goblet + 573: go-kart + 574: golf ball + 575: golf cart + 576: gondola + 577: gong + 578: gown + 579: grand piano + 580: greenhouse + 581: grille + 582: grocery store + 583: guillotine + 584: barrette + 585: hair spray + 586: half-track + 587: hammer + 588: hamper + 589: hair dryer + 590: hand-held computer + 591: handkerchief + 592: hard disk drive + 593: harmonica + 594: harp + 595: harvester + 596: hatchet + 597: holster + 598: home theater + 599: honeycomb + 600: hook + 601: hoop skirt + 602: horizontal bar + 603: horse-drawn vehicle + 604: hourglass + 605: iPod + 606: clothes iron + 607: jack-o'-lantern + 608: jeans + 609: jeep + 610: T-shirt + 611: jigsaw puzzle + 612: pulled rickshaw + 613: joystick + 614: kimono + 615: knee pad + 616: knot + 617: lab coat + 618: ladle + 619: lampshade + 620: laptop computer + 621: lawn mower + 622: lens cap + 623: paper knife + 624: library + 625: lifeboat + 626: lighter + 627: limousine + 628: ocean liner + 629: lipstick + 630: slip-on shoe + 631: lotion + 632: speaker + 633: loupe + 634: sawmill + 635: magnetic compass + 636: mail bag + 637: mailbox + 638: tights + 639: tank suit + 640: manhole cover + 641: maraca + 642: marimba + 643: mask + 644: match + 645: maypole + 646: maze + 647: measuring cup + 648: medicine chest + 649: megalith + 650: microphone + 651: microwave oven + 652: military uniform + 653: milk can + 654: minibus + 655: miniskirt + 656: minivan + 657: missile + 658: mitten + 659: mixing bowl + 660: mobile home + 661: Model T + 662: modem + 663: monastery + 664: monitor + 665: moped + 666: mortar + 667: square academic cap + 668: mosque + 669: mosquito net + 670: scooter + 671: mountain bike + 672: tent + 673: computer mouse + 674: mousetrap + 675: moving van + 676: muzzle + 677: nail + 678: neck brace + 679: necklace + 680: nipple + 681: notebook computer + 682: obelisk + 683: oboe + 684: ocarina + 685: odometer + 686: oil filter + 687: organ + 688: oscilloscope + 689: overskirt + 690: bullock cart + 691: oxygen mask + 692: packet + 693: paddle + 694: paddle wheel + 695: padlock + 696: paintbrush + 697: pajamas + 698: palace + 699: pan flute + 700: paper towel + 701: parachute + 702: parallel bars + 703: park bench + 704: parking meter + 705: passenger car + 706: patio + 707: payphone + 708: pedestal + 709: pencil case + 710: pencil sharpener + 711: perfume + 712: Petri dish + 713: photocopier + 714: plectrum + 715: Pickelhaube + 716: picket fence + 717: pickup truck + 718: pier + 719: piggy bank + 720: pill bottle + 721: pillow + 722: ping-pong ball + 723: pinwheel + 724: pirate ship + 725: pitcher + 726: hand plane + 727: planetarium + 728: plastic bag + 729: plate rack + 730: plow + 731: plunger + 732: Polaroid camera + 733: pole + 734: police van + 735: poncho + 736: billiard table + 737: soda bottle + 738: pot + 739: potter's wheel + 740: power drill + 741: prayer rug + 742: printer + 743: prison + 744: projectile + 745: projector + 746: hockey puck + 747: punching bag + 748: purse + 749: quill + 750: quilt + 751: race car + 752: racket + 753: radiator + 754: radio + 755: radio telescope + 756: rain barrel + 757: recreational vehicle + 758: reel + 759: reflex camera + 760: refrigerator + 761: remote control + 762: restaurant + 763: revolver + 764: rifle + 765: rocking chair + 766: rotisserie + 767: eraser + 768: rugby ball + 769: ruler + 770: running shoe + 771: safe + 772: safety pin + 773: salt shaker + 774: sandal + 775: sarong + 776: saxophone + 777: scabbard + 778: weighing scale + 779: school bus + 780: schooner + 781: scoreboard + 782: CRT screen + 783: screw + 784: screwdriver + 785: seat belt + 786: sewing machine + 787: shield + 788: shoe store + 789: shoji + 790: shopping basket + 791: shopping cart + 792: shovel + 793: shower cap + 794: shower curtain + 795: ski + 796: ski mask + 797: sleeping bag + 798: slide rule + 799: sliding door + 800: slot machine + 801: snorkel + 802: snowmobile + 803: snowplow + 804: soap dispenser + 805: soccer ball + 806: sock + 807: solar thermal collector + 808: sombrero + 809: soup bowl + 810: space bar + 811: space heater + 812: space shuttle + 813: spatula + 814: motorboat + 815: spider web + 816: spindle + 817: sports car + 818: spotlight + 819: stage + 820: steam locomotive + 821: through arch bridge + 822: steel drum + 823: stethoscope + 824: scarf + 825: stone wall + 826: stopwatch + 827: stove + 828: strainer + 829: tram + 830: stretcher + 831: couch + 832: stupa + 833: submarine + 834: suit + 835: sundial + 836: sunglass + 837: sunglasses + 838: sunscreen + 839: suspension bridge + 840: mop + 841: sweatshirt + 842: swimsuit + 843: swing + 844: switch + 845: syringe + 846: table lamp + 847: tank + 848: tape player + 849: teapot + 850: teddy bear + 851: television + 852: tennis ball + 853: thatched roof + 854: front curtain + 855: thimble + 856: threshing machine + 857: throne + 858: tile roof + 859: toaster + 860: tobacco shop + 861: toilet seat + 862: torch + 863: totem pole + 864: tow truck + 865: toy store + 866: tractor + 867: semi-trailer truck + 868: tray + 869: trench coat + 870: tricycle + 871: trimaran + 872: tripod + 873: triumphal arch + 874: trolleybus + 875: trombone + 876: tub + 877: turnstile + 878: typewriter keyboard + 879: umbrella + 880: unicycle + 881: upright piano + 882: vacuum cleaner + 883: vase + 884: vault + 885: velvet + 886: vending machine + 887: vestment + 888: viaduct + 889: violin + 890: volleyball + 891: waffle iron + 892: wall clock + 893: wallet + 894: wardrobe + 895: military aircraft + 896: sink + 897: washing machine + 898: water bottle + 899: water jug + 900: water tower + 901: whiskey jug + 902: whistle + 903: wig + 904: window screen + 905: window shade + 906: Windsor tie + 907: wine bottle + 908: wing + 909: wok + 910: wooden spoon + 911: wool + 912: split-rail fence + 913: shipwreck + 914: yawl + 915: yurt + 916: website + 917: comic book + 918: crossword + 919: traffic sign + 920: traffic light + 921: dust jacket + 922: menu + 923: plate + 924: guacamole + 925: consomme + 926: hot pot + 927: trifle + 928: ice cream + 929: ice pop + 930: baguette + 931: bagel + 932: pretzel + 933: cheeseburger + 934: hot dog + 935: mashed potato + 936: cabbage + 937: broccoli + 938: cauliflower + 939: zucchini + 940: spaghetti squash + 941: acorn squash + 942: butternut squash + 943: cucumber + 944: artichoke + 945: bell pepper + 946: cardoon + 947: mushroom + 948: Granny Smith + 949: strawberry + 950: orange + 951: lemon + 952: fig + 953: pineapple + 954: banana + 955: jackfruit + 956: custard apple + 957: pomegranate + 958: hay + 959: carbonara + 960: chocolate syrup + 961: dough + 962: meatloaf + 963: pizza + 964: pot pie + 965: burrito + 966: red wine + 967: espresso + 968: cup + 969: eggnog + 970: alp + 971: bubble + 972: cliff + 973: coral reef + 974: geyser + 975: lakeshore + 976: promontory + 977: shoal + 978: seashore + 979: valley + 980: volcano + 981: baseball player + 982: bridegroom + 983: scuba diver + 984: rapeseed + 985: daisy + 986: yellow lady's slipper + 987: corn + 988: acorn + 989: rose hip + 990: horse chestnut seed + 991: coral fungus + 992: agaric + 993: gyromitra + 994: stinkhorn mushroom + 995: earth star + 996: hen-of-the-woods + 997: bolete + 998: ear + 999: toilet paper + + +# Download script/URL (optional) +download: data/scripts/get_imagenet.sh diff --git a/algorithm/yolov5-master/data/Objects365.yaml b/algorithm/yolov5-master/data/Objects365.yaml new file mode 100644 index 0000000..05b26a1 --- /dev/null +++ b/algorithm/yolov5-master/data/Objects365.yaml @@ -0,0 +1,438 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Objects365 dataset https://www.objects365.org/ by Megvii +# Example usage: python train.py --data Objects365.yaml +# parent +# ├── yolov5 +# └── datasets +# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/Objects365 # dataset root dir +train: images/train # train images (relative to 'path') 1742289 images +val: images/val # val images (relative to 'path') 80000 images +test: # test images (optional) + +# Classes +names: + 0: Person + 1: Sneakers + 2: Chair + 3: Other Shoes + 4: Hat + 5: Car + 6: Lamp + 7: Glasses + 8: Bottle + 9: Desk + 10: Cup + 11: Street Lights + 12: Cabinet/shelf + 13: Handbag/Satchel + 14: Bracelet + 15: Plate + 16: Picture/Frame + 17: Helmet + 18: Book + 19: Gloves + 20: Storage box + 21: Boat + 22: Leather Shoes + 23: Flower + 24: Bench + 25: Potted Plant + 26: Bowl/Basin + 27: Flag + 28: Pillow + 29: Boots + 30: Vase + 31: Microphone + 32: Necklace + 33: Ring + 34: SUV + 35: Wine Glass + 36: Belt + 37: Monitor/TV + 38: Backpack + 39: Umbrella + 40: Traffic Light + 41: Speaker + 42: Watch + 43: Tie + 44: Trash bin Can + 45: Slippers + 46: Bicycle + 47: Stool + 48: Barrel/bucket + 49: Van + 50: Couch + 51: Sandals + 52: Basket + 53: Drum + 54: Pen/Pencil + 55: Bus + 56: Wild Bird + 57: High Heels + 58: Motorcycle + 59: Guitar + 60: Carpet + 61: Cell Phone + 62: Bread + 63: Camera + 64: Canned + 65: Truck + 66: Traffic cone + 67: Cymbal + 68: Lifesaver + 69: Towel + 70: Stuffed Toy + 71: Candle + 72: Sailboat + 73: Laptop + 74: Awning + 75: Bed + 76: Faucet + 77: Tent + 78: Horse + 79: Mirror + 80: Power outlet + 81: Sink + 82: Apple + 83: Air Conditioner + 84: Knife + 85: Hockey Stick + 86: Paddle + 87: Pickup Truck + 88: Fork + 89: Traffic Sign + 90: Balloon + 91: Tripod + 92: Dog + 93: Spoon + 94: Clock + 95: Pot + 96: Cow + 97: Cake + 98: Dinning Table + 99: Sheep + 100: Hanger + 101: Blackboard/Whiteboard + 102: Napkin + 103: Other Fish + 104: Orange/Tangerine + 105: Toiletry + 106: Keyboard + 107: Tomato + 108: Lantern + 109: Machinery Vehicle + 110: Fan + 111: Green Vegetables + 112: Banana + 113: Baseball Glove + 114: Airplane + 115: Mouse + 116: Train + 117: Pumpkin + 118: Soccer + 119: Skiboard + 120: Luggage + 121: Nightstand + 122: Tea pot + 123: Telephone + 124: Trolley + 125: Head Phone + 126: Sports Car + 127: Stop Sign + 128: Dessert + 129: Scooter + 130: Stroller + 131: Crane + 132: Remote + 133: Refrigerator + 134: Oven + 135: Lemon + 136: Duck + 137: Baseball Bat + 138: Surveillance Camera + 139: Cat + 140: Jug + 141: Broccoli + 142: Piano + 143: Pizza + 144: Elephant + 145: Skateboard + 146: Surfboard + 147: Gun + 148: Skating and Skiing shoes + 149: Gas stove + 150: Donut + 151: Bow Tie + 152: Carrot + 153: Toilet + 154: Kite + 155: Strawberry + 156: Other Balls + 157: Shovel + 158: Pepper + 159: Computer Box + 160: Toilet Paper + 161: Cleaning Products + 162: Chopsticks + 163: Microwave + 164: Pigeon + 165: Baseball + 166: Cutting/chopping Board + 167: Coffee Table + 168: Side Table + 169: Scissors + 170: Marker + 171: Pie + 172: Ladder + 173: Snowboard + 174: Cookies + 175: Radiator + 176: Fire Hydrant + 177: Basketball + 178: Zebra + 179: Grape + 180: Giraffe + 181: Potato + 182: Sausage + 183: Tricycle + 184: Violin + 185: Egg + 186: Fire Extinguisher + 187: Candy + 188: Fire Truck + 189: Billiards + 190: Converter + 191: Bathtub + 192: Wheelchair + 193: Golf Club + 194: Briefcase + 195: Cucumber + 196: Cigar/Cigarette + 197: Paint Brush + 198: Pear + 199: Heavy Truck + 200: Hamburger + 201: Extractor + 202: Extension Cord + 203: Tong + 204: Tennis Racket + 205: Folder + 206: American Football + 207: earphone + 208: Mask + 209: Kettle + 210: Tennis + 211: Ship + 212: Swing + 213: Coffee Machine + 214: Slide + 215: Carriage + 216: Onion + 217: Green beans + 218: Projector + 219: Frisbee + 220: Washing Machine/Drying Machine + 221: Chicken + 222: Printer + 223: Watermelon + 224: Saxophone + 225: Tissue + 226: Toothbrush + 227: Ice cream + 228: Hot-air balloon + 229: Cello + 230: French Fries + 231: Scale + 232: Trophy + 233: Cabbage + 234: Hot dog + 235: Blender + 236: Peach + 237: Rice + 238: Wallet/Purse + 239: Volleyball + 240: Deer + 241: Goose + 242: Tape + 243: Tablet + 244: Cosmetics + 245: Trumpet + 246: Pineapple + 247: Golf Ball + 248: Ambulance + 249: Parking meter + 250: Mango + 251: Key + 252: Hurdle + 253: Fishing Rod + 254: Medal + 255: Flute + 256: Brush + 257: Penguin + 258: Megaphone + 259: Corn + 260: Lettuce + 261: Garlic + 262: Swan + 263: Helicopter + 264: Green Onion + 265: Sandwich + 266: Nuts + 267: Speed Limit Sign + 268: Induction Cooker + 269: Broom + 270: Trombone + 271: Plum + 272: Rickshaw + 273: Goldfish + 274: Kiwi fruit + 275: Router/modem + 276: Poker Card + 277: Toaster + 278: Shrimp + 279: Sushi + 280: Cheese + 281: Notepaper + 282: Cherry + 283: Pliers + 284: CD + 285: Pasta + 286: Hammer + 287: Cue + 288: Avocado + 289: Hamimelon + 290: Flask + 291: Mushroom + 292: Screwdriver + 293: Soap + 294: Recorder + 295: Bear + 296: Eggplant + 297: Board Eraser + 298: Coconut + 299: Tape Measure/Ruler + 300: Pig + 301: Showerhead + 302: Globe + 303: Chips + 304: Steak + 305: Crosswalk Sign + 306: Stapler + 307: Camel + 308: Formula 1 + 309: Pomegranate + 310: Dishwasher + 311: Crab + 312: Hoverboard + 313: Meat ball + 314: Rice Cooker + 315: Tuba + 316: Calculator + 317: Papaya + 318: Antelope + 319: Parrot + 320: Seal + 321: Butterfly + 322: Dumbbell + 323: Donkey + 324: Lion + 325: Urinal + 326: Dolphin + 327: Electric Drill + 328: Hair Dryer + 329: Egg tart + 330: Jellyfish + 331: Treadmill + 332: Lighter + 333: Grapefruit + 334: Game board + 335: Mop + 336: Radish + 337: Baozi + 338: Target + 339: French + 340: Spring Rolls + 341: Monkey + 342: Rabbit + 343: Pencil Case + 344: Yak + 345: Red Cabbage + 346: Binoculars + 347: Asparagus + 348: Barbell + 349: Scallop + 350: Noddles + 351: Comb + 352: Dumpling + 353: Oyster + 354: Table Tennis paddle + 355: Cosmetics Brush/Eyeliner Pencil + 356: Chainsaw + 357: Eraser + 358: Lobster + 359: Durian + 360: Okra + 361: Lipstick + 362: Cosmetics Mirror + 363: Curling + 364: Table Tennis + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from tqdm import tqdm + + from utils.general import Path, check_requirements, download, np, xyxy2xywhn + + check_requirements(('pycocotools>=2.0',)) + from pycocotools.coco import COCO + + # Make Directories + dir = Path(yaml['path']) # dataset root dir + for p in 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + for q in 'train', 'val': + (dir / p / q).mkdir(parents=True, exist_ok=True) + + # Train, Val Splits + for split, patches in [('train', 50 + 1), ('val', 43 + 1)]: + print(f"Processing {split} in {patches} patches ...") + images, labels = dir / 'images' / split, dir / 'labels' / split + + # Download + url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/" + if split == 'train': + download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json + download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8) + elif split == 'val': + download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json + download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8) + download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8) + + # Move + for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'): + f.rename(images / f.name) # move to /images/{split} + + # Labels + coco = COCO(dir / f'zhiyuan_objv2_{split}.json') + names = [x["name"] for x in coco.loadCats(coco.getCatIds())] + for cid, cat in enumerate(names): + catIds = coco.getCatIds(catNms=[cat]) + imgIds = coco.getImgIds(catIds=catIds) + for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'): + width, height = im["width"], im["height"] + path = Path(im["file_name"]) # image filename + try: + with open(labels / path.with_suffix('.txt').name, 'a') as file: + annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None) + for a in coco.loadAnns(annIds): + x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner) + xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4) + x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped + file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n") + except Exception as e: + print(e) diff --git a/algorithm/yolov5-master/data/SKU-110K.yaml b/algorithm/yolov5-master/data/SKU-110K.yaml new file mode 100644 index 0000000..edae717 --- /dev/null +++ b/algorithm/yolov5-master/data/SKU-110K.yaml @@ -0,0 +1,53 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail +# Example usage: python train.py --data SKU-110K.yaml +# parent +# ├── yolov5 +# └── datasets +# └── SKU-110K ← downloads here (13.6 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/SKU-110K # dataset root dir +train: train.txt # train images (relative to 'path') 8219 images +val: val.txt # val images (relative to 'path') 588 images +test: test.txt # test images (optional) 2936 images + +# Classes +names: + 0: object + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import shutil + from tqdm import tqdm + from utils.general import np, pd, Path, download, xyxy2xywh + + + # Download + dir = Path(yaml['path']) # dataset root dir + parent = Path(dir.parent) # download dir + urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz'] + download(urls, dir=parent, delete=False) + + # Rename directories + if dir.exists(): + shutil.rmtree(dir) + (parent / 'SKU110K_fixed').rename(dir) # rename dir + (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir + + # Convert labels + names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names + for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv': + x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations + images, unique_images = x[:, 0], np.unique(x[:, 0]) + with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f: + f.writelines(f'./images/{s}\n' for s in unique_images) + for im in tqdm(unique_images, desc=f'Converting {dir / d}'): + cls = 0 # single-class dataset + with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f: + for r in x[images == im]: + w, h = r[6], r[7] # image width, height + xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance + f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label diff --git a/algorithm/yolov5-master/data/VOC.yaml b/algorithm/yolov5-master/data/VOC.yaml new file mode 100644 index 0000000..27d3810 --- /dev/null +++ b/algorithm/yolov5-master/data/VOC.yaml @@ -0,0 +1,100 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford +# Example usage: python train.py --data VOC.yaml +# parent +# ├── yolov5 +# └── datasets +# └── VOC ← downloads here (2.8 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/VOC +train: # train images (relative to 'path') 16551 images + - images/train2012 + - images/train2007 + - images/val2012 + - images/val2007 +val: # val images (relative to 'path') 4952 images + - images/test2007 +test: # test images (optional) + - images/test2007 + +# Classes +names: + 0: aeroplane + 1: bicycle + 2: bird + 3: boat + 4: bottle + 5: bus + 6: car + 7: cat + 8: chair + 9: cow + 10: diningtable + 11: dog + 12: horse + 13: motorbike + 14: person + 15: pottedplant + 16: sheep + 17: sofa + 18: train + 19: tvmonitor + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import xml.etree.ElementTree as ET + + from tqdm import tqdm + from utils.general import download, Path + + + def convert_label(path, lb_path, year, image_id): + def convert_box(size, box): + dw, dh = 1. / size[0], 1. / size[1] + x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2] + return x * dw, y * dh, w * dw, h * dh + + in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') + out_file = open(lb_path, 'w') + tree = ET.parse(in_file) + root = tree.getroot() + size = root.find('size') + w = int(size.find('width').text) + h = int(size.find('height').text) + + names = list(yaml['names'].values()) # names list + for obj in root.iter('object'): + cls = obj.find('name').text + if cls in names and int(obj.find('difficult').text) != 1: + xmlbox = obj.find('bndbox') + bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) + cls_id = names.index(cls) # class id + out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') + + + # Download + dir = Path(yaml['path']) # dataset root dir + url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' + urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images + f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images + f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images + download(urls, dir=dir / 'images', delete=False, curl=True, threads=3) + + # Convert + path = dir / 'images/VOCdevkit' + for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): + imgs_path = dir / 'images' / f'{image_set}{year}' + lbs_path = dir / 'labels' / f'{image_set}{year}' + imgs_path.mkdir(exist_ok=True, parents=True) + lbs_path.mkdir(exist_ok=True, parents=True) + + with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f: + image_ids = f.read().strip().split() + for id in tqdm(image_ids, desc=f'{image_set}{year}'): + f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path + lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path + f.rename(imgs_path / f.name) # move image + convert_label(path, lb_path, year, id) # convert labels to YOLO format diff --git a/algorithm/yolov5-master/data/VisDrone.yaml b/algorithm/yolov5-master/data/VisDrone.yaml new file mode 100644 index 0000000..a8bcf8e --- /dev/null +++ b/algorithm/yolov5-master/data/VisDrone.yaml @@ -0,0 +1,70 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University +# Example usage: python train.py --data VisDrone.yaml +# parent +# ├── yolov5 +# └── datasets +# └── VisDrone ← downloads here (2.3 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/VisDrone # dataset root dir +train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images +val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images +test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images + +# Classes +names: + 0: pedestrian + 1: people + 2: bicycle + 3: car + 4: van + 5: truck + 6: tricycle + 7: awning-tricycle + 8: bus + 9: motor + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from utils.general import download, os, Path + + def visdrone2yolo(dir): + from PIL import Image + from tqdm import tqdm + + def convert_box(size, box): + # Convert VisDrone box to YOLO xywh box + dw = 1. / size[0] + dh = 1. / size[1] + return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh + + (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory + pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') + for f in pbar: + img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size + lines = [] + with open(f, 'r') as file: # read annotation.txt + for row in [x.split(',') for x in file.read().strip().splitlines()]: + if row[4] == '0': # VisDrone 'ignored regions' class 0 + continue + cls = int(row[5]) - 1 + box = convert_box(img_size, tuple(map(int, row[:4]))) + lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") + with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: + fl.writelines(lines) # write label.txt + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip'] + download(urls, dir=dir, curl=True, threads=4) + + # Convert + for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': + visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels diff --git a/algorithm/yolov5-master/data/coco.yaml b/algorithm/yolov5-master/data/coco.yaml new file mode 100644 index 0000000..d64dfc7 --- /dev/null +++ b/algorithm/yolov5-master/data/coco.yaml @@ -0,0 +1,116 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# COCO 2017 dataset http://cocodataset.org by Microsoft +# Example usage: python train.py --data coco.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco ← downloads here (20.1 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco # dataset root dir +train: train2017.txt # train images (relative to 'path') 118287 images +val: val2017.txt # val images (relative to 'path') 5000 images +test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + + +# Download script/URL (optional) +download: | + from utils.general import download, Path + + + # Download labels + segments = False # segment or box labels + dir = Path(yaml['path']) # dataset root dir + url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' + urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels + download(urls, dir=dir.parent) + + # Download data + urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images + 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images + 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) + download(urls, dir=dir / 'images', threads=3) diff --git a/algorithm/yolov5-master/data/coco128-seg.yaml b/algorithm/yolov5-master/data/coco128-seg.yaml new file mode 100644 index 0000000..5e81910 --- /dev/null +++ b/algorithm/yolov5-master/data/coco128-seg.yaml @@ -0,0 +1,101 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# Example usage: python train.py --data coco128.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco128-seg ← downloads here (7 MB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco128-seg # dataset root dir +train: images/train2017 # train images (relative to 'path') 128 images +val: images/train2017 # val images (relative to 'path') 128 images +test: # test images (optional) + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + + +# Download script/URL (optional) +download: https://ultralytics.com/assets/coco128-seg.zip diff --git a/algorithm/yolov5-master/data/coco128.yaml b/algorithm/yolov5-master/data/coco128.yaml new file mode 100644 index 0000000..1255673 --- /dev/null +++ b/algorithm/yolov5-master/data/coco128.yaml @@ -0,0 +1,101 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# Example usage: python train.py --data coco128.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco128 ← downloads here (7 MB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco128 # dataset root dir +train: images/train2017 # train images (relative to 'path') 128 images +val: images/train2017 # val images (relative to 'path') 128 images +test: # test images (optional) + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + + +# Download script/URL (optional) +download: https://ultralytics.com/assets/coco128.zip diff --git a/algorithm/yolov5-master/data/hyps/hyp.Objects365.yaml b/algorithm/yolov5-master/data/hyps/hyp.Objects365.yaml new file mode 100644 index 0000000..7497174 --- /dev/null +++ b/algorithm/yolov5-master/data/hyps/hyp.Objects365.yaml @@ -0,0 +1,34 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for Objects365 training +# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve +# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.00258 +lrf: 0.17 +momentum: 0.779 +weight_decay: 0.00058 +warmup_epochs: 1.33 +warmup_momentum: 0.86 +warmup_bias_lr: 0.0711 +box: 0.0539 +cls: 0.299 +cls_pw: 0.825 +obj: 0.632 +obj_pw: 1.0 +iou_t: 0.2 +anchor_t: 3.44 +anchors: 3.2 +fl_gamma: 0.0 +hsv_h: 0.0188 +hsv_s: 0.704 +hsv_v: 0.36 +degrees: 0.0 +translate: 0.0902 +scale: 0.491 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.0 +copy_paste: 0.0 diff --git a/algorithm/yolov5-master/data/hyps/hyp.VOC.yaml b/algorithm/yolov5-master/data/hyps/hyp.VOC.yaml new file mode 100644 index 0000000..0aa4e7d --- /dev/null +++ b/algorithm/yolov5-master/data/hyps/hyp.VOC.yaml @@ -0,0 +1,40 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for VOC training +# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve +# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials + +# YOLOv5 Hyperparameter Evolution Results +# Best generation: 467 +# Last generation: 996 +# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss +# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865 + +lr0: 0.00334 +lrf: 0.15135 +momentum: 0.74832 +weight_decay: 0.00025 +warmup_epochs: 3.3835 +warmup_momentum: 0.59462 +warmup_bias_lr: 0.18657 +box: 0.02 +cls: 0.21638 +cls_pw: 0.5 +obj: 0.51728 +obj_pw: 0.67198 +iou_t: 0.2 +anchor_t: 3.3744 +fl_gamma: 0.0 +hsv_h: 0.01041 +hsv_s: 0.54703 +hsv_v: 0.27739 +degrees: 0.0 +translate: 0.04591 +scale: 0.75544 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 0.85834 +mixup: 0.04266 +copy_paste: 0.0 +anchors: 3.412 diff --git a/algorithm/yolov5-master/data/hyps/hyp.no-augmentation.yaml b/algorithm/yolov5-master/data/hyps/hyp.no-augmentation.yaml new file mode 100644 index 0000000..8fbd5b2 --- /dev/null +++ b/algorithm/yolov5-master/data/hyps/hyp.no-augmentation.yaml @@ -0,0 +1,35 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters when using Albumentations frameworks +# python train.py --hyp hyp.no-augmentation.yaml +# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +# this parameters are all zero since we want to use albumentation framework +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0 # image HSV-Hue augmentation (fraction) +hsv_s: 00 # image HSV-Saturation augmentation (fraction) +hsv_v: 0 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0 # image translation (+/- fraction) +scale: 0 # image scale (+/- gain) +shear: 0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.0 # image flip left-right (probability) +mosaic: 0.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/algorithm/yolov5-master/data/hyps/hyp.scratch-high.yaml b/algorithm/yolov5-master/data/hyps/hyp.scratch-high.yaml new file mode 100644 index 0000000..123cc84 --- /dev/null +++ b/algorithm/yolov5-master/data/hyps/hyp.scratch-high.yaml @@ -0,0 +1,34 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for high-augmentation COCO training from scratch +# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.1 # image mixup (probability) +copy_paste: 0.1 # segment copy-paste (probability) diff --git a/algorithm/yolov5-master/data/hyps/hyp.scratch-low.yaml b/algorithm/yolov5-master/data/hyps/hyp.scratch-low.yaml new file mode 100644 index 0000000..b9ef1d5 --- /dev/null +++ b/algorithm/yolov5-master/data/hyps/hyp.scratch-low.yaml @@ -0,0 +1,34 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for low-augmentation COCO training from scratch +# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.5 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 1.0 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/algorithm/yolov5-master/data/hyps/hyp.scratch-med.yaml b/algorithm/yolov5-master/data/hyps/hyp.scratch-med.yaml new file mode 100644 index 0000000..d6867d7 --- /dev/null +++ b/algorithm/yolov5-master/data/hyps/hyp.scratch-med.yaml @@ -0,0 +1,34 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for medium-augmentation COCO training from scratch +# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.1 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/algorithm/yolov5-master/data/images/bus.jpg b/algorithm/yolov5-master/data/images/bus.jpg new file mode 100644 index 0000000..b43e311 Binary files /dev/null and b/algorithm/yolov5-master/data/images/bus.jpg differ diff --git a/algorithm/yolov5-master/data/images/zidane.jpg b/algorithm/yolov5-master/data/images/zidane.jpg new file mode 100644 index 0000000..92d72ea Binary files /dev/null and b/algorithm/yolov5-master/data/images/zidane.jpg differ diff --git a/algorithm/yolov5-master/data/scripts/download_weights.sh b/algorithm/yolov5-master/data/scripts/download_weights.sh new file mode 100644 index 0000000..31e0a15 --- /dev/null +++ b/algorithm/yolov5-master/data/scripts/download_weights.sh @@ -0,0 +1,22 @@ +#!/bin/bash +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Download latest models from https://github.com/ultralytics/yolov5/releases +# Example usage: bash data/scripts/download_weights.sh +# parent +# └── yolov5 +# ├── yolov5s.pt ← downloads here +# ├── yolov5m.pt +# └── ... + +python - <= cls >= 0, f'incorrect class index {cls}' + + # Write YOLO label + if id not in shapes: + shapes[id] = Image.open(file).size + box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True) + with open((labels / id).with_suffix('.txt'), 'a') as f: + f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt + except Exception as e: + print(f'WARNING: skipping one label for {file}: {e}') + + + # Download manually from https://challenge.xviewdataset.org + dir = Path(yaml['path']) # dataset root dir + # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels + # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images + # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels) + # download(urls, dir=dir, delete=False) + + # Convert labels + convert_labels(dir / 'xView_train.geojson') + + # Move images + images = Path(dir / 'images') + images.mkdir(parents=True, exist_ok=True) + Path(dir / 'train_images').rename(dir / 'images' / 'train') + Path(dir / 'val_images').rename(dir / 'images' / 'val') + + # Split + autosplit(dir / 'images' / 'train') diff --git a/algorithm/yolov5-master/detect.py b/algorithm/yolov5-master/detect.py new file mode 100644 index 0000000..3f32d7a --- /dev/null +++ b/algorithm/yolov5-master/detect.py @@ -0,0 +1,261 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) +from utils.plots import Annotator, colors, save_one_box +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s.pt', # model path or triton URL + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/detect', # save results to project/name + name='exp', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + pred = model(im, augment=augment, visualize=visualize) + + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + s += '%gx%g ' % im.shape[2:] # print string + gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh + imc = im0.copy() if save_crop else im0 # for save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Write results + for *xyxy, conf, cls in reversed(det): + if save_txt: # Write to file + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(f'{txt_path}.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + annotator.box_label(xyxy, label, color=colors(c, True)) + if save_crop: + save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL') + parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') + parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') + parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/export.py b/algorithm/yolov5-master/export.py new file mode 100644 index 0000000..e167b20 --- /dev/null +++ b/algorithm/yolov5-master/export.py @@ -0,0 +1,672 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit + +Format | `export.py --include` | Model +--- | --- | --- +PyTorch | - | yolov5s.pt +TorchScript | `torchscript` | yolov5s.torchscript +ONNX | `onnx` | yolov5s.onnx +OpenVINO | `openvino` | yolov5s_openvino_model/ +TensorRT | `engine` | yolov5s.engine +CoreML | `coreml` | yolov5s.mlmodel +TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ +TensorFlow GraphDef | `pb` | yolov5s.pb +TensorFlow Lite | `tflite` | yolov5s.tflite +TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov5s_web_model/ +PaddlePaddle | `paddle` | yolov5s_paddle_model/ + +Requirements: + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU + +Usage: + $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ... + +Inference: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle + +TensorFlow.js: + $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example + $ npm install + $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model + $ npm start +""" + +import argparse +import contextlib +import json +import os +import platform +import re +import subprocess +import sys +import time +import warnings +from pathlib import Path + +import pandas as pd +import torch +from torch.utils.mobile_optimizer import optimize_for_mobile + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +if platform.system() != 'Windows': + ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.experimental import attempt_load +from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel +from utils.dataloaders import LoadImages +from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version, + check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save) +from utils.torch_utils import select_device, smart_inference_mode + +MACOS = platform.system() == 'Darwin' # macOS environment + + +def export_formats(): + # YOLOv5 export formats + x = [ + ['PyTorch', '-', '.pt', True, True], + ['TorchScript', 'torchscript', '.torchscript', True, True], + ['ONNX', 'onnx', '.onnx', True, True], + ['OpenVINO', 'openvino', '_openvino_model', True, False], + ['TensorRT', 'engine', '.engine', False, True], + ['CoreML', 'coreml', '.mlmodel', True, False], + ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True], + ['TensorFlow GraphDef', 'pb', '.pb', True, True], + ['TensorFlow Lite', 'tflite', '.tflite', True, False], + ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False], + ['TensorFlow.js', 'tfjs', '_web_model', False, False], + ['PaddlePaddle', 'paddle', '_paddle_model', True, True],] + return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU']) + + +def try_export(inner_func): + # YOLOv5 export decorator, i..e @try_export + inner_args = get_default_args(inner_func) + + def outer_func(*args, **kwargs): + prefix = inner_args['prefix'] + try: + with Profile() as dt: + f, model = inner_func(*args, **kwargs) + LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)') + return f, model + except Exception as e: + LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}') + return None, None + + return outer_func + + +@try_export +def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')): + # YOLOv5 TorchScript model export + LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...') + f = file.with_suffix('.torchscript') + + ts = torch.jit.trace(model, im, strict=False) + d = {'shape': im.shape, 'stride': int(max(model.stride)), 'names': model.names} + extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap() + if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html + optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) + else: + ts.save(str(f), _extra_files=extra_files) + return f, None + + +@try_export +def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')): + # YOLOv5 ONNX export + check_requirements('onnx>=1.12.0') + import onnx + + LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...') + f = file.with_suffix('.onnx') + + output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0'] + if dynamic: + dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640) + if isinstance(model, SegmentationModel): + dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85) + dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160) + elif isinstance(model, DetectionModel): + dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85) + + torch.onnx.export( + model.cpu() if dynamic else model, # --dynamic only compatible with cpu + im.cpu() if dynamic else im, + f, + verbose=False, + opset_version=opset, + do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False + input_names=['images'], + output_names=output_names, + dynamic_axes=dynamic or None) + + # Checks + model_onnx = onnx.load(f) # load onnx model + onnx.checker.check_model(model_onnx) # check onnx model + + # Metadata + d = {'stride': int(max(model.stride)), 'names': model.names} + for k, v in d.items(): + meta = model_onnx.metadata_props.add() + meta.key, meta.value = k, str(v) + onnx.save(model_onnx, f) + + # Simplify + if simplify: + try: + cuda = torch.cuda.is_available() + check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1')) + import onnxsim + + LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') + model_onnx, check = onnxsim.simplify(model_onnx) + assert check, 'assert check failed' + onnx.save(model_onnx, f) + except Exception as e: + LOGGER.info(f'{prefix} simplifier failure: {e}') + return f, model_onnx + + +@try_export +def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')): + # YOLOv5 OpenVINO export + check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/ + import openvino.inference_engine as ie + + LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...') + f = str(file).replace('.pt', f'_openvino_model{os.sep}') + + args = [ + 'mo', + '--input_model', + str(file.with_suffix('.onnx')), + '--output_dir', + f, + '--data_type', + ('FP16' if half else 'FP32'),] + subprocess.run(args, check=True, env=os.environ) # export + yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml + return f, None + + +@try_export +def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')): + # YOLOv5 Paddle export + check_requirements(('paddlepaddle', 'x2paddle')) + import x2paddle + from x2paddle.convert import pytorch2paddle + + LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...') + f = str(file).replace('.pt', f'_paddle_model{os.sep}') + + pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im]) # export + yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml + return f, None + + +@try_export +def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')): + # YOLOv5 CoreML export + check_requirements('coremltools') + import coremltools as ct + + LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...') + f = file.with_suffix('.mlmodel') + + ts = torch.jit.trace(model, im, strict=False) # TorchScript model + ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])]) + bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None) + if bits < 32: + if MACOS: # quantization only supported on macOS + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress numpy==1.20 float warning + ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) + else: + print(f'{prefix} quantization only supported on macOS, skipping...') + ct_model.save(f) + return f, ct_model + + +@try_export +def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')): + # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt + assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`' + try: + import tensorrt as trt + except Exception: + if platform.system() == 'Linux': + check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com') + import tensorrt as trt + + if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 + grid = model.model[-1].anchor_grid + model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] + export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 + model.model[-1].anchor_grid = grid + else: # TensorRT >= 8 + check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0 + export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 + onnx = file.with_suffix('.onnx') + + LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...') + assert onnx.exists(), f'failed to export ONNX file: {onnx}' + f = file.with_suffix('.engine') # TensorRT engine file + logger = trt.Logger(trt.Logger.INFO) + if verbose: + logger.min_severity = trt.Logger.Severity.VERBOSE + + builder = trt.Builder(logger) + config = builder.create_builder_config() + config.max_workspace_size = workspace * 1 << 30 + # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice + + flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) + network = builder.create_network(flag) + parser = trt.OnnxParser(network, logger) + if not parser.parse_from_file(str(onnx)): + raise RuntimeError(f'failed to load ONNX file: {onnx}') + + inputs = [network.get_input(i) for i in range(network.num_inputs)] + outputs = [network.get_output(i) for i in range(network.num_outputs)] + for inp in inputs: + LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}') + for out in outputs: + LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}') + + if dynamic: + if im.shape[0] <= 1: + LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument') + profile = builder.create_optimization_profile() + for inp in inputs: + profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape) + config.add_optimization_profile(profile) + + LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}') + if builder.platform_has_fast_fp16 and half: + config.set_flag(trt.BuilderFlag.FP16) + with builder.build_engine(network, config) as engine, open(f, 'wb') as t: + t.write(engine.serialize()) + return f, None + + +@try_export +def export_saved_model(model, + im, + file, + dynamic, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25, + keras=False, + prefix=colorstr('TensorFlow SavedModel:')): + # YOLOv5 TensorFlow SavedModel export + try: + import tensorflow as tf + except Exception: + check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}") + import tensorflow as tf + from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 + + from models.tf import TFModel + + LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') + f = str(file).replace('.pt', '_saved_model') + batch_size, ch, *imgsz = list(im.shape) # BCHW + + tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) + im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow + _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) + inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size) + outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) + keras_model = tf.keras.Model(inputs=inputs, outputs=outputs) + keras_model.trainable = False + keras_model.summary() + if keras: + keras_model.save(f, save_format='tf') + else: + spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype) + m = tf.function(lambda x: keras_model(x)) # full model + m = m.get_concrete_function(spec) + frozen_func = convert_variables_to_constants_v2(m) + tfm = tf.Module() + tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec]) + tfm.__call__(im) + tf.saved_model.save(tfm, + f, + options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version( + tf.__version__, '2.6') else tf.saved_model.SaveOptions()) + return f, keras_model + + +@try_export +def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')): + # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow + import tensorflow as tf + from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 + + LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') + f = file.with_suffix('.pb') + + m = tf.function(lambda x: keras_model(x)) # full model + m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) + frozen_func = convert_variables_to_constants_v2(m) + frozen_func.graph.as_graph_def() + tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False) + return f, None + + +@try_export +def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')): + # YOLOv5 TensorFlow Lite export + import tensorflow as tf + + LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') + batch_size, ch, *imgsz = list(im.shape) # BCHW + f = str(file).replace('.pt', '-fp16.tflite') + + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + converter.target_spec.supported_types = [tf.float16] + converter.optimizations = [tf.lite.Optimize.DEFAULT] + if int8: + from models.tf import representative_dataset_gen + dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False) + converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100) + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] + converter.target_spec.supported_types = [] + converter.inference_input_type = tf.uint8 # or tf.int8 + converter.inference_output_type = tf.uint8 # or tf.int8 + converter.experimental_new_quantizer = True + f = str(file).replace('.pt', '-int8.tflite') + if nms or agnostic_nms: + converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS) + + tflite_model = converter.convert() + open(f, 'wb').write(tflite_model) + return f, None + + +@try_export +def export_edgetpu(file, prefix=colorstr('Edge TPU:')): + # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/ + cmd = 'edgetpu_compiler --version' + help_url = 'https://coral.ai/docs/edgetpu/compiler/' + assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}' + if subprocess.run(f'{cmd} > /dev/null 2>&1', shell=True).returncode != 0: + LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}') + sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system + for c in ( + 'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -', + 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', + 'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'): + subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True) + ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1] + + LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...') + f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model + f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model + + subprocess.run([ + 'edgetpu_compiler', + '-s', + '-d', + '-k', + '10', + '--out_dir', + str(file.parent), + f_tfl,], check=True) + return f, None + + +@try_export +def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')): + # YOLOv5 TensorFlow.js export + check_requirements('tensorflowjs') + import tensorflowjs as tfjs + + LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...') + f = str(file).replace('.pt', '_web_model') # js dir + f_pb = file.with_suffix('.pb') # *.pb path + f_json = f'{f}/model.json' # *.json path + + args = [ + 'tensorflowjs_converter', + '--input_format=tf_frozen_model', + '--quantize_uint8' if int8 else '', + '--output_node_names=Identity,Identity_1,Identity_2,Identity_3', + str(f_pb), + str(f),] + subprocess.run([arg for arg in args if arg], check=True) + + json = Path(f_json).read_text() + with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order + subst = re.sub( + r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, ' + r'"Identity_1": {"name": "Identity_1"}, ' + r'"Identity_2": {"name": "Identity_2"}, ' + r'"Identity_3": {"name": "Identity_3"}}}', json) + j.write(subst) + return f, None + + +def add_tflite_metadata(file, metadata, num_outputs): + # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata + with contextlib.suppress(ImportError): + # check_requirements('tflite_support') + from tflite_support import flatbuffers + from tflite_support import metadata as _metadata + from tflite_support import metadata_schema_py_generated as _metadata_fb + + tmp_file = Path('/tmp/meta.txt') + with open(tmp_file, 'w') as meta_f: + meta_f.write(str(metadata)) + + model_meta = _metadata_fb.ModelMetadataT() + label_file = _metadata_fb.AssociatedFileT() + label_file.name = tmp_file.name + model_meta.associatedFiles = [label_file] + + subgraph = _metadata_fb.SubGraphMetadataT() + subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()] + subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs + model_meta.subgraphMetadata = [subgraph] + + b = flatbuffers.Builder(0) + b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER) + metadata_buf = b.Output() + + populator = _metadata.MetadataPopulator.with_model_file(file) + populator.load_metadata_buffer(metadata_buf) + populator.load_associated_files([str(tmp_file)]) + populator.populate() + tmp_file.unlink() + + +@smart_inference_mode() +def run( + data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path' + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=(640, 640), # image (height, width) + batch_size=1, # batch size + device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu + include=('torchscript', 'onnx'), # include formats + half=False, # FP16 half-precision export + inplace=False, # set YOLOv5 Detect() inplace=True + keras=False, # use Keras + optimize=False, # TorchScript: optimize for mobile + int8=False, # CoreML/TF INT8 quantization + dynamic=False, # ONNX/TF/TensorRT: dynamic axes + simplify=False, # ONNX: simplify model + opset=12, # ONNX: opset version + verbose=False, # TensorRT: verbose log + workspace=4, # TensorRT: workspace size (GB) + nms=False, # TF: add NMS to model + agnostic_nms=False, # TF: add agnostic NMS to model + topk_per_class=100, # TF.js NMS: topk per class to keep + topk_all=100, # TF.js NMS: topk for all classes to keep + iou_thres=0.45, # TF.js NMS: IoU threshold + conf_thres=0.25, # TF.js NMS: confidence threshold +): + t = time.time() + include = [x.lower() for x in include] # to lowercase + fmts = tuple(export_formats()['Argument'][1:]) # --include arguments + flags = [x in include for x in fmts] + assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}' + jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans + file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights + + # Load PyTorch model + device = select_device(device) + if half: + assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0' + assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both' + model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model + + # Checks + imgsz *= 2 if len(imgsz) == 1 else 1 # expand + if optimize: + assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu' + + # Input + gs = int(max(model.stride)) # grid size (max stride) + imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples + im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection + + # Update model + model.eval() + for k, m in model.named_modules(): + if isinstance(m, Detect): + m.inplace = inplace + m.dynamic = dynamic + m.export = True + + for _ in range(2): + y = model(im) # dry runs + if half and not coreml: + im, model = im.half(), model.half() # to FP16 + shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape + metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata + LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)") + + # Exports + f = [''] * len(fmts) # exported filenames + warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning + if jit: # TorchScript + f[0], _ = export_torchscript(model, im, file, optimize) + if engine: # TensorRT required before ONNX + f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose) + if onnx or xml: # OpenVINO requires ONNX + f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify) + if xml: # OpenVINO + f[3], _ = export_openvino(file, metadata, half) + if coreml: # CoreML + f[4], _ = export_coreml(model, im, file, int8, half) + if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats + assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.' + assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.' + f[5], s_model = export_saved_model(model.cpu(), + im, + file, + dynamic, + tf_nms=nms or agnostic_nms or tfjs, + agnostic_nms=agnostic_nms or tfjs, + topk_per_class=topk_per_class, + topk_all=topk_all, + iou_thres=iou_thres, + conf_thres=conf_thres, + keras=keras) + if pb or tfjs: # pb prerequisite to tfjs + f[6], _ = export_pb(s_model, file) + if tflite or edgetpu: + f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms) + if edgetpu: + f[8], _ = export_edgetpu(file) + add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) + if tfjs: + f[9], _ = export_tfjs(file, int8) + if paddle: # PaddlePaddle + f[10], _ = export_paddle(model, im, file, metadata) + + # Finish + f = [str(x) for x in f if x] # filter out '' and None + if any(f): + cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type + det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel) + dir = Path('segment' if seg else 'classify' if cls else '') + h = '--half' if half else '' # --half FP16 inference arg + s = '# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference' if cls else \ + '# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference' if seg else '' + LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)' + f"\nResults saved to {colorstr('bold', file.parent.resolve())}" + f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}" + f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}" + f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}" + f'\nVisualize: https://netron.app') + return f # return list of exported files/dirs + + +def parse_opt(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--half', action='store_true', help='FP16 half-precision export') + parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True') + parser.add_argument('--keras', action='store_true', help='TF: use Keras') + parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile') + parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization') + parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes') + parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model') + parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version') + parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log') + parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)') + parser.add_argument('--nms', action='store_true', help='TF: add NMS to model') + parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model') + parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep') + parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep') + parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold') + parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold') + parser.add_argument( + '--include', + nargs='+', + default=['torchscript'], + help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle') + opt = parser.parse_known_args()[0] if known else parser.parse_args() + print_args(vars(opt)) + return opt + + +def main(opt): + for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]): + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/hubconf.py b/algorithm/yolov5-master/hubconf.py new file mode 100644 index 0000000..41af8e3 --- /dev/null +++ b/algorithm/yolov5-master/hubconf.py @@ -0,0 +1,169 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5 + +Usage: + import torch + model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model + model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch + model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model + model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo +""" + +import torch + + +def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): + """Creates or loads a YOLOv5 model + + Arguments: + name (str): model name 'yolov5s' or path 'path/to/best.pt' + pretrained (bool): load pretrained weights into the model + channels (int): number of input channels + classes (int): number of model classes + autoshape (bool): apply YOLOv5 .autoshape() wrapper to model + verbose (bool): print all information to screen + device (str, torch.device, None): device to use for model parameters + + Returns: + YOLOv5 model + """ + from pathlib import Path + + from models.common import AutoShape, DetectMultiBackend + from models.experimental import attempt_load + from models.yolo import ClassificationModel, DetectionModel, SegmentationModel + from utils.downloads import attempt_download + from utils.general import LOGGER, check_requirements, intersect_dicts, logging + from utils.torch_utils import select_device + + if not verbose: + LOGGER.setLevel(logging.WARNING) + check_requirements(exclude=('opencv-python', 'tensorboard', 'thop')) + name = Path(name) + path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name # checkpoint path + try: + device = select_device(device) + if pretrained and channels == 3 and classes == 80: + try: + model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model + if autoshape: + if model.pt and isinstance(model.model, ClassificationModel): + LOGGER.warning('WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. ' + 'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).') + elif model.pt and isinstance(model.model, SegmentationModel): + LOGGER.warning('WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. ' + 'You will not be able to run inference with this model.') + else: + model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS + except Exception: + model = attempt_load(path, device=device, fuse=False) # arbitrary model + else: + cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path + model = DetectionModel(cfg, channels, classes) # create model + if pretrained: + ckpt = torch.load(attempt_download(path), map_location=device) # load + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect + model.load_state_dict(csd, strict=False) # load + if len(ckpt['model'].names) == classes: + model.names = ckpt['model'].names # set class names attribute + if not verbose: + LOGGER.setLevel(logging.INFO) # reset to default + return model.to(device) + + except Exception as e: + help_url = 'https://github.com/ultralytics/yolov5/issues/36' + s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.' + raise Exception(s) from e + + +def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None): + # YOLOv5 custom or local model + return _create(path, autoshape=autoshape, verbose=_verbose, device=device) + + +def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-nano model https://github.com/ultralytics/yolov5 + return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-small model https://github.com/ultralytics/yolov5 + return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-medium model https://github.com/ultralytics/yolov5 + return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-large model https://github.com/ultralytics/yolov5 + return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 + return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device) + + +if __name__ == '__main__': + import argparse + from pathlib import Path + + import numpy as np + from PIL import Image + + from utils.general import cv2, print_args + + # Argparser + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default='yolov5s', help='model name') + opt = parser.parse_args() + print_args(vars(opt)) + + # Model + model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) + # model = custom(path='path/to/model.pt') # custom + + # Images + imgs = [ + 'data/images/zidane.jpg', # filename + Path('data/images/zidane.jpg'), # Path + 'https://ultralytics.com/images/zidane.jpg', # URI + cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV + Image.open('data/images/bus.jpg'), # PIL + np.zeros((320, 640, 3))] # numpy + + # Inference + results = model(imgs, size=320) # batched inference + + # Results + results.print() + results.save() diff --git a/algorithm/yolov5-master/models/__init__.py b/algorithm/yolov5-master/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5-master/models/common.py b/algorithm/yolov5-master/models/common.py new file mode 100644 index 0000000..aa8ae67 --- /dev/null +++ b/algorithm/yolov5-master/models/common.py @@ -0,0 +1,870 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Common modules +""" + +import ast +import contextlib +import json +import math +import platform +import warnings +import zipfile +from collections import OrderedDict, namedtuple +from copy import copy +from pathlib import Path +from urllib.parse import urlparse + +import cv2 +import numpy as np +import pandas as pd +import requests +import torch +import torch.nn as nn +from PIL import Image +from torch.cuda import amp + +from utils import TryExcept +from utils.dataloaders import exif_transpose, letterbox +from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr, + increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy, + xyxy2xywh, yaml_load) +from utils.plots import Annotator, colors, save_one_box +from utils.torch_utils import copy_attr, smart_inference_mode + + +def autopad(k, p=None, d=1): # kernel, padding, dilation + # Pad to 'same' shape outputs + if d > 1: + k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + + +class Conv(nn.Module): + # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation) + default_act = nn.SiLU() # default activation + + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): + super().__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def forward_fuse(self, x): + return self.act(self.conv(x)) + + +class DWConv(Conv): + # Depth-wise convolution + def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation + super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) + + +class DWConvTranspose2d(nn.ConvTranspose2d): + # Depth-wise transpose convolution + def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out + super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) + + +class TransformerLayer(nn.Module): + # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) + def __init__(self, c, num_heads): + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + + +class TransformerBlock(nn.Module): + # Vision Transformer https://arxiv.org/abs/2010.11929 + def __init__(self, c1, c2, num_heads, num_layers): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) + self.c2 = c2 + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2).permute(2, 0, 1) + return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) + + +class Bottleneck(nn.Module): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2, 3, 1, g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class BottleneckCSP(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) + self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) + self.cv4 = Conv(2 * c_, c2, 1, 1) + self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) + self.act = nn.SiLU() + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) + + +class CrossConv(nn.Module): + # Cross Convolution Downsample + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): + # ch_in, ch_out, kernel, stride, groups, expansion, shortcut + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, (1, k), (1, s)) + self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class C3(nn.Module): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) + + +class C3x(C3): + # C3 module with cross-convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n))) + + +class C3TR(C3): + # C3 module with TransformerBlock() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = TransformerBlock(c_, c_, 4, n) + + +class C3SPP(C3): + # C3 module with SPP() + def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = SPP(c_, c_, k) + + +class C3Ghost(C3): + # C3 module with GhostBottleneck() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) + + +class SPP(nn.Module): + # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 + def __init__(self, c1, c2, k=(5, 9, 13)): + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + + def forward(self, x): + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + + +class SPPF(nn.Module): + # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher + def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * 4, c2, 1, 1) + self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) + + def forward(self, x): + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) + + +class Focus(nn.Module): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act) + # self.contract = Contract(gain=2) + + def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) + return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1)) + # return self.conv(self.contract(x)) + + +class GhostConv(nn.Module): + # Ghost Convolution https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups + super().__init__() + c_ = c2 // 2 # hidden channels + self.cv1 = Conv(c1, c_, k, s, None, g, act=act) + self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act) + + def forward(self, x): + y = self.cv1(x) + return torch.cat((y, self.cv2(y)), 1) + + +class GhostBottleneck(nn.Module): + # Ghost Bottleneck https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride + super().__init__() + c_ = c2 // 2 + self.conv = nn.Sequential( + GhostConv(c1, c_, 1, 1), # pw + DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw + GhostConv(c_, c2, 1, 1, act=False)) # pw-linear + self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, + act=False)) if s == 2 else nn.Identity() + + def forward(self, x): + return self.conv(x) + self.shortcut(x) + + +class Contract(nn.Module): + # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' + s = self.gain + x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) + x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) + return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) + + +class Expand(nn.Module): + # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' + s = self.gain + x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) + x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) + return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) + + +class Concat(nn.Module): + # Concatenate a list of tensors along dimension + def __init__(self, dimension=1): + super().__init__() + self.d = dimension + + def forward(self, x): + return torch.cat(x, self.d) + + +class DetectMultiBackend(nn.Module): + # YOLOv5 MultiBackend class for python inference on various backends + def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True): + # Usage: + # PyTorch: weights = *.pt + # TorchScript: *.torchscript + # ONNX Runtime: *.onnx + # ONNX OpenCV DNN: *.onnx --dnn + # OpenVINO: *_openvino_model + # CoreML: *.mlmodel + # TensorRT: *.engine + # TensorFlow SavedModel: *_saved_model + # TensorFlow GraphDef: *.pb + # TensorFlow Lite: *.tflite + # TensorFlow Edge TPU: *_edgetpu.tflite + # PaddlePaddle: *_paddle_model + from models.experimental import attempt_download, attempt_load # scoped to avoid circular import + + super().__init__() + w = str(weights[0] if isinstance(weights, list) else weights) + pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w) + fp16 &= pt or jit or onnx or engine # FP16 + nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) + stride = 32 # default stride + cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA + if not (pt or triton): + w = attempt_download(w) # download if not local + + if pt: # PyTorch + model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse) + stride = max(int(model.stride.max()), 32) # model stride + names = model.module.names if hasattr(model, 'module') else model.names # get class names + model.half() if fp16 else model.float() + self.model = model # explicitly assign for to(), cpu(), cuda(), half() + elif jit: # TorchScript + LOGGER.info(f'Loading {w} for TorchScript inference...') + extra_files = {'config.txt': ''} # model metadata + model = torch.jit.load(w, _extra_files=extra_files, map_location=device) + model.half() if fp16 else model.float() + if extra_files['config.txt']: # load metadata dict + d = json.loads(extra_files['config.txt'], + object_hook=lambda d: {int(k) if k.isdigit() else k: v + for k, v in d.items()}) + stride, names = int(d['stride']), d['names'] + elif dnn: # ONNX OpenCV DNN + LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') + check_requirements('opencv-python>=4.5.4') + net = cv2.dnn.readNetFromONNX(w) + elif onnx: # ONNX Runtime + LOGGER.info(f'Loading {w} for ONNX Runtime inference...') + check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) + import onnxruntime + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] + session = onnxruntime.InferenceSession(w, providers=providers) + output_names = [x.name for x in session.get_outputs()] + meta = session.get_modelmeta().custom_metadata_map # metadata + if 'stride' in meta: + stride, names = int(meta['stride']), eval(meta['names']) + elif xml: # OpenVINO + LOGGER.info(f'Loading {w} for OpenVINO inference...') + check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/ + from openvino.runtime import Core, Layout, get_batch + ie = Core() + if not Path(w).is_file(): # if not *.xml + w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir + network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin')) + if network.get_parameters()[0].get_layout().empty: + network.get_parameters()[0].set_layout(Layout('NCHW')) + batch_dim = get_batch(network) + if batch_dim.is_static: + batch_size = batch_dim.get_length() + executable_network = ie.compile_model(network, device_name='CPU') # device_name="MYRIAD" for Intel NCS2 + stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata + elif engine: # TensorRT + LOGGER.info(f'Loading {w} for TensorRT inference...') + import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download + check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 + if device.type == 'cpu': + device = torch.device('cuda:0') + Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) + logger = trt.Logger(trt.Logger.INFO) + with open(w, 'rb') as f, trt.Runtime(logger) as runtime: + model = runtime.deserialize_cuda_engine(f.read()) + context = model.create_execution_context() + bindings = OrderedDict() + output_names = [] + fp16 = False # default updated below + dynamic = False + for i in range(model.num_bindings): + name = model.get_binding_name(i) + dtype = trt.nptype(model.get_binding_dtype(i)) + if model.binding_is_input(i): + if -1 in tuple(model.get_binding_shape(i)): # dynamic + dynamic = True + context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2])) + if dtype == np.float16: + fp16 = True + else: # output + output_names.append(name) + shape = tuple(context.get_binding_shape(i)) + im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) + bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) + binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) + batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size + elif coreml: # CoreML + LOGGER.info(f'Loading {w} for CoreML inference...') + import coremltools as ct + model = ct.models.MLModel(w) + elif saved_model: # TF SavedModel + LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') + import tensorflow as tf + keras = False # assume TF1 saved_model + model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) + elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') + import tensorflow as tf + + def wrap_frozen_graph(gd, inputs, outputs): + x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), []) # wrapped + ge = x.graph.as_graph_element + return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) + + def gd_outputs(gd): + name_list, input_list = [], [] + for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef + name_list.append(node.name) + input_list.extend(node.input) + return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp')) + + gd = tf.Graph().as_graph_def() # TF GraphDef + with open(w, 'rb') as f: + gd.ParseFromString(f.read()) + frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd)) + elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python + try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu + from tflite_runtime.interpreter import Interpreter, load_delegate + except ImportError: + import tensorflow as tf + Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate, + if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime + LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') + delegate = { + 'Linux': 'libedgetpu.so.1', + 'Darwin': 'libedgetpu.1.dylib', + 'Windows': 'edgetpu.dll'}[platform.system()] + interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) + else: # TFLite + LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') + interpreter = Interpreter(model_path=w) # load TFLite model + interpreter.allocate_tensors() # allocate + input_details = interpreter.get_input_details() # inputs + output_details = interpreter.get_output_details() # outputs + # load metadata + with contextlib.suppress(zipfile.BadZipFile): + with zipfile.ZipFile(w, 'r') as model: + meta_file = model.namelist()[0] + meta = ast.literal_eval(model.read(meta_file).decode('utf-8')) + stride, names = int(meta['stride']), meta['names'] + elif tfjs: # TF.js + raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported') + elif paddle: # PaddlePaddle + LOGGER.info(f'Loading {w} for PaddlePaddle inference...') + check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle') + import paddle.inference as pdi + if not Path(w).is_file(): # if not *.pdmodel + w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir + weights = Path(w).with_suffix('.pdiparams') + config = pdi.Config(str(w), str(weights)) + if cuda: + config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) + predictor = pdi.create_predictor(config) + input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) + output_names = predictor.get_output_names() + elif triton: # NVIDIA Triton Inference Server + LOGGER.info(f'Using {w} as Triton Inference Server...') + check_requirements('tritonclient[all]') + from utils.triton import TritonRemoteModel + model = TritonRemoteModel(url=w) + nhwc = model.runtime.startswith('tensorflow') + else: + raise NotImplementedError(f'ERROR: {w} is not a supported format') + + # class names + if 'names' not in locals(): + names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)} + if names[0] == 'n01440764' and len(names) == 1000: # ImageNet + names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names + + self.__dict__.update(locals()) # assign all variables to self + + def forward(self, im, augment=False, visualize=False): + # YOLOv5 MultiBackend inference + b, ch, h, w = im.shape # batch, channel, height, width + if self.fp16 and im.dtype != torch.float16: + im = im.half() # to FP16 + if self.nhwc: + im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) + + if self.pt: # PyTorch + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + elif self.jit: # TorchScript + y = self.model(im) + elif self.dnn: # ONNX OpenCV DNN + im = im.cpu().numpy() # torch to numpy + self.net.setInput(im) + y = self.net.forward() + elif self.onnx: # ONNX Runtime + im = im.cpu().numpy() # torch to numpy + y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) + elif self.xml: # OpenVINO + im = im.cpu().numpy() # FP32 + y = list(self.executable_network([im]).values()) + elif self.engine: # TensorRT + if self.dynamic and im.shape != self.bindings['images'].shape: + i = self.model.get_binding_index('images') + self.context.set_binding_shape(i, im.shape) # reshape if dynamic + self.bindings['images'] = self.bindings['images']._replace(shape=im.shape) + for name in self.output_names: + i = self.model.get_binding_index(name) + self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) + s = self.bindings['images'].shape + assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" + self.binding_addrs['images'] = int(im.data_ptr()) + self.context.execute_v2(list(self.binding_addrs.values())) + y = [self.bindings[x].data for x in sorted(self.output_names)] + elif self.coreml: # CoreML + im = im.cpu().numpy() + im = Image.fromarray((im[0] * 255).astype('uint8')) + # im = im.resize((192, 320), Image.ANTIALIAS) + y = self.model.predict({'image': im}) # coordinates are xywh normalized + if 'confidence' in y: + box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels + conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) + y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) + else: + y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) + elif self.paddle: # PaddlePaddle + im = im.cpu().numpy().astype(np.float32) + self.input_handle.copy_from_cpu(im) + self.predictor.run() + y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] + elif self.triton: # NVIDIA Triton Inference Server + y = self.model(im) + else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) + im = im.cpu().numpy() + if self.saved_model: # SavedModel + y = self.model(im, training=False) if self.keras else self.model(im) + elif self.pb: # GraphDef + y = self.frozen_func(x=self.tf.constant(im)) + else: # Lite or Edge TPU + input = self.input_details[0] + int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model + if int8: + scale, zero_point = input['quantization'] + im = (im / scale + zero_point).astype(np.uint8) # de-scale + self.interpreter.set_tensor(input['index'], im) + self.interpreter.invoke() + y = [] + for output in self.output_details: + x = self.interpreter.get_tensor(output['index']) + if int8: + scale, zero_point = output['quantization'] + x = (x.astype(np.float32) - zero_point) * scale # re-scale + y.append(x) + y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] + y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels + + if isinstance(y, (list, tuple)): + return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] + else: + return self.from_numpy(y) + + def from_numpy(self, x): + return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x + + def warmup(self, imgsz=(1, 3, 640, 640)): + # Warmup model by running inference once + warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton + if any(warmup_types) and (self.device.type != 'cpu' or self.triton): + im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input + for _ in range(2 if self.jit else 1): # + self.forward(im) # warmup + + @staticmethod + def _model_type(p='path/to/model.pt'): + # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx + # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] + from export import export_formats + from utils.downloads import is_url + sf = list(export_formats().Suffix) # export suffixes + if not is_url(p, check=False): + check_suffix(p, sf) # checks + url = urlparse(p) # if url may be Triton inference server + types = [s in Path(p).name for s in sf] + types[8] &= not types[9] # tflite &= not edgetpu + triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc]) + return types + [triton] + + @staticmethod + def _load_metadata(f=Path('path/to/meta.yaml')): + # Load metadata from meta.yaml if it exists + if f.exists(): + d = yaml_load(f) + return d['stride'], d['names'] # assign stride, names + return None, None + + +class AutoShape(nn.Module): + # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS + conf = 0.25 # NMS confidence threshold + iou = 0.45 # NMS IoU threshold + agnostic = False # NMS class-agnostic + multi_label = False # NMS multiple labels per box + classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs + max_det = 1000 # maximum number of detections per image + amp = False # Automatic Mixed Precision (AMP) inference + + def __init__(self, model, verbose=True): + super().__init__() + if verbose: + LOGGER.info('Adding AutoShape... ') + copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes + self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance + self.pt = not self.dmb or model.pt # PyTorch model + self.model = model.eval() + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.inplace = False # Detect.inplace=False for safe multithread inference + m.export = True # do not output loss values + + def _apply(self, fn): + # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers + self = super()._apply(fn) + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + @smart_inference_mode() + def forward(self, ims, size=640, augment=False, profile=False): + # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are: + # file: ims = 'data/images/zidane.jpg' # str or PosixPath + # URI: = 'https://ultralytics.com/images/zidane.jpg' + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) + # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + + dt = (Profile(), Profile(), Profile()) + with dt[0]: + if isinstance(size, int): # expand + size = (size, size) + p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param + autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference + if isinstance(ims, torch.Tensor): # torch + with amp.autocast(autocast): + return self.model(ims.to(p.device).type_as(p), augment=augment) # inference + + # Pre-process + n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images + shape0, shape1, files = [], [], [] # image and inference shapes, filenames + for i, im in enumerate(ims): + f = f'image{i}' # filename + if isinstance(im, (str, Path)): # filename or uri + im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im + im = np.asarray(exif_transpose(im)) + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f + files.append(Path(f).with_suffix('.jpg').name) + if im.shape[0] < 5: # image in CHW + im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) + im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input + s = im.shape[:2] # HWC + shape0.append(s) # image shape + g = max(size) / max(s) # gain + shape1.append([int(y * g) for y in s]) + ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update + shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape + x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad + x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW + x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 + + with amp.autocast(autocast): + # Inference + with dt[1]: + y = self.model(x, augment=augment) # forward + + # Post-process + with dt[2]: + y = non_max_suppression(y if self.dmb else y[0], + self.conf, + self.iou, + self.classes, + self.agnostic, + self.multi_label, + max_det=self.max_det) # NMS + for i in range(n): + scale_boxes(shape1, y[i][:, :4], shape0[i]) + + return Detections(ims, y, files, dt, self.names, x.shape) + + +class Detections: + # YOLOv5 detections class for inference results + def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): + super().__init__() + d = pred[0].device # device + gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations + self.ims = ims # list of images as numpy arrays + self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) + self.names = names # class names + self.files = files # image filenames + self.times = times # profiling times + self.xyxy = pred # xyxy pixels + self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels + self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized + self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized + self.n = len(self.pred) # number of images (batch size) + self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms) + self.s = tuple(shape) # inference BCHW shape + + def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')): + s, crops = '', [] + for i, (im, pred) in enumerate(zip(self.ims, self.pred)): + s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string + if pred.shape[0]: + for c in pred[:, -1].unique(): + n = (pred[:, -1] == c).sum() # detections per class + s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + s = s.rstrip(', ') + if show or save or render or crop: + annotator = Annotator(im, example=str(self.names)) + for *box, conf, cls in reversed(pred): # xyxy, confidence, class + label = f'{self.names[int(cls)]} {conf:.2f}' + if crop: + file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None + crops.append({ + 'box': box, + 'conf': conf, + 'cls': cls, + 'label': label, + 'im': save_one_box(box, im, file=file, save=save)}) + else: # all others + annotator.box_label(box, label if labels else '', color=colors(cls)) + im = annotator.im + else: + s += '(no detections)' + + im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np + if show: + if is_jupyter(): + from IPython.display import display + display(im) + else: + im.show(self.files[i]) + if save: + f = self.files[i] + im.save(save_dir / f) # save + if i == self.n - 1: + LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") + if render: + self.ims[i] = np.asarray(im) + if pprint: + s = s.lstrip('\n') + return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t + if crop: + if save: + LOGGER.info(f'Saved results to {save_dir}\n') + return crops + + @TryExcept('Showing images is not supported in this environment') + def show(self, labels=True): + self._run(show=True, labels=labels) # show results + + def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): + save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir + self._run(save=True, labels=labels, save_dir=save_dir) # save results + + def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): + save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None + return self._run(crop=True, save=save, save_dir=save_dir) # crop results + + def render(self, labels=True): + self._run(render=True, labels=labels) # render results + return self.ims + + def pandas(self): + # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) + new = copy(self) # return copy + ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns + cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns + for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): + a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new + + def tolist(self): + # return a list of Detections objects, i.e. 'for result in results.tolist():' + r = range(self.n) # iterable + x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] + # for d in x: + # for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: + # setattr(d, k, getattr(d, k)[0]) # pop out of list + return x + + def print(self): + LOGGER.info(self.__str__()) + + def __len__(self): # override len(results) + return self.n + + def __str__(self): # override print(results) + return self._run(pprint=True) # print results + + def __repr__(self): + return f'YOLOv5 {self.__class__} instance\n' + self.__str__() + + +class Proto(nn.Module): + # YOLOv5 mask Proto module for segmentation models + def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks + super().__init__() + self.cv1 = Conv(c1, c_, k=3) + self.upsample = nn.Upsample(scale_factor=2, mode='nearest') + self.cv2 = Conv(c_, c_, k=3) + self.cv3 = Conv(c_, c2) + + def forward(self, x): + return self.cv3(self.cv2(self.upsample(self.cv1(x)))) + + +class Classify(nn.Module): + # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2) + def __init__(self, + c1, + c2, + k=1, + s=1, + p=None, + g=1, + dropout_p=0.0): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability + super().__init__() + c_ = 1280 # efficientnet_b0 size + self.conv = Conv(c1, c_, k, s, autopad(k, p), g) + self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1) + self.drop = nn.Dropout(p=dropout_p, inplace=True) + self.linear = nn.Linear(c_, c2) # to x(b,c2) + + def forward(self, x): + if isinstance(x, list): + x = torch.cat(x, 1) + return self.linear(self.drop(self.pool(self.conv(x)).flatten(1))) diff --git a/algorithm/yolov5-master/models/experimental.py b/algorithm/yolov5-master/models/experimental.py new file mode 100644 index 0000000..02d35b9 --- /dev/null +++ b/algorithm/yolov5-master/models/experimental.py @@ -0,0 +1,111 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Experimental modules +""" +import math + +import numpy as np +import torch +import torch.nn as nn + +from utils.downloads import attempt_download + + +class Sum(nn.Module): + # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 + def __init__(self, n, weight=False): # n: number of inputs + super().__init__() + self.weight = weight # apply weights boolean + self.iter = range(n - 1) # iter object + if weight: + self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights + + def forward(self, x): + y = x[0] # no weight + if self.weight: + w = torch.sigmoid(self.w) * 2 + for i in self.iter: + y = y + x[i + 1] * w[i] + else: + for i in self.iter: + y = y + x[i + 1] + return y + + +class MixConv2d(nn.Module): + # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595 + def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy + super().__init__() + n = len(k) # number of convolutions + if equal_ch: # equal c_ per group + i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices + c_ = [(i == g).sum() for g in range(n)] # intermediate channels + else: # equal weight.numel() per group + b = [c2] + [0] * n + a = np.eye(n + 1, n, k=-1) + a -= np.roll(a, 1, axis=1) + a *= np.array(k) ** 2 + a[0] = 1 + c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b + + self.m = nn.ModuleList([ + nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.SiLU() + + def forward(self, x): + return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) + + +class Ensemble(nn.ModuleList): + # Ensemble of models + def __init__(self): + super().__init__() + + def forward(self, x, augment=False, profile=False, visualize=False): + y = [module(x, augment, profile, visualize)[0] for module in self] + # y = torch.stack(y).max(0)[0] # max ensemble + # y = torch.stack(y).mean(0) # mean ensemble + y = torch.cat(y, 1) # nms ensemble + return y, None # inference, train output + + +def attempt_load(weights, device=None, inplace=True, fuse=True): + # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a + from models.yolo import Detect, Model + + model = Ensemble() + for w in weights if isinstance(weights, list) else [weights]: + ckpt = torch.load(attempt_download(w), map_location='cpu') # load + ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model + + # Model compatibility updates + if not hasattr(ckpt, 'stride'): + ckpt.stride = torch.tensor([32.]) + if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)): + ckpt.names = dict(enumerate(ckpt.names)) # convert to dict + + model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode + + # Module compatibility updates + for m in model.modules(): + t = type(m) + if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): + m.inplace = inplace # torch 1.7.0 compatibility + if t is Detect and not isinstance(m.anchor_grid, list): + delattr(m, 'anchor_grid') + setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) + elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): + m.recompute_scale_factor = None # torch 1.11.0 compatibility + + # Return model + if len(model) == 1: + return model[-1] + + # Return detection ensemble + print(f'Ensemble created with {weights}\n') + for k in 'names', 'nc', 'yaml': + setattr(model, k, getattr(model[0], k)) + model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride + assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}' + return model diff --git a/algorithm/yolov5-master/models/hub/anchors.yaml b/algorithm/yolov5-master/models/hub/anchors.yaml new file mode 100644 index 0000000..e4d7beb --- /dev/null +++ b/algorithm/yolov5-master/models/hub/anchors.yaml @@ -0,0 +1,59 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Default anchors for COCO data + + +# P5 ------------------------------------------------------------------------------------------------------------------- +# P5-640: +anchors_p5_640: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + + +# P6 ------------------------------------------------------------------------------------------------------------------- +# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 +anchors_p6_640: + - [9,11, 21,19, 17,41] # P3/8 + - [43,32, 39,70, 86,64] # P4/16 + - [65,131, 134,130, 120,265] # P5/32 + - [282,180, 247,354, 512,387] # P6/64 + +# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 +anchors_p6_1280: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 +anchors_p6_1920: + - [28,41, 67,59, 57,141] # P3/8 + - [144,103, 129,227, 270,205] # P4/16 + - [209,452, 455,396, 358,812] # P5/32 + - [653,922, 1109,570, 1387,1187] # P6/64 + + +# P7 ------------------------------------------------------------------------------------------------------------------- +# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 +anchors_p7_640: + - [11,11, 13,30, 29,20] # P3/8 + - [30,46, 61,38, 39,92] # P4/16 + - [78,80, 146,66, 79,163] # P5/32 + - [149,150, 321,143, 157,303] # P6/64 + - [257,402, 359,290, 524,372] # P7/128 + +# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 +anchors_p7_1280: + - [19,22, 54,36, 32,77] # P3/8 + - [70,83, 138,71, 75,173] # P4/16 + - [165,159, 148,334, 375,151] # P5/32 + - [334,317, 251,626, 499,474] # P6/64 + - [750,326, 534,814, 1079,818] # P7/128 + +# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 +anchors_p7_1920: + - [29,34, 81,55, 47,115] # P3/8 + - [105,124, 207,107, 113,259] # P4/16 + - [247,238, 222,500, 563,227] # P5/32 + - [501,476, 376,939, 749,711] # P6/64 + - [1126,489, 801,1222, 1618,1227] # P7/128 diff --git a/algorithm/yolov5-master/models/hub/yolov3-spp.yaml b/algorithm/yolov5-master/models/hub/yolov3-spp.yaml new file mode 100644 index 0000000..c669821 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov3-spp.yaml @@ -0,0 +1,51 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3-SPP head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, SPP, [512, [5, 9, 13]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov3-tiny.yaml b/algorithm/yolov5-master/models/hub/yolov3-tiny.yaml new file mode 100644 index 0000000..b28b443 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov3-tiny.yaml @@ -0,0 +1,41 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,14, 23,27, 37,58] # P4/16 + - [81,82, 135,169, 344,319] # P5/32 + +# YOLOv3-tiny backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [16, 3, 1]], # 0 + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 + [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 + ] + +# YOLOv3-tiny head +head: + [[-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) + + [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov3.yaml b/algorithm/yolov5-master/models/hub/yolov3.yaml new file mode 100644 index 0000000..d1ef912 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov3.yaml @@ -0,0 +1,51 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3 head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5-bifpn.yaml b/algorithm/yolov5-master/models/hub/yolov5-bifpn.yaml new file mode 100644 index 0000000..504815f --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5-bifpn.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 BiFPN head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5-fpn.yaml b/algorithm/yolov5-master/models/hub/yolov5-fpn.yaml new file mode 100644 index 0000000..a23e9c6 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5-fpn.yaml @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 FPN head +head: + [[-1, 3, C3, [1024, False]], # 10 (P5/32-large) + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [512, 1, 1]], + [-1, 3, C3, [512, False]], # 14 (P4/16-medium) + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Conv, [256, 1, 1]], + [-1, 3, C3, [256, False]], # 18 (P3/8-small) + + [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5-p2.yaml b/algorithm/yolov5-master/models/hub/yolov5-p2.yaml new file mode 100644 index 0000000..554117d --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5-p2.yaml @@ -0,0 +1,54 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 2], 1, Concat, [1]], # cat backbone P2 + [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P3 + [-1, 3, C3, [256, False]], # 24 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 27 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 30 (P5/32-large) + + [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5-p34.yaml b/algorithm/yolov5-master/models/hub/yolov5-p34.yaml new file mode 100644 index 0000000..dbf0f85 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5-p34.yaml @@ -0,0 +1,41 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ], # 0-P1/2 + [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 + [ -1, 3, C3, [ 128 ] ], + [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 + [ -1, 6, C3, [ 256 ] ], + [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 + [ -1, 9, C3, [ 512 ] ], + [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 + [ -1, 3, C3, [ 1024 ] ], + [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 + ] + +# YOLOv5 v6.0 head with (P3, P4) outputs +head: + [ [ -1, 1, Conv, [ 512, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 + [ -1, 3, C3, [ 512, False ] ], # 13 + + [ -1, 1, Conv, [ 256, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 + [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) + + [ -1, 1, Conv, [ 256, 3, 2 ] ], + [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 + [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) + + [ [ 17, 20 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5-p6.yaml b/algorithm/yolov5-master/models/hub/yolov5-p6.yaml new file mode 100644 index 0000000..a17202f --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5-p6.yaml @@ -0,0 +1,56 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5-p7.yaml b/algorithm/yolov5-master/models/hub/yolov5-p7.yaml new file mode 100644 index 0000000..edd7d13 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5-p7.yaml @@ -0,0 +1,67 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 + [-1, 3, C3, [1280]], + [-1, 1, SPPF, [1280, 5]], # 13 + ] + +# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs +head: + [[-1, 1, Conv, [1024, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 10], 1, Concat, [1]], # cat backbone P6 + [-1, 3, C3, [1024, False]], # 17 + + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 21 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 25 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 29 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 26], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 32 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 35 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) + + [-1, 1, Conv, [1024, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P7 + [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) + + [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5-panet.yaml b/algorithm/yolov5-master/models/hub/yolov5-panet.yaml new file mode 100644 index 0000000..ccfbf90 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5-panet.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 PANet head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5l6.yaml b/algorithm/yolov5-master/models/hub/yolov5l6.yaml new file mode 100644 index 0000000..632c2cb --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5l6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5m6.yaml b/algorithm/yolov5-master/models/hub/yolov5m6.yaml new file mode 100644 index 0000000..ecc53fd --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5m6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5n6.yaml b/algorithm/yolov5-master/models/hub/yolov5n6.yaml new file mode 100644 index 0000000..0c0c71d --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5n6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5s-LeakyReLU.yaml b/algorithm/yolov5-master/models/hub/yolov5s-LeakyReLU.yaml new file mode 100644 index 0000000..3a179bf --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5s-LeakyReLU.yaml @@ -0,0 +1,49 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +activation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5s-ghost.yaml b/algorithm/yolov5-master/models/hub/yolov5s-ghost.yaml new file mode 100644 index 0000000..ff9519c --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5s-ghost.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3Ghost, [128]], + [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3Ghost, [256]], + [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3Ghost, [512]], + [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3Ghost, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, GhostConv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3Ghost, [512, False]], # 13 + + [-1, 1, GhostConv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) + + [-1, 1, GhostConv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) + + [-1, 1, GhostConv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5s-transformer.yaml b/algorithm/yolov5-master/models/hub/yolov5s-transformer.yaml new file mode 100644 index 0000000..100d7c4 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5s-transformer.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5s6.yaml b/algorithm/yolov5-master/models/hub/yolov5s6.yaml new file mode 100644 index 0000000..a28fb55 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5s6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5-master/models/hub/yolov5x6.yaml b/algorithm/yolov5-master/models/hub/yolov5x6.yaml new file mode 100644 index 0000000..ba795c4 --- /dev/null +++ b/algorithm/yolov5-master/models/hub/yolov5x6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5-master/models/segment/yolov5l-seg.yaml b/algorithm/yolov5-master/models/segment/yolov5l-seg.yaml new file mode 100644 index 0000000..4782de1 --- /dev/null +++ b/algorithm/yolov5-master/models/segment/yolov5l-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/segment/yolov5m-seg.yaml b/algorithm/yolov5-master/models/segment/yolov5m-seg.yaml new file mode 100644 index 0000000..07ec25b --- /dev/null +++ b/algorithm/yolov5-master/models/segment/yolov5m-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/segment/yolov5n-seg.yaml b/algorithm/yolov5-master/models/segment/yolov5n-seg.yaml new file mode 100644 index 0000000..c28225a --- /dev/null +++ b/algorithm/yolov5-master/models/segment/yolov5n-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/segment/yolov5s-seg.yaml b/algorithm/yolov5-master/models/segment/yolov5s-seg.yaml new file mode 100644 index 0000000..a827814 --- /dev/null +++ b/algorithm/yolov5-master/models/segment/yolov5s-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.5 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/segment/yolov5x-seg.yaml b/algorithm/yolov5-master/models/segment/yolov5x-seg.yaml new file mode 100644 index 0000000..5d0c452 --- /dev/null +++ b/algorithm/yolov5-master/models/segment/yolov5x-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/tf.py b/algorithm/yolov5-master/models/tf.py new file mode 100644 index 0000000..8290cf2 --- /dev/null +++ b/algorithm/yolov5-master/models/tf.py @@ -0,0 +1,608 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +TensorFlow, Keras and TFLite versions of YOLOv5 +Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127 + +Usage: + $ python models/tf.py --weights yolov5s.pt + +Export: + $ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs +""" + +import argparse +import sys +from copy import deepcopy +from pathlib import Path + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +# ROOT = ROOT.relative_to(Path.cwd()) # relative + +import numpy as np +import tensorflow as tf +import torch +import torch.nn as nn +from tensorflow import keras + +from models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv, + DWConvTranspose2d, Focus, autopad) +from models.experimental import MixConv2d, attempt_load +from models.yolo import Detect, Segment +from utils.activations import SiLU +from utils.general import LOGGER, make_divisible, print_args + + +class TFBN(keras.layers.Layer): + # TensorFlow BatchNormalization wrapper + def __init__(self, w=None): + super().__init__() + self.bn = keras.layers.BatchNormalization( + beta_initializer=keras.initializers.Constant(w.bias.numpy()), + gamma_initializer=keras.initializers.Constant(w.weight.numpy()), + moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), + moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), + epsilon=w.eps) + + def call(self, inputs): + return self.bn(inputs) + + +class TFPad(keras.layers.Layer): + # Pad inputs in spatial dimensions 1 and 2 + def __init__(self, pad): + super().__init__() + if isinstance(pad, int): + self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) + else: # tuple/list + self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]]) + + def call(self, inputs): + return tf.pad(inputs, self.pad, mode='constant', constant_values=0) + + +class TFConv(keras.layers.Layer): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + # ch_in, ch_out, weights, kernel, stride, padding, groups + super().__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) + # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch + conv = keras.layers.Conv2D( + filters=c2, + kernel_size=k, + strides=s, + padding='SAME' if s == 1 else 'VALID', + use_bias=not hasattr(w, 'bn'), + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) + self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) + self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity + self.act = activations(w.act) if act else tf.identity + + def call(self, inputs): + return self.act(self.bn(self.conv(inputs))) + + +class TFDWConv(keras.layers.Layer): + # Depthwise convolution + def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None): + # ch_in, ch_out, weights, kernel, stride, padding, groups + super().__init__() + assert c2 % c1 == 0, f'TFDWConv() output={c2} must be a multiple of input={c1} channels' + conv = keras.layers.DepthwiseConv2D( + kernel_size=k, + depth_multiplier=c2 // c1, + strides=s, + padding='SAME' if s == 1 else 'VALID', + use_bias=not hasattr(w, 'bn'), + depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) + self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) + self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity + self.act = activations(w.act) if act else tf.identity + + def call(self, inputs): + return self.act(self.bn(self.conv(inputs))) + + +class TFDWConvTranspose2d(keras.layers.Layer): + # Depthwise ConvTranspose2d + def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None): + # ch_in, ch_out, weights, kernel, stride, padding, groups + super().__init__() + assert c1 == c2, f'TFDWConv() output={c2} must be equal to input={c1} channels' + assert k == 4 and p1 == 1, 'TFDWConv() only valid for k=4 and p1=1' + weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy() + self.c1 = c1 + self.conv = [ + keras.layers.Conv2DTranspose(filters=1, + kernel_size=k, + strides=s, + padding='VALID', + output_padding=p2, + use_bias=True, + kernel_initializer=keras.initializers.Constant(weight[..., i:i + 1]), + bias_initializer=keras.initializers.Constant(bias[i])) for i in range(c1)] + + def call(self, inputs): + return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1] + + +class TFFocus(keras.layers.Layer): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) + + def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) + # inputs = inputs / 255 # normalize 0-255 to 0-1 + inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]] + return self.conv(tf.concat(inputs, 3)) + + +class TFBottleneck(keras.layers.Layer): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class TFCrossConv(keras.layers.Layer): + # Cross Convolution + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None): + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1) + self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class TFConv2d(keras.layers.Layer): + # Substitution for PyTorch nn.Conv2D + def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): + super().__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + self.conv = keras.layers.Conv2D(filters=c2, + kernel_size=k, + strides=s, + padding='VALID', + use_bias=bias, + kernel_initializer=keras.initializers.Constant( + w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None) + + def call(self, inputs): + return self.conv(inputs) + + +class TFBottleneckCSP(keras.layers.Layer): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) + self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3) + self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4) + self.bn = TFBN(w.bn) + self.act = lambda x: keras.activations.swish(x) + self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + y1 = self.cv3(self.m(self.cv1(inputs))) + y2 = self.cv2(inputs) + return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) + + +class TFC3(keras.layers.Layer): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + +class TFC3x(keras.layers.Layer): + # 3 module with cross-convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential([ + TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + +class TFSPP(keras.layers.Layer): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, k=(5, 9, 13), w=None): + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) + self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k] + + def call(self, inputs): + x = self.cv1(inputs) + return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) + + +class TFSPPF(keras.layers.Layer): + # Spatial pyramid pooling-Fast layer + def __init__(self, c1, c2, k=5, w=None): + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) + self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME') + + def call(self, inputs): + x = self.cv1(inputs) + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3)) + + +class TFDetect(keras.layers.Layer): + # TF YOLOv5 Detect layer + def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer + super().__init__() + self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [tf.zeros(1)] * self.nl # init grid + self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) + self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2]) + self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] + self.training = False # set to False after building model + self.imgsz = imgsz + for i in range(self.nl): + ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] + self.grid[i] = self._make_grid(nx, ny) + + def call(self, inputs): + z = [] # inference output + x = [] + for i in range(self.nl): + x.append(self.m[i](inputs[i])) + # x(bs,20,20,255) to x(bs,3,20,20,85) + ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] + x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no]) + + if not self.training: # inference + y = x[i] + grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5 + anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4 + xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy + wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid + # Normalize xywh to 0-1 to reduce calibration error + xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) + wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) + y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1) + z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) + + return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),) + + @staticmethod + def _make_grid(nx=20, ny=20): + # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) + return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) + + +class TFSegment(TFDetect): + # YOLOv5 Segment head for segmentation models + def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None): + super().__init__(nc, anchors, ch, imgsz, w) + self.nm = nm # number of masks + self.npr = npr # number of protos + self.no = 5 + nc + self.nm # number of outputs per anchor + self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv + self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos + self.detect = TFDetect.call + + def call(self, x): + p = self.proto(x[0]) + # p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos + p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160) + x = self.detect(self, x) + return (x, p) if self.training else (x[0], p) + + +class TFProto(keras.layers.Layer): + + def __init__(self, c1, c_=256, c2=32, w=None): + super().__init__() + self.cv1 = TFConv(c1, c_, k=3, w=w.cv1) + self.upsample = TFUpsample(None, scale_factor=2, mode='nearest') + self.cv2 = TFConv(c_, c_, k=3, w=w.cv2) + self.cv3 = TFConv(c_, c2, w=w.cv3) + + def call(self, inputs): + return self.cv3(self.cv2(self.upsample(self.cv1(inputs)))) + + +class TFUpsample(keras.layers.Layer): + # TF version of torch.nn.Upsample() + def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w' + super().__init__() + assert scale_factor % 2 == 0, 'scale_factor must be multiple of 2' + self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode) + # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) + # with default arguments: align_corners=False, half_pixel_centers=False + # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x, + # size=(x.shape[1] * 2, x.shape[2] * 2)) + + def call(self, inputs): + return self.upsample(inputs) + + +class TFConcat(keras.layers.Layer): + # TF version of torch.concat() + def __init__(self, dimension=1, w=None): + super().__init__() + assert dimension == 1, 'convert only NCHW to NHWC concat' + self.d = 3 + + def call(self, inputs): + return tf.concat(inputs, self.d) + + +def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") + anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + m_str = m + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except NameError: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [ + nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv, + BottleneckCSP, C3, C3x]: + c1, c2 = ch[f], args[0] + c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 + + args = [c1, c2, *args[1:]] + if m in [BottleneckCSP, C3, C3x]: + args.insert(2, n) + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) + elif m in [Detect, Segment]: + args.append([ch[x + 1] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + if m is Segment: + args[3] = make_divisible(args[3] * gw, 8) + args.append(imgsz) + else: + c2 = ch[f] + + tf_m = eval('TF' + m_str.replace('nn.', '')) + m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ + else tf_m(*args, w=model.model[i]) # module + + torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum(x.numel() for x in torch_m_.parameters()) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + ch.append(c2) + return keras.Sequential(layers), sorted(save) + + +class TFModel: + # TF YOLOv5 model + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes + super().__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + + # Define model + if nc and nc != self.yaml['nc']: + LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") + self.yaml['nc'] = nc # override yaml value + self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) + + def predict(self, + inputs, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25): + y = [] # outputs + x = inputs + for m in self.model.layers: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + x = m(x) # run + y.append(x if m.i in self.savelist else None) # save output + + # Add TensorFlow NMS + if tf_nms: + boxes = self._xywh2xyxy(x[0][..., :4]) + probs = x[0][:, :, 4:5] + classes = x[0][:, :, 5:] + scores = probs * classes + if agnostic_nms: + nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres) + else: + boxes = tf.expand_dims(boxes, 2) + nms = tf.image.combined_non_max_suppression(boxes, + scores, + topk_per_class, + topk_all, + iou_thres, + conf_thres, + clip_boxes=False) + return (nms,) + return x # output [1,6300,85] = [xywh, conf, class0, class1, ...] + # x = x[0] # [x(1,6300,85), ...] to x(6300,85) + # xywh = x[..., :4] # x(6300,4) boxes + # conf = x[..., 4:5] # x(6300,1) confidences + # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes + # return tf.concat([conf, cls, xywh], 1) + + @staticmethod + def _xywh2xyxy(xywh): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) + return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) + + +class AgnosticNMS(keras.layers.Layer): + # TF Agnostic NMS + def call(self, input, topk_all, iou_thres, conf_thres): + # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450 + return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), + input, + fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), + name='agnostic_nms') + + @staticmethod + def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS + boxes, classes, scores = x + class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) + scores_inp = tf.reduce_max(scores, -1) + selected_inds = tf.image.non_max_suppression(boxes, + scores_inp, + max_output_size=topk_all, + iou_threshold=iou_thres, + score_threshold=conf_thres) + selected_boxes = tf.gather(boxes, selected_inds) + padded_boxes = tf.pad(selected_boxes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], + mode='CONSTANT', + constant_values=0.0) + selected_scores = tf.gather(scores_inp, selected_inds) + padded_scores = tf.pad(selected_scores, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode='CONSTANT', + constant_values=-1.0) + selected_classes = tf.gather(class_inds, selected_inds) + padded_classes = tf.pad(selected_classes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode='CONSTANT', + constant_values=-1.0) + valid_detections = tf.shape(selected_inds)[0] + return padded_boxes, padded_scores, padded_classes, valid_detections + + +def activations(act=nn.SiLU): + # Returns TF activation from input PyTorch activation + if isinstance(act, nn.LeakyReLU): + return lambda x: keras.activations.relu(x, alpha=0.1) + elif isinstance(act, nn.Hardswish): + return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667 + elif isinstance(act, (nn.SiLU, SiLU)): + return lambda x: keras.activations.swish(x) + else: + raise Exception(f'no matching TensorFlow activation found for PyTorch activation {act}') + + +def representative_dataset_gen(dataset, ncalib=100): + # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays + for n, (path, img, im0s, vid_cap, string) in enumerate(dataset): + im = np.transpose(img, [1, 2, 0]) + im = np.expand_dims(im, axis=0).astype(np.float32) + im /= 255 + yield [im] + if n >= ncalib: + break + + +def run( + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=(640, 640), # inference size h,w + batch_size=1, # batch size + dynamic=False, # dynamic batch size +): + # PyTorch model + im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image + model = attempt_load(weights, device=torch.device('cpu'), inplace=True, fuse=False) + _ = model(im) # inference + model.info() + + # TensorFlow model + im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image + tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) + _ = tf_model.predict(im) # inference + + # Keras model + im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) + keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) + keras_model.summary() + + LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.') + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--dynamic', action='store_true', help='dynamic batch size') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/models/yolo.py b/algorithm/yolov5-master/models/yolo.py new file mode 100644 index 0000000..ed21c06 --- /dev/null +++ b/algorithm/yolov5-master/models/yolo.py @@ -0,0 +1,391 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +YOLO-specific modules + +Usage: + $ python models/yolo.py --cfg yolov5s.yaml +""" + +import argparse +import contextlib +import os +import platform +import sys +from copy import deepcopy +from pathlib import Path + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +if platform.system() != 'Windows': + ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import * +from models.experimental import * +from utils.autoanchor import check_anchor_order +from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args +from utils.plots import feature_visualization +from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device, + time_sync) + +try: + import thop # for FLOPs computation +except ImportError: + thop = None + + +class Detect(nn.Module): + # YOLOv5 Detect head for detection models + stride = None # strides computed during build + dynamic = False # force grid reconstruction + export = False # export mode + + def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer + super().__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid + self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid + self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + self.inplace = inplace # use inplace ops (e.g. slice assignment) + + def forward(self, x): + z = [] # inference output + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) + + if isinstance(self, Segment): # (boxes + masks) + xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) + xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy + wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh + y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) + else: # Detect (boxes only) + xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) + xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy + wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, self.na * nx * ny, self.no)) + + return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) + + def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')): + d = self.anchors[i].device + t = self.anchors[i].dtype + shape = 1, self.na, ny, nx, 2 # grid shape + y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) + yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility + grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 + anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) + return grid, anchor_grid + + +class Segment(Detect): + # YOLOv5 Segment head for segmentation models + def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True): + super().__init__(nc, anchors, ch, inplace) + self.nm = nm # number of masks + self.npr = npr # number of protos + self.no = 5 + nc + self.nm # number of outputs per anchor + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + self.proto = Proto(ch[0], self.npr, self.nm) # protos + self.detect = Detect.forward + + def forward(self, x): + p = self.proto(x[0]) + x = self.detect(self, x) + return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) + + +class BaseModel(nn.Module): + # YOLOv5 base model + def forward(self, x, profile=False, visualize=False): + return self._forward_once(x, profile, visualize) # single-scale inference, train + + def _forward_once(self, x, profile=False, visualize=False): + y, dt = [], [] # outputs + for m in self.model: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + if profile: + self._profile_one_layer(m, x, dt) + x = m(x) # run + y.append(x if m.i in self.save else None) # save output + if visualize: + feature_visualization(x, m.type, m.i, save_dir=visualize) + return x + + def _profile_one_layer(self, m, x, dt): + c = m == self.model[-1] # is final layer, copy input as inplace fix + o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs + t = time_sync() + for _ in range(10): + m(x.copy() if c else x) + dt.append((time_sync() - t) * 100) + if m == self.model[0]: + LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module") + LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') + if c: + LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") + + def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers + LOGGER.info('Fusing layers... ') + for m in self.model.modules(): + if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'): + m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv + delattr(m, 'bn') # remove batchnorm + m.forward = m.forward_fuse # update forward + self.info() + return self + + def info(self, verbose=False, img_size=640): # print model information + model_info(self, verbose, img_size) + + def _apply(self, fn): + # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers + self = super()._apply(fn) + m = self.model[-1] # Detect() + if isinstance(m, (Detect, Segment)): + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + +class DetectionModel(BaseModel): + # YOLOv5 detection model + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes + super().__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + self.yaml_file = Path(cfg).name + with open(cfg, encoding='ascii', errors='ignore') as f: + self.yaml = yaml.safe_load(f) # model dict + + # Define model + ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels + if nc and nc != self.yaml['nc']: + LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") + self.yaml['nc'] = nc # override yaml value + if anchors: + LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') + self.yaml['anchors'] = round(anchors) # override yaml value + self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist + self.names = [str(i) for i in range(self.yaml['nc'])] # default names + self.inplace = self.yaml.get('inplace', True) + + # Build strides, anchors + m = self.model[-1] # Detect() + if isinstance(m, (Detect, Segment)): + s = 256 # 2x min stride + m.inplace = self.inplace + forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) + m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases() # only run once + + # Init weights, biases + initialize_weights(self) + self.info() + LOGGER.info('') + + def forward(self, x, augment=False, profile=False, visualize=False): + if augment: + return self._forward_augment(x) # augmented inference, None + return self._forward_once(x, profile, visualize) # single-scale inference, train + + def _forward_augment(self, x): + img_size = x.shape[-2:] # height, width + s = [1, 0.83, 0.67] # scales + f = [None, 3, None] # flips (2-ud, 3-lr) + y = [] # outputs + for si, fi in zip(s, f): + xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) + yi = self._forward_once(xi)[0] # forward + # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + yi = self._descale_pred(yi, fi, si, img_size) + y.append(yi) + y = self._clip_augmented(y) # clip augmented tails + return torch.cat(y, 1), None # augmented inference, train + + def _descale_pred(self, p, flips, scale, img_size): + # de-scale predictions following augmented inference (inverse operation) + if self.inplace: + p[..., :4] /= scale # de-scale + if flips == 2: + p[..., 1] = img_size[0] - p[..., 1] # de-flip ud + elif flips == 3: + p[..., 0] = img_size[1] - p[..., 0] # de-flip lr + else: + x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale + if flips == 2: + y = img_size[0] - y # de-flip ud + elif flips == 3: + x = img_size[1] - x # de-flip lr + p = torch.cat((x, y, wh, p[..., 4:]), -1) + return p + + def _clip_augmented(self, y): + # Clip YOLOv5 augmented inference tails + nl = self.model[-1].nl # number of detection layers (P3-P5) + g = sum(4 ** x for x in range(nl)) # grid points + e = 1 # exclude layer count + i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices + y[0] = y[0][:, :-i] # large + i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices + y[-1] = y[-1][:, i:] # small + return y + + def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + +Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility + + +class SegmentationModel(DetectionModel): + # YOLOv5 segmentation model + def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None): + super().__init__(cfg, ch, nc, anchors) + + +class ClassificationModel(BaseModel): + # YOLOv5 classification model + def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index + super().__init__() + self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg) + + def _from_detection_model(self, model, nc=1000, cutoff=10): + # Create a YOLOv5 classification model from a YOLOv5 detection model + if isinstance(model, DetectMultiBackend): + model = model.model # unwrap DetectMultiBackend + model.model = model.model[:cutoff] # backbone + m = model.model[-1] # last layer + ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module + c = Classify(ch, nc) # Classify() + c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type + model.model[-1] = c # replace + self.model = model.model + self.stride = model.stride + self.save = [] + self.nc = nc + + def _from_yaml(self, cfg): + # Create a YOLOv5 classification model from a *.yaml file + self.model = None + + +def parse_model(d, ch): # model_dict, input_channels(3) + # Parse a YOLOv5 model.yaml dictionary + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") + anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation') + if act: + Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() + LOGGER.info(f"{colorstr('activation:')} {act}") # print + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + with contextlib.suppress(NameError): + args[j] = eval(a) if isinstance(a, str) else a # eval strings + + n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in { + Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, + BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}: + c1, c2 = ch[f], args[0] + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, 8) + + args = [c1, c2, *args[1:]] + if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}: + args.insert(2, n) # number of repeats + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum(ch[x] for x in f) + # TODO: channel, gw, gd + elif m in {Detect, Segment}: + args.append([ch[x] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + if m is Segment: + args[3] = make_divisible(args[3] * gw, 8) + elif m is Contract: + c2 = ch[f] * args[0] ** 2 + elif m is Expand: + c2 = ch[f] // args[0] ** 2 + else: + c2 = ch[f] + + m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum(x.numel() for x in m_.parameters()) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + if i == 0: + ch = [] + ch.append(c2) + return nn.Sequential(*layers), sorted(save) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') + parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--profile', action='store_true', help='profile model speed') + parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer') + parser.add_argument('--test', action='store_true', help='test all yolo*.yaml') + opt = parser.parse_args() + opt.cfg = check_yaml(opt.cfg) # check YAML + print_args(vars(opt)) + device = select_device(opt.device) + + # Create model + im = torch.rand(opt.batch_size, 3, 640, 640).to(device) + model = Model(opt.cfg).to(device) + + # Options + if opt.line_profile: # profile layer by layer + model(im, profile=True) + + elif opt.profile: # profile forward-backward + results = profile(input=im, ops=[model], n=3) + + elif opt.test: # test all models + for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'): + try: + _ = Model(cfg) + except Exception as e: + print(f'Error in {cfg}: {e}') + + else: # report fused model summary + model.fuse() diff --git a/algorithm/yolov5-master/models/yolov5l.yaml b/algorithm/yolov5-master/models/yolov5l.yaml new file mode 100644 index 0000000..ce8a5de --- /dev/null +++ b/algorithm/yolov5-master/models/yolov5l.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/yolov5m.yaml b/algorithm/yolov5-master/models/yolov5m.yaml new file mode 100644 index 0000000..ad13ab3 --- /dev/null +++ b/algorithm/yolov5-master/models/yolov5m.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/yolov5n.yaml b/algorithm/yolov5-master/models/yolov5n.yaml new file mode 100644 index 0000000..8a28a40 --- /dev/null +++ b/algorithm/yolov5-master/models/yolov5n.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/yolov5s.yaml b/algorithm/yolov5-master/models/yolov5s.yaml new file mode 100644 index 0000000..f35beab --- /dev/null +++ b/algorithm/yolov5-master/models/yolov5s.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/models/yolov5x.yaml b/algorithm/yolov5-master/models/yolov5x.yaml new file mode 100644 index 0000000..f617a02 --- /dev/null +++ b/algorithm/yolov5-master/models/yolov5x.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5-master/requirements.txt b/algorithm/yolov5-master/requirements.txt new file mode 100644 index 0000000..db4851e --- /dev/null +++ b/algorithm/yolov5-master/requirements.txt @@ -0,0 +1,50 @@ +# YOLOv5 requirements +# Usage: pip install -r requirements.txt + +# Base ------------------------------------------------------------------------ +gitpython>=3.1.30 +matplotlib>=3.2.2 +numpy>=1.18.5 +opencv-python>=4.1.1 +Pillow>=7.1.2 +psutil # system resources +PyYAML>=5.3.1 +requests>=2.23.0 +scipy>=1.4.1 +# thop>=0.1.1 # FLOPs computation +# torch>=1.7.0 # see https://pytorch.org/get-started/locally (recommended) +# torchvision>=0.8.1 +tqdm>=4.64.0 +# protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 + +# Logging --------------------------------------------------------------------- +tensorboard>=2.4.1 +# clearml>=1.2.0 +# comet + +# Plotting -------------------------------------------------------------------- +pandas>=1.1.4 +seaborn>=0.11.0 + +# Export ---------------------------------------------------------------------- +# coremltools>=6.0 # CoreML export +# onnx>=1.12.0 # ONNX export +# onnx-simplifier>=0.4.1 # ONNX simplifier +# nvidia-pyindex # TensorRT export +# nvidia-tensorrt # TensorRT export +# scikit-learn<=1.1.2 # CoreML quantization +# tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos) +# tensorflowjs>=3.9.0 # TF.js export +# openvino-dev # OpenVINO export + +# Deploy ---------------------------------------------------------------------- +setuptools>=65.5.1 # Snyk vulnerability fix +# tritonclient[all]~=2.24.0 + +# Extras ---------------------------------------------------------------------- +# ipython # interactive notebook +# mss # screenshots +# albumentations>=1.0.3 +# pycocotools>=2.0.6 # COCO mAP +# roboflow +# ultralytics # HUB https://hub.ultralytics.com diff --git a/algorithm/yolov5-master/segment/predict.py b/algorithm/yolov5-master/segment/predict.py new file mode 100644 index 0000000..d82df89 --- /dev/null +++ b/algorithm/yolov5-master/segment/predict.py @@ -0,0 +1,284 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 segmentation inference on images, videos, directories, streams, etc. + +Usage - sources: + $ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python segment/predict.py --weights yolov5s-seg.pt # PyTorch + yolov5s-seg.torchscript # TorchScript + yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-seg_openvino_model # OpenVINO + yolov5s-seg.engine # TensorRT + yolov5s-seg.mlmodel # CoreML (macOS-only) + yolov5s-seg_saved_model # TensorFlow SavedModel + yolov5s-seg.pb # TensorFlow GraphDef + yolov5s-seg.tflite # TensorFlow Lite + yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-seg_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, non_max_suppression, print_args, scale_boxes, scale_segments, + strip_optimizer) +from utils.plots import Annotator, colors, save_one_box +from utils.segment.general import masks2segments, process_mask, process_mask_native +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s-seg.pt', # model.pt path(s) + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/predict-seg', # save results to project/name + name='exp', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride + retina_masks=False, +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + pred, proto = model(im, augment=augment, visualize=visualize)[:2] + + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32) + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + s += '%gx%g ' % im.shape[2:] # print string + imc = im0.copy() if save_crop else im0 # for save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + if len(det): + if retina_masks: + # scale bbox first the crop masks + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size + masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC + else: + masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size + + # Segments + if save_txt: + segments = [ + scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True) + for x in reversed(masks2segments(masks))] + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Mask plotting + annotator.masks( + masks, + colors=[colors(x, True) for x in det[:, 5]], + im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / + 255 if retina_masks else im[i]) + + # Write results + for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): + if save_txt: # Write to file + seg = segments[j].reshape(-1) # (n,2) to (n*2) + line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format + with open(f'{txt_path}.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + annotator.box_label(xyxy, label, color=colors(c, True)) + # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3) + if save_crop: + save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + if cv2.waitKey(1) == ord('q'): # 1 millisecond + exit() + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)') + parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/predict-seg', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') + parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') + parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/segment/train.py b/algorithm/yolov5-master/segment/train.py new file mode 100644 index 0000000..8ed75ba --- /dev/null +++ b/algorithm/yolov5-master/segment/train.py @@ -0,0 +1,664 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Train a YOLOv5 segment model on a segment dataset +Models and datasets download automatically from the latest YOLOv5 release. + +Usage - Single-GPU training: + $ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended) + $ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 + +Models: https://github.com/ultralytics/yolov5/tree/master/models +Datasets: https://github.com/ultralytics/yolov5/tree/master/data +Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data +""" + +import argparse +import math +import os +import random +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +import yaml +from torch.optim import lr_scheduler +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import segment.val as validate # for end-of-epoch mAP +from models.experimental import attempt_load +from models.yolo import SegmentationModel +from utils.autoanchor import check_anchors +from utils.autobatch import check_train_batch_size +from utils.callbacks import Callbacks +from utils.downloads import attempt_download, is_url +from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, + check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, + get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, + labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save) +from utils.loggers import GenericLogger +from utils.plots import plot_evolve, plot_labels +from utils.segment.dataloaders import create_dataloader +from utils.segment.loss import ComputeLoss +from utils.segment.metrics import KEYS, fitness +from utils.segment.plots import plot_images_and_masks, plot_results_with_masks +from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, + smart_resume, torch_distributed_zero_first) + +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +GIT_INFO = check_git_info() + + +def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary + save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, mask_ratio = \ + Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ + opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze, opt.mask_ratio + # callbacks.run('on_pretrain_routine_start') + + # Directories + w = save_dir / 'weights' # weights dir + (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir + last, best = w / 'last.pt', w / 'best.pt' + + # Hyperparameters + if isinstance(hyp, str): + with open(hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) + opt.hyp = hyp.copy() # for saving hyps to checkpoints + + # Save run settings + if not evolve: + yaml_save(save_dir / 'hyp.yaml', hyp) + yaml_save(save_dir / 'opt.yaml', vars(opt)) + + # Loggers + data_dict = None + if RANK in {-1, 0}: + logger = GenericLogger(opt=opt, console_logger=LOGGER) + + # Config + plots = not evolve and not opt.noplots # create plots + overlap = not opt.no_overlap + cuda = device.type != 'cpu' + init_seeds(opt.seed + 1 + RANK, deterministic=True) + with torch_distributed_zero_first(LOCAL_RANK): + data_dict = data_dict or check_dataset(data) # check if None + train_path, val_path = data_dict['train'], data_dict['val'] + nc = 1 if single_cls else int(data_dict['nc']) # number of classes + names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names + is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset + + # Model + check_suffix(weights, '.pt') # check weights + pretrained = weights.endswith('.pt') + if pretrained: + with torch_distributed_zero_first(LOCAL_RANK): + weights = attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak + model = SegmentationModel(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) + exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(csd, strict=False) # load + LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report + else: + model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + amp = check_amp(model) # check AMP + + # Freeze + freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) + if any(x in k for x in freeze): + LOGGER.info(f'freezing {k}') + v.requires_grad = False + + # Image size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple + + # Batch size + if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size + batch_size = check_train_batch_size(model, imgsz, amp) + logger.update_params({'batch_size': batch_size}) + # loggers.on_params_update({"batch_size": batch_size}) + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay + optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) + + # Scheduler + if opt.cos_lr: + lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + else: + lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # Resume + best_fitness, start_epoch = 0.0, 0 + if pretrained: + if resume: + best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) + del ckpt, csd + + # DP mode + if cuda and RANK == -1 and torch.cuda.device_count() > 1: + LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' + 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and RANK != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + LOGGER.info('Using SyncBatchNorm()') + + # Trainloader + train_loader, dataset = create_dataloader( + train_path, + imgsz, + batch_size // WORLD_SIZE, + gs, + single_cls, + hyp=hyp, + augment=True, + cache=None if opt.cache == 'val' else opt.cache, + rect=opt.rect, + rank=LOCAL_RANK, + workers=workers, + image_weights=opt.image_weights, + quad=opt.quad, + prefix=colorstr('train: '), + shuffle=True, + mask_downsample_ratio=mask_ratio, + overlap_mask=overlap, + ) + labels = np.concatenate(dataset.labels, 0) + mlc = int(labels[:, 0].max()) # max label class + assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' + + # Process 0 + if RANK in {-1, 0}: + val_loader = create_dataloader(val_path, + imgsz, + batch_size // WORLD_SIZE * 2, + gs, + single_cls, + hyp=hyp, + cache=None if noval else opt.cache, + rect=True, + rank=-1, + workers=workers * 2, + pad=0.5, + mask_downsample_ratio=mask_ratio, + overlap_mask=overlap, + prefix=colorstr('val: '))[0] + + if not resume: + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor + model.half().float() # pre-reduce anchor precision + + if plots: + plot_labels(labels, names, save_dir) + # callbacks.run('on_pretrain_routine_end', labels, names) + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Model attributes + nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) + hyp['box'] *= 3 / nl # scale to layers + hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers + hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers + hyp['label_smoothing'] = opt.label_smoothing + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nb = len(train_loader) # number of batches + nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + last_opt_step = -1 + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = torch.cuda.amp.GradScaler(enabled=amp) + stopper, stop = EarlyStopping(patience=opt.patience), False + compute_loss = ComputeLoss(model, overlap=overlap) # init loss class + # callbacks.run('on_train_start') + LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' + f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting training for {epochs} epochs...') + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + # callbacks.run('on_train_epoch_start') + model.train() + + # Update image weights (optional, single-GPU only) + if opt.image_weights: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + + # Update mosaic border (optional) + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(4, device=device) # mean losses + if RANK != -1: + train_loader.sampler.set_epoch(epoch) + pbar = enumerate(train_loader) + LOGGER.info(('\n' + '%11s' * 8) % + ('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) + if RANK in {-1, 0}: + pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------ + # callbacks.run('on_train_batch_start') + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + with torch.cuda.amp.autocast(amp): + pred = model(imgs) # forward + loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float()) + if RANK != -1: + loss *= WORLD_SIZE # gradient averaged between devices in DDP mode + if opt.quad: + loss *= 4. + + # Backward + scaler.scale(loss).backward() + + # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html + if ni - last_opt_step >= accumulate: + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + last_opt_step = ni + + # Log + if RANK in {-1, 0}: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) + pbar.set_description(('%11s' * 2 + '%11.4g' * 6) % + (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) + # callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths) + # if callbacks.stop_training: + # return + + # Mosaic plots + if plots: + if ni < 3: + plot_images_and_masks(imgs, targets, masks, paths, save_dir / f'train_batch{ni}.jpg') + if ni == 10: + files = sorted(save_dir.glob('train*.jpg')) + logger.log_images(files, 'Mosaics', epoch) + # end batch ------------------------------------------------------------------------------------------------ + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for loggers + scheduler.step() + + if RANK in {-1, 0}: + # mAP + # callbacks.run('on_train_epoch_end', epoch=epoch) + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) + final_epoch = (epoch + 1 == epochs) or stopper.possible_stop + if not noval or final_epoch: # Calculate mAP + results, maps, _ = validate.run(data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + half=amp, + model=ema.ema, + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + plots=False, + callbacks=callbacks, + compute_loss=compute_loss, + mask_downsample_ratio=mask_ratio, + overlap=overlap) + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + stop = stopper(epoch=epoch, fitness=fi) # early stop check + if fi > best_fitness: + best_fitness = fi + log_vals = list(mloss) + list(results) + lr + # callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) + # Log val metrics and media + metrics_dict = dict(zip(KEYS, log_vals)) + logger.log_metrics(metrics_dict, epoch) + + # Save model + if (not nosave) or (final_epoch and not evolve): # if save + ckpt = { + 'epoch': epoch, + 'best_fitness': best_fitness, + 'model': deepcopy(de_parallel(model)).half(), + 'ema': deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': optimizer.state_dict(), + 'opt': vars(opt), + 'git': GIT_INFO, # {remote, branch, commit} if a git repo + 'date': datetime.now().isoformat()} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + if opt.save_period > 0 and epoch % opt.save_period == 0: + torch.save(ckpt, w / f'epoch{epoch}.pt') + logger.log_model(w / f'epoch{epoch}.pt') + del ckpt + # callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) + + # EarlyStopping + if RANK != -1: # if DDP training + broadcast_list = [stop if RANK == 0 else None] + dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks + if RANK != 0: + stop = broadcast_list[0] + if stop: + break # must break all DDP ranks + + # end epoch ---------------------------------------------------------------------------------------------------- + # end training ----------------------------------------------------------------------------------------------------- + if RANK in {-1, 0}: + LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if f is best: + LOGGER.info(f'\nValidating {f}...') + results, _, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + model=attempt_load(f, device).half(), + iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + save_json=is_coco, + verbose=True, + plots=plots, + callbacks=callbacks, + compute_loss=compute_loss, + mask_downsample_ratio=mask_ratio, + overlap=overlap) # val best model with plots + if is_coco: + # callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) + metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr)) + logger.log_metrics(metrics_dict, epoch) + + # callbacks.run('on_train_end', last, best, epoch, results) + # on train end callback using genericLogger + logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs) + if not opt.evolve: + logger.log_model(best, epoch) + if plots: + plot_results_with_masks(file=save_dir / 'results.csv') # save results.png + files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] + files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") + logger.log_images(files, 'Results', epoch + 1) + logger.log_images(sorted(save_dir.glob('val*.jpg')), 'Validation', epoch + 1) + torch.cuda.empty_cache() + return results + + +def parse_opt(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s-seg.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.yaml', help='dataset.yaml path') + parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=100, help='total training epochs') + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--noval', action='store_true', help='only validate final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') + parser.add_argument('--noplots', action='store_true', help='save no plot files') + parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--project', default=ROOT / 'runs/train-seg', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') + parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') + parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') + parser.add_argument('--seed', type=int, default=0, help='Global training seed') + parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + + # Instance Segmentation Args + parser.add_argument('--mask-ratio', type=int, default=4, help='Downsample the truth masks to saving memory') + parser.add_argument('--no-overlap', action='store_true', help='Overlap masks train faster at slightly less mAP') + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt, callbacks=Callbacks()): + # Checks + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements() + + # Resume + if opt.resume and not opt.evolve: # resume from specified or most recent last.pt + last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) + opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml + opt_data = opt.data # original dataset + if opt_yaml.is_file(): + with open(opt_yaml, errors='ignore') as f: + d = yaml.safe_load(f) + else: + d = torch.load(last, map_location='cpu')['opt'] + opt = argparse.Namespace(**d) # replace + opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate + if is_url(opt_data): + opt.data = check_file(opt_data) # avoid HUB resume auth timeout + else: + opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ + check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks + assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + if opt.evolve: + if opt.project == str(ROOT / 'runs/train-seg'): # if default project name, rename to runs/evolve-seg + opt.project = str(ROOT / 'runs/evolve-seg') + opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume + if opt.name == 'cfg': + opt.name = Path(opt.cfg).stem # use model.yaml as name + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' + assert not opt.image_weights, f'--image-weights {msg}' + assert not opt.evolve, f'--evolve {msg}' + assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' + assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' + assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + torch.cuda.set_device(LOCAL_RANK) + device = torch.device('cuda', LOCAL_RANK) + dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + + # Train + if not opt.evolve: + train(opt.hyp, opt, device, callbacks) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = { + 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr + 'box': (1, 0.02, 0.2), # box loss gain + 'cls': (1, 0.2, 4.0), # cls loss gain + 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (0, 0.1, 0.7), # IoU training threshold + 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (1, 0.0, 0.9), # image scale (+/- gain) + 'shear': (1, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (1, 0.0, 1.0), # image mixup (probability) + 'mixup': (1, 0.0, 1.0), # image mixup (probability) + 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) + + with open(opt.hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + if 'anchors' not in hyp: # anchors commented in hyp.yaml + hyp['anchors'] = 3 + if opt.noautoanchor: + del hyp['anchors'], meta['anchors'] + opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' + if opt.bucket: + # download evolve.csv if exists + subprocess.run([ + 'gsutil', + 'cp', + f'gs://{opt.bucket}/evolve.csv', + str(evolve_csv),]) + + for _ in range(opt.evolve): # generations to evolve + if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 12] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device, callbacks) + callbacks = Callbacks() + # Write mutation results + print_mutation(KEYS[4:16], results, hyp.copy(), save_dir, opt.bucket) + + # Plot results + plot_evolve(evolve_csv) + LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' + f"Results saved to {colorstr('bold', save_dir)}\n" + f'Usage example: $ python train.py --hyp {evolve_yaml}') + + +def run(**kwargs): + # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/segment/tutorial.ipynb b/algorithm/yolov5-master/segment/tutorial.ipynb new file mode 100644 index 0000000..cb52045 --- /dev/null +++ b/algorithm/yolov5-master/segment/tutorial.ipynb @@ -0,0 +1,594 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "
\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wbvMlHd_QwMG", + "outputId": "171b23f0-71b9-4cbf-b666-6fa2ecef70c8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + ] + } + ], + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Predict\n", + "\n", + "`segment/predict.py` runs YOLOv5 instance segmentation inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict`. Example inference sources are:\n", + "\n", + "```shell\n", + "python segment/predict.py --source 0 # webcam\n", + " img.jpg # image \n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zR9ZbuQCH7FX", + "outputId": "3f67f1c7-f15e-4fa5-d251-967c3b77eaad" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/predict: \u001b[0mweights=['yolov5s-seg.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\n", + "100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\n", + "\n", + "Fusing layers... \n", + "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\n", + "Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/predict-seg/exp\u001b[0m\n" + ] + } + ], + "source": [ + "!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\n", + "#display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WQPtK1QYVaD_", + "outputId": "9d751d8c-bee8-4339-cf30-9854ca530449" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip ...\n", + "Downloading http://images.cocodataset.org/zips/val2017.zip ...\n", + "######################################################################## 100.0%\n", + "######################################################################## 100.0%\n" + ] + } + ], + "source": [ + "# Download COCO val\n", + "!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X58w8JLpMnjH", + "outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Fusing layers... \n", + "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n", + " all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n", + "Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n" + ] + } + ], + "source": [ + "# Validate YOLOv5s-seg on COCO val\n", + "!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZY2VXXXu74w5" + }, + "source": [ + "# 3. Train\n", + "\n", + "

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n", + "- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\n", + "

\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Train on Custom Data with Roboflow 🌟 NEW\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", + "\n", + "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/](https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/?ref=ultralytics)\n", + "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JTz7kpmHsg-5qwVz2d2IH3AaenI1tv0N?usp=sharing)\n", + "
\n", + "\n", + "

Label images lightning fast (including with model-assisted labeling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "outputs": [], + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML']\n", + "\n", + "if logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train-seg\n", + "elif logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'ClearML':\n", + " import clearml; clearml.browser_login()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1NcFxRcFdJ_O", + "outputId": "3a3e0cf7-e79c-47a5-c8e7-2d26eeeab988" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n", + "Downloading https://ultralytics.com/assets/coco128-seg.zip to coco128-seg.zip...\n", + "100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n", + "Dataset download success ✅ (1.9s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", + " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", + " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 19 [-1, 14] 1 0 models.common.Concat [1] \n", + " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", + " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 22 [-1, 10] 1 0 models.common.Concat [1] \n", + " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", + " 24 [17, 20, 23] 1 615133 models.yolo.Segment [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 128, [128, 256, 512]]\n", + "Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n", + "\n", + "Transferred 367/367 items from yolov5s-seg.pt\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lay2WsTjNJzP" + }, + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "\n", + "\n", + "\"ClearML" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GMusP4OAxFu6" + }, + "outputs": [], + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s-seg') # yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "YOLOv5 Segmentation Tutorial", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/algorithm/yolov5-master/segment/val.py b/algorithm/yolov5-master/segment/val.py new file mode 100644 index 0000000..a7f95fe --- /dev/null +++ b/algorithm/yolov5-master/segment/val.py @@ -0,0 +1,473 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Validate a trained YOLOv5 segment model on a segment dataset + +Usage: + $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images) + $ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments + +Usage - formats: + $ python segment/val.py --weights yolov5s-seg.pt # PyTorch + yolov5s-seg.torchscript # TorchScript + yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-seg_openvino_label # OpenVINO + yolov5s-seg.engine # TensorRT + yolov5s-seg.mlmodel # CoreML (macOS-only) + yolov5s-seg_saved_model # TensorFlow SavedModel + yolov5s-seg.pb # TensorFlow GraphDef + yolov5s-seg.tflite # TensorFlow Lite + yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-seg_paddle_model # PaddlePaddle +""" + +import argparse +import json +import os +import subprocess +import sys +from multiprocessing.pool import ThreadPool +from pathlib import Path + +import numpy as np +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import torch.nn.functional as F + +from models.common import DetectMultiBackend +from models.yolo import SegmentationModel +from utils.callbacks import Callbacks +from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, + check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path, + non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh) +from utils.metrics import ConfusionMatrix, box_iou +from utils.plots import output_to_target, plot_val_study +from utils.segment.dataloaders import create_dataloader +from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image +from utils.segment.metrics import Metrics, ap_per_class_box_and_mask +from utils.segment.plots import plot_images_and_masks +from utils.torch_utils import de_parallel, select_device, smart_inference_mode + + +def save_one_txt(predn, save_conf, shape, file): + # Save one txt result + gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh + for *xyxy, conf, cls in predn.tolist(): + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(file, 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + +def save_one_json(predn, jdict, path, class_map, pred_masks): + # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} + from pycocotools.mask import encode + + def single_encode(x): + rle = encode(np.asarray(x[:, :, None], order='F', dtype='uint8'))[0] + rle['counts'] = rle['counts'].decode('utf-8') + return rle + + image_id = int(path.stem) if path.stem.isnumeric() else path.stem + box = xyxy2xywh(predn[:, :4]) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + pred_masks = np.transpose(pred_masks, (2, 0, 1)) + with ThreadPool(NUM_THREADS) as pool: + rles = pool.map(single_encode, pred_masks) + for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())): + jdict.append({ + 'image_id': image_id, + 'category_id': class_map[int(p[5])], + 'bbox': [round(x, 3) for x in b], + 'score': round(p[4], 5), + 'segmentation': rles[i]}) + + +def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False): + """ + Return correct prediction matrix + Arguments: + detections (array[N, 6]), x1, y1, x2, y2, conf, class + labels (array[M, 5]), class, x1, y1, x2, y2 + Returns: + correct (array[N, 10]), for 10 IoU levels + """ + if masks: + if overlap: + nl = len(labels) + index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1 + gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640) + gt_masks = torch.where(gt_masks == index, 1.0, 0.0) + if gt_masks.shape[1:] != pred_masks.shape[1:]: + gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode='bilinear', align_corners=False)[0] + gt_masks = gt_masks.gt_(0.5) + iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1)) + else: # boxes + iou = box_iou(labels[:, 1:], detections[:, :4]) + + correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) + correct_class = labels[:, 0:1] == detections[:, 5] + for i in range(len(iouv)): + x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + # matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True + return torch.tensor(correct, dtype=torch.bool, device=iouv.device) + + +@smart_inference_mode() +def run( + data, + weights=None, # model.pt path(s) + batch_size=32, # batch size + imgsz=640, # inference size (pixels) + conf_thres=0.001, # confidence threshold + iou_thres=0.6, # NMS IoU threshold + max_det=300, # maximum detections per image + task='val', # train, val, test, speed or study + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + single_cls=False, # treat as single-class dataset + augment=False, # augmented inference + verbose=False, # verbose output + save_txt=False, # save results to *.txt + save_hybrid=False, # save label+prediction hybrid results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_json=False, # save a COCO-JSON results file + project=ROOT / 'runs/val-seg', # save to project/name + name='exp', # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + save_dir=Path(''), + plots=True, + overlap=False, + mask_downsample_ratio=1, + compute_loss=None, + callbacks=Callbacks(), +): + if save_json: + check_requirements('pycocotools>=2.0.6') + process = process_mask_native # more accurate + else: + process = process_mask # faster + + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != 'cpu' # half precision only supported on CUDA + model.half() if half else model.float() + nm = de_parallel(model).model[-1].nm # number of masks + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + + # Data + data = check_dataset(data) # check + + # Configure + model.eval() + cuda = device.type != 'cpu' + is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset + nc = 1 if single_cls else int(data['nc']) # number of classes + iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Dataloader + if not training: + if pt and not single_cls: # check --weights are trained on --data + ncm = model.model.nc + assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ + f'classes). Pass correct combination of --weights and --data that are trained together.' + model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup + pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks + task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images + dataloader = create_dataloader(data[task], + imgsz, + batch_size, + stride, + single_cls, + pad=pad, + rect=rect, + workers=workers, + prefix=colorstr(f'{task}: '), + overlap_mask=overlap, + mask_downsample_ratio=mask_downsample_ratio)[0] + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = model.names if hasattr(model, 'names') else model.module.names # get class names + if isinstance(names, (list, tuple)): # old format + names = dict(enumerate(names)) + class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) + s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R', + 'mAP50', 'mAP50-95)') + dt = Profile(), Profile(), Profile() + metrics = Metrics() + loss = torch.zeros(4, device=device) + jdict, stats = [], [] + # callbacks.run('on_val_start') + pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar + for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar): + # callbacks.run('on_val_batch_start') + with dt[0]: + if cuda: + im = im.to(device, non_blocking=True) + targets = targets.to(device) + masks = masks.to(device) + masks = masks.float() + im = im.half() if half else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + nb, _, height, width = im.shape # batch size, channels, height, width + + # Inference + with dt[1]: + preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None) + + # Loss + if compute_loss: + loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls + + # NMS + targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + with dt[2]: + preds = non_max_suppression(preds, + conf_thres, + iou_thres, + labels=lb, + multi_label=True, + agnostic=single_cls, + max_det=max_det, + nm=nm) + + # Metrics + plot_masks = [] # masks for plotting + for si, (pred, proto) in enumerate(zip(preds, protos)): + labels = targets[targets[:, 0] == si, 1:] + nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions + path, shape = Path(paths[si]), shapes[si][0] + correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + seen += 1 + + if npr == 0: + if nl: + stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0])) + if plots: + confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) + continue + + # Masks + midx = [si] if overlap else targets[:, 0] == si + gt_masks = masks[midx] + pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:]) + + # Predictions + if single_cls: + pred[:, 5] = 0 + predn = pred.clone() + scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred + + # Evaluate + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + correct_bboxes = process_batch(predn, labelsn, iouv) + correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True) + if plots: + confusion_matrix.process_batch(predn, labelsn) + stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls) + + pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8) + if plots and batch_i < 3: + plot_masks.append(pred_masks[:15]) # filter top 15 to plot + + # Save/log + if save_txt: + save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') + if save_json: + pred_masks = scale_image(im[si].shape[1:], + pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]) + save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary + # callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) + + # Plot images + if plots and batch_i < 3: + if len(plot_masks): + plot_masks = torch.cat(plot_masks, dim=0) + plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) + plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths, + save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred + + # callbacks.run('on_val_batch_end') + + # Compute metrics + stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names) + metrics.update(results) + nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class + + # Print results + pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format + LOGGER.info(pf % ('all', seen, nt.sum(), *metrics.mean_results())) + if nt.sum() == 0: + LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels') + + # Print results per class + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): + for i, c in enumerate(metrics.ap_class_index): + LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i))) + + # Print speeds + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + if not training: + shape = (batch_size, 3, imgsz, imgsz) + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + # callbacks.run('on_val_end') + + mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results() + + # Save JSON + if save_json and len(jdict): + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights + anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations + pred_json = str(save_dir / f'{w}_predictions.json') # predictions + LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...') + with open(pred_json, 'w') as f: + json.dump(jdict, f) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + anno = COCO(anno_json) # init annotations api + pred = anno.loadRes(pred_json) # init predictions api + results = [] + for eval in COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm'): + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5) + map_bbox, map50_bbox, map_mask, map50_mask = results + except Exception as e: + LOGGER.info(f'pycocotools unable to run: {e}') + + # Return results + model.float() # for training + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask + return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.yaml', help='dataset.yaml path') + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)') + parser.add_argument('--batch-size', type=int, default=32, help='batch size') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image') + parser.add_argument('--task', default='val', help='train, val, test, speed or study') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--verbose', action='store_true', help='report mAP by class') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file') + parser.add_argument('--project', default=ROOT / 'runs/val-seg', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + # opt.save_json |= opt.data.endswith('coco.yaml') + opt.save_txt |= opt.save_hybrid + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + + if opt.task in ('train', 'val', 'test'): # run normally + if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 + LOGGER.warning(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results') + if opt.save_hybrid: + LOGGER.warning('WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone') + run(**vars(opt)) + + else: + weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] + opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results + if opt.task == 'speed': # speed benchmarks + # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... + opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False + for opt.weights in weights: + run(**vars(opt), plots=False) + + elif opt.task == 'study': # speed vs mAP benchmarks + # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... + for opt.weights in weights: + f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to + x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis + for opt.imgsz in x: # img-size + LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...') + r, _, t = run(**vars(opt), plots=False) + y.append(r + t) # results and times + np.savetxt(f, y, fmt='%10.4g') # save + subprocess.run(['zip', '-r', 'study.zip', 'study_*.txt']) + plot_val_study(x=x) # plot + else: + raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5-master/setup.cfg b/algorithm/yolov5-master/setup.cfg new file mode 100644 index 0000000..d7c4cb3 --- /dev/null +++ b/algorithm/yolov5-master/setup.cfg @@ -0,0 +1,54 @@ +# Project-wide configuration file, can be used for package metadata and other toll configurations +# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments +# Local usage: pip install pre-commit, pre-commit run --all-files + +[metadata] +license_file = LICENSE +description_file = README.md + +[tool:pytest] +norecursedirs = + .git + dist + build +addopts = + --doctest-modules + --durations=25 + --color=yes + +[flake8] +max-line-length = 120 +exclude = .tox,*.egg,build,temp +select = E,W,F +doctests = True +verbose = 2 +# https://pep8.readthedocs.io/en/latest/intro.html#error-codes +format = pylint +# see: https://www.flake8rules.com/ +ignore = E731,F405,E402,F401,W504,E127,E231,E501,F403 + # E731: Do not assign a lambda expression, use a def + # F405: name may be undefined, or defined from star imports: module + # E402: module level import not at top of file + # F401: module imported but unused + # W504: line break after binary operator + # E127: continuation line over-indented for visual indent + # E231: missing whitespace after ‘,’, ‘;’, or ‘:’ + # E501: line too long + # F403: ‘from module import *’ used; unable to detect undefined names + +[isort] +# https://pycqa.github.io/isort/docs/configuration/options.html +line_length = 120 +# see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html +multi_line_output = 0 + +[yapf] +based_on_style = pep8 +spaces_before_comment = 2 +COLUMN_LIMIT = 120 +COALESCE_BRACKETS = True +SPACES_AROUND_POWER_OPERATOR = True +SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False +SPLIT_BEFORE_CLOSING_BRACKET = False +SPLIT_BEFORE_FIRST_ARGUMENT = False +# EACH_DICT_ENTRY_ON_SEPARATE_LINE = False diff --git a/algorithm/yolov5-master/train.py b/algorithm/yolov5-master/train.py new file mode 100644 index 0000000..c4e3aac --- /dev/null +++ b/algorithm/yolov5-master/train.py @@ -0,0 +1,640 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Train a YOLOv5 model on a custom dataset. +Models and datasets download automatically from the latest YOLOv5 release. + +Usage - Single-GPU training: + $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) + $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3 + +Models: https://github.com/ultralytics/yolov5/tree/master/models +Datasets: https://github.com/ultralytics/yolov5/tree/master/data +Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data +""" + +import argparse +import math +import os +import random +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +import yaml +from torch.optim import lr_scheduler +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import val as validate # for end-of-epoch mAP +from models.experimental import attempt_load +from models.yolo import Model +from utils.autoanchor import check_anchors +from utils.autobatch import check_train_batch_size +from utils.callbacks import Callbacks +from utils.dataloaders import create_dataloader +from utils.downloads import attempt_download, is_url +from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, + check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, + get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, + labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, + yaml_save) +from utils.loggers import Loggers +from utils.loggers.comet.comet_utils import check_comet_resume +from utils.loss import ComputeLoss +from utils.metrics import fitness +from utils.plots import plot_evolve +from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, + smart_resume, torch_distributed_zero_first) + +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +GIT_INFO = check_git_info() + + +def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary + save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ + Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ + opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze + callbacks.run('on_pretrain_routine_start') + + # Directories + w = save_dir / 'weights' # weights dir + (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir + last, best = w / 'last.pt', w / 'best.pt' + + # Hyperparameters + if isinstance(hyp, str): + with open(hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) + opt.hyp = hyp.copy() # for saving hyps to checkpoints + + # Save run settings + if not evolve: + yaml_save(save_dir / 'hyp.yaml', hyp) + yaml_save(save_dir / 'opt.yaml', vars(opt)) + + # Loggers + data_dict = None + if RANK in {-1, 0}: + loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance + + # Register actions + for k in methods(loggers): + callbacks.register_action(k, callback=getattr(loggers, k)) + + # Process custom dataset artifact link + data_dict = loggers.remote_dataset + if resume: # If resuming runs from remote artifact + weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size + + # Config + plots = not evolve and not opt.noplots # create plots + cuda = device.type != 'cpu' + init_seeds(opt.seed + 1 + RANK, deterministic=True) + with torch_distributed_zero_first(LOCAL_RANK): + data_dict = data_dict or check_dataset(data) # check if None + train_path, val_path = data_dict['train'], data_dict['val'] + nc = 1 if single_cls else int(data_dict['nc']) # number of classes + names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names + is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset + + # Model + check_suffix(weights, '.pt') # check weights + pretrained = weights.endswith('.pt') + if pretrained: + with torch_distributed_zero_first(LOCAL_RANK): + weights = attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak + model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(csd, strict=False) # load + LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report + else: + model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + amp = check_amp(model) # check AMP + + # Freeze + freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) + if any(x in k for x in freeze): + LOGGER.info(f'freezing {k}') + v.requires_grad = False + + # Image size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple + + # Batch size + if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size + batch_size = check_train_batch_size(model, imgsz, amp) + loggers.on_params_update({'batch_size': batch_size}) + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay + optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) + + # Scheduler + if opt.cos_lr: + lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + else: + lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # Resume + best_fitness, start_epoch = 0.0, 0 + if pretrained: + if resume: + best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) + del ckpt, csd + + # DP mode + if cuda and RANK == -1 and torch.cuda.device_count() > 1: + LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' + 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and RANK != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + LOGGER.info('Using SyncBatchNorm()') + + # Trainloader + train_loader, dataset = create_dataloader(train_path, + imgsz, + batch_size // WORLD_SIZE, + gs, + single_cls, + hyp=hyp, + augment=True, + cache=None if opt.cache == 'val' else opt.cache, + rect=opt.rect, + rank=LOCAL_RANK, + workers=workers, + image_weights=opt.image_weights, + quad=opt.quad, + prefix=colorstr('train: '), + shuffle=True, + seed=opt.seed) + labels = np.concatenate(dataset.labels, 0) + mlc = int(labels[:, 0].max()) # max label class + assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' + + # Process 0 + if RANK in {-1, 0}: + val_loader = create_dataloader(val_path, + imgsz, + batch_size // WORLD_SIZE * 2, + gs, + single_cls, + hyp=hyp, + cache=None if noval else opt.cache, + rect=True, + rank=-1, + workers=workers * 2, + pad=0.5, + prefix=colorstr('val: '))[0] + + if not resume: + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor + model.half().float() # pre-reduce anchor precision + + callbacks.run('on_pretrain_routine_end', labels, names) + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Model attributes + nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) + hyp['box'] *= 3 / nl # scale to layers + hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers + hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers + hyp['label_smoothing'] = opt.label_smoothing + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nb = len(train_loader) # number of batches + nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + last_opt_step = -1 + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = torch.cuda.amp.GradScaler(enabled=amp) + stopper, stop = EarlyStopping(patience=opt.patience), False + compute_loss = ComputeLoss(model) # init loss class + callbacks.run('on_train_start') + LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' + f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting training for {epochs} epochs...') + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + callbacks.run('on_train_epoch_start') + model.train() + + # Update image weights (optional, single-GPU only) + if opt.image_weights: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + + # Update mosaic border (optional) + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(3, device=device) # mean losses + if RANK != -1: + train_loader.sampler.set_epoch(epoch) + pbar = enumerate(train_loader) + LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) + if RANK in {-1, 0}: + pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- + callbacks.run('on_train_batch_start') + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + with torch.cuda.amp.autocast(amp): + pred = model(imgs) # forward + loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size + if RANK != -1: + loss *= WORLD_SIZE # gradient averaged between devices in DDP mode + if opt.quad: + loss *= 4. + + # Backward + scaler.scale(loss).backward() + + # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html + if ni - last_opt_step >= accumulate: + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + last_opt_step = ni + + # Log + if RANK in {-1, 0}: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) + pbar.set_description(('%11s' * 2 + '%11.4g' * 5) % + (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) + callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss)) + if callbacks.stop_training: + return + # end batch ------------------------------------------------------------------------------------------------ + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for loggers + scheduler.step() + + if RANK in {-1, 0}: + # mAP + callbacks.run('on_train_epoch_end', epoch=epoch) + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) + final_epoch = (epoch + 1 == epochs) or stopper.possible_stop + if not noval or final_epoch: # Calculate mAP + results, maps, _ = validate.run(data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + half=amp, + model=ema.ema, + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + plots=False, + callbacks=callbacks, + compute_loss=compute_loss) + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + stop = stopper(epoch=epoch, fitness=fi) # early stop check + if fi > best_fitness: + best_fitness = fi + log_vals = list(mloss) + list(results) + lr + callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) + + # Save model + if (not nosave) or (final_epoch and not evolve): # if save + ckpt = { + 'epoch': epoch, + 'best_fitness': best_fitness, + 'model': deepcopy(de_parallel(model)).half(), + 'ema': deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': optimizer.state_dict(), + 'opt': vars(opt), + 'git': GIT_INFO, # {remote, branch, commit} if a git repo + 'date': datetime.now().isoformat()} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + if opt.save_period > 0 and epoch % opt.save_period == 0: + torch.save(ckpt, w / f'epoch{epoch}.pt') + del ckpt + callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) + + # EarlyStopping + if RANK != -1: # if DDP training + broadcast_list = [stop if RANK == 0 else None] + dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks + if RANK != 0: + stop = broadcast_list[0] + if stop: + break # must break all DDP ranks + + # end epoch ---------------------------------------------------------------------------------------------------- + # end training ----------------------------------------------------------------------------------------------------- + if RANK in {-1, 0}: + LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if f is best: + LOGGER.info(f'\nValidating {f}...') + results, _, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + model=attempt_load(f, device).half(), + iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + save_json=is_coco, + verbose=True, + plots=plots, + callbacks=callbacks, + compute_loss=compute_loss) # val best model with plots + if is_coco: + callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) + + callbacks.run('on_train_end', last, best, epoch, results) + + torch.cuda.empty_cache() + return results + + +def parse_opt(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=100, help='total training epochs') + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--noval', action='store_true', help='only validate final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') + parser.add_argument('--noplots', action='store_true', help='save no plot files') + parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') + parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') + parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') + parser.add_argument('--seed', type=int, default=0, help='Global training seed') + parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + + # Logger arguments + parser.add_argument('--entity', default=None, help='Entity') + parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option') + parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') + parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt, callbacks=Callbacks()): + # Checks + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements() + + # Resume (from specified or most recent last.pt) + if opt.resume and not check_comet_resume(opt) and not opt.evolve: + last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) + opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml + opt_data = opt.data # original dataset + if opt_yaml.is_file(): + with open(opt_yaml, errors='ignore') as f: + d = yaml.safe_load(f) + else: + d = torch.load(last, map_location='cpu')['opt'] + opt = argparse.Namespace(**d) # replace + opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate + if is_url(opt_data): + opt.data = check_file(opt_data) # avoid HUB resume auth timeout + else: + opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ + check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks + assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + if opt.evolve: + if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve + opt.project = str(ROOT / 'runs/evolve') + opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume + if opt.name == 'cfg': + opt.name = Path(opt.cfg).stem # use model.yaml as name + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' + assert not opt.image_weights, f'--image-weights {msg}' + assert not opt.evolve, f'--evolve {msg}' + assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' + assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' + assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + torch.cuda.set_device(LOCAL_RANK) + device = torch.device('cuda', LOCAL_RANK) + dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + + # Train + if not opt.evolve: + train(opt.hyp, opt, device, callbacks) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = { + 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr + 'box': (1, 0.02, 0.2), # box loss gain + 'cls': (1, 0.2, 4.0), # cls loss gain + 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (0, 0.1, 0.7), # IoU training threshold + 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (1, 0.0, 0.9), # image scale (+/- gain) + 'shear': (1, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (1, 0.0, 1.0), # image mixup (probability) + 'mixup': (1, 0.0, 1.0), # image mixup (probability) + 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) + + with open(opt.hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + if 'anchors' not in hyp: # anchors commented in hyp.yaml + hyp['anchors'] = 3 + if opt.noautoanchor: + del hyp['anchors'], meta['anchors'] + opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' + if opt.bucket: + # download evolve.csv if exists + subprocess.run([ + 'gsutil', + 'cp', + f'gs://{opt.bucket}/evolve.csv', + str(evolve_csv),]) + + for _ in range(opt.evolve): # generations to evolve + if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 7] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device, callbacks) + callbacks = Callbacks() + # Write mutation results + keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss', + 'val/obj_loss', 'val/cls_loss') + print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) + + # Plot results + plot_evolve(evolve_csv) + LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' + f"Results saved to {colorstr('bold', save_dir)}\n" + f'Usage example: $ python train.py --hyp {evolve_yaml}') + + +def run(**kwargs): + # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git 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\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "
\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wbvMlHd_QwMG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f9f016ad-3dcf-4bd2-e1c3-d5b79efc6f32" + }, + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Detect\n", + "\n", + "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n", + "\n", + "```shell\n", + "python detect.py --source 0 # webcam\n", + " img.jpg # image \n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zR9ZbuQCH7FX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b4db5c49-f501-4505-cf0d-a1d35236c485" + }, + "source": [ + "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n", + "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n", + "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n", + "100% 14.1M/14.1M [00:00<00:00, 116MB/s] \n", + "\n", + "Fusing layers... \n", + "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 17.0ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 14.3ms\n", + "Speed: 0.5ms pre-process, 15.7ms inference, 18.6ms NMS per image at shape (1, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WQPtK1QYVaD_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "1f7df330663048998adcf8a45bc8f69b", + "e896e6096dd244c59d7955e2035cd729", + "a6ff238c29984b24bf6d0bd175c19430", + "3c085ba3f3fd4c3c8a6bb41b41ce1479", + "16b0c8aa6e0f427e8a54d3791abb7504", + "c7b2dd0f78384cad8e400b282996cdf5", + "6a27e43b0e434edd82ee63f0a91036ca", + "cce0e6c0c4ec442cb47e65c674e02e92", + "c5b9f38e2f0d4f9aa97fe87265263743", + "df554fb955c7454696beac5a82889386", + "74e9112a87a242f4831b7d68c7da6333" + ] + }, + "outputId": "c7d0a0d2-abfb-44c3-d60d-f99d0e7aabad" + }, + "source": [ + "# Download COCO val\n", + "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n", + "!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0.00/780M [00:00

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n", + "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n", + "

\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Train on Custom Data with Roboflow 🌟 NEW\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", + "\n", + "- Custom Training Example: [https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/](https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/?ref=ultralytics)\n", + "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)\n", + "
\n", + "\n", + "

Label images lightning fast (including with model-assisted labeling)" + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'ClearML' #@param ['ClearML', 'Comet', 'TensorBoard']\n", + "\n", + "if logger == 'ClearML':\n", + " %pip install -q clearml\n", + " import clearml; clearml.browser_login()\n", + "elif logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train" + ], + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1NcFxRcFdJ_O", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "721b9028-767f-4a05-c964-692c245f7398" + }, + "source": [ + "# Train YOLOv5s on COCO128 for 3 epochs\n", + "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n", + "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n", + "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n", + "Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n", + "100% 6.66M/6.66M [00:00<00:00, 261MB/s]\n", + "Dataset download success ✅ (0.3s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", + " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", + " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 19 [-1, 14] 1 0 models.common.Concat [1] \n", + " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", + " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 22 [-1, 10] 1 0 models.common.Concat [1] \n", + " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", + " 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n", + "Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n", + "\n", + "Transferred 349/349 items from yolov5s.pt\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1911.57it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 229.69it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ], + "metadata": { + "id": "nWOsI5wJR1o3" + } + }, + { + "cell_type": "markdown", + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "\n", + "\n", + "\"ClearML" + ], + "metadata": { + "id": "Lay2WsTjNJzP" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GMusP4OAxFu6" + }, + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) # yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ], + "execution_count": null, + "outputs": [] + } + ] +} diff --git a/algorithm/yolov5-master/utils/__init__.py b/algorithm/yolov5-master/utils/__init__.py new file mode 100644 index 0000000..33db6b0 --- /dev/null +++ b/algorithm/yolov5-master/utils/__init__.py @@ -0,0 +1,82 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +utils/initialization +""" + +import contextlib +import platform +import threading + + +def emojis(str=''): + # Return platform-dependent emoji-safe version of string + return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str + + +class TryExcept(contextlib.ContextDecorator): + # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager + def __init__(self, msg=''): + self.msg = msg + + def __enter__(self): + pass + + def __exit__(self, exc_type, value, traceback): + if value: + print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}")) + return True + + +def threaded(func): + # Multi-threads a target function and returns thread. Usage: @threaded decorator + def wrapper(*args, **kwargs): + thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) + thread.start() + return thread + + return wrapper + + +def join_threads(verbose=False): + # Join all daemon threads, i.e. atexit.register(lambda: join_threads()) + main_thread = threading.current_thread() + for t in threading.enumerate(): + if t is not main_thread: + if verbose: + print(f'Joining thread {t.name}') + t.join() + + +def notebook_init(verbose=True): + # Check system software and hardware + print('Checking setup...') + + import os + import shutil + + from utils.general import check_font, check_requirements, is_colab + from utils.torch_utils import select_device # imports + + check_font() + + import psutil + + if is_colab(): + shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory + + # System info + display = None + if verbose: + gb = 1 << 30 # bytes to GiB (1024 ** 3) + ram = psutil.virtual_memory().total + total, used, free = shutil.disk_usage('/') + with contextlib.suppress(Exception): # clear display if ipython is installed + from IPython import display + display.clear_output() + s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)' + else: + s = '' + + select_device(newline=False) + print(emojis(f'Setup complete ✅ {s}')) + return display diff --git a/algorithm/yolov5-master/utils/activations.py b/algorithm/yolov5-master/utils/activations.py new file mode 100644 index 0000000..084ce8c --- /dev/null +++ b/algorithm/yolov5-master/utils/activations.py @@ -0,0 +1,103 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Activation functions +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class SiLU(nn.Module): + # SiLU activation https://arxiv.org/pdf/1606.08415.pdf + @staticmethod + def forward(x): + return x * torch.sigmoid(x) + + +class Hardswish(nn.Module): + # Hard-SiLU activation + @staticmethod + def forward(x): + # return x * F.hardsigmoid(x) # for TorchScript and CoreML + return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX + + +class Mish(nn.Module): + # Mish activation https://github.com/digantamisra98/Mish + @staticmethod + def forward(x): + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + # Mish activation memory-efficient + class F(torch.autograd.Function): + + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + return self.F.apply(x) + + +class FReLU(nn.Module): + # FReLU activation https://arxiv.org/abs/2007.11824 + def __init__(self, c1, k=3): # ch_in, kernel + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + return torch.max(x, self.bn(self.conv(x))) + + +class AconC(nn.Module): + r""" ACON activation (activate or not) + AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter + according to "Activate or Not: Learning Customized Activation" . + """ + + def __init__(self, c1): + super().__init__() + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) + + def forward(self, x): + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x + + +class MetaAconC(nn.Module): + r""" ACON activation (activate or not) + MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network + according to "Activate or Not: Learning Customized Activation" . + """ + + def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r + super().__init__() + c2 = max(r, c1 // r) + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) + self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) + # self.bn1 = nn.BatchNorm2d(c2) + # self.bn2 = nn.BatchNorm2d(c1) + + def forward(self, x): + y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) + # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 + # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable + beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(beta * dpx) + self.p2 * x diff --git a/algorithm/yolov5-master/utils/augmentations.py b/algorithm/yolov5-master/utils/augmentations.py new file mode 100644 index 0000000..7ab75f1 --- /dev/null +++ b/algorithm/yolov5-master/utils/augmentations.py @@ -0,0 +1,397 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Image augmentation functions +""" + +import math +import random + +import cv2 +import numpy as np +import torch +import torchvision.transforms as T +import torchvision.transforms.functional as TF + +from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy +from utils.metrics import bbox_ioa + +IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean +IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation + + +class Albumentations: + # YOLOv5 Albumentations class (optional, only used if package is installed) + def __init__(self, size=640): + self.transform = None + prefix = colorstr('albumentations: ') + try: + import albumentations as A + check_version(A.__version__, '1.0.3', hard=True) # version requirement + + T = [ + A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), + A.Blur(p=0.01), + A.MedianBlur(p=0.01), + A.ToGray(p=0.01), + A.CLAHE(p=0.01), + A.RandomBrightnessContrast(p=0.0), + A.RandomGamma(p=0.0), + A.ImageCompression(quality_lower=75, p=0.0)] # transforms + self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) + + LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) + except ImportError: # package not installed, skip + pass + except Exception as e: + LOGGER.info(f'{prefix}{e}') + + def __call__(self, im, labels, p=1.0): + if self.transform and random.random() < p: + new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed + im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) + return im, labels + + +def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): + # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std + return TF.normalize(x, mean, std, inplace=inplace) + + +def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): + # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean + for i in range(3): + x[:, i] = x[:, i] * std[i] + mean[i] + return x + + +def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): + # HSV color-space augmentation + if hgain or sgain or vgain: + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) + dtype = im.dtype # uint8 + + x = np.arange(0, 256, dtype=r.dtype) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) + cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed + + +def hist_equalize(im, clahe=True, bgr=False): + # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 + yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB + + +def replicate(im, labels): + # Replicate labels + h, w = im.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return im, labels + + +def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + # Resize and pad image while meeting stride-multiple constraints + shape = im.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better val mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return im, ratio, (dw, dh) + + +def random_perspective(im, + targets=(), + segments=(), + degrees=10, + translate=.1, + scale=.1, + shear=10, + perspective=0.0, + border=(0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = im.shape[0] + border[0] * 2 # shape(h,w,c) + width = im.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -im.shape[1] / 2 # x translation (pixels) + C[1, 2] = -im.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(im[:, :, ::-1]) # base + # ax[1].imshow(im2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + if n: + use_segments = any(x.any() for x in segments) and len(segments) == n + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) + targets = targets[i] + targets[:, 1:5] = new[i] + + return im, targets + + +def copy_paste(im, labels, segments, p=0.5): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + if p and n: + h, w, c = im.shape # height, width, channels + im_new = np.zeros(im.shape, np.uint8) + for j in random.sample(range(n), k=round(p * n)): + l, s = labels[j], segments[j] + box = w - l[3], l[2], w - l[1], l[4] + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + if (ioa < 0.30).all(): # allow 30% obscuration of existing labels + labels = np.concatenate((labels, [[l[0], *box]]), 0) + segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) + cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) + + result = cv2.flip(im, 1) # augment segments (flip left-right) + i = cv2.flip(im_new, 1).astype(bool) + im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug + + return im, labels, segments + + +def cutout(im, labels, p=0.5): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + if random.random() < p: + h, w = im.shape[:2] + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) # create random masks + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def mixup(im, labels, im2, labels2): + # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf + r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 + im = (im * r + im2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + return im, labels + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) + # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates + + +def classify_albumentations( + augment=True, + size=224, + scale=(0.08, 1.0), + ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 + hflip=0.5, + vflip=0.0, + jitter=0.4, + mean=IMAGENET_MEAN, + std=IMAGENET_STD, + auto_aug=False): + # YOLOv5 classification Albumentations (optional, only used if package is installed) + prefix = colorstr('albumentations: ') + try: + import albumentations as A + from albumentations.pytorch import ToTensorV2 + check_version(A.__version__, '1.0.3', hard=True) # version requirement + if augment: # Resize and crop + T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] + if auto_aug: + # TODO: implement AugMix, AutoAug & RandAug in albumentation + LOGGER.info(f'{prefix}auto augmentations are currently not supported') + else: + if hflip > 0: + T += [A.HorizontalFlip(p=hflip)] + if vflip > 0: + T += [A.VerticalFlip(p=vflip)] + if jitter > 0: + color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue + T += [A.ColorJitter(*color_jitter, 0)] + else: # Use fixed crop for eval set (reproducibility) + T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] + T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor + LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) + return A.Compose(T) + + except ImportError: # package not installed, skip + LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)') + except Exception as e: + LOGGER.info(f'{prefix}{e}') + + +def classify_transforms(size=224): + # Transforms to apply if albumentations not installed + assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' + # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) + return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) + + +class LetterBox: + # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) + def __init__(self, size=(640, 640), auto=False, stride=32): + super().__init__() + self.h, self.w = (size, size) if isinstance(size, int) else size + self.auto = auto # pass max size integer, automatically solve for short side using stride + self.stride = stride # used with auto + + def __call__(self, im): # im = np.array HWC + imh, imw = im.shape[:2] + r = min(self.h / imh, self.w / imw) # ratio of new/old + h, w = round(imh * r), round(imw * r) # resized image + hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w + top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) + im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) + im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) + return im_out + + +class CenterCrop: + # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) + def __init__(self, size=640): + super().__init__() + self.h, self.w = (size, size) if isinstance(size, int) else size + + def __call__(self, im): # im = np.array HWC + imh, imw = im.shape[:2] + m = min(imh, imw) # min dimension + top, left = (imh - m) // 2, (imw - m) // 2 + return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) + + +class ToTensor: + # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) + def __init__(self, half=False): + super().__init__() + self.half = half + + def __call__(self, im): # im = np.array HWC in BGR order + im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous + im = torch.from_numpy(im) # to torch + im = im.half() if self.half else im.float() # uint8 to fp16/32 + im /= 255.0 # 0-255 to 0.0-1.0 + return im diff --git a/algorithm/yolov5-master/utils/autoanchor.py b/algorithm/yolov5-master/utils/autoanchor.py new file mode 100644 index 0000000..bb5cf6e --- /dev/null +++ b/algorithm/yolov5-master/utils/autoanchor.py @@ -0,0 +1,169 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +AutoAnchor utils +""" + +import random + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +from utils import TryExcept +from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr + +PREFIX = colorstr('AutoAnchor: ') + + +def check_anchor_order(m): + # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary + a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da and (da.sign() != ds.sign()): # same order + LOGGER.info(f'{PREFIX}Reversing anchor order') + m.anchors[:] = m.anchors.flip(0) + + +@TryExcept(f'{PREFIX}ERROR') +def check_anchors(dataset, model, thr=4.0, imgsz=640): + # Check anchor fit to data, recompute if necessary + m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh + + def metric(k): # compute metric + r = wh[:, None] / k[None] + x = torch.min(r, 1 / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1 / thr).float().mean() # best possible recall + return bpr, aat + + stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides + anchors = m.anchors.clone() * stride # current anchors + bpr, aat = metric(anchors.cpu().view(-1, 2)) + s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). ' + if bpr > 0.98: # threshold to recompute + LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅') + else: + LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...') + na = m.anchors.numel() // 2 # number of anchors + anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + new_bpr = metric(anchors)[0] + if new_bpr > bpr: # replace anchors + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchors[:] = anchors.clone().view_as(m.anchors) + check_anchor_order(m) # must be in pixel-space (not grid-space) + m.anchors /= stride + s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)' + else: + s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)' + LOGGER.info(s) + + +def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ Creates kmeans-evolved anchors from training dataset + + Arguments: + dataset: path to data.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + from scipy.cluster.vq import kmeans + + npr = np.random + thr = 1 / thr + + def metric(k, wh): # compute metrics + r = wh[:, None] / k[None] + x = torch.min(r, 1 / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k, verbose=True): + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr + s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \ + f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \ + f'past_thr={x[x > thr].mean():.3f}-mean: ' + for x in k: + s += '%i,%i, ' % (round(x[0]), round(x[1])) + if verbose: + LOGGER.info(s[:-2]) + return k + + if isinstance(dataset, str): # *.yaml file + with open(dataset, errors='ignore') as f: + data_dict = yaml.safe_load(f) # model dict + from utils.dataloaders import LoadImagesAndLabels + dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + LOGGER.info(f'{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size') + wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels + # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 + + # Kmeans init + try: + LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...') + assert n <= len(wh) # apply overdetermined constraint + s = wh.std(0) # sigmas for whitening + k = kmeans(wh / s, n, iter=30)[0] * s # points + assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar + except Exception: + LOGGER.warning(f'{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init') + k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init + wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) + k = print_results(k, verbose=False) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma + pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' + if verbose: + print_results(k, verbose) + + return print_results(k).astype(np.float32) diff --git a/algorithm/yolov5-master/utils/autobatch.py b/algorithm/yolov5-master/utils/autobatch.py new file mode 100644 index 0000000..bdeb91c --- /dev/null +++ b/algorithm/yolov5-master/utils/autobatch.py @@ -0,0 +1,72 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Auto-batch utils +""" + +from copy import deepcopy + +import numpy as np +import torch + +from utils.general import LOGGER, colorstr +from utils.torch_utils import profile + + +def check_train_batch_size(model, imgsz=640, amp=True): + # Check YOLOv5 training batch size + with torch.cuda.amp.autocast(amp): + return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size + + +def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): + # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory + # Usage: + # import torch + # from utils.autobatch import autobatch + # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) + # print(autobatch(model)) + + # Check device + prefix = colorstr('AutoBatch: ') + LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}') + device = next(model.parameters()).device # get model device + if device.type == 'cpu': + LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}') + return batch_size + if torch.backends.cudnn.benchmark: + LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}') + return batch_size + + # Inspect CUDA memory + gb = 1 << 30 # bytes to GiB (1024 ** 3) + d = str(device).upper() # 'CUDA:0' + properties = torch.cuda.get_device_properties(device) # device properties + t = properties.total_memory / gb # GiB total + r = torch.cuda.memory_reserved(device) / gb # GiB reserved + a = torch.cuda.memory_allocated(device) / gb # GiB allocated + f = t - (r + a) # GiB free + LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') + + # Profile batch sizes + batch_sizes = [1, 2, 4, 8, 16] + try: + img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] + results = profile(img, model, n=3, device=device) + except Exception as e: + LOGGER.warning(f'{prefix}{e}') + + # Fit a solution + y = [x[2] for x in results if x] # memory [2] + p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit + b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) + if None in results: # some sizes failed + i = results.index(None) # first fail index + if b >= batch_sizes[i]: # y intercept above failure point + b = batch_sizes[max(i - 1, 0)] # select prior safe point + if b < 1 or b > 1024: # b outside of safe range + b = batch_size + LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.') + + fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted + LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅') + return b diff --git a/algorithm/yolov5-master/utils/aws/__init__.py b/algorithm/yolov5-master/utils/aws/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5-master/utils/aws/mime.sh b/algorithm/yolov5-master/utils/aws/mime.sh new file mode 100644 index 0000000..c319a83 --- /dev/null +++ b/algorithm/yolov5-master/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/algorithm/yolov5-master/utils/aws/resume.py b/algorithm/yolov5-master/utils/aws/resume.py new file mode 100644 index 0000000..b21731c --- /dev/null +++ b/algorithm/yolov5-master/utils/aws/resume.py @@ -0,0 +1,40 @@ +# Resume all interrupted trainings in yolov5/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[2] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +port = 0 # --master_port +path = Path('').resolve() +for last in path.rglob('*/**/last.pt'): + ckpt = torch.load(last) + if ckpt['optimizer'] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: + opt = yaml.safe_load(f) + + # Get device count + d = opt['device'].split(',') # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' + else: # single-GPU + cmd = f'python train.py --resume {last}' + + cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/algorithm/yolov5-master/utils/aws/userdata.sh b/algorithm/yolov5-master/utils/aws/userdata.sh new file mode 100644 index 0000000..5fc1332 --- /dev/null +++ b/algorithm/yolov5-master/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolov5 ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 + cd yolov5 + bash data/scripts/get_coco.sh && echo "COCO done." & + sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/algorithm/yolov5-master/utils/callbacks.py b/algorithm/yolov5-master/utils/callbacks.py new file mode 100644 index 0000000..166d893 --- /dev/null +++ b/algorithm/yolov5-master/utils/callbacks.py @@ -0,0 +1,76 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Callback utils +""" + +import threading + + +class Callbacks: + """" + Handles all registered callbacks for YOLOv5 Hooks + """ + + def __init__(self): + # Define the available callbacks + self._callbacks = { + 'on_pretrain_routine_start': [], + 'on_pretrain_routine_end': [], + 'on_train_start': [], + 'on_train_epoch_start': [], + 'on_train_batch_start': [], + 'optimizer_step': [], + 'on_before_zero_grad': [], + 'on_train_batch_end': [], + 'on_train_epoch_end': [], + 'on_val_start': [], + 'on_val_batch_start': [], + 'on_val_image_end': [], + 'on_val_batch_end': [], + 'on_val_end': [], + 'on_fit_epoch_end': [], # fit = train + val + 'on_model_save': [], + 'on_train_end': [], + 'on_params_update': [], + 'teardown': [],} + self.stop_training = False # set True to interrupt training + + def register_action(self, hook, name='', callback=None): + """ + Register a new action to a callback hook + + Args: + hook: The callback hook name to register the action to + name: The name of the action for later reference + callback: The callback to fire + """ + assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" + assert callable(callback), f"callback '{callback}' is not callable" + self._callbacks[hook].append({'name': name, 'callback': callback}) + + def get_registered_actions(self, hook=None): + """" + Returns all the registered actions by callback hook + + Args: + hook: The name of the hook to check, defaults to all + """ + return self._callbacks[hook] if hook else self._callbacks + + def run(self, hook, *args, thread=False, **kwargs): + """ + Loop through the registered actions and fire all callbacks on main thread + + Args: + hook: The name of the hook to check, defaults to all + args: Arguments to receive from YOLOv5 + thread: (boolean) Run callbacks in daemon thread + kwargs: Keyword Arguments to receive from YOLOv5 + """ + + assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" + for logger in self._callbacks[hook]: + if thread: + threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start() + else: + logger['callback'](*args, **kwargs) diff --git a/algorithm/yolov5-master/utils/dataloaders.py b/algorithm/yolov5-master/utils/dataloaders.py new file mode 100644 index 0000000..7687a2b --- /dev/null +++ b/algorithm/yolov5-master/utils/dataloaders.py @@ -0,0 +1,1221 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Dataloaders and dataset utils +""" + +import contextlib +import glob +import hashlib +import json +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import Pool, ThreadPool +from pathlib import Path +from threading import Thread +from urllib.parse import urlparse + +import numpy as np +import psutil +import torch +import torch.nn.functional as F +import torchvision +import yaml +from PIL import ExifTags, Image, ImageOps +from torch.utils.data import DataLoader, Dataset, dataloader, distributed +from tqdm import tqdm + +from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste, + letterbox, mixup, random_perspective) +from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements, + check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, + xywh2xyxy, xywhn2xyxy, xyxy2xywhn) +from utils.torch_utils import torch_distributed_zero_first + +# Parameters +HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes +VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == 'Orientation': + break + + +def get_hash(paths): + # Returns a single hash value of a list of paths (files or dirs) + size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes + h = hashlib.sha256(str(size).encode()) # hash sizes + h.update(''.join(paths).encode()) # hash paths + return h.hexdigest() # return hash + + +def exif_size(img): + # Returns exif-corrected PIL size + s = img.size # (width, height) + with contextlib.suppress(Exception): + rotation = dict(img._getexif().items())[orientation] + if rotation in [6, 8]: # rotation 270 or 90 + s = (s[1], s[0]) + return s + + +def exif_transpose(image): + """ + Transpose a PIL image accordingly if it has an EXIF Orientation tag. + Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() + + :param image: The image to transpose. + :return: An image. + """ + exif = image.getexif() + orientation = exif.get(0x0112, 1) # default 1 + if orientation > 1: + method = { + 2: Image.FLIP_LEFT_RIGHT, + 3: Image.ROTATE_180, + 4: Image.FLIP_TOP_BOTTOM, + 5: Image.TRANSPOSE, + 6: Image.ROTATE_270, + 7: Image.TRANSVERSE, + 8: Image.ROTATE_90}.get(orientation) + if method is not None: + image = image.transpose(method) + del exif[0x0112] + image.info['exif'] = exif.tobytes() + return image + + +def seed_worker(worker_id): + # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader + worker_seed = torch.initial_seed() % 2 ** 32 + np.random.seed(worker_seed) + random.seed(worker_seed) + + +def create_dataloader(path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix='', + shuffle=False, + seed=0): + if rect and shuffle: + LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') + shuffle = False + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = LoadImagesAndLabels( + path, + imgsz, + batch_size, + augment=augment, # augmentation + hyp=hyp, # hyperparameters + rect=rect, # rectangular batches + cache_images=cache, + single_cls=single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix) + + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() # number of CUDA devices + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + seed + RANK) + return loader(dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, + worker_init_fn=seed_worker, + generator=generator), dataset + + +class InfiniteDataLoader(dataloader.DataLoader): + """ Dataloader that reuses workers + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + return len(self.batch_sampler.sampler) + + def __iter__(self): + for _ in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler: + """ Sampler that repeats forever + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + self.sampler = sampler + + def __iter__(self): + while True: + yield from iter(self.sampler) + + +class LoadScreenshots: + # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` + def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): + # source = [screen_number left top width height] (pixels) + check_requirements('mss') + import mss + + source, *params = source.split() + self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0 + if len(params) == 1: + self.screen = int(params[0]) + elif len(params) == 4: + left, top, width, height = (int(x) for x in params) + elif len(params) == 5: + self.screen, left, top, width, height = (int(x) for x in params) + self.img_size = img_size + self.stride = stride + self.transforms = transforms + self.auto = auto + self.mode = 'stream' + self.frame = 0 + self.sct = mss.mss() + + # Parse monitor shape + monitor = self.sct.monitors[self.screen] + self.top = monitor['top'] if top is None else (monitor['top'] + top) + self.left = monitor['left'] if left is None else (monitor['left'] + left) + self.width = width or monitor['width'] + self.height = height or monitor['height'] + self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height} + + def __iter__(self): + return self + + def __next__(self): + # mss screen capture: get raw pixels from the screen as np array + im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR + s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: ' + + if self.transforms: + im = self.transforms(im0) # transforms + else: + im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize + im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + im = np.ascontiguousarray(im) # contiguous + self.frame += 1 + return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s + + +class LoadImages: + # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` + def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line + path = Path(path).read_text().rsplit() + files = [] + for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: + p = str(Path(p).resolve()) + if '*' in p: + files.extend(sorted(glob.glob(p, recursive=True))) # glob + elif os.path.isdir(p): + files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir + elif os.path.isfile(p): + files.append(p) # files + else: + raise FileNotFoundError(f'{p} does not exist') + + images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS] + videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.stride = stride + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = 'image' + self.auto = auto + self.transforms = transforms # optional + self.vid_stride = vid_stride # video frame-rate stride + if any(videos): + self._new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, f'No images or videos found in {p}. ' \ + f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}' + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = 'video' + for _ in range(self.vid_stride): + self.cap.grab() + ret_val, im0 = self.cap.retrieve() + while not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + path = self.files[self.count] + self._new_video(path) + ret_val, im0 = self.cap.read() + + self.frame += 1 + # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False + s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ' + + else: + # Read image + self.count += 1 + im0 = cv2.imread(path) # BGR + assert im0 is not None, f'Image Not Found {path}' + s = f'image {self.count}/{self.nf} {path}: ' + + if self.transforms: + im = self.transforms(im0) # transforms + else: + im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize + im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + im = np.ascontiguousarray(im) # contiguous + + return path, im, im0, self.cap, s + + def _new_video(self, path): + # Create a new video capture object + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) + self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees + # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 + + def _cv2_rotate(self, im): + # Rotate a cv2 video manually + if self.orientation == 0: + return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) + elif self.orientation == 180: + return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) + elif self.orientation == 90: + return cv2.rotate(im, cv2.ROTATE_180) + return im + + def __len__(self): + return self.nf # number of files + + +class LoadStreams: + # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` + def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + torch.backends.cudnn.benchmark = True # faster for fixed-size inference + self.mode = 'stream' + self.img_size = img_size + self.stride = stride + self.vid_stride = vid_stride # video frame-rate stride + sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources] + n = len(sources) + self.sources = [clean_str(x) for x in sources] # clean source names for later + self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n + for i, s in enumerate(sources): # index, source + # Start thread to read frames from video stream + st = f'{i + 1}/{n}: {s}... ' + if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video + # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc' + check_requirements(('pafy', 'youtube_dl==2020.12.2')) + import pafy + s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL + s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam + if s == 0: + assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.' + assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.' + cap = cv2.VideoCapture(s) + assert cap.isOpened(), f'{st}Failed to open {s}' + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan + self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback + self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback + + _, self.imgs[i] = cap.read() # guarantee first frame + self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) + LOGGER.info(f'{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)') + self.threads[i].start() + LOGGER.info('') # newline + + # check for common shapes + s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + self.auto = auto and self.rect + self.transforms = transforms # optional + if not self.rect: + LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.') + + def update(self, i, cap, stream): + # Read stream `i` frames in daemon thread + n, f = 0, self.frames[i] # frame number, frame array + while cap.isOpened() and n < f: + n += 1 + cap.grab() # .read() = .grab() followed by .retrieve() + if n % self.vid_stride == 0: + success, im = cap.retrieve() + if success: + self.imgs[i] = im + else: + LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.') + self.imgs[i] = np.zeros_like(self.imgs[i]) + cap.open(stream) # re-open stream if signal was lost + time.sleep(0.0) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + im0 = self.imgs.copy() + if self.transforms: + im = np.stack([self.transforms(x) for x in im0]) # transforms + else: + im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize + im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW + im = np.ascontiguousarray(im) # contiguous + + return self.sources, im, im0, None, '' + + def __len__(self): + return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + # Define label paths as a function of image paths + sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}' # /images/, /labels/ substrings + return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths] + + +class LoadImagesAndLabels(Dataset): + # YOLOv5 train_loader/val_loader, loads images and labels for training and validation + cache_version = 0.6 # dataset labels *.cache version + rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] + + def __init__(self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0.0, + min_items=0, + prefix=''): + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + self.path = path + self.albumentations = Albumentations(size=img_size) if augment else None + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / '**' / '*.*'), recursive=True) + # f = list(p.rglob('*.*')) # pathlib + elif p.is_file(): # file + with open(p) as t: + t = t.read().strip().splitlines() + parent = str(p.parent) + os.sep + f += [x.replace('./', parent, 1) if x.startswith('./') else x for x in t] # to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) + else: + raise FileNotFoundError(f'{prefix}{p} does not exist') + self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib + assert self.im_files, f'{prefix}No images found' + except Exception as e: + raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e + + # Check cache + self.label_files = img2label_paths(self.im_files) # labels + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') + try: + cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict + assert cache['version'] == self.cache_version # matches current version + assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash + except Exception: + cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops + + # Display cache + nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total + if exists and LOCAL_RANK in {-1, 0}: + d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt' + tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results + if cache['msgs']: + LOGGER.info('\n'.join(cache['msgs'])) # display warnings + assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}' + + # Read cache + [cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items + labels, shapes, self.segments = zip(*cache.values()) + nl = len(np.concatenate(labels, 0)) # number of labels + assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}' + self.labels = list(labels) + self.shapes = np.array(shapes) + self.im_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + + # Filter images + if min_items: + include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int) + LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset') + self.im_files = [self.im_files[i] for i in include] + self.label_files = [self.label_files[i] for i in include] + self.labels = [self.labels[i] for i in include] + self.segments = [self.segments[i] for i in include] + self.shapes = self.shapes[include] # wh + + # Create indices + n = len(self.shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = range(n) + + # Update labels + include_class = [] # filter labels to include only these classes (optional) + include_class_array = np.array(include_class).reshape(1, -1) + for i, (label, segment) in enumerate(zip(self.labels, self.segments)): + if include_class: + j = (label[:, 0:1] == include_class_array).any(1) + self.labels[i] = label[j] + if segment: + self.segments[i] = segment[j] + if single_cls: # single-class training, merge all classes into 0 + self.labels[i][:, 0] = 0 + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.im_files = [self.im_files[i] for i in irect] + self.label_files = [self.label_files[i] for i in irect] + self.labels = [self.labels[i] for i in irect] + self.segments = [self.segments[i] for i in irect] + self.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride + + # Cache images into RAM/disk for faster training + if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix): + cache_images = False + self.ims = [None] * n + self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files] + if cache_images: + b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes + self.im_hw0, self.im_hw = [None] * n, [None] * n + fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image + results = ThreadPool(NUM_THREADS).imap(fcn, range(n)) + pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) + for i, x in pbar: + if cache_images == 'disk': + b += self.npy_files[i].stat().st_size + else: # 'ram' + self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) + b += self.ims[i].nbytes + pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})' + pbar.close() + + def check_cache_ram(self, safety_margin=0.1, prefix=''): + # Check image caching requirements vs available memory + b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes + n = min(self.n, 30) # extrapolate from 30 random images + for _ in range(n): + im = cv2.imread(random.choice(self.im_files)) # sample image + ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio + b += im.nbytes * ratio ** 2 + mem_required = b * self.n / n # GB required to cache dataset into RAM + mem = psutil.virtual_memory() + cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question + if not cache: + LOGGER.info(f'{prefix}{mem_required / gb:.1f}GB RAM required, ' + f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, ' + f"{'caching images ✅' if cache else 'not caching images ⚠️'}") + return cache + + def cache_labels(self, path=Path('./labels.cache'), prefix=''): + # Cache dataset labels, check images and read shapes + x = {} # dict + nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages + desc = f'{prefix}Scanning {path.parent / path.stem}...' + with Pool(NUM_THREADS) as pool: + pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), + desc=desc, + total=len(self.im_files), + bar_format=TQDM_BAR_FORMAT) + for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: + nm += nm_f + nf += nf_f + ne += ne_f + nc += nc_f + if im_file: + x[im_file] = [lb, shape, segments] + if msg: + msgs.append(msg) + pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt' + + pbar.close() + if msgs: + LOGGER.info('\n'.join(msgs)) + if nf == 0: + LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}') + x['hash'] = get_hash(self.label_files + self.im_files) + x['results'] = nf, nm, ne, nc, len(self.im_files) + x['msgs'] = msgs # warnings + x['version'] = self.cache_version # cache version + try: + np.save(path, x) # save cache for next time + path.with_suffix('.cache.npy').rename(path) # remove .npy suffix + LOGGER.info(f'{prefix}New cache created: {path}') + except Exception as e: + LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}') # not writeable + return x + + def __len__(self): + return len(self.im_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + if mosaic: + # Load mosaic + img, labels = self.load_mosaic(index) + shapes = None + + # MixUp augmentation + if random.random() < hyp['mixup']: + img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1))) + + else: + # Load image + img, (h0, w0), (h, w) = self.load_image(index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + img, labels = random_perspective(img, + labels, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + nl = len(labels) # number of labels + if nl: + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3) + + if self.augment: + # Albumentations + img, labels = self.albumentations(img, labels) + nl = len(labels) # update after albumentations + + # HSV color-space + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Flip up-down + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nl: + labels[:, 2] = 1 - labels[:, 2] + + # Flip left-right + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nl: + labels[:, 1] = 1 - labels[:, 1] + + # Cutouts + # labels = cutout(img, labels, p=0.5) + # nl = len(labels) # update after cutout + + labels_out = torch.zeros((nl, 6)) + if nl: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.im_files[index], shapes + + def load_image(self, i): + # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw) + im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i], + if im is None: # not cached in RAM + if fn.exists(): # load npy + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + assert im is not None, f'Image Not Found {f}' + h0, w0 = im.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # ratio + if r != 1: # if sizes are not equal + interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA + im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp) + return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized + return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized + + def cache_images_to_disk(self, i): + # Saves an image as an *.npy file for faster loading + f = self.npy_files[i] + if not f.exists(): + np.save(f.as_posix(), cv2.imread(self.im_files[i])) + + def load_mosaic(self, index): + # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic + labels4, segments4 = [], [] + s = self.img_size + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + random.shuffle(indices) + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) + img4, labels4 = random_perspective(img4, + labels4, + segments4, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img4, labels4 + + def load_mosaic9(self, index): + # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic + labels9, segments9 = [], [] + s = self.img_size + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices + random.shuffle(indices) + hp, wp = -1, -1 # height, width previous + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img9 + if i == 0: # center + img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + h0, w0 = h, w + c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates + elif i == 1: # top + c = s, s - h, s + w, s + elif i == 2: # top right + c = s + wp, s - h, s + wp + w, s + elif i == 3: # right + c = s + w0, s, s + w0 + w, s + h + elif i == 4: # bottom right + c = s + w0, s + hp, s + w0 + w, s + hp + h + elif i == 5: # bottom + c = s + w0 - w, s + h0, s + w0, s + h0 + h + elif i == 6: # bottom left + c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h + elif i == 7: # left + c = s - w, s + h0 - h, s, s + h0 + elif i == 8: # top left + c = s - w, s + h0 - hp - h, s, s + h0 - hp + + padx, pady = c[:2] + x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] + labels9.append(labels) + segments9.extend(segments) + + # Image + img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] + hp, wp = h, w # height, width previous + + # Offset + yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y + img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] + + # Concat/clip labels + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] + + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate + + # Augment + img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste']) + img9, labels9 = random_perspective(img9, + labels9, + segments9, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img9, labels9 + + @staticmethod + def collate_fn(batch): + im, label, path, shapes = zip(*batch) # transposed + for i, lb in enumerate(label): + lb[:, 0] = i # add target image index for build_targets() + return torch.stack(im, 0), torch.cat(label, 0), path, shapes + + @staticmethod + def collate_fn4(batch): + im, label, path, shapes = zip(*batch) # transposed + n = len(shapes) // 4 + im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] + + ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) + wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) + s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale + for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW + i *= 4 + if random.random() < 0.5: + im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', + align_corners=False)[0].type(im[i].type()) + lb = label[i] + else: + im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2) + lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s + im4.append(im1) + label4.append(lb) + + for i, lb in enumerate(label4): + lb[:, 0] = i # add target image index for build_targets() + + return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def flatten_recursive(path=DATASETS_DIR / 'coco128'): + # Flatten a recursive directory by bringing all files to top level + new_path = Path(f'{str(path)}_flat') + if os.path.exists(new_path): + shutil.rmtree(new_path) # delete output folder + os.makedirs(new_path) # make new output folder + for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders import *; extract_boxes() + # Convert detection dataset into classification dataset, with one directory per class + path = Path(path) # images dir + shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None # remove existing + files = list(path.rglob('*.*')) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in IMG_FORMATS: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file) as f: + lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' + + +def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False): + """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + Usage: from utils.dataloaders import *; autosplit() + Arguments + path: Path to images directory + weights: Train, val, test weights (list, tuple) + annotated_only: Only use images with an annotated txt file + """ + path = Path(path) # images dir + files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only + n = len(files) # number of files + random.seed(0) # for reproducibility + indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + + txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files + for x in txt: + if (path.parent / x).exists(): + (path.parent / x).unlink() # remove existing + + print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) + for i, img in tqdm(zip(indices, files), total=n): + if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label + with open(path.parent / txt[i], 'a') as f: + f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n') # add image to txt file + + +def verify_image_label(args): + # Verify one image-label pair + im_file, lb_file, prefix = args + nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', [] # number (missing, found, empty, corrupt), message, segments + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' + assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}' + if im.format.lower() in ('jpg', 'jpeg'): + with open(im_file, 'rb') as f: + f.seek(-2, 2) + if f.read() != b'\xff\xd9': # corrupt JPEG + ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100) + msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved' + + # verify labels + if os.path.isfile(lb_file): + nf = 1 # label found + with open(lb_file) as f: + lb = [x.split() for x in f.read().strip().splitlines() if len(x)] + if any(len(x) > 6 for x in lb): # is segment + classes = np.array([x[0] for x in lb], dtype=np.float32) + segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...) + lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) + lb = np.array(lb, dtype=np.float32) + nl = len(lb) + if nl: + assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected' + assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}' + assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}' + _, i = np.unique(lb, axis=0, return_index=True) + if len(i) < nl: # duplicate row check + lb = lb[i] # remove duplicates + if segments: + segments = [segments[x] for x in i] + msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed' + else: + ne = 1 # label empty + lb = np.zeros((0, 5), dtype=np.float32) + else: + nm = 1 # label missing + lb = np.zeros((0, 5), dtype=np.float32) + return im_file, lb, shape, segments, nm, nf, ne, nc, msg + except Exception as e: + nc = 1 + msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}' + return [None, None, None, None, nm, nf, ne, nc, msg] + + +class HUBDatasetStats(): + """ Class for generating HUB dataset JSON and `-hub` dataset directory + + Arguments + path: Path to data.yaml or data.zip (with data.yaml inside data.zip) + autodownload: Attempt to download dataset if not found locally + + Usage + from utils.dataloaders import HUBDatasetStats + stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1 + stats = HUBDatasetStats('path/to/coco128.zip') # usage 2 + stats.get_json(save=False) + stats.process_images() + """ + + def __init__(self, path='coco128.yaml', autodownload=False): + # Initialize class + zipped, data_dir, yaml_path = self._unzip(Path(path)) + try: + with open(check_yaml(yaml_path), errors='ignore') as f: + data = yaml.safe_load(f) # data dict + if zipped: + data['path'] = data_dir + except Exception as e: + raise Exception('error/HUB/dataset_stats/yaml_load') from e + + check_dataset(data, autodownload) # download dataset if missing + self.hub_dir = Path(data['path'] + '-hub') + self.im_dir = self.hub_dir / 'images' + self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images + self.stats = {'nc': data['nc'], 'names': list(data['names'].values())} # statistics dictionary + self.data = data + + @staticmethod + def _find_yaml(dir): + # Return data.yaml file + files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml')) # try root level first and then recursive + assert files, f'No *.yaml file found in {dir}' + if len(files) > 1: + files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name + assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed' + assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}' + return files[0] + + def _unzip(self, path): + # Unzip data.zip + if not str(path).endswith('.zip'): # path is data.yaml + return False, None, path + assert Path(path).is_file(), f'Error unzipping {path}, file not found' + unzip_file(path, path=path.parent) + dir = path.with_suffix('') # dataset directory == zip name + assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/' + return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path + + def _hub_ops(self, f, max_dim=1920): + # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing + f_new = self.im_dir / Path(f).name # dataset-hub image filename + try: # use PIL + im = Image.open(f) + r = max_dim / max(im.height, im.width) # ratio + if r < 1.0: # image too large + im = im.resize((int(im.width * r), int(im.height * r))) + im.save(f_new, 'JPEG', quality=50, optimize=True) # save + except Exception as e: # use OpenCV + LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}') + im = cv2.imread(f) + im_height, im_width = im.shape[:2] + r = max_dim / max(im_height, im_width) # ratio + if r < 1.0: # image too large + im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) + cv2.imwrite(str(f_new), im) + + def get_json(self, save=False, verbose=False): + # Return dataset JSON for Ultralytics HUB + def _round(labels): + # Update labels to integer class and 6 decimal place floats + return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] + + for split in 'train', 'val', 'test': + if self.data.get(split) is None: + self.stats[split] = None # i.e. no test set + continue + dataset = LoadImagesAndLabels(self.data[split]) # load dataset + x = np.array([ + np.bincount(label[:, 0].astype(int), minlength=self.data['nc']) + for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics')]) # shape(128x80) + self.stats[split] = { + 'instance_stats': { + 'total': int(x.sum()), + 'per_class': x.sum(0).tolist()}, + 'image_stats': { + 'total': dataset.n, + 'unlabelled': int(np.all(x == 0, 1).sum()), + 'per_class': (x > 0).sum(0).tolist()}, + 'labels': [{ + str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]} + + # Save, print and return + if save: + stats_path = self.hub_dir / 'stats.json' + print(f'Saving {stats_path.resolve()}...') + with open(stats_path, 'w') as f: + json.dump(self.stats, f) # save stats.json + if verbose: + print(json.dumps(self.stats, indent=2, sort_keys=False)) + return self.stats + + def process_images(self): + # Compress images for Ultralytics HUB + for split in 'train', 'val', 'test': + if self.data.get(split) is None: + continue + dataset = LoadImagesAndLabels(self.data[split]) # load dataset + desc = f'{split} images' + for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc): + pass + print(f'Done. All images saved to {self.im_dir}') + return self.im_dir + + +# Classification dataloaders ------------------------------------------------------------------------------------------- +class ClassificationDataset(torchvision.datasets.ImageFolder): + """ + YOLOv5 Classification Dataset. + Arguments + root: Dataset path + transform: torchvision transforms, used by default + album_transform: Albumentations transforms, used if installed + """ + + def __init__(self, root, augment, imgsz, cache=False): + super().__init__(root=root) + self.torch_transforms = classify_transforms(imgsz) + self.album_transforms = classify_albumentations(augment, imgsz) if augment else None + self.cache_ram = cache is True or cache == 'ram' + self.cache_disk = cache == 'disk' + self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im + + def __getitem__(self, i): + f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image + if self.cache_ram and im is None: + im = self.samples[i][3] = cv2.imread(f) + elif self.cache_disk: + if not fn.exists(): # load npy + np.save(fn.as_posix(), cv2.imread(f)) + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + if self.album_transforms: + sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image'] + else: + sample = self.torch_transforms(im) + return sample, j + + +def create_classification_dataloader(path, + imgsz=224, + batch_size=16, + augment=True, + cache=False, + rank=-1, + workers=8, + shuffle=True): + # Returns Dataloader object to be used with YOLOv5 Classifier + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache) + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) + sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + RANK) + return InfiniteDataLoader(dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + worker_init_fn=seed_worker, + generator=generator) # or DataLoader(persistent_workers=True) diff --git a/algorithm/yolov5-master/utils/docker/Dockerfile b/algorithm/yolov5-master/utils/docker/Dockerfile new file mode 100644 index 0000000..b5d2af9 --- /dev/null +++ b/algorithm/yolov5-master/utils/docker/Dockerfile @@ -0,0 +1,75 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference + +# Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch +# FROM docker.io/pytorch/pytorch:latest +FROM pytorch/pytorch:latest + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg +# RUN alias python=python3 + +# Security updates +# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796 +RUN apt upgrade --no-install-recommends -y openssl + +# Create working directory +RUN rm -rf /usr/src/app && mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +# COPY . /usr/src/app (issues as not a .git directory) +RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \ + coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3' + # tensorflow tensorflowjs \ + +# Set environment variables +ENV OMP_NUM_THREADS=1 + +# Cleanup +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t + +# Pull and Run with local directory access +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t + +# Kill all +# sudo docker kill $(sudo docker ps -q) + +# Kill all image-based +# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) + +# DockerHub tag update +# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew + +# Clean up +# sudo docker system prune -a --volumes + +# Update Ubuntu drivers +# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ + +# DDP test +# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 + +# GCP VM from Image +# docker.io/ultralytics/yolov5:latest diff --git a/algorithm/yolov5-master/utils/docker/Dockerfile-arm64 b/algorithm/yolov5-master/utils/docker/Dockerfile-arm64 new file mode 100644 index 0000000..7023c6a --- /dev/null +++ b/algorithm/yolov5-master/utils/docker/Dockerfile-arm64 @@ -0,0 +1,41 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu +FROM arm64v8/ubuntu:rolling + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev +# RUN alias python=python3 + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ + coremltools onnx onnxruntime + # tensorflow-aarch64 tensorflowjs \ + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +# COPY . /usr/src/app (issues as not a .git directory) +RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/algorithm/yolov5-master/utils/docker/Dockerfile-cpu b/algorithm/yolov5-master/utils/docker/Dockerfile-cpu new file mode 100644 index 0000000..06bad9a --- /dev/null +++ b/algorithm/yolov5-master/utils/docker/Dockerfile-cpu @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu +FROM ubuntu:rolling + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg +# RUN alias python=python3 + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ + coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3' \ + # tensorflow tensorflowjs \ + --extra-index-url https://download.pytorch.org/whl/cpu + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +# COPY . /usr/src/app (issues as not a .git directory) +RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/algorithm/yolov5-master/utils/downloads.py b/algorithm/yolov5-master/utils/downloads.py new file mode 100644 index 0000000..643b529 --- /dev/null +++ b/algorithm/yolov5-master/utils/downloads.py @@ -0,0 +1,128 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Download utils +""" + +import logging +import os +import subprocess +import urllib +from pathlib import Path + +import requests +import torch + + +def is_url(url, check=True): + # Check if string is URL and check if URL exists + try: + url = str(url) + result = urllib.parse.urlparse(url) + assert all([result.scheme, result.netloc]) # check if is url + return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online + except (AssertionError, urllib.request.HTTPError): + return False + + +def gsutil_getsize(url=''): + # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du + output = subprocess.check_output(['gsutil', 'du', url], shell=True, encoding='utf-8') + if output: + return int(output.split()[0]) + return 0 + + +def url_getsize(url='https://ultralytics.com/images/bus.jpg'): + # Return downloadable file size in bytes + response = requests.head(url, allow_redirects=True) + return int(response.headers.get('content-length', -1)) + + +def curl_download(url, filename, *, silent: bool = False) -> bool: + """ + Download a file from a url to a filename using curl. + """ + silent_option = 'sS' if silent else '' # silent + proc = subprocess.run([ + 'curl', + '-#', + f'-{silent_option}L', + url, + '--output', + filename, + '--retry', + '9', + '-C', + '-',]) + return proc.returncode == 0 + + +def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): + # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes + from utils.general import LOGGER + + file = Path(file) + assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" + try: # url1 + LOGGER.info(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO) + assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check + except Exception as e: # url2 + if file.exists(): + file.unlink() # remove partial downloads + LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...') + # curl download, retry and resume on fail + curl_download(url2 or url, file) + finally: + if not file.exists() or file.stat().st_size < min_bytes: # check + if file.exists(): + file.unlink() # remove partial downloads + LOGGER.info(f'ERROR: {assert_msg}\n{error_msg}') + LOGGER.info('') + + +def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'): + # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc. + from utils.general import LOGGER + + def github_assets(repository, version='latest'): + # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...]) + if version != 'latest': + version = f'tags/{version}' # i.e. tags/v7.0 + response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api + return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets + + file = Path(str(file).strip().replace("'", '')) + if not file.exists(): + # URL specified + name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc. + if str(file).startswith(('http:/', 'https:/')): # download + url = str(file).replace(':/', '://') # Pathlib turns :// -> :/ + file = name.split('?')[0] # parse authentication https://url.com/file.txt?auth... + if Path(file).is_file(): + LOGGER.info(f'Found {url} locally at {file}') # file already exists + else: + safe_download(file=file, url=url, min_bytes=1E5) + return file + + # GitHub assets + assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] # default + try: + tag, assets = github_assets(repo, release) + except Exception: + try: + tag, assets = github_assets(repo) # latest release + except Exception: + try: + tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] + except Exception: + tag = release + + file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) + if name in assets: + safe_download(file, + url=f'https://github.com/{repo}/releases/download/{tag}/{name}', + min_bytes=1E5, + error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}') + + return str(file) diff --git a/algorithm/yolov5-master/utils/flask_rest_api/README.md b/algorithm/yolov5-master/utils/flask_rest_api/README.md new file mode 100644 index 0000000..a726acb --- /dev/null +++ b/algorithm/yolov5-master/utils/flask_rest_api/README.md @@ -0,0 +1,73 @@ +# Flask REST API + +[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are +commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API +created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). + +## Requirements + +[Flask](https://palletsprojects.com/p/flask/) is required. Install with: + +```shell +$ pip install Flask +``` + +## Run + +After Flask installation run: + +```shell +$ python3 restapi.py --port 5000 +``` + +Then use [curl](https://curl.se/) to perform a request: + +```shell +$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s' +``` + +The model inference results are returned as a JSON response: + +```json +[ + { + "class": 0, + "confidence": 0.8900438547, + "height": 0.9318675399, + "name": "person", + "width": 0.3264600933, + "xcenter": 0.7438579798, + "ycenter": 0.5207948685 + }, + { + "class": 0, + "confidence": 0.8440024257, + "height": 0.7155083418, + "name": "person", + "width": 0.6546785235, + "xcenter": 0.427829951, + "ycenter": 0.6334488392 + }, + { + "class": 27, + "confidence": 0.3771208823, + "height": 0.3902671337, + "name": "tie", + "width": 0.0696444362, + "xcenter": 0.3675483763, + "ycenter": 0.7991207838 + }, + { + "class": 27, + "confidence": 0.3527112305, + "height": 0.1540903747, + "name": "tie", + "width": 0.0336618312, + "xcenter": 0.7814827561, + "ycenter": 0.5065554976 + } +] +``` + +An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given +in `example_request.py` diff --git a/algorithm/yolov5-master/utils/flask_rest_api/example_request.py b/algorithm/yolov5-master/utils/flask_rest_api/example_request.py new file mode 100644 index 0000000..952e5dc --- /dev/null +++ b/algorithm/yolov5-master/utils/flask_rest_api/example_request.py @@ -0,0 +1,19 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Perform test request +""" + +import pprint + +import requests + +DETECTION_URL = 'http://localhost:5000/v1/object-detection/yolov5s' +IMAGE = 'zidane.jpg' + +# Read image +with open(IMAGE, 'rb') as f: + image_data = f.read() + +response = requests.post(DETECTION_URL, files={'image': image_data}).json() + +pprint.pprint(response) diff --git a/algorithm/yolov5-master/utils/flask_rest_api/restapi.py b/algorithm/yolov5-master/utils/flask_rest_api/restapi.py new file mode 100644 index 0000000..9258b1a --- /dev/null +++ b/algorithm/yolov5-master/utils/flask_rest_api/restapi.py @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run a Flask REST API exposing one or more YOLOv5s models +""" + +import argparse +import io + +import torch +from flask import Flask, request +from PIL import Image + +app = Flask(__name__) +models = {} + +DETECTION_URL = '/v1/object-detection/' + + +@app.route(DETECTION_URL, methods=['POST']) +def predict(model): + if request.method != 'POST': + return + + if request.files.get('image'): + # Method 1 + # with request.files["image"] as f: + # im = Image.open(io.BytesIO(f.read())) + + # Method 2 + im_file = request.files['image'] + im_bytes = im_file.read() + im = Image.open(io.BytesIO(im_bytes)) + + if model in models: + results = models[model](im, size=640) # reduce size=320 for faster inference + return results.pandas().xyxy[0].to_json(orient='records') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Flask API exposing YOLOv5 model') + parser.add_argument('--port', default=5000, type=int, help='port number') + parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s') + opt = parser.parse_args() + + for m in opt.model: + models[m] = torch.hub.load('ultralytics/yolov5', m, force_reload=True, skip_validation=True) + + app.run(host='0.0.0.0', port=opt.port) # debug=True causes Restarting with stat diff --git a/algorithm/yolov5-master/utils/general.py b/algorithm/yolov5-master/utils/general.py new file mode 100644 index 0000000..7462046 --- /dev/null +++ b/algorithm/yolov5-master/utils/general.py @@ -0,0 +1,1140 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +General utils +""" + +import contextlib +import glob +import inspect +import logging +import logging.config +import math +import os +import platform +import random +import re +import signal +import subprocess +import sys +import time +import urllib +from copy import deepcopy +from datetime import datetime +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from subprocess import check_output +from tarfile import is_tarfile +from typing import Optional +from zipfile import ZipFile, is_zipfile + +import cv2 +import numpy as np +import pandas as pd +import pkg_resources as pkg +import torch +import torchvision +import yaml + +from utils import TryExcept, emojis +from utils.downloads import curl_download, gsutil_getsize +from utils.metrics import box_iou, fitness + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +RANK = int(os.getenv('RANK', -1)) + +# Settings +NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads +DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory +AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode +VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode +TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format +FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf + +torch.set_printoptions(linewidth=320, precision=5, profile='long') +np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +pd.options.display.max_columns = 10 +cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) +os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads +os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) + + +def is_ascii(s=''): + # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) + s = str(s) # convert list, tuple, None, etc. to str + return len(s.encode().decode('ascii', 'ignore')) == len(s) + + +def is_chinese(s='人工智能'): + # Is string composed of any Chinese characters? + return bool(re.search('[\u4e00-\u9fff]', str(s))) + + +def is_colab(): + # Is environment a Google Colab instance? + return 'google.colab' in sys.modules + + +def is_jupyter(): + """ + Check if the current script is running inside a Jupyter Notebook. + Verified on Colab, Jupyterlab, Kaggle, Paperspace. + + Returns: + bool: True if running inside a Jupyter Notebook, False otherwise. + """ + with contextlib.suppress(Exception): + from IPython import get_ipython + return get_ipython() is not None + return False + + +def is_kaggle(): + # Is environment a Kaggle Notebook? + return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com' + + +def is_docker() -> bool: + """Check if the process runs inside a docker container.""" + if Path('/.dockerenv').exists(): + return True + try: # check if docker is in control groups + with open('/proc/self/cgroup') as file: + return any('docker' in line for line in file) + except OSError: + return False + + +def is_writeable(dir, test=False): + # Return True if directory has write permissions, test opening a file with write permissions if test=True + if not test: + return os.access(dir, os.W_OK) # possible issues on Windows + file = Path(dir) / 'tmp.txt' + try: + with open(file, 'w'): # open file with write permissions + pass + file.unlink() # remove file + return True + except OSError: + return False + + +LOGGING_NAME = 'yolov5' + + +def set_logging(name=LOGGING_NAME, verbose=True): + # sets up logging for the given name + rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings + level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR + logging.config.dictConfig({ + 'version': 1, + 'disable_existing_loggers': False, + 'formatters': { + name: { + 'format': '%(message)s'}}, + 'handlers': { + name: { + 'class': 'logging.StreamHandler', + 'formatter': name, + 'level': level,}}, + 'loggers': { + name: { + 'level': level, + 'handlers': [name], + 'propagate': False,}}}) + + +set_logging(LOGGING_NAME) # run before defining LOGGER +LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.) +if platform.system() == 'Windows': + for fn in LOGGER.info, LOGGER.warning: + setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging + + +def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'): + # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required. + env = os.getenv(env_var) + if env: + path = Path(env) # use environment variable + else: + cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs + path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir + path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable + path.mkdir(exist_ok=True) # make if required + return path + + +CONFIG_DIR = user_config_dir() # Ultralytics settings dir + + +class Profile(contextlib.ContextDecorator): + # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager + def __init__(self, t=0.0): + self.t = t + self.cuda = torch.cuda.is_available() + + def __enter__(self): + self.start = self.time() + return self + + def __exit__(self, type, value, traceback): + self.dt = self.time() - self.start # delta-time + self.t += self.dt # accumulate dt + + def time(self): + if self.cuda: + torch.cuda.synchronize() + return time.time() + + +class Timeout(contextlib.ContextDecorator): + # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager + def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True): + self.seconds = int(seconds) + self.timeout_message = timeout_msg + self.suppress = bool(suppress_timeout_errors) + + def _timeout_handler(self, signum, frame): + raise TimeoutError(self.timeout_message) + + def __enter__(self): + if platform.system() != 'Windows': # not supported on Windows + signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM + signal.alarm(self.seconds) # start countdown for SIGALRM to be raised + + def __exit__(self, exc_type, exc_val, exc_tb): + if platform.system() != 'Windows': + signal.alarm(0) # Cancel SIGALRM if it's scheduled + if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError + return True + + +class WorkingDirectory(contextlib.ContextDecorator): + # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager + def __init__(self, new_dir): + self.dir = new_dir # new dir + self.cwd = Path.cwd().resolve() # current dir + + def __enter__(self): + os.chdir(self.dir) + + def __exit__(self, exc_type, exc_val, exc_tb): + os.chdir(self.cwd) + + +def methods(instance): + # Get class/instance methods + return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith('__')] + + +def print_args(args: Optional[dict] = None, show_file=True, show_func=False): + # Print function arguments (optional args dict) + x = inspect.currentframe().f_back # previous frame + file, _, func, _, _ = inspect.getframeinfo(x) + if args is None: # get args automatically + args, _, _, frm = inspect.getargvalues(x) + args = {k: v for k, v in frm.items() if k in args} + try: + file = Path(file).resolve().relative_to(ROOT).with_suffix('') + except ValueError: + file = Path(file).stem + s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '') + LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items())) + + +def init_seeds(seed=0, deterministic=False): + # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe + # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 + if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213 + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.deterministic = True + os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' + os.environ['PYTHONHASHSEED'] = str(seed) + + +def intersect_dicts(da, db, exclude=()): + # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values + return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape} + + +def get_default_args(func): + # Get func() default arguments + signature = inspect.signature(func) + return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty} + + +def get_latest_run(search_dir='.'): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) + return max(last_list, key=os.path.getctime) if last_list else '' + + +def file_age(path=__file__): + # Return days since last file update + dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta + return dt.days # + dt.seconds / 86400 # fractional days + + +def file_date(path=__file__): + # Return human-readable file modification date, i.e. '2021-3-26' + t = datetime.fromtimestamp(Path(path).stat().st_mtime) + return f'{t.year}-{t.month}-{t.day}' + + +def file_size(path): + # Return file/dir size (MB) + mb = 1 << 20 # bytes to MiB (1024 ** 2) + path = Path(path) + if path.is_file(): + return path.stat().st_size / mb + elif path.is_dir(): + return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb + else: + return 0.0 + + +def check_online(): + # Check internet connectivity + import socket + + def run_once(): + # Check once + try: + socket.create_connection(('1.1.1.1', 443), 5) # check host accessibility + return True + except OSError: + return False + + return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues + + +def git_describe(path=ROOT): # path must be a directory + # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe + try: + assert (Path(path) / '.git').is_dir() + return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1] + except Exception: + return '' + + +@TryExcept() +@WorkingDirectory(ROOT) +def check_git_status(repo='ultralytics/yolov5', branch='master'): + # YOLOv5 status check, recommend 'git pull' if code is out of date + url = f'https://github.com/{repo}' + msg = f', for updates see {url}' + s = colorstr('github: ') # string + assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg + assert check_online(), s + 'skipping check (offline)' + msg + + splits = re.split(pattern=r'\s', string=check_output('git remote -v', shell=True).decode()) + matches = [repo in s for s in splits] + if any(matches): + remote = splits[matches.index(True) - 1] + else: + remote = 'ultralytics' + check_output(f'git remote add {remote} {url}', shell=True) + check_output(f'git fetch {remote}', shell=True, timeout=5) # git fetch + local_branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out + n = int(check_output(f'git rev-list {local_branch}..{remote}/{branch} --count', shell=True)) # commits behind + if n > 0: + pull = 'git pull' if remote == 'origin' else f'git pull {remote} {branch}' + s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update." + else: + s += f'up to date with {url} ✅' + LOGGER.info(s) + + +@WorkingDirectory(ROOT) +def check_git_info(path='.'): + # YOLOv5 git info check, return {remote, branch, commit} + check_requirements('gitpython') + import git + try: + repo = git.Repo(path) + remote = repo.remotes.origin.url.replace('.git', '') # i.e. 'https://github.com/ultralytics/yolov5' + commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d' + try: + branch = repo.active_branch.name # i.e. 'main' + except TypeError: # not on any branch + branch = None # i.e. 'detached HEAD' state + return {'remote': remote, 'branch': branch, 'commit': commit} + except git.exc.InvalidGitRepositoryError: # path is not a git dir + return {'remote': None, 'branch': None, 'commit': None} + + +def check_python(minimum='3.7.0'): + # Check current python version vs. required python version + check_version(platform.python_version(), minimum, name='Python ', hard=True) + + +def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False): + # Check version vs. required version + current, minimum = (pkg.parse_version(x) for x in (current, minimum)) + result = (current == minimum) if pinned else (current >= minimum) # bool + s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed' # string + if hard: + assert result, emojis(s) # assert min requirements met + if verbose and not result: + LOGGER.warning(s) + return result + + +@TryExcept() +def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=''): + # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str) + prefix = colorstr('red', 'bold', 'requirements:') + check_python() # check python version + if isinstance(requirements, Path): # requirements.txt file + file = requirements.resolve() + assert file.exists(), f'{prefix} {file} not found, check failed.' + with file.open() as f: + requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude] + elif isinstance(requirements, str): + requirements = [requirements] + + s = '' + n = 0 + for r in requirements: + try: + pkg.require(r) + except (pkg.VersionConflict, pkg.DistributionNotFound): # exception if requirements not met + s += f'"{r}" ' + n += 1 + + if s and install and AUTOINSTALL: # check environment variable + LOGGER.info(f"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...") + try: + # assert check_online(), "AutoUpdate skipped (offline)" + LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode()) + source = file if 'file' in locals() else requirements + s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ + f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" + LOGGER.info(s) + except Exception as e: + LOGGER.warning(f'{prefix} ❌ {e}') + + +def check_img_size(imgsz, s=32, floor=0): + # Verify image size is a multiple of stride s in each dimension + if isinstance(imgsz, int): # integer i.e. img_size=640 + new_size = max(make_divisible(imgsz, int(s)), floor) + else: # list i.e. img_size=[640, 480] + imgsz = list(imgsz) # convert to list if tuple + new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] + if new_size != imgsz: + LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}') + return new_size + + +def check_imshow(warn=False): + # Check if environment supports image displays + try: + assert not is_jupyter() + assert not is_docker() + cv2.imshow('test', np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + if warn: + LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}') + return False + + +def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''): + # Check file(s) for acceptable suffix + if file and suffix: + if isinstance(suffix, str): + suffix = [suffix] + for f in file if isinstance(file, (list, tuple)) else [file]: + s = Path(f).suffix.lower() # file suffix + if len(s): + assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}' + + +def check_yaml(file, suffix=('.yaml', '.yml')): + # Search/download YAML file (if necessary) and return path, checking suffix + return check_file(file, suffix) + + +def check_file(file, suffix=''): + # Search/download file (if necessary) and return path + check_suffix(file, suffix) # optional + file = str(file) # convert to str() + if os.path.isfile(file) or not file: # exists + return file + elif file.startswith(('http:/', 'https:/')): # download + url = file # warning: Pathlib turns :// -> :/ + file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth + if os.path.isfile(file): + LOGGER.info(f'Found {url} locally at {file}') # file already exists + else: + LOGGER.info(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, file) + assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check + return file + elif file.startswith('clearml://'): # ClearML Dataset ID + assert 'clearml' in sys.modules, "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." + return file + else: # search + files = [] + for d in 'data', 'models', 'utils': # search directories + files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file + assert len(files), f'File not found: {file}' # assert file was found + assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique + return files[0] # return file + + +def check_font(font=FONT, progress=False): + # Download font to CONFIG_DIR if necessary + font = Path(font) + file = CONFIG_DIR / font.name + if not font.exists() and not file.exists(): + url = f'https://ultralytics.com/assets/{font.name}' + LOGGER.info(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, str(file), progress=progress) + + +def check_dataset(data, autodownload=True): + # Download, check and/or unzip dataset if not found locally + + # Download (optional) + extract_dir = '' + if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): + download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1) + data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml')) + extract_dir, autodownload = data.parent, False + + # Read yaml (optional) + if isinstance(data, (str, Path)): + data = yaml_load(data) # dictionary + + # Checks + for k in 'train', 'val', 'names': + assert k in data, emojis(f"data.yaml '{k}:' field missing ❌") + if isinstance(data['names'], (list, tuple)): # old array format + data['names'] = dict(enumerate(data['names'])) # convert to dict + assert all(isinstance(k, int) for k in data['names'].keys()), 'data.yaml names keys must be integers, i.e. 2: car' + data['nc'] = len(data['names']) + + # Resolve paths + path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.' + if not path.is_absolute(): + path = (ROOT / path).resolve() + data['path'] = path # download scripts + for k in 'train', 'val', 'test': + if data.get(k): # prepend path + if isinstance(data[k], str): + x = (path / data[k]).resolve() + if not x.exists() and data[k].startswith('../'): + x = (path / data[k][3:]).resolve() + data[k] = str(x) + else: + data[k] = [str((path / x).resolve()) for x in data[k]] + + # Parse yaml + train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download')) + if val: + val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path + if not all(x.exists() for x in val): + LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]) + if not s or not autodownload: + raise Exception('Dataset not found ❌') + t = time.time() + if s.startswith('http') and s.endswith('.zip'): # URL + f = Path(s).name # filename + LOGGER.info(f'Downloading {s} to {f}...') + torch.hub.download_url_to_file(s, f) + Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root + unzip_file(f, path=DATASETS_DIR) # unzip + Path(f).unlink() # remove zip + r = None # success + elif s.startswith('bash '): # bash script + LOGGER.info(f'Running {s} ...') + r = subprocess.run(s, shell=True) + else: # python script + r = exec(s, {'yaml': data}) # return None + dt = f'({round(time.time() - t, 1)}s)' + s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f'failure {dt} ❌' + LOGGER.info(f'Dataset download {s}') + check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts + return data # dictionary + + +def check_amp(model): + # Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation + from models.common import AutoShape, DetectMultiBackend + + def amp_allclose(model, im): + # All close FP32 vs AMP results + m = AutoShape(model, verbose=False) # model + a = m(im).xywhn[0] # FP32 inference + m.amp = True + b = m(im).xywhn[0] # AMP inference + return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance + + prefix = colorstr('AMP: ') + device = next(model.parameters()).device # get model device + if device.type in ('cpu', 'mps'): + return False # AMP only used on CUDA devices + f = ROOT / 'data' / 'images' / 'bus.jpg' # image to check + im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3)) + try: + assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im) + LOGGER.info(f'{prefix}checks passed ✅') + return True + except Exception: + help_url = 'https://github.com/ultralytics/yolov5/issues/7908' + LOGGER.warning(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}') + return False + + +def yaml_load(file='data.yaml'): + # Single-line safe yaml loading + with open(file, errors='ignore') as f: + return yaml.safe_load(f) + + +def yaml_save(file='data.yaml', data={}): + # Single-line safe yaml saving + with open(file, 'w') as f: + yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False) + + +def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')): + # Unzip a *.zip file to path/, excluding files containing strings in exclude list + if path is None: + path = Path(file).parent # default path + with ZipFile(file) as zipObj: + for f in zipObj.namelist(): # list all archived filenames in the zip + if all(x not in f for x in exclude): + zipObj.extract(f, path=path) + + +def url2file(url): + # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt + url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/ + return Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth + + +def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3): + # Multithreaded file download and unzip function, used in data.yaml for autodownload + def download_one(url, dir): + # Download 1 file + success = True + if os.path.isfile(url): + f = Path(url) # filename + else: # does not exist + f = dir / Path(url).name + LOGGER.info(f'Downloading {url} to {f}...') + for i in range(retry + 1): + if curl: + success = curl_download(url, f, silent=(threads > 1)) + else: + torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download + success = f.is_file() + if success: + break + elif i < retry: + LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...') + else: + LOGGER.warning(f'❌ Failed to download {url}...') + + if unzip and success and (f.suffix == '.gz' or is_zipfile(f) or is_tarfile(f)): + LOGGER.info(f'Unzipping {f}...') + if is_zipfile(f): + unzip_file(f, dir) # unzip + elif is_tarfile(f): + subprocess.run(['tar', 'xf', f, '--directory', f.parent], check=True) # unzip + elif f.suffix == '.gz': + subprocess.run(['tar', 'xfz', f, '--directory', f.parent], check=True) # unzip + if delete: + f.unlink() # remove zip + + dir = Path(dir) + dir.mkdir(parents=True, exist_ok=True) # make directory + if threads > 1: + pool = ThreadPool(threads) + pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded + pool.close() + pool.join() + else: + for u in [url] if isinstance(url, (str, Path)) else url: + download_one(u, dir) + + +def make_divisible(x, divisor): + # Returns nearest x divisible by divisor + if isinstance(divisor, torch.Tensor): + divisor = int(divisor.max()) # to int + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + # Cleans a string by replacing special characters with underscore _ + return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s) + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +def colorstr(*input): + # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') + *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string + colors = { + 'black': '\033[30m', # basic colors + 'red': '\033[31m', + 'green': '\033[32m', + 'yellow': '\033[33m', + 'blue': '\033[34m', + 'magenta': '\033[35m', + 'cyan': '\033[36m', + 'white': '\033[37m', + 'bright_black': '\033[90m', # bright colors + 'bright_red': '\033[91m', + 'bright_green': '\033[92m', + 'bright_yellow': '\033[93m', + 'bright_blue': '\033[94m', + 'bright_magenta': '\033[95m', + 'bright_cyan': '\033[96m', + 'bright_white': '\033[97m', + 'end': '\033[0m', # misc + 'bold': '\033[1m', + 'underline': '\033[4m'} + return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] + + +def labels_to_class_weights(labels, nc=80): + # Get class weights (inverse frequency) from training labels + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(int) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights).float() + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + # Produces image weights based on class_weights and image contents + # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample + class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels]) + return (class_weights.reshape(1, nc) * class_counts).sum(1) + + +def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) + # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + return [ + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] + + +def xyxy2xywh(x): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center + y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center + y[..., 2] = x[..., 2] - x[..., 0] # width + y[..., 3] = x[..., 3] - x[..., 1] # height + return y + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x + y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y + y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x + y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y + return y + + +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x + y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y + y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x + y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y + return y + + +def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right + if clip: + clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center + y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center + y[..., 2] = (x[..., 2] - x[..., 0]) / w # width + y[..., 3] = (x[..., 3] - x[..., 1]) / h # height + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + # Convert normalized segments into pixel segments, shape (n,2) + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = w * x[..., 0] + padw # top left x + y[..., 1] = h * x[..., 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + x, y, = x[inside], y[inside] + return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy + + +def segments2boxes(segments): + # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + # Up-sample an (n,2) segment + for i, s in enumerate(segments): + s = np.concatenate((s, s[0:1, :]), axis=0) + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy + return segments + + +def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): + # Rescale boxes (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + clip_boxes(boxes, img0_shape) + return boxes + + +def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + segments[:, 0] -= pad[0] # x padding + segments[:, 1] -= pad[1] # y padding + segments /= gain + clip_segments(segments, img0_shape) + if normalize: + segments[:, 0] /= img0_shape[1] # width + segments[:, 1] /= img0_shape[0] # height + return segments + + +def clip_boxes(boxes, shape): + # Clip boxes (xyxy) to image shape (height, width) + if isinstance(boxes, torch.Tensor): # faster individually + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 + else: # np.array (faster grouped) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + + +def clip_segments(segments, shape): + # Clip segments (xy1,xy2,...) to image shape (height, width) + if isinstance(segments, torch.Tensor): # faster individually + segments[:, 0].clamp_(0, shape[1]) # x + segments[:, 1].clamp_(0, shape[0]) # y + else: # np.array (faster grouped) + segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x + segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0, # number of masks +): + """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + # Checks + assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) + prediction = prediction[0] # select only inference output + + device = prediction.device + mps = 'mps' in device.type # Apple MPS + if mps: # MPS not fully supported yet, convert tensors to CPU before NMS + prediction = prediction.cpu() + bs = prediction.shape[0] # batch size + nc = prediction.shape[2] - nm - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + # min_wh = 2 # (pixels) minimum box width and height + max_wh = 7680 # (pixels) maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 0.5 + 0.05 * bs # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + mi = 5 + nc # mask start index + output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + lb = labels[xi] + v = torch.zeros((len(lb), nc + nm + 5), device=x.device) + v[:, :4] = lb[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box/Mask + box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) + mask = x[:, mi:] # zero columns if no masks + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) + else: # best class only + conf, j = x[:, 5:mi].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + i = i[:max_det] # limit detections + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if mps: + output[xi] = output[xi].to(device) + if (time.time() - t) > time_limit: + LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded') + break # time limit exceeded + + return output + + +def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() + # Strip optimizer from 'f' to finalize training, optionally save as 's' + x = torch.load(f, map_location=torch.device('cpu')) + if x.get('ema'): + x['model'] = x['ema'] # replace model with ema + for k in 'optimizer', 'best_fitness', 'ema', 'updates': # keys + x[k] = None + x['epoch'] = -1 + x['model'].half() # to FP16 + for p in x['model'].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1E6 # filesize + LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB") + + +def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')): + evolve_csv = save_dir / 'evolve.csv' + evolve_yaml = save_dir / 'hyp_evolve.yaml' + keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps] + keys = tuple(x.strip() for x in keys) + vals = results + tuple(hyp.values()) + n = len(keys) + + # Download (optional) + if bucket: + url = f'gs://{bucket}/evolve.csv' + if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0): + subprocess.run(['gsutil', 'cp', f'{url}', f'{save_dir}']) # download evolve.csv if larger than local + + # Log to evolve.csv + s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header + with open(evolve_csv, 'a') as f: + f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n') + + # Save yaml + with open(evolve_yaml, 'w') as f: + data = pd.read_csv(evolve_csv, skipinitialspace=True) + data = data.rename(columns=lambda x: x.strip()) # strip keys + i = np.argmax(fitness(data.values[:, :4])) # + generations = len(data) + f.write('# YOLOv5 Hyperparameter Evolution Results\n' + f'# Best generation: {i}\n' + + f'# Last generation: {generations - 1}\n' + '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + + '\n' + '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n') + yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False) + + # Print to screen + LOGGER.info(prefix + f'{generations} generations finished, current result:\n' + prefix + + ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' + prefix + ', '.join(f'{x:20.5g}' + for x in vals) + '\n\n') + + if bucket: + subprocess.run(['gsutil', 'cp', f'{evolve_csv}', f'{evolve_yaml}', f'gs://{bucket}']) # upload + + +def apply_classifier(x, model, img, im0): + # Apply a second stage classifier to YOLO outputs + # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_boxes(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for a in d: + cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + + im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction + x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections + + return x + + +def increment_path(path, exist_ok=False, sep='', mkdir=False): + # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. + path = Path(path) # os-agnostic + if path.exists() and not exist_ok: + path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '') + + # Method 1 + for n in range(2, 9999): + p = f'{path}{sep}{n}{suffix}' # increment path + if not os.path.exists(p): # + break + path = Path(p) + + # Method 2 (deprecated) + # dirs = glob.glob(f"{path}{sep}*") # similar paths + # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs] + # i = [int(m.groups()[0]) for m in matches if m] # indices + # n = max(i) + 1 if i else 2 # increment number + # path = Path(f"{path}{sep}{n}{suffix}") # increment path + + if mkdir: + path.mkdir(parents=True, exist_ok=True) # make directory + + return path + + +# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------ +imshow_ = cv2.imshow # copy to avoid recursion errors + + +def imread(path, flags=cv2.IMREAD_COLOR): + return cv2.imdecode(np.fromfile(path, np.uint8), flags) + + +def imwrite(path, im): + try: + cv2.imencode(Path(path).suffix, im)[1].tofile(path) + return True + except Exception: + return False + + +def imshow(path, im): + imshow_(path.encode('unicode_escape').decode(), im) + + +cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine + +# Variables ------------------------------------------------------------------------------------------------------------ diff --git a/algorithm/yolov5-master/utils/google_app_engine/Dockerfile b/algorithm/yolov5-master/utils/google_app_engine/Dockerfile new file mode 100644 index 0000000..0155618 --- /dev/null +++ b/algorithm/yolov5-master/utils/google_app_engine/Dockerfile @@ -0,0 +1,25 @@ +FROM gcr.io/google-appengine/python + +# Create a virtualenv for dependencies. This isolates these packages from +# system-level packages. +# Use -p python3 or -p python3.7 to select python version. Default is version 2. +RUN virtualenv /env -p python3 + +# Setting these environment variables are the same as running +# source /env/bin/activate. +ENV VIRTUAL_ENV /env +ENV PATH /env/bin:$PATH + +RUN apt-get update && apt-get install -y python-opencv + +# Copy the application's requirements.txt and run pip to install all +# dependencies into the virtualenv. +ADD requirements.txt /app/requirements.txt +RUN pip install -r /app/requirements.txt + +# Add the application source code. +ADD . /app + +# Run a WSGI server to serve the application. gunicorn must be declared as +# a dependency in requirements.txt. +CMD gunicorn -b :$PORT main:app diff --git a/algorithm/yolov5-master/utils/google_app_engine/additional_requirements.txt b/algorithm/yolov5-master/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 0000000..d5b7675 --- /dev/null +++ b/algorithm/yolov5-master/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,5 @@ +# add these requirements in your app on top of the existing ones +pip==21.1 +Flask==1.0.2 +gunicorn==19.10.0 +werkzeug>=2.2.3 # not directly required, pinned by Snyk to avoid a vulnerability diff --git a/algorithm/yolov5-master/utils/google_app_engine/app.yaml b/algorithm/yolov5-master/utils/google_app_engine/app.yaml new file mode 100644 index 0000000..5056b7c --- /dev/null +++ b/algorithm/yolov5-master/utils/google_app_engine/app.yaml @@ -0,0 +1,14 @@ +runtime: custom +env: flex + +service: yolov5app + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 diff --git a/algorithm/yolov5-master/utils/loggers/__init__.py b/algorithm/yolov5-master/utils/loggers/__init__.py new file mode 100644 index 0000000..9de1f22 --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/__init__.py @@ -0,0 +1,401 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Logging utils +""" + +import os +import warnings +from pathlib import Path + +import pkg_resources as pkg +import torch +from torch.utils.tensorboard import SummaryWriter + +from utils.general import LOGGER, colorstr, cv2 +from utils.loggers.clearml.clearml_utils import ClearmlLogger +from utils.loggers.wandb.wandb_utils import WandbLogger +from utils.plots import plot_images, plot_labels, plot_results +from utils.torch_utils import de_parallel + +LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet') # *.csv, TensorBoard, Weights & Biases, ClearML +RANK = int(os.getenv('RANK', -1)) + +try: + import wandb + + assert hasattr(wandb, '__version__') # verify package import not local dir + if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}: + try: + wandb_login_success = wandb.login(timeout=30) + except wandb.errors.UsageError: # known non-TTY terminal issue + wandb_login_success = False + if not wandb_login_success: + wandb = None +except (ImportError, AssertionError): + wandb = None + +try: + import clearml + + assert hasattr(clearml, '__version__') # verify package import not local dir +except (ImportError, AssertionError): + clearml = None + +try: + if RANK not in [0, -1]: + comet_ml = None + else: + import comet_ml + + assert hasattr(comet_ml, '__version__') # verify package import not local dir + from utils.loggers.comet import CometLogger + +except (ModuleNotFoundError, ImportError, AssertionError): + comet_ml = None + + +class Loggers(): + # YOLOv5 Loggers class + def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): + self.save_dir = save_dir + self.weights = weights + self.opt = opt + self.hyp = hyp + self.plots = not opt.noplots # plot results + self.logger = logger # for printing results to console + self.include = include + self.keys = [ + 'train/box_loss', + 'train/obj_loss', + 'train/cls_loss', # train loss + 'metrics/precision', + 'metrics/recall', + 'metrics/mAP_0.5', + 'metrics/mAP_0.5:0.95', # metrics + 'val/box_loss', + 'val/obj_loss', + 'val/cls_loss', # val loss + 'x/lr0', + 'x/lr1', + 'x/lr2'] # params + self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95'] + for k in LOGGERS: + setattr(self, k, None) # init empty logger dictionary + self.csv = True # always log to csv + + # Messages + if not clearml: + prefix = colorstr('ClearML: ') + s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML" + self.logger.info(s) + if not comet_ml: + prefix = colorstr('Comet: ') + s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet" + self.logger.info(s) + # TensorBoard + s = self.save_dir + if 'tb' in self.include and not self.opt.evolve: + prefix = colorstr('TensorBoard: ') + self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/") + self.tb = SummaryWriter(str(s)) + + # W&B + if wandb and 'wandb' in self.include: + self.opt.hyp = self.hyp # add hyperparameters + self.wandb = WandbLogger(self.opt) + else: + self.wandb = None + + # ClearML + if clearml and 'clearml' in self.include: + try: + self.clearml = ClearmlLogger(self.opt, self.hyp) + except Exception: + self.clearml = None + prefix = colorstr('ClearML: ') + LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' + f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme') + + else: + self.clearml = None + + # Comet + if comet_ml and 'comet' in self.include: + if isinstance(self.opt.resume, str) and self.opt.resume.startswith('comet://'): + run_id = self.opt.resume.split('/')[-1] + self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id) + + else: + self.comet_logger = CometLogger(self.opt, self.hyp) + + else: + self.comet_logger = None + + @property + def remote_dataset(self): + # Get data_dict if custom dataset artifact link is provided + data_dict = None + if self.clearml: + data_dict = self.clearml.data_dict + if self.wandb: + data_dict = self.wandb.data_dict + if self.comet_logger: + data_dict = self.comet_logger.data_dict + + return data_dict + + def on_train_start(self): + if self.comet_logger: + self.comet_logger.on_train_start() + + def on_pretrain_routine_start(self): + if self.comet_logger: + self.comet_logger.on_pretrain_routine_start() + + def on_pretrain_routine_end(self, labels, names): + # Callback runs on pre-train routine end + if self.plots: + plot_labels(labels, names, self.save_dir) + paths = self.save_dir.glob('*labels*.jpg') # training labels + if self.wandb: + self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for x in paths]}) + # if self.clearml: + # pass # ClearML saves these images automatically using hooks + if self.comet_logger: + self.comet_logger.on_pretrain_routine_end(paths) + + def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): + log_dict = dict(zip(self.keys[:3], vals)) + # Callback runs on train batch end + # ni: number integrated batches (since train start) + if self.plots: + if ni < 3: + f = self.save_dir / f'train_batch{ni}.jpg' # filename + plot_images(imgs, targets, paths, f) + if ni == 0 and self.tb and not self.opt.sync_bn: + log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)) + if ni == 10 and (self.wandb or self.clearml): + files = sorted(self.save_dir.glob('train*.jpg')) + if self.wandb: + self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) + if self.clearml: + self.clearml.log_debug_samples(files, title='Mosaics') + + if self.comet_logger: + self.comet_logger.on_train_batch_end(log_dict, step=ni) + + def on_train_epoch_end(self, epoch): + # Callback runs on train epoch end + if self.wandb: + self.wandb.current_epoch = epoch + 1 + + if self.comet_logger: + self.comet_logger.on_train_epoch_end(epoch) + + def on_val_start(self): + if self.comet_logger: + self.comet_logger.on_val_start() + + def on_val_image_end(self, pred, predn, path, names, im): + # Callback runs on val image end + if self.wandb: + self.wandb.val_one_image(pred, predn, path, names, im) + if self.clearml: + self.clearml.log_image_with_boxes(path, pred, names, im) + + def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): + if self.comet_logger: + self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out) + + def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + # Callback runs on val end + if self.wandb or self.clearml: + files = sorted(self.save_dir.glob('val*.jpg')) + if self.wandb: + self.wandb.log({'Validation': [wandb.Image(str(f), caption=f.name) for f in files]}) + if self.clearml: + self.clearml.log_debug_samples(files, title='Validation') + + if self.comet_logger: + self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) + + def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): + # Callback runs at the end of each fit (train+val) epoch + x = dict(zip(self.keys, vals)) + if self.csv: + file = self.save_dir / 'results.csv' + n = len(x) + 1 # number of cols + s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header + with open(file, 'a') as f: + f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') + + if self.tb: + for k, v in x.items(): + self.tb.add_scalar(k, v, epoch) + elif self.clearml: # log to ClearML if TensorBoard not used + for k, v in x.items(): + title, series = k.split('/') + self.clearml.task.get_logger().report_scalar(title, series, v, epoch) + + if self.wandb: + if best_fitness == fi: + best_results = [epoch] + vals[3:7] + for i, name in enumerate(self.best_keys): + self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary + self.wandb.log(x) + self.wandb.end_epoch() + + if self.clearml: + self.clearml.current_epoch_logged_images = set() # reset epoch image limit + self.clearml.current_epoch += 1 + + if self.comet_logger: + self.comet_logger.on_fit_epoch_end(x, epoch=epoch) + + def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + # Callback runs on model save event + if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1: + if self.wandb: + self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) + if self.clearml: + self.clearml.task.update_output_model(model_path=str(last), + model_name='Latest Model', + auto_delete_file=False) + + if self.comet_logger: + self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) + + def on_train_end(self, last, best, epoch, results): + # Callback runs on training end, i.e. saving best model + if self.plots: + plot_results(file=self.save_dir / 'results.csv') # save results.png + files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] + files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter + self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}") + + if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles + for f in files: + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') + + if self.wandb: + self.wandb.log(dict(zip(self.keys[3:10], results))) + self.wandb.log({'Results': [wandb.Image(str(f), caption=f.name) for f in files]}) + # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model + if not self.opt.evolve: + wandb.log_artifact(str(best if best.exists() else last), + type='model', + name=f'run_{self.wandb.wandb_run.id}_model', + aliases=['latest', 'best', 'stripped']) + self.wandb.finish_run() + + if self.clearml and not self.opt.evolve: + self.clearml.task.update_output_model(model_path=str(best if best.exists() else last), + name='Best Model', + auto_delete_file=False) + + if self.comet_logger: + final_results = dict(zip(self.keys[3:10], results)) + self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results) + + def on_params_update(self, params: dict): + # Update hyperparams or configs of the experiment + if self.wandb: + self.wandb.wandb_run.config.update(params, allow_val_change=True) + if self.comet_logger: + self.comet_logger.on_params_update(params) + + +class GenericLogger: + """ + YOLOv5 General purpose logger for non-task specific logging + Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...) + Arguments + opt: Run arguments + console_logger: Console logger + include: loggers to include + """ + + def __init__(self, opt, console_logger, include=('tb', 'wandb')): + # init default loggers + self.save_dir = Path(opt.save_dir) + self.include = include + self.console_logger = console_logger + self.csv = self.save_dir / 'results.csv' # CSV logger + if 'tb' in self.include: + prefix = colorstr('TensorBoard: ') + self.console_logger.info( + f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/") + self.tb = SummaryWriter(str(self.save_dir)) + + if wandb and 'wandb' in self.include: + self.wandb = wandb.init(project=web_project_name(str(opt.project)), + name=None if opt.name == 'exp' else opt.name, + config=opt) + else: + self.wandb = None + + def log_metrics(self, metrics, epoch): + # Log metrics dictionary to all loggers + if self.csv: + keys, vals = list(metrics.keys()), list(metrics.values()) + n = len(metrics) + 1 # number of cols + s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header + with open(self.csv, 'a') as f: + f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') + + if self.tb: + for k, v in metrics.items(): + self.tb.add_scalar(k, v, epoch) + + if self.wandb: + self.wandb.log(metrics, step=epoch) + + def log_images(self, files, name='Images', epoch=0): + # Log images to all loggers + files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path + files = [f for f in files if f.exists()] # filter by exists + + if self.tb: + for f in files: + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') + + if self.wandb: + self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) + + def log_graph(self, model, imgsz=(640, 640)): + # Log model graph to all loggers + if self.tb: + log_tensorboard_graph(self.tb, model, imgsz) + + def log_model(self, model_path, epoch=0, metadata={}): + # Log model to all loggers + if self.wandb: + art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata) + art.add_file(str(model_path)) + wandb.log_artifact(art) + + def update_params(self, params): + # Update the parameters logged + if self.wandb: + wandb.run.config.update(params, allow_val_change=True) + + +def log_tensorboard_graph(tb, model, imgsz=(640, 640)): + # Log model graph to TensorBoard + try: + p = next(model.parameters()) # for device, type + imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand + im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress jit trace warning + tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) + except Exception as e: + LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}') + + +def web_project_name(project): + # Convert local project name to web project name + if not project.startswith('runs/train'): + return project + suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else '' + return f'YOLOv5{suffix}' diff --git a/algorithm/yolov5-master/utils/loggers/clearml/README.md b/algorithm/yolov5-master/utils/loggers/clearml/README.md new file mode 100644 index 0000000..ca41c04 --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/clearml/README.md @@ -0,0 +1,237 @@ +# ClearML Integration + +Clear|MLClear|ML + +## About ClearML + +[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️. + +🔨 Track every YOLOv5 training run in the experiment manager + +🔧 Version and easily access your custom training data with the integrated ClearML Data Versioning Tool + +🔦 Remotely train and monitor your YOLOv5 training runs using ClearML Agent + +🔬 Get the very best mAP using ClearML Hyperparameter Optimization + +🔭 Turn your newly trained YOLOv5 model into an API with just a few commands using ClearML Serving + +
+And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline! +
+
+ +![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif) + +
+
+ +## 🦾 Setting Things Up + +To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one: + +Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go! + +1. Install the `clearml` python package: + + ```bash + pip install clearml + ``` + +1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions: + + ```bash + clearml-init + ``` + +That's it! You're done 😎 + +
+ +## 🚀 Training YOLOv5 With ClearML + +To enable ClearML experiment tracking, simply install the ClearML pip package. + +```bash +pip install clearml>=1.2.0 +``` + +This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager. + +If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. +PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name! + +```bash +python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache +``` + +or with custom project and task name: + +```bash +python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache +``` + +This will capture: + +- Source code + uncommitted changes +- Installed packages +- (Hyper)parameters +- Model files (use `--save-period n` to save a checkpoint every n epochs) +- Console output +- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...) +- General info such as machine details, runtime, creation date etc. +- All produced plots such as label correlogram and confusion matrix +- Images with bounding boxes per epoch +- Mosaic per epoch +- Validation images per epoch +- ... + +That's a lot right? 🤯 +Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them! + +There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works! + +
+ +## 🔗 Dataset Version Management + +Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment! + +![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif) + +### Prepare Your Dataset + +The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure: + +``` +.. +|_ yolov5 +|_ datasets + |_ coco128 + |_ images + |_ labels + |_ LICENSE + |_ README.txt +``` + +But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure. + +Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls. + +Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`. + +``` +.. +|_ yolov5 +|_ datasets + |_ coco128 + |_ images + |_ labels + |_ coco128.yaml # <---- HERE! + |_ LICENSE + |_ README.txt +``` + +### Upload Your Dataset + +To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command: + +```bash +cd coco128 +clearml-data sync --project YOLOv5 --name coco128 --folder . +``` + +The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other: + +```bash +# Optionally add --parent if you want to base +# this version on another dataset version, so no duplicate files are uploaded! +clearml-data create --name coco128 --project YOLOv5 +clearml-data add --files . +clearml-data close +``` + +### Run Training Using A ClearML Dataset + +Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models! + +```bash +python train.py --img 640 --batch 16 --epochs 3 --data clearml:// --weights yolov5s.pt --cache +``` + +
+ +## 👀 Hyperparameter Optimization + +Now that we have our experiments and data versioned, it's time to take a look at what we can build on top! + +Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does! + +To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters. + +You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead. + +```bash +# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch +pip install optuna +python utils/loggers/clearml/hpo.py +``` + +![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png) + +## 🤯 Remote Execution (advanced) + +Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. +This is where the ClearML Agent comes into play. Check out what the agent can do here: + +- [YouTube video](https://youtu.be/MX3BrXnaULs) +- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent) + +In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager. + +You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running: + +```bash +clearml-agent daemon --queue [--docker] +``` + +### Cloning, Editing And Enqueuing + +With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too! + +🪄 Clone the experiment by right-clicking it + +🎯 Edit the hyperparameters to what you wish them to be + +⏳ Enqueue the task to any of the queues by right-clicking it + +![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif) + +### Executing A Task Remotely + +Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on! + +To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated: + +```python +# ... +# Loggers +data_dict = None +if RANK in {-1, 0}: + loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance + if loggers.clearml: + loggers.clearml.task.execute_remotely(queue="my_queue") # <------ ADD THIS LINE + # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML + data_dict = loggers.clearml.data_dict +# ... +``` + +When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead! + +### Autoscaling workers + +ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying! + +Check out the autoscalers getting started video below. + +[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E) diff --git a/algorithm/yolov5-master/utils/loggers/clearml/__init__.py b/algorithm/yolov5-master/utils/loggers/clearml/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5-master/utils/loggers/clearml/clearml_utils.py b/algorithm/yolov5-master/utils/loggers/clearml/clearml_utils.py new file mode 100644 index 0000000..2764abe --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/clearml/clearml_utils.py @@ -0,0 +1,164 @@ +"""Main Logger class for ClearML experiment tracking.""" +import glob +import re +from pathlib import Path + +import numpy as np +import yaml + +from utils.plots import Annotator, colors + +try: + import clearml + from clearml import Dataset, Task + + assert hasattr(clearml, '__version__') # verify package import not local dir +except (ImportError, AssertionError): + clearml = None + + +def construct_dataset(clearml_info_string): + """Load in a clearml dataset and fill the internal data_dict with its contents. + """ + dataset_id = clearml_info_string.replace('clearml://', '') + dataset = Dataset.get(dataset_id=dataset_id) + dataset_root_path = Path(dataset.get_local_copy()) + + # We'll search for the yaml file definition in the dataset + yaml_filenames = list(glob.glob(str(dataset_root_path / '*.yaml')) + glob.glob(str(dataset_root_path / '*.yml'))) + if len(yaml_filenames) > 1: + raise ValueError('More than one yaml file was found in the dataset root, cannot determine which one contains ' + 'the dataset definition this way.') + elif len(yaml_filenames) == 0: + raise ValueError('No yaml definition found in dataset root path, check that there is a correct yaml file ' + 'inside the dataset root path.') + with open(yaml_filenames[0]) as f: + dataset_definition = yaml.safe_load(f) + + assert set(dataset_definition.keys()).issuperset( + {'train', 'test', 'val', 'nc', 'names'} + ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" + + data_dict = dict() + data_dict['train'] = str( + (dataset_root_path / dataset_definition['train']).resolve()) if dataset_definition['train'] else None + data_dict['test'] = str( + (dataset_root_path / dataset_definition['test']).resolve()) if dataset_definition['test'] else None + data_dict['val'] = str( + (dataset_root_path / dataset_definition['val']).resolve()) if dataset_definition['val'] else None + data_dict['nc'] = dataset_definition['nc'] + data_dict['names'] = dataset_definition['names'] + + return data_dict + + +class ClearmlLogger: + """Log training runs, datasets, models, and predictions to ClearML. + + This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, + this information includes hyperparameters, system configuration and metrics, model metrics, code information and + basic data metrics and analyses. + + By providing additional command line arguments to train.py, datasets, + models and predictions can also be logged. + """ + + def __init__(self, opt, hyp): + """ + - Initialize ClearML Task, this object will capture the experiment + - Upload dataset version to ClearML Data if opt.upload_dataset is True + + arguments: + opt (namespace) -- Commandline arguments for this run + hyp (dict) -- Hyperparameters for this run + + """ + self.current_epoch = 0 + # Keep tracked of amount of logged images to enforce a limit + self.current_epoch_logged_images = set() + # Maximum number of images to log to clearML per epoch + self.max_imgs_to_log_per_epoch = 16 + # Get the interval of epochs when bounding box images should be logged + self.bbox_interval = opt.bbox_interval + self.clearml = clearml + self.task = None + self.data_dict = None + if self.clearml: + self.task = Task.init( + project_name=opt.project if opt.project != 'runs/train' else 'YOLOv5', + task_name=opt.name if opt.name != 'exp' else 'Training', + tags=['YOLOv5'], + output_uri=True, + reuse_last_task_id=opt.exist_ok, + auto_connect_frameworks={'pytorch': False} + # We disconnect pytorch auto-detection, because we added manual model save points in the code + ) + # ClearML's hooks will already grab all general parameters + # Only the hyperparameters coming from the yaml config file + # will have to be added manually! + self.task.connect(hyp, name='Hyperparameters') + self.task.connect(opt, name='Args') + + # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent + self.task.set_base_docker('ultralytics/yolov5:latest', + docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', + docker_setup_bash_script='pip install clearml') + + # Get ClearML Dataset Version if requested + if opt.data.startswith('clearml://'): + # data_dict should have the following keys: + # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) + self.data_dict = construct_dataset(opt.data) + # Set data to data_dict because wandb will crash without this information and opt is the best way + # to give it to them + opt.data = self.data_dict + + def log_debug_samples(self, files, title='Debug Samples'): + """ + Log files (images) as debug samples in the ClearML task. + + arguments: + files (List(PosixPath)) a list of file paths in PosixPath format + title (str) A title that groups together images with the same values + """ + for f in files: + if f.exists(): + it = re.search(r'_batch(\d+)', f.name) + iteration = int(it.groups()[0]) if it else 0 + self.task.get_logger().report_image(title=title, + series=f.name.replace(it.group(), ''), + local_path=str(f), + iteration=iteration) + + def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): + """ + Draw the bounding boxes on a single image and report the result as a ClearML debug sample. + + arguments: + image_path (PosixPath) the path the original image file + boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] + class_names (dict): dict containing mapping of class int to class name + image (Tensor): A torch tensor containing the actual image data + """ + if len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0: + # Log every bbox_interval times and deduplicate for any intermittend extra eval runs + if self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images: + im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2)) + annotator = Annotator(im=im, pil=True) + for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])): + color = colors(i) + + class_name = class_names[int(class_nr)] + confidence_percentage = round(float(conf) * 100, 2) + label = f'{class_name}: {confidence_percentage}%' + + if conf > conf_threshold: + annotator.rectangle(box.cpu().numpy(), outline=color) + annotator.box_label(box.cpu().numpy(), label=label, color=color) + + annotated_image = annotator.result() + self.task.get_logger().report_image(title='Bounding Boxes', + series=image_path.name, + iteration=self.current_epoch, + image=annotated_image) + self.current_epoch_logged_images.add(image_path) diff --git a/algorithm/yolov5-master/utils/loggers/clearml/hpo.py b/algorithm/yolov5-master/utils/loggers/clearml/hpo.py new file mode 100644 index 0000000..ee518b0 --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/clearml/hpo.py @@ -0,0 +1,84 @@ +from clearml import Task +# Connecting ClearML with the current process, +# from here on everything is logged automatically +from clearml.automation import HyperParameterOptimizer, UniformParameterRange +from clearml.automation.optuna import OptimizerOptuna + +task = Task.init(project_name='Hyper-Parameter Optimization', + task_name='YOLOv5', + task_type=Task.TaskTypes.optimizer, + reuse_last_task_id=False) + +# Example use case: +optimizer = HyperParameterOptimizer( + # This is the experiment we want to optimize + base_task_id='', + # here we define the hyper-parameters to optimize + # Notice: The parameter name should exactly match what you see in the UI: / + # For Example, here we see in the base experiment a section Named: "General" + # under it a parameter named "batch_size", this becomes "General/batch_size" + # If you have `argparse` for example, then arguments will appear under the "Args" section, + # and you should instead pass "Args/batch_size" + hyper_parameters=[ + UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1), + UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0), + UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98), + UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001), + UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0), + UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95), + UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2), + UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2), + UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0), + UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0), + UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0), + UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0), + UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7), + UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0), + UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0), + UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1), + UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9), + UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9), + UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0), + UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9), + UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9), + UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0), + UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001), + UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0), + UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0), + UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0), + UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0), + UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)], + # this is the objective metric we want to maximize/minimize + objective_metric_title='metrics', + objective_metric_series='mAP_0.5', + # now we decide if we want to maximize it or minimize it (accuracy we maximize) + objective_metric_sign='max', + # let us limit the number of concurrent experiments, + # this in turn will make sure we do dont bombard the scheduler with experiments. + # if we have an auto-scaler connected, this, by proxy, will limit the number of machine + max_number_of_concurrent_tasks=1, + # this is the optimizer class (actually doing the optimization) + # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band) + optimizer_class=OptimizerOptuna, + # If specified only the top K performing Tasks will be kept, the others will be automatically archived + save_top_k_tasks_only=5, # 5, + compute_time_limit=None, + total_max_jobs=20, + min_iteration_per_job=None, + max_iteration_per_job=None, +) + +# report every 10 seconds, this is way too often, but we are testing here +optimizer.set_report_period(10 / 60) +# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent +# an_optimizer.start_locally(job_complete_callback=job_complete_callback) +# set the time limit for the optimization process (2 hours) +optimizer.set_time_limit(in_minutes=120.0) +# Start the optimization process in the local environment +optimizer.start_locally() +# wait until process is done (notice we are controlling the optimization process in the background) +optimizer.wait() +# make sure background optimization stopped +optimizer.stop() + +print('We are done, good bye') diff --git a/algorithm/yolov5-master/utils/loggers/comet/README.md b/algorithm/yolov5-master/utils/loggers/comet/README.md new file mode 100644 index 0000000..47e6a45 --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/comet/README.md @@ -0,0 +1,258 @@ + + +# YOLOv5 with Comet + +This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2) + +# About Comet + +Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models. + +Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! +Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! + +# Getting Started + +## Install Comet + +```shell +pip install comet_ml +``` + +## Configure Comet Credentials + +There are two ways to configure Comet with YOLOv5. + +You can either set your credentials through environment variables + +**Environment Variables** + +```shell +export COMET_API_KEY= +export COMET_PROJECT_NAME= # This will default to 'yolov5' +``` + +Or create a `.comet.config` file in your working directory and set your credentials there. + +**Comet Configuration File** + +``` +[comet] +api_key= +project_name= # This will default to 'yolov5' +``` + +## Run the Training Script + +```shell +# Train YOLOv5s on COCO128 for 5 epochs +python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt +``` + +That's it! Comet will automatically log your hyperparameters, command line arguments, training and validation metrics. You can visualize and analyze your runs in the Comet UI + +yolo-ui + +# Try out an Example! + +Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +Or better yet, try it out yourself in this Colab Notebook + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing) + +# Log automatically + +By default, Comet will log the following items + +## Metrics + +- Box Loss, Object Loss, Classification Loss for the training and validation data +- mAP_0.5, mAP_0.5:0.95 metrics for the validation data. +- Precision and Recall for the validation data + +## Parameters + +- Model Hyperparameters +- All parameters passed through the command line options + +## Visualizations + +- Confusion Matrix of the model predictions on the validation data +- Plots for the PR and F1 curves across all classes +- Correlogram of the Class Labels + +# Configure Comet Logging + +Comet can be configured to log additional data either through command line flags passed to the training script +or through environment variables. + +```shell +export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online +export COMET_MODEL_NAME= #Set the name for the saved model. Defaults to yolov5 +export COMET_LOG_CONFUSION_MATRIX=false # Set to disable logging a Comet Confusion Matrix. Defaults to true +export COMET_MAX_IMAGE_UPLOADS= # Controls how many total image predictions to log to Comet. Defaults to 100. +export COMET_LOG_PER_CLASS_METRICS=true # Set to log evaluation metrics for each detected class at the end of training. Defaults to false +export COMET_DEFAULT_CHECKPOINT_FILENAME= # Set this if you would like to resume training from a different checkpoint. Defaults to 'last.pt' +export COMET_LOG_BATCH_LEVEL_METRICS=true # Set this if you would like to log training metrics at the batch level. Defaults to false. +export COMET_LOG_PREDICTIONS=true # Set this to false to disable logging model predictions +``` + +## Logging Checkpoints with Comet + +Logging Models to Comet is disabled by default. To enable it, pass the `save-period` argument to the training script. This will save the +logged checkpoints to Comet based on the interval value provided by `save-period` + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--save-period 1 +``` + +## Logging Model Predictions + +By default, model predictions (images, ground truth labels and bounding boxes) will be logged to Comet. + +You can control the frequency of logged predictions and the associated images by passing the `bbox_interval` command line argument. Predictions can be visualized using Comet's Object Detection Custom Panel. This frequency corresponds to every Nth batch of data per epoch. In the example below, we are logging every 2nd batch of data for each epoch. + +**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly. + +Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--bbox_interval 2 +``` + +### Controlling the number of Prediction Images logged to Comet + +When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable. + +```shell +env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--bbox_interval 1 +``` + +### Logging Class Level Metrics + +Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision, recall, f1 for each class. + +```shell +env COMET_LOG_PER_CLASS_METRICS=true python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt +``` + +## Uploading a Dataset to Comet Artifacts + +If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag. + +The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--upload_dataset +``` + +You can find the uploaded dataset in the Artifacts tab in your Comet Workspace +artifact-1 + +You can preview the data directly in the Comet UI. +artifact-2 + +Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file +artifact-3 + +### Using a saved Artifact + +If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL. + +``` +# contents of artifact.yaml file +path: "comet:///:" +``` + +Then pass this file to your training script in the following way + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data artifact.yaml \ +--weights yolov5s.pt +``` + +Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. +artifact-4 + +## Resuming a Training Run + +If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path. + +The Run Path has the following format `comet:////`. + +This will restore the run to its state before the interruption, which includes restoring the model from a checkpoint, restoring all hyperparameters and training arguments and downloading Comet dataset Artifacts if they were used in the original run. The resumed run will continue logging to the existing Experiment in the Comet UI + +```shell +python train.py \ +--resume "comet://" +``` + +## Hyperparameter Search with the Comet Optimizer + +YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualize hyperparameter sweeps in the Comet UI. + +### Configuring an Optimizer Sweep + +To configure the Comet Optimizer, you will have to create a JSON file with the information about the sweep. An example file has been provided in `utils/loggers/comet/optimizer_config.json` + +```shell +python utils/loggers/comet/hpo.py \ + --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" +``` + +The `hpo.py` script accepts the same arguments as `train.py`. If you wish to pass additional arguments to your sweep simply add them after +the script. + +```shell +python utils/loggers/comet/hpo.py \ + --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" \ + --save-period 1 \ + --bbox_interval 1 +``` + +### Running a Sweep in Parallel + +```shell +comet optimizer -j utils/loggers/comet/hpo.py \ + utils/loggers/comet/optimizer_config.json" +``` + +### Visualizing Results + +Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +hyperparameter-yolo diff --git a/algorithm/yolov5-master/utils/loggers/comet/__init__.py b/algorithm/yolov5-master/utils/loggers/comet/__init__.py new file mode 100644 index 0000000..d459984 --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/comet/__init__.py @@ -0,0 +1,508 @@ +import glob +import json +import logging +import os +import sys +from pathlib import Path + +logger = logging.getLogger(__name__) + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +try: + import comet_ml + + # Project Configuration + config = comet_ml.config.get_config() + COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') +except (ModuleNotFoundError, ImportError): + comet_ml = None + COMET_PROJECT_NAME = None + +import PIL +import torch +import torchvision.transforms as T +import yaml + +from utils.dataloaders import img2label_paths +from utils.general import check_dataset, scale_boxes, xywh2xyxy +from utils.metrics import box_iou + +COMET_PREFIX = 'comet://' + +COMET_MODE = os.getenv('COMET_MODE', 'online') + +# Model Saving Settings +COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') + +# Dataset Artifact Settings +COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true' + +# Evaluation Settings +COMET_LOG_CONFUSION_MATRIX = os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true' +COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true' +COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100)) + +# Confusion Matrix Settings +CONF_THRES = float(os.getenv('CONF_THRES', 0.001)) +IOU_THRES = float(os.getenv('IOU_THRES', 0.6)) + +# Batch Logging Settings +COMET_LOG_BATCH_METRICS = os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true' +COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1) +COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1) +COMET_LOG_PER_CLASS_METRICS = os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true' + +RANK = int(os.getenv('RANK', -1)) + +to_pil = T.ToPILImage() + + +class CometLogger: + """Log metrics, parameters, source code, models and much more + with Comet + """ + + def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwargs) -> None: + self.job_type = job_type + self.opt = opt + self.hyp = hyp + + # Comet Flags + self.comet_mode = COMET_MODE + + self.save_model = opt.save_period > -1 + self.model_name = COMET_MODEL_NAME + + # Batch Logging Settings + self.log_batch_metrics = COMET_LOG_BATCH_METRICS + self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL + + # Dataset Artifact Settings + self.upload_dataset = self.opt.upload_dataset if self.opt.upload_dataset else COMET_UPLOAD_DATASET + self.resume = self.opt.resume + + # Default parameters to pass to Experiment objects + self.default_experiment_kwargs = { + 'log_code': False, + 'log_env_gpu': True, + 'log_env_cpu': True, + 'project_name': COMET_PROJECT_NAME,} + self.default_experiment_kwargs.update(experiment_kwargs) + self.experiment = self._get_experiment(self.comet_mode, run_id) + + self.data_dict = self.check_dataset(self.opt.data) + self.class_names = self.data_dict['names'] + self.num_classes = self.data_dict['nc'] + + self.logged_images_count = 0 + self.max_images = COMET_MAX_IMAGE_UPLOADS + + if run_id is None: + self.experiment.log_other('Created from', 'YOLOv5') + if not isinstance(self.experiment, comet_ml.OfflineExperiment): + workspace, project_name, experiment_id = self.experiment.url.split('/')[-3:] + self.experiment.log_other( + 'Run Path', + f'{workspace}/{project_name}/{experiment_id}', + ) + self.log_parameters(vars(opt)) + self.log_parameters(self.opt.hyp) + self.log_asset_data( + self.opt.hyp, + name='hyperparameters.json', + metadata={'type': 'hyp-config-file'}, + ) + self.log_asset( + f'{self.opt.save_dir}/opt.yaml', + metadata={'type': 'opt-config-file'}, + ) + + self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX + + if hasattr(self.opt, 'conf_thres'): + self.conf_thres = self.opt.conf_thres + else: + self.conf_thres = CONF_THRES + if hasattr(self.opt, 'iou_thres'): + self.iou_thres = self.opt.iou_thres + else: + self.iou_thres = IOU_THRES + + self.log_parameters({'val_iou_threshold': self.iou_thres, 'val_conf_threshold': self.conf_thres}) + + self.comet_log_predictions = COMET_LOG_PREDICTIONS + if self.opt.bbox_interval == -1: + self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10 + else: + self.comet_log_prediction_interval = self.opt.bbox_interval + + if self.comet_log_predictions: + self.metadata_dict = {} + self.logged_image_names = [] + + self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS + + self.experiment.log_others({ + 'comet_mode': COMET_MODE, + 'comet_max_image_uploads': COMET_MAX_IMAGE_UPLOADS, + 'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS, + 'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS, + 'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX, + 'comet_model_name': COMET_MODEL_NAME,}) + + # Check if running the Experiment with the Comet Optimizer + if hasattr(self.opt, 'comet_optimizer_id'): + self.experiment.log_other('optimizer_id', self.opt.comet_optimizer_id) + self.experiment.log_other('optimizer_objective', self.opt.comet_optimizer_objective) + self.experiment.log_other('optimizer_metric', self.opt.comet_optimizer_metric) + self.experiment.log_other('optimizer_parameters', json.dumps(self.hyp)) + + def _get_experiment(self, mode, experiment_id=None): + if mode == 'offline': + if experiment_id is not None: + return comet_ml.ExistingOfflineExperiment( + previous_experiment=experiment_id, + **self.default_experiment_kwargs, + ) + + return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,) + + else: + try: + if experiment_id is not None: + return comet_ml.ExistingExperiment( + previous_experiment=experiment_id, + **self.default_experiment_kwargs, + ) + + return comet_ml.Experiment(**self.default_experiment_kwargs) + + except ValueError: + logger.warning('COMET WARNING: ' + 'Comet credentials have not been set. ' + 'Comet will default to offline logging. ' + 'Please set your credentials to enable online logging.') + return self._get_experiment('offline', experiment_id) + + return + + def log_metrics(self, log_dict, **kwargs): + self.experiment.log_metrics(log_dict, **kwargs) + + def log_parameters(self, log_dict, **kwargs): + self.experiment.log_parameters(log_dict, **kwargs) + + def log_asset(self, asset_path, **kwargs): + self.experiment.log_asset(asset_path, **kwargs) + + def log_asset_data(self, asset, **kwargs): + self.experiment.log_asset_data(asset, **kwargs) + + def log_image(self, img, **kwargs): + self.experiment.log_image(img, **kwargs) + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + if not self.save_model: + return + + model_metadata = { + 'fitness_score': fitness_score[-1], + 'epochs_trained': epoch + 1, + 'save_period': opt.save_period, + 'total_epochs': opt.epochs,} + + model_files = glob.glob(f'{path}/*.pt') + for model_path in model_files: + name = Path(model_path).name + + self.experiment.log_model( + self.model_name, + file_or_folder=model_path, + file_name=name, + metadata=model_metadata, + overwrite=True, + ) + + def check_dataset(self, data_file): + with open(data_file) as f: + data_config = yaml.safe_load(f) + + if data_config['path'].startswith(COMET_PREFIX): + path = data_config['path'].replace(COMET_PREFIX, '') + data_dict = self.download_dataset_artifact(path) + + return data_dict + + self.log_asset(self.opt.data, metadata={'type': 'data-config-file'}) + + return check_dataset(data_file) + + def log_predictions(self, image, labelsn, path, shape, predn): + if self.logged_images_count >= self.max_images: + return + detections = predn[predn[:, 4] > self.conf_thres] + iou = box_iou(labelsn[:, 1:], detections[:, :4]) + mask, _ = torch.where(iou > self.iou_thres) + if len(mask) == 0: + return + + filtered_detections = detections[mask] + filtered_labels = labelsn[mask] + + image_id = path.split('/')[-1].split('.')[0] + image_name = f'{image_id}_curr_epoch_{self.experiment.curr_epoch}' + if image_name not in self.logged_image_names: + native_scale_image = PIL.Image.open(path) + self.log_image(native_scale_image, name=image_name) + self.logged_image_names.append(image_name) + + metadata = [] + for cls, *xyxy in filtered_labels.tolist(): + metadata.append({ + 'label': f'{self.class_names[int(cls)]}-gt', + 'score': 100, + 'box': { + 'x': xyxy[0], + 'y': xyxy[1], + 'x2': xyxy[2], + 'y2': xyxy[3]},}) + for *xyxy, conf, cls in filtered_detections.tolist(): + metadata.append({ + 'label': f'{self.class_names[int(cls)]}', + 'score': conf * 100, + 'box': { + 'x': xyxy[0], + 'y': xyxy[1], + 'x2': xyxy[2], + 'y2': xyxy[3]},}) + + self.metadata_dict[image_name] = metadata + self.logged_images_count += 1 + + return + + def preprocess_prediction(self, image, labels, shape, pred): + nl, _ = labels.shape[0], pred.shape[0] + + # Predictions + if self.opt.single_cls: + pred[:, 5] = 0 + + predn = pred.clone() + scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) + + labelsn = None + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred + + return predn, labelsn + + def add_assets_to_artifact(self, artifact, path, asset_path, split): + img_paths = sorted(glob.glob(f'{asset_path}/*')) + label_paths = img2label_paths(img_paths) + + for image_file, label_file in zip(img_paths, label_paths): + image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file]) + + try: + artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split}) + artifact.add(label_file, logical_path=label_logical_path, metadata={'split': split}) + except ValueError as e: + logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.') + logger.error(f'COMET ERROR: {e}') + continue + + return artifact + + def upload_dataset_artifact(self): + dataset_name = self.data_dict.get('dataset_name', 'yolov5-dataset') + path = str((ROOT / Path(self.data_dict['path'])).resolve()) + + metadata = self.data_dict.copy() + for key in ['train', 'val', 'test']: + split_path = metadata.get(key) + if split_path is not None: + metadata[key] = split_path.replace(path, '') + + artifact = comet_ml.Artifact(name=dataset_name, artifact_type='dataset', metadata=metadata) + for key in metadata.keys(): + if key in ['train', 'val', 'test']: + if isinstance(self.upload_dataset, str) and (key != self.upload_dataset): + continue + + asset_path = self.data_dict.get(key) + if asset_path is not None: + artifact = self.add_assets_to_artifact(artifact, path, asset_path, key) + + self.experiment.log_artifact(artifact) + + return + + def download_dataset_artifact(self, artifact_path): + logged_artifact = self.experiment.get_artifact(artifact_path) + artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name) + logged_artifact.download(artifact_save_dir) + + metadata = logged_artifact.metadata + data_dict = metadata.copy() + data_dict['path'] = artifact_save_dir + + metadata_names = metadata.get('names') + if type(metadata_names) == dict: + data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()} + elif type(metadata_names) == list: + data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} + else: + raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary" + + data_dict = self.update_data_paths(data_dict) + return data_dict + + def update_data_paths(self, data_dict): + path = data_dict.get('path', '') + + for split in ['train', 'val', 'test']: + if data_dict.get(split): + split_path = data_dict.get(split) + data_dict[split] = (f'{path}/{split_path}' if isinstance(split, str) else [ + f'{path}/{x}' for x in split_path]) + + return data_dict + + def on_pretrain_routine_end(self, paths): + if self.opt.resume: + return + + for path in paths: + self.log_asset(str(path)) + + if self.upload_dataset: + if not self.resume: + self.upload_dataset_artifact() + + return + + def on_train_start(self): + self.log_parameters(self.hyp) + + def on_train_epoch_start(self): + return + + def on_train_epoch_end(self, epoch): + self.experiment.curr_epoch = epoch + + return + + def on_train_batch_start(self): + return + + def on_train_batch_end(self, log_dict, step): + self.experiment.curr_step = step + if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0): + self.log_metrics(log_dict, step=step) + + return + + def on_train_end(self, files, save_dir, last, best, epoch, results): + if self.comet_log_predictions: + curr_epoch = self.experiment.curr_epoch + self.experiment.log_asset_data(self.metadata_dict, 'image-metadata.json', epoch=curr_epoch) + + for f in files: + self.log_asset(f, metadata={'epoch': epoch}) + self.log_asset(f'{save_dir}/results.csv', metadata={'epoch': epoch}) + + if not self.opt.evolve: + model_path = str(best if best.exists() else last) + name = Path(model_path).name + if self.save_model: + self.experiment.log_model( + self.model_name, + file_or_folder=model_path, + file_name=name, + overwrite=True, + ) + + # Check if running Experiment with Comet Optimizer + if hasattr(self.opt, 'comet_optimizer_id'): + metric = results.get(self.opt.comet_optimizer_metric) + self.experiment.log_other('optimizer_metric_value', metric) + + self.finish_run() + + def on_val_start(self): + return + + def on_val_batch_start(self): + return + + def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): + if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)): + return + + for si, pred in enumerate(outputs): + if len(pred) == 0: + continue + + image = images[si] + labels = targets[targets[:, 0] == si, 1:] + shape = shapes[si] + path = paths[si] + predn, labelsn = self.preprocess_prediction(image, labels, shape, pred) + if labelsn is not None: + self.log_predictions(image, labelsn, path, shape, predn) + + return + + def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + if self.comet_log_per_class_metrics: + if self.num_classes > 1: + for i, c in enumerate(ap_class): + class_name = self.class_names[c] + self.experiment.log_metrics( + { + 'mAP@.5': ap50[i], + 'mAP@.5:.95': ap[i], + 'precision': p[i], + 'recall': r[i], + 'f1': f1[i], + 'true_positives': tp[i], + 'false_positives': fp[i], + 'support': nt[c]}, + prefix=class_name) + + if self.comet_log_confusion_matrix: + epoch = self.experiment.curr_epoch + class_names = list(self.class_names.values()) + class_names.append('background') + num_classes = len(class_names) + + self.experiment.log_confusion_matrix( + matrix=confusion_matrix.matrix, + max_categories=num_classes, + labels=class_names, + epoch=epoch, + column_label='Actual Category', + row_label='Predicted Category', + file_name=f'confusion-matrix-epoch-{epoch}.json', + ) + + def on_fit_epoch_end(self, result, epoch): + self.log_metrics(result, epoch=epoch) + + def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: + self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) + + def on_params_update(self, params): + self.log_parameters(params) + + def finish_run(self): + self.experiment.end() diff --git a/algorithm/yolov5-master/utils/loggers/comet/comet_utils.py b/algorithm/yolov5-master/utils/loggers/comet/comet_utils.py new file mode 100644 index 0000000..2760076 --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/comet/comet_utils.py @@ -0,0 +1,150 @@ +import logging +import os +from urllib.parse import urlparse + +try: + import comet_ml +except (ModuleNotFoundError, ImportError): + comet_ml = None + +import yaml + +logger = logging.getLogger(__name__) + +COMET_PREFIX = 'comet://' +COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') +COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv('COMET_DEFAULT_CHECKPOINT_FILENAME', 'last.pt') + + +def download_model_checkpoint(opt, experiment): + model_dir = f'{opt.project}/{experiment.name}' + os.makedirs(model_dir, exist_ok=True) + + model_name = COMET_MODEL_NAME + model_asset_list = experiment.get_model_asset_list(model_name) + + if len(model_asset_list) == 0: + logger.error(f'COMET ERROR: No checkpoints found for model name : {model_name}') + return + + model_asset_list = sorted( + model_asset_list, + key=lambda x: x['step'], + reverse=True, + ) + logged_checkpoint_map = {asset['fileName']: asset['assetId'] for asset in model_asset_list} + + resource_url = urlparse(opt.weights) + checkpoint_filename = resource_url.query + + if checkpoint_filename: + asset_id = logged_checkpoint_map.get(checkpoint_filename) + else: + asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME) + checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME + + if asset_id is None: + logger.error(f'COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment') + return + + try: + logger.info(f'COMET INFO: Downloading checkpoint {checkpoint_filename}') + asset_filename = checkpoint_filename + + model_binary = experiment.get_asset(asset_id, return_type='binary', stream=False) + model_download_path = f'{model_dir}/{asset_filename}' + with open(model_download_path, 'wb') as f: + f.write(model_binary) + + opt.weights = model_download_path + + except Exception as e: + logger.warning('COMET WARNING: Unable to download checkpoint from Comet') + logger.exception(e) + + +def set_opt_parameters(opt, experiment): + """Update the opts Namespace with parameters + from Comet's ExistingExperiment when resuming a run + + Args: + opt (argparse.Namespace): Namespace of command line options + experiment (comet_ml.APIExperiment): Comet API Experiment object + """ + asset_list = experiment.get_asset_list() + resume_string = opt.resume + + for asset in asset_list: + if asset['fileName'] == 'opt.yaml': + asset_id = asset['assetId'] + asset_binary = experiment.get_asset(asset_id, return_type='binary', stream=False) + opt_dict = yaml.safe_load(asset_binary) + for key, value in opt_dict.items(): + setattr(opt, key, value) + opt.resume = resume_string + + # Save hyperparameters to YAML file + # Necessary to pass checks in training script + save_dir = f'{opt.project}/{experiment.name}' + os.makedirs(save_dir, exist_ok=True) + + hyp_yaml_path = f'{save_dir}/hyp.yaml' + with open(hyp_yaml_path, 'w') as f: + yaml.dump(opt.hyp, f) + opt.hyp = hyp_yaml_path + + +def check_comet_weights(opt): + """Downloads model weights from Comet and updates the + weights path to point to saved weights location + + Args: + opt (argparse.Namespace): Command Line arguments passed + to YOLOv5 training script + + Returns: + None/bool: Return True if weights are successfully downloaded + else return None + """ + if comet_ml is None: + return + + if isinstance(opt.weights, str): + if opt.weights.startswith(COMET_PREFIX): + api = comet_ml.API() + resource = urlparse(opt.weights) + experiment_path = f'{resource.netloc}{resource.path}' + experiment = api.get(experiment_path) + download_model_checkpoint(opt, experiment) + return True + + return None + + +def check_comet_resume(opt): + """Restores run parameters to its original state based on the model checkpoint + and logged Experiment parameters. + + Args: + opt (argparse.Namespace): Command Line arguments passed + to YOLOv5 training script + + Returns: + None/bool: Return True if the run is restored successfully + else return None + """ + if comet_ml is None: + return + + if isinstance(opt.resume, str): + if opt.resume.startswith(COMET_PREFIX): + api = comet_ml.API() + resource = urlparse(opt.resume) + experiment_path = f'{resource.netloc}{resource.path}' + experiment = api.get(experiment_path) + set_opt_parameters(opt, experiment) + download_model_checkpoint(opt, experiment) + + return True + + return None diff --git a/algorithm/yolov5-master/utils/loggers/comet/hpo.py b/algorithm/yolov5-master/utils/loggers/comet/hpo.py new file mode 100644 index 0000000..fc49115 --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/comet/hpo.py @@ -0,0 +1,118 @@ +import argparse +import json +import logging +import os +import sys +from pathlib import Path + +import comet_ml + +logger = logging.getLogger(__name__) + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +from train import train +from utils.callbacks import Callbacks +from utils.general import increment_path +from utils.torch_utils import select_device + +# Project Configuration +config = comet_ml.config.get_config() +COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') + + +def get_args(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=300, help='total training epochs') + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--noval', action='store_true', help='only validate final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') + parser.add_argument('--noplots', action='store_true', help='save no plot files') + parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') + parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') + parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') + parser.add_argument('--seed', type=int, default=0, help='Global training seed') + parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + + # Weights & Biases arguments + parser.add_argument('--entity', default=None, help='W&B: Entity') + parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option') + parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval') + parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use') + + # Comet Arguments + parser.add_argument('--comet_optimizer_config', type=str, help='Comet: Path to a Comet Optimizer Config File.') + parser.add_argument('--comet_optimizer_id', type=str, help='Comet: ID of the Comet Optimizer sweep.') + parser.add_argument('--comet_optimizer_objective', type=str, help="Comet: Set to 'minimize' or 'maximize'.") + parser.add_argument('--comet_optimizer_metric', type=str, help='Comet: Metric to Optimize.') + parser.add_argument('--comet_optimizer_workers', + type=int, + default=1, + help='Comet: Number of Parallel Workers to use with the Comet Optimizer.') + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def run(parameters, opt): + hyp_dict = {k: v for k, v in parameters.items() if k not in ['epochs', 'batch_size']} + + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) + opt.batch_size = parameters.get('batch_size') + opt.epochs = parameters.get('epochs') + + device = select_device(opt.device, batch_size=opt.batch_size) + train(hyp_dict, opt, device, callbacks=Callbacks()) + + +if __name__ == '__main__': + opt = get_args(known=True) + + opt.weights = str(opt.weights) + opt.cfg = str(opt.cfg) + opt.data = str(opt.data) + opt.project = str(opt.project) + + optimizer_id = os.getenv('COMET_OPTIMIZER_ID') + if optimizer_id is None: + with open(opt.comet_optimizer_config) as f: + optimizer_config = json.load(f) + optimizer = comet_ml.Optimizer(optimizer_config) + else: + optimizer = comet_ml.Optimizer(optimizer_id) + + opt.comet_optimizer_id = optimizer.id + status = optimizer.status() + + opt.comet_optimizer_objective = status['spec']['objective'] + opt.comet_optimizer_metric = status['spec']['metric'] + + logger.info('COMET INFO: Starting Hyperparameter Sweep') + for parameter in optimizer.get_parameters(): + run(parameter['parameters'], opt) diff --git a/algorithm/yolov5-master/utils/loggers/wandb/__init__.py b/algorithm/yolov5-master/utils/loggers/wandb/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5-master/utils/loggers/wandb/wandb_utils.py b/algorithm/yolov5-master/utils/loggers/wandb/wandb_utils.py new file mode 100644 index 0000000..c8ab381 --- /dev/null +++ b/algorithm/yolov5-master/utils/loggers/wandb/wandb_utils.py @@ -0,0 +1,193 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# WARNING ⚠️ wandb is deprecated and will be removed in future release. +# See supported integrations at https://github.com/ultralytics/yolov5#integrations + +import logging +import os +import sys +from contextlib import contextmanager +from pathlib import Path + +from utils.general import LOGGER, colorstr + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +RANK = int(os.getenv('RANK', -1)) +DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \ + f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.' + +try: + import wandb + + assert hasattr(wandb, '__version__') # verify package import not local dir + LOGGER.warning(DEPRECATION_WARNING) +except (ImportError, AssertionError): + wandb = None + + +class WandbLogger(): + """Log training runs, datasets, models, and predictions to Weights & Biases. + + This logger sends information to W&B at wandb.ai. By default, this information + includes hyperparameters, system configuration and metrics, model metrics, + and basic data metrics and analyses. + + By providing additional command line arguments to train.py, datasets, + models and predictions can also be logged. + + For more on how this logger is used, see the Weights & Biases documentation: + https://docs.wandb.com/guides/integrations/yolov5 + """ + + def __init__(self, opt, run_id=None, job_type='Training'): + """ + - Initialize WandbLogger instance + - Upload dataset if opt.upload_dataset is True + - Setup training processes if job_type is 'Training' + + arguments: + opt (namespace) -- Commandline arguments for this run + run_id (str) -- Run ID of W&B run to be resumed + job_type (str) -- To set the job_type for this run + + """ + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run = wandb, wandb.run if wandb else None + self.val_artifact, self.train_artifact = None, None + self.train_artifact_path, self.val_artifact_path = None, None + self.result_artifact = None + self.val_table, self.result_table = None, None + self.max_imgs_to_log = 16 + self.data_dict = None + if self.wandb: + self.wandb_run = wandb.init(config=opt, + resume='allow', + project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, + entity=opt.entity, + name=opt.name if opt.name != 'exp' else None, + job_type=job_type, + id=run_id, + allow_val_change=True) if not wandb.run else wandb.run + + if self.wandb_run: + if self.job_type == 'Training': + if isinstance(opt.data, dict): + # This means another dataset manager has already processed the dataset info (e.g. ClearML) + # and they will have stored the already processed dict in opt.data + self.data_dict = opt.data + self.setup_training(opt) + + def setup_training(self, opt): + """ + Setup the necessary processes for training YOLO models: + - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX + - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded + - Setup log_dict, initialize bbox_interval + + arguments: + opt (namespace) -- commandline arguments for this run + + """ + self.log_dict, self.current_epoch = {}, 0 + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + model_dir, _ = self.download_model_artifact(opt) + if model_dir: + self.weights = Path(model_dir) / 'last.pt' + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str( + self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \ + config.hyp, config.imgsz + + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + if opt.evolve or opt.noplots: + self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + """ + Log the model checkpoint as W&B artifact + + arguments: + path (Path) -- Path of directory containing the checkpoints + opt (namespace) -- Command line arguments for this run + epoch (int) -- Current epoch number + fitness_score (float) -- fitness score for current epoch + best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. + """ + model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', + type='model', + metadata={ + 'original_url': str(path), + 'epochs_trained': epoch + 1, + 'save period': opt.save_period, + 'project': opt.project, + 'total_epochs': opt.epochs, + 'fitness_score': fitness_score}) + model_artifact.add_file(str(path / 'last.pt'), name='last.pt') + wandb.log_artifact(model_artifact, + aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) + LOGGER.info(f'Saving model artifact on epoch {epoch + 1}') + + def val_one_image(self, pred, predn, path, names, im): + pass + + def log(self, log_dict): + """ + save the metrics to the logging dictionary + + arguments: + log_dict (Dict) -- metrics/media to be logged in current step + """ + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self): + """ + commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. + + arguments: + best_result (boolean): Boolean representing if the result of this evaluation is best or not + """ + if self.wandb_run: + with all_logging_disabled(): + try: + wandb.log(self.log_dict) + except BaseException as e: + LOGGER.info( + f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}' + ) + self.wandb_run.finish() + self.wandb_run = None + self.log_dict = {} + + def finish_run(self): + """ + Log metrics if any and finish the current W&B run + """ + if self.wandb_run: + if self.log_dict: + with all_logging_disabled(): + wandb.log(self.log_dict) + wandb.run.finish() + LOGGER.warning(DEPRECATION_WARNING) + + +@contextmanager +def all_logging_disabled(highest_level=logging.CRITICAL): + """ source - https://gist.github.com/simon-weber/7853144 + A context manager that will prevent any logging messages triggered during the body from being processed. + :param highest_level: the maximum logging level in use. + This would only need to be changed if a custom level greater than CRITICAL is defined. + """ + previous_level = logging.root.manager.disable + logging.disable(highest_level) + try: + yield + finally: + logging.disable(previous_level) diff --git a/algorithm/yolov5-master/utils/loss.py b/algorithm/yolov5-master/utils/loss.py new file mode 100644 index 0000000..9b9c3d9 --- /dev/null +++ b/algorithm/yolov5-master/utils/loss.py @@ -0,0 +1,234 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Loss functions +""" + +import torch +import torch.nn as nn + +from utils.metrics import bbox_iou +from utils.torch_utils import de_parallel + + +def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 + # return positive, negative label smoothing BCE targets + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=0.05): + super().__init__() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class FocalLoss(nn.Module): + # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +class ComputeLoss: + sort_obj_iou = False + + # Compute losses + def __init__(self, model, autobalance=False): + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + m = de_parallel(model).model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 + self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + self.na = m.na # number of anchors + self.nc = m.nc # number of classes + self.nl = m.nl # number of layers + self.anchors = m.anchors + self.device = device + + def __call__(self, p, targets): # predictions, targets + lcls = torch.zeros(1, device=self.device) # class loss + lbox = torch.zeros(1, device=self.device) # box loss + lobj = torch.zeros(1, device=self.device) # object loss + tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj + + n = b.shape[0] # number of targets + if n: + # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0 + pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions + + # Regression + pxy = pxy.sigmoid() * 2 - 0.5 + pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + iou = iou.detach().clamp(0).type(tobj.dtype) + if self.sort_obj_iou: + j = iou.argsort() + b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] + if self.gr < 1: + iou = (1.0 - self.gr) + self.gr * iou + tobj[b, a, gj, gi] = iou # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(pcls, self.cn, device=self.device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(pcls, t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() + + def build_targets(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=self.device) # normalized to gridspace gain + ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device).float() * g # offsets + + for i in range(self.nl): + anchors, shape = self.anchors[i], p[i].shape + gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain # shape(3,n,7) + if nt: + # Matches + r = t[..., 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1 < g) & (gxy > 1)).T + l, m = ((gxi % 1 < g) & (gxi > 1)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors + a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class + gij = (gxy - offsets).long() + gi, gj = gij.T # grid indices + + # Append + indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch diff --git a/algorithm/yolov5-master/utils/metrics.py b/algorithm/yolov5-master/utils/metrics.py new file mode 100644 index 0000000..95f364c --- /dev/null +++ b/algorithm/yolov5-master/utils/metrics.py @@ -0,0 +1,360 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Model validation metrics +""" + +import math +import warnings +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from utils import TryExcept, threaded + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def smooth(y, f=0.05): + # Box filter of fraction f + nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) + p = np.ones(nf // 2) # ones padding + yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded + return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed + + +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''): + """ Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes, nt = np.unique(target_cls, return_counts=True) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = nt[ci] # number of labels + n_p = i.sum() # number of predictions + if n_p == 0 or n_l == 0: + continue + + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + eps) # recall curve + r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) + if plot and j == 0: + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + eps) + names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data + names = dict(enumerate(names)) # to dict + if plot: + plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names) + plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1') + plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision') + plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall') + + i = smooth(f1.mean(0), 0.1).argmax() # max F1 index + p, r, f1 = p[:, i], r[:, i], f1[:, i] + tp = (r * nt).round() # true positives + fp = (tp / (p + eps) - tp).round() # false positives + return tp, fp, p, r, f1, ap, unique_classes.astype(int) + + +def compute_ap(recall, precision): + """ Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + # Returns + Average precision, precision curve, recall curve + """ + + # Append sentinel values to beginning and end + mrec = np.concatenate(([0.0], recall, [1.0])) + mpre = np.concatenate(([1.0], precision, [0.0])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = 'interp' # methods: 'continuous', 'interp' + if method == 'interp': + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix + def __init__(self, nc, conf=0.25, iou_thres=0.45): + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + if detections is None: + gt_classes = labels.int() + for gc in gt_classes: + self.matrix[self.nc, gc] += 1 # background FN + return + + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(int) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[detection_classes[m1[j]], gc] += 1 # correct + else: + self.matrix[self.nc, gc] += 1 # true background + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[dc, self.nc] += 1 # predicted background + + def tp_fp(self): + tp = self.matrix.diagonal() # true positives + fp = self.matrix.sum(1) - tp # false positives + # fn = self.matrix.sum(0) - tp # false negatives (missed detections) + return tp[:-1], fp[:-1] # remove background class + + @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure') + def plot(self, normalize=True, save_dir='', names=()): + import seaborn as sn + + array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) + nc, nn = self.nc, len(names) # number of classes, names + sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size + labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels + ticklabels = (names + ['background']) if labels else 'auto' + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered + sn.heatmap(array, + ax=ax, + annot=nc < 30, + annot_kws={ + 'size': 8}, + cmap='Blues', + fmt='.2f', + square=True, + vmin=0.0, + xticklabels=ticklabels, + yticklabels=ticklabels).set_facecolor((1, 1, 1)) + ax.set_xlabel('True') + ax.set_ylabel('Predicted') + ax.set_title('Confusion Matrix') + fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) + plt.close(fig) + + def print(self): + for i in range(self.nc + 1): + print(' '.join(map(str, self.matrix[i]))) + + +def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4) + + # Get the coordinates of bounding boxes + if xywh: # transform from xywh to xyxy + (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) + w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 + b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ + b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ + else: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) + b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) + w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps) + w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps) + + # Intersection area + inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * \ + (b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp(0) + + # Union Area + union = w1 * h1 + w2 * h2 - inter + eps + + # IoU + iou = inter / union + if CIoU or DIoU or GIoU: + cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width + ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 + if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + return iou - rho2 / c2 # DIoU + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf + return iou # IoU + + +def box_iou(box1, box2, eps=1e-7): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) + + +def bbox_ioa(box1, box2, eps=1e-7): + """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 + box1: np.array of shape(4) + box2: np.array of shape(nx4) + returns: np.array of shape(n) + """ + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1 + b2_x1, b2_y1, b2_x2, b2_y2 = box2.T + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ + (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps + + # Intersection over box2 area + return inter_area / box2_area + + +def wh_iou(wh1, wh2, eps=1e-7): + # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + + +@threaded +def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()): + # Precision-recall curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) + ax.set_xlabel('Recall') + ax.set_ylabel('Precision') + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') + ax.set_title('Precision-Recall Curve') + fig.savefig(save_dir, dpi=250) + plt.close(fig) + + +@threaded +def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'): + # Metric-confidence curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) + + y = smooth(py.mean(0), 0.05) + ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') + ax.set_title(f'{ylabel}-Confidence Curve') + fig.savefig(save_dir, dpi=250) + plt.close(fig) diff --git a/algorithm/yolov5-master/utils/plots.py b/algorithm/yolov5-master/utils/plots.py new file mode 100644 index 0000000..24c618c --- /dev/null +++ b/algorithm/yolov5-master/utils/plots.py @@ -0,0 +1,560 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Plotting utils +""" + +import contextlib +import math +import os +from copy import copy +from pathlib import Path +from urllib.error import URLError + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sn +import torch +from PIL import Image, ImageDraw, ImageFont + +from utils import TryExcept, threaded +from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path, + is_ascii, xywh2xyxy, xyxy2xywh) +from utils.metrics import fitness +from utils.segment.general import scale_image + +# Settings +RANK = int(os.getenv('RANK', -1)) +matplotlib.rc('font', **{'size': 11}) +matplotlib.use('Agg') # for writing to files only + + +class Colors: + # Ultralytics color palette https://ultralytics.com/ + def __init__(self): + # hex = matplotlib.colors.TABLEAU_COLORS.values() + hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', + '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') + self.palette = [self.hex2rgb(f'#{c}') for c in hexs] + self.n = len(self.palette) + + def __call__(self, i, bgr=False): + c = self.palette[int(i) % self.n] + return (c[2], c[1], c[0]) if bgr else c + + @staticmethod + def hex2rgb(h): # rgb order (PIL) + return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + + +colors = Colors() # create instance for 'from utils.plots import colors' + + +def check_pil_font(font=FONT, size=10): + # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary + font = Path(font) + font = font if font.exists() else (CONFIG_DIR / font.name) + try: + return ImageFont.truetype(str(font) if font.exists() else font.name, size) + except Exception: # download if missing + try: + check_font(font) + return ImageFont.truetype(str(font), size) + except TypeError: + check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374 + except URLError: # not online + return ImageFont.load_default() + + +class Annotator: + # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations + def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): + assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' + non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic + self.pil = pil or non_ascii + if self.pil: # use PIL + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font, + size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)) + else: # use cv2 + self.im = im + self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width + + def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)): + # Add one xyxy box to image with label + if self.pil or not is_ascii(label): + self.draw.rectangle(box, width=self.lw, outline=color) # box + if label: + w, h = self.font.getsize(label) # text width, height (WARNING: deprecated) in 9.2.0 + # _, _, w, h = self.font.getbbox(label) # text width, height (New) + outside = box[1] - h >= 0 # label fits outside box + self.draw.rectangle( + (box[0], box[1] - h if outside else box[1], box[0] + w + 1, + box[1] + 1 if outside else box[1] + h + 1), + fill=color, + ) + # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0 + self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font) + else: # cv2 + p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3])) + cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA) + if label: + tf = max(self.lw - 1, 1) # font thickness + w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height + outside = p1[1] - h >= 3 + p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3 + cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled + cv2.putText(self.im, + label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), + 0, + self.lw / 3, + txt_color, + thickness=tf, + lineType=cv2.LINE_AA) + + def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False): + """Plot masks at once. + Args: + masks (tensor): predicted masks on cuda, shape: [n, h, w] + colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n] + im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1] + alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque + """ + if self.pil: + # convert to numpy first + self.im = np.asarray(self.im).copy() + if len(masks) == 0: + self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255 + colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0 + colors = colors[:, None, None] # shape(n,1,1,3) + masks = masks.unsqueeze(3) # shape(n,h,w,1) + masks_color = masks * (colors * alpha) # shape(n,h,w,3) + + inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1) + mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3) + + im_gpu = im_gpu.flip(dims=[0]) # flip channel + im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3) + im_gpu = im_gpu * inv_alph_masks[-1] + mcs + im_mask = (im_gpu * 255).byte().cpu().numpy() + self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape) + if self.pil: + # convert im back to PIL and update draw + self.fromarray(self.im) + + def rectangle(self, xy, fill=None, outline=None, width=1): + # Add rectangle to image (PIL-only) + self.draw.rectangle(xy, fill, outline, width) + + def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'): + # Add text to image (PIL-only) + if anchor == 'bottom': # start y from font bottom + w, h = self.font.getsize(text) # text width, height + xy[1] += 1 - h + self.draw.text(xy, text, fill=txt_color, font=self.font) + + def fromarray(self, im): + # Update self.im from a numpy array + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + + def result(self): + # Return annotated image as array + return np.asarray(self.im) + + +def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')): + """ + x: Features to be visualized + module_type: Module type + stage: Module stage within model + n: Maximum number of feature maps to plot + save_dir: Directory to save results + """ + if 'Detect' not in module_type: + batch, channels, height, width = x.shape # batch, channels, height, width + if height > 1 and width > 1: + f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename + + blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels + n = min(n, channels) # number of plots + fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols + ax = ax.ravel() + plt.subplots_adjust(wspace=0.05, hspace=0.05) + for i in range(n): + ax[i].imshow(blocks[i].squeeze()) # cmap='gray' + ax[i].axis('off') + + LOGGER.info(f'Saving {f}... ({n}/{channels})') + plt.savefig(f, dpi=300, bbox_inches='tight') + plt.close() + np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save + + +def hist2d(x, y, n=100): + # 2d histogram used in labels.png and evolve.png + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + from scipy.signal import butter, filtfilt + + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype='low', analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def output_to_target(output, max_det=300): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting + targets = [] + for i, o in enumerate(output): + box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1) + j = torch.full((conf.shape[0], 1), i) + targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1)) + return torch.cat(targets, 0).numpy() + + +@threaded +def plot_images(images, targets, paths=None, fname='images.jpg', names=None): + # Plot image grid with labels + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + max_size = 1920 # max image size + max_subplots = 16 # max image subplots, i.e. 4x4 + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs ** 0.5) # number of subplots (square) + if np.max(images[0]) <= 1: + images *= 255 # de-normalise (optional) + + # Build Image + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, im in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + im = im.transpose(1, 2, 0) + mosaic[y:y + h, x:x + w, :] = im + + # Resize (optional) + scale = max_size / ns / max(h, w) + if scale < 1: + h = math.ceil(scale * h) + w = math.ceil(scale * w) + mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) + + # Annotate + fs = int((h + w) * ns * 0.01) # font size + annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) + for i in range(i + 1): + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders + if paths: + annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames + if len(targets) > 0: + ti = targets[targets[:, 0] == i] # image targets + boxes = xywh2xyxy(ti[:, 2:6]).T + classes = ti[:, 1].astype('int') + labels = ti.shape[1] == 6 # labels if no conf column + conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale < 1: # absolute coords need scale if image scales + boxes *= scale + boxes[[0, 2]] += x + boxes[[1, 3]] += y + for j, box in enumerate(boxes.T.tolist()): + cls = classes[j] + color = colors(cls) + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' + annotator.box_label(box, label, color=color) + annotator.im.save(fname) # save + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): + # Plot LR simulating training for full epochs + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]['lr']) + plt.plot(y, '.-', label='LR') + plt.xlabel('epoch') + plt.ylabel('LR') + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / 'LR.png', dpi=200) + plt.close() + + +def plot_val_txt(): # from utils.plots import *; plot_val() + # Plot val.txt histograms + x = np.loadtxt('val.txt', dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect('equal') + plt.savefig('hist2d.png', dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig('hist1d.png', dpi=200) + + +def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() + # Plot targets.txt histograms + x = np.loadtxt('targets.txt', dtype=np.float32).T + s = ['x targets', 'y targets', 'width targets', 'height targets'] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}') + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig('targets.jpg', dpi=200) + + +def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study() + # Plot file=study.txt generated by val.py (or plot all study*.txt in dir) + save_dir = Path(file).parent if file else Path(dir) + plot2 = False # plot additional results + if plot2: + ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: + for f in sorted(save_dir.glob('study*.txt')): + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + if plot2: + s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)'] + for i in range(7): + ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot(y[5, 1:j], + y[3, 1:j] * 1E2, + '.-', + linewidth=2, + markersize=8, + label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) + + ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + 'k.-', + linewidth=2, + markersize=8, + alpha=.25, + label='EfficientDet') + + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(25, 55) + ax2.set_xlabel('GPU Speed (ms/img)') + ax2.set_ylabel('COCO AP val') + ax2.legend(loc='lower right') + f = save_dir / 'study.png' + print(f'Saving {f}...') + plt.savefig(f, dpi=300) + + +@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 +def plot_labels(labels, names=(), save_dir=Path('')): + # plot dataset labels + LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") + c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) + + # seaborn correlogram + sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use('svg') # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + with contextlib.suppress(Exception): # color histogram bars by class + [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195 + ax[0].set_ylabel('instances') + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10) + else: + ax[0].set_xlabel('classes') + sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) + sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + for cls, *box in labels[:1000]: + ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot + ax[1].imshow(img) + ax[1].axis('off') + + for a in [0, 1, 2, 3]: + for s in ['top', 'right', 'left', 'bottom']: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / 'labels.jpg', dpi=200) + matplotlib.use('Agg') + plt.close() + + +def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')): + # Show classification image grid with labels (optional) and predictions (optional) + from utils.augmentations import denormalize + + names = names or [f'class{i}' for i in range(1000)] + blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im), + dim=0) # select batch index 0, block by channels + n = min(len(blocks), nmax) # number of plots + m = min(8, round(n ** 0.5)) # 8 x 8 default + fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols + ax = ax.ravel() if m > 1 else [ax] + # plt.subplots_adjust(wspace=0.05, hspace=0.05) + for i in range(n): + ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0)) + ax[i].axis('off') + if labels is not None: + s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '') + ax[i].set_title(s, fontsize=8, verticalalignment='top') + plt.savefig(f, dpi=300, bbox_inches='tight') + plt.close() + if verbose: + LOGGER.info(f'Saving {f}') + if labels is not None: + LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax])) + if pred is not None: + LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax])) + return f + + +def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve() + # Plot evolve.csv hyp evolution results + evolve_csv = Path(evolve_csv) + data = pd.read_csv(evolve_csv) + keys = [x.strip() for x in data.columns] + x = data.values + f = fitness(x) + j = np.argmax(f) # max fitness index + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc('font', **{'size': 8}) + print(f'Best results from row {j} of {evolve_csv}:') + for i, k in enumerate(keys[7:]): + v = x[:, 7 + i] + mu = v[j] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none') + plt.plot(mu, f.max(), 'k+', markersize=15) + plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print(f'{k:>15}: {mu:.3g}') + f = evolve_csv.with_suffix('.png') # filename + plt.savefig(f, dpi=200) + plt.close() + print(f'Saved {f}') + + +def plot_results(file='path/to/results.csv', dir=''): + # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') + save_dir = Path(file).parent if file else Path(dir) + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + files = list(save_dir.glob('results*.csv')) + assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' + for f in files: + try: + data = pd.read_csv(f) + s = [x.strip() for x in data.columns] + x = data.values[:, 0] + for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]): + y = data.values[:, j].astype('float') + # y[y == 0] = np.nan # don't show zero values + ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8) + ax[i].set_title(s[j], fontsize=12) + # if j in [8, 9, 10]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + LOGGER.info(f'Warning: Plotting error for {f}: {e}') + ax[1].legend() + fig.savefig(save_dir / 'results.png', dpi=200) + plt.close() + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=''): + # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] + files = list(Path(save_dir).glob('frames*.txt')) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = (results[0] - results[0].min()) # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace('frames_', '') + a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) + a.set_title(s[i]) + a.set_xlabel('time (s)') + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ['top', 'right']: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print(f'Warning: Plotting error for {f}; {e}') + ax[1].legend() + plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) + + +def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True): + # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop + xyxy = torch.tensor(xyxy).view(-1, 4) + b = xyxy2xywh(xyxy) # boxes + if square: + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square + b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad + xyxy = xywh2xyxy(b).long() + clip_boxes(xyxy, im.shape) + crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)] + if save: + file.parent.mkdir(parents=True, exist_ok=True) # make directory + f = str(increment_path(file).with_suffix('.jpg')) + # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue + Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB + return crop diff --git a/algorithm/yolov5-master/utils/segment/__init__.py b/algorithm/yolov5-master/utils/segment/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5-master/utils/segment/augmentations.py b/algorithm/yolov5-master/utils/segment/augmentations.py new file mode 100644 index 0000000..169adde --- /dev/null +++ b/algorithm/yolov5-master/utils/segment/augmentations.py @@ -0,0 +1,104 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Image augmentation functions +""" + +import math +import random + +import cv2 +import numpy as np + +from ..augmentations import box_candidates +from ..general import resample_segments, segment2box + + +def mixup(im, labels, segments, im2, labels2, segments2): + # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf + r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 + im = (im * r + im2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + segments = np.concatenate((segments, segments2), 0) + return im, labels, segments + + +def random_perspective(im, + targets=(), + segments=(), + degrees=10, + translate=.1, + scale=.1, + shear=10, + perspective=0.0, + border=(0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = im.shape[0] + border[0] * 2 # shape(h,w,c) + width = im.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -im.shape[1] / 2 # x translation (pixels) + C[1, 2] = -im.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels) + T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(im[:, :, ::-1]) # base + # ax[1].imshow(im2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + new_segments = [] + if n: + new = np.zeros((n, 4)) + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + new_segments.append(xy) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01) + targets = targets[i] + targets[:, 1:5] = new[i] + new_segments = np.array(new_segments)[i] + + return im, targets, new_segments diff --git a/algorithm/yolov5-master/utils/segment/dataloaders.py b/algorithm/yolov5-master/utils/segment/dataloaders.py new file mode 100644 index 0000000..097a5d5 --- /dev/null +++ b/algorithm/yolov5-master/utils/segment/dataloaders.py @@ -0,0 +1,332 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Dataloaders +""" + +import os +import random + +import cv2 +import numpy as np +import torch +from torch.utils.data import DataLoader, distributed + +from ..augmentations import augment_hsv, copy_paste, letterbox +from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, seed_worker +from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn +from ..torch_utils import torch_distributed_zero_first +from .augmentations import mixup, random_perspective + +RANK = int(os.getenv('RANK', -1)) + + +def create_dataloader(path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix='', + shuffle=False, + mask_downsample_ratio=1, + overlap_mask=False, + seed=0): + if rect and shuffle: + LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') + shuffle = False + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = LoadImagesAndLabelsAndMasks( + path, + imgsz, + batch_size, + augment=augment, # augmentation + hyp=hyp, # hyperparameters + rect=rect, # rectangular batches + cache_images=cache, + single_cls=single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix, + downsample_ratio=mask_downsample_ratio, + overlap=overlap_mask) + + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() # number of CUDA devices + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + seed + RANK) + return loader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn, + worker_init_fn=seed_worker, + generator=generator, + ), dataset + + +class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing + + def __init__( + self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0, + min_items=0, + prefix='', + downsample_ratio=1, + overlap=False, + ): + super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls, + stride, pad, min_items, prefix) + self.downsample_ratio = downsample_ratio + self.overlap = overlap + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + masks = [] + if mosaic: + # Load mosaic + img, labels, segments = self.load_mosaic(index) + shapes = None + + # MixUp augmentation + if random.random() < hyp['mixup']: + img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1))) + + else: + # Load image + img, (h0, w0), (h, w) = self.load_image(index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy + segments = self.segments[index].copy() + if len(segments): + for i_s in range(len(segments)): + segments[i_s] = xyn2xy( + segments[i_s], + ratio[0] * w, + ratio[1] * h, + padw=pad[0], + padh=pad[1], + ) + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + img, labels, segments = random_perspective(img, + labels, + segments=segments, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + nl = len(labels) # number of labels + if nl: + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) + if self.overlap: + masks, sorted_idx = polygons2masks_overlap(img.shape[:2], + segments, + downsample_ratio=self.downsample_ratio) + masks = masks[None] # (640, 640) -> (1, 640, 640) + labels = labels[sorted_idx] + else: + masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio) + + masks = (torch.from_numpy(masks) if len(masks) else torch.zeros(1 if self.overlap else nl, img.shape[0] // + self.downsample_ratio, img.shape[1] // + self.downsample_ratio)) + # TODO: albumentations support + if self.augment: + # Albumentations + # there are some augmentation that won't change boxes and masks, + # so just be it for now. + img, labels = self.albumentations(img, labels) + nl = len(labels) # update after albumentations + + # HSV color-space + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Flip up-down + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nl: + labels[:, 2] = 1 - labels[:, 2] + masks = torch.flip(masks, dims=[1]) + + # Flip left-right + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nl: + labels[:, 1] = 1 - labels[:, 1] + masks = torch.flip(masks, dims=[2]) + + # Cutouts # labels = cutout(img, labels, p=0.5) + + labels_out = torch.zeros((nl, 6)) + if nl: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + img = np.ascontiguousarray(img) + + return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks) + + def load_mosaic(self, index): + # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic + labels4, segments4 = [], [] + s = self.img_size + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y + + # 3 additional image indices + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + labels, segments = self.labels[index].copy(), self.segments[index].copy() + + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) + img4, labels4, segments4 = random_perspective(img4, + labels4, + segments4, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + return img4, labels4, segments4 + + @staticmethod + def collate_fn(batch): + img, label, path, shapes, masks = zip(*batch) # transposed + batched_masks = torch.cat(masks, 0) + for i, l in enumerate(label): + l[:, 0] = i # add target image index for build_targets() + return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks + + +def polygon2mask(img_size, polygons, color=1, downsample_ratio=1): + """ + Args: + img_size (tuple): The image size. + polygons (np.ndarray): [N, M], N is the number of polygons, + M is the number of points(Be divided by 2). + """ + mask = np.zeros(img_size, dtype=np.uint8) + polygons = np.asarray(polygons) + polygons = polygons.astype(np.int32) + shape = polygons.shape + polygons = polygons.reshape(shape[0], -1, 2) + cv2.fillPoly(mask, polygons, color=color) + nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio) + # NOTE: fillPoly firstly then resize is trying the keep the same way + # of loss calculation when mask-ratio=1. + mask = cv2.resize(mask, (nw, nh)) + return mask + + +def polygons2masks(img_size, polygons, color, downsample_ratio=1): + """ + Args: + img_size (tuple): The image size. + polygons (list[np.ndarray]): each polygon is [N, M], + N is the number of polygons, + M is the number of points(Be divided by 2). + """ + masks = [] + for si in range(len(polygons)): + mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio) + masks.append(mask) + return np.array(masks) + + +def polygons2masks_overlap(img_size, segments, downsample_ratio=1): + """Return a (640, 640) overlap mask.""" + masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio), + dtype=np.int32 if len(segments) > 255 else np.uint8) + areas = [] + ms = [] + for si in range(len(segments)): + mask = polygon2mask( + img_size, + [segments[si].reshape(-1)], + downsample_ratio=downsample_ratio, + color=1, + ) + ms.append(mask) + areas.append(mask.sum()) + areas = np.asarray(areas) + index = np.argsort(-areas) + ms = np.array(ms)[index] + for i in range(len(segments)): + mask = ms[i] * (i + 1) + masks = masks + mask + masks = np.clip(masks, a_min=0, a_max=i + 1) + return masks, index diff --git a/algorithm/yolov5-master/utils/segment/general.py b/algorithm/yolov5-master/utils/segment/general.py new file mode 100644 index 0000000..9da8945 --- /dev/null +++ b/algorithm/yolov5-master/utils/segment/general.py @@ -0,0 +1,160 @@ +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + + +def crop_mask(masks, boxes): + """ + "Crop" predicted masks by zeroing out everything not in the predicted bbox. + Vectorized by Chong (thanks Chong). + + Args: + - masks should be a size [h, w, n] tensor of masks + - boxes should be a size [n, 4] tensor of bbox coords in relative point form + """ + + n, h, w = masks.shape + x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n) + r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1) + c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1) + + return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) + + +def process_mask_upsample(protos, masks_in, bboxes, shape): + """ + Crop after upsample. + protos: [mask_dim, mask_h, mask_w] + masks_in: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape: input_image_size, (h, w) + + return: h, w, n + """ + + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) + masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.5) + + +def process_mask(protos, masks_in, bboxes, shape, upsample=False): + """ + Crop before upsample. + proto_out: [mask_dim, mask_h, mask_w] + out_masks: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape:input_image_size, (h, w) + + return: h, w, n + """ + + c, mh, mw = protos.shape # CHW + ih, iw = shape + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW + + downsampled_bboxes = bboxes.clone() + downsampled_bboxes[:, 0] *= mw / iw + downsampled_bboxes[:, 2] *= mw / iw + downsampled_bboxes[:, 3] *= mh / ih + downsampled_bboxes[:, 1] *= mh / ih + + masks = crop_mask(masks, downsampled_bboxes) # CHW + if upsample: + masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + return masks.gt_(0.5) + + +def process_mask_native(protos, masks_in, bboxes, shape): + """ + Crop after upsample. + protos: [mask_dim, mask_h, mask_w] + masks_in: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape: input_image_size, (h, w) + + return: h, w, n + """ + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) + gain = min(mh / shape[0], mw / shape[1]) # gain = old / new + pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(mh - pad[1]), int(mw - pad[0]) + masks = masks[:, top:bottom, left:right] + + masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.5) + + +def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): + """ + img1_shape: model input shape, [h, w] + img0_shape: origin pic shape, [h, w, 3] + masks: [h, w, num] + """ + # Rescale coordinates (xyxy) from im1_shape to im0_shape + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + # masks = masks.permute(2, 0, 1).contiguous() + # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] + # masks = masks.permute(1, 2, 0).contiguous() + masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) + + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + + +def mask_iou(mask1, mask2, eps=1e-7): + """ + mask1: [N, n] m1 means number of predicted objects + mask2: [M, n] m2 means number of gt objects + Note: n means image_w x image_h + + return: masks iou, [N, M] + """ + intersection = torch.matmul(mask1, mask2.t()).clamp(0) + union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def masks_iou(mask1, mask2, eps=1e-7): + """ + mask1: [N, n] m1 means number of predicted objects + mask2: [N, n] m2 means number of gt objects + Note: n means image_w x image_h + + return: masks iou, (N, ) + """ + intersection = (mask1 * mask2).sum(1).clamp(0) # (N, ) + union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def masks2segments(masks, strategy='largest'): + # Convert masks(n,160,160) into segments(n,xy) + segments = [] + for x in masks.int().cpu().numpy().astype('uint8'): + c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + if c: + if strategy == 'concat': # concatenate all segments + c = np.concatenate([x.reshape(-1, 2) for x in c]) + elif strategy == 'largest': # select largest segment + c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) + else: + c = np.zeros((0, 2)) # no segments found + segments.append(c.astype('float32')) + return segments diff --git a/algorithm/yolov5-master/utils/segment/loss.py b/algorithm/yolov5-master/utils/segment/loss.py new file mode 100644 index 0000000..2a8a4c6 --- /dev/null +++ b/algorithm/yolov5-master/utils/segment/loss.py @@ -0,0 +1,186 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..general import xywh2xyxy +from ..loss import FocalLoss, smooth_BCE +from ..metrics import bbox_iou +from ..torch_utils import de_parallel +from .general import crop_mask + + +class ComputeLoss: + # Compute losses + def __init__(self, model, autobalance=False, overlap=False): + self.sort_obj_iou = False + self.overlap = overlap + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + self.device = device + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + m = de_parallel(model).model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 + self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + self.na = m.na # number of anchors + self.nc = m.nc # number of classes + self.nl = m.nl # number of layers + self.nm = m.nm # number of masks + self.anchors = m.anchors + self.device = device + + def __call__(self, preds, targets, masks): # predictions, targets, model + p, proto = preds + bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width + lcls = torch.zeros(1, device=self.device) + lbox = torch.zeros(1, device=self.device) + lobj = torch.zeros(1, device=self.device) + lseg = torch.zeros(1, device=self.device) + tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj + + n = b.shape[0] # number of targets + if n: + pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions + + # Box regression + pxy = pxy.sigmoid() * 2 - 0.5 + pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + iou = iou.detach().clamp(0).type(tobj.dtype) + if self.sort_obj_iou: + j = iou.argsort() + b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] + if self.gr < 1: + iou = (1.0 - self.gr) + self.gr * iou + tobj[b, a, gj, gi] = iou # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(pcls, self.cn, device=self.device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(pcls, t) # BCE + + # Mask regression + if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample + masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0] + marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized + mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) + for bi in b.unique(): + j = b == bi # matching index + if self.overlap: + mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) + else: + mask_gti = masks[tidxs[i]][j] + lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + lseg *= self.hyp['box'] / bs + + loss = lbox + lobj + lcls + lseg + return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() + + def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): + # Mask loss for one image + pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) + loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none') + return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() + + def build_targets(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], [] + gain = torch.ones(8, device=self.device) # normalized to gridspace gain + ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + if self.overlap: + batch = p[0].shape[0] + ti = [] + for i in range(batch): + num = (targets[:, 0] == i).sum() # find number of targets of each image + ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) + ti = torch.cat(ti, 1) # (na, nt) + else: + ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) + targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device).float() * g # offsets + + for i in range(self.nl): + anchors, shape = self.anchors[i], p[i].shape + gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain # shape(3,n,7) + if nt: + # Matches + r = t[..., 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1 < g) & (gxy > 1)).T + l, m = ((gxi % 1 < g) & (gxi > 1)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors + (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class + gij = (gxy - offsets).long() + gi, gj = gij.T # grid indices + + # Append + indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + tidxs.append(tidx) + xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized + + return tcls, tbox, indices, anch, tidxs, xywhn diff --git a/algorithm/yolov5-master/utils/segment/metrics.py b/algorithm/yolov5-master/utils/segment/metrics.py new file mode 100644 index 0000000..c9f137e --- /dev/null +++ b/algorithm/yolov5-master/utils/segment/metrics.py @@ -0,0 +1,210 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Model validation metrics +""" + +import numpy as np + +from ..metrics import ap_per_class + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9] + return (x[:, :8] * w).sum(1) + + +def ap_per_class_box_and_mask( + tp_m, + tp_b, + conf, + pred_cls, + target_cls, + plot=False, + save_dir='.', + names=(), +): + """ + Args: + tp_b: tp of boxes. + tp_m: tp of masks. + other arguments see `func: ap_per_class`. + """ + results_boxes = ap_per_class(tp_b, + conf, + pred_cls, + target_cls, + plot=plot, + save_dir=save_dir, + names=names, + prefix='Box')[2:] + results_masks = ap_per_class(tp_m, + conf, + pred_cls, + target_cls, + plot=plot, + save_dir=save_dir, + names=names, + prefix='Mask')[2:] + + results = { + 'boxes': { + 'p': results_boxes[0], + 'r': results_boxes[1], + 'ap': results_boxes[3], + 'f1': results_boxes[2], + 'ap_class': results_boxes[4]}, + 'masks': { + 'p': results_masks[0], + 'r': results_masks[1], + 'ap': results_masks[3], + 'f1': results_masks[2], + 'ap_class': results_masks[4]}} + return results + + +class Metric: + + def __init__(self) -> None: + self.p = [] # (nc, ) + self.r = [] # (nc, ) + self.f1 = [] # (nc, ) + self.all_ap = [] # (nc, 10) + self.ap_class_index = [] # (nc, ) + + @property + def ap50(self): + """AP@0.5 of all classes. + Return: + (nc, ) or []. + """ + return self.all_ap[:, 0] if len(self.all_ap) else [] + + @property + def ap(self): + """AP@0.5:0.95 + Return: + (nc, ) or []. + """ + return self.all_ap.mean(1) if len(self.all_ap) else [] + + @property + def mp(self): + """mean precision of all classes. + Return: + float. + """ + return self.p.mean() if len(self.p) else 0.0 + + @property + def mr(self): + """mean recall of all classes. + Return: + float. + """ + return self.r.mean() if len(self.r) else 0.0 + + @property + def map50(self): + """Mean AP@0.5 of all classes. + Return: + float. + """ + return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 + + @property + def map(self): + """Mean AP@0.5:0.95 of all classes. + Return: + float. + """ + return self.all_ap.mean() if len(self.all_ap) else 0.0 + + def mean_results(self): + """Mean of results, return mp, mr, map50, map""" + return (self.mp, self.mr, self.map50, self.map) + + def class_result(self, i): + """class-aware result, return p[i], r[i], ap50[i], ap[i]""" + return (self.p[i], self.r[i], self.ap50[i], self.ap[i]) + + def get_maps(self, nc): + maps = np.zeros(nc) + self.map + for i, c in enumerate(self.ap_class_index): + maps[c] = self.ap[i] + return maps + + def update(self, results): + """ + Args: + results: tuple(p, r, ap, f1, ap_class) + """ + p, r, all_ap, f1, ap_class_index = results + self.p = p + self.r = r + self.all_ap = all_ap + self.f1 = f1 + self.ap_class_index = ap_class_index + + +class Metrics: + """Metric for boxes and masks.""" + + def __init__(self) -> None: + self.metric_box = Metric() + self.metric_mask = Metric() + + def update(self, results): + """ + Args: + results: Dict{'boxes': Dict{}, 'masks': Dict{}} + """ + self.metric_box.update(list(results['boxes'].values())) + self.metric_mask.update(list(results['masks'].values())) + + def mean_results(self): + return self.metric_box.mean_results() + self.metric_mask.mean_results() + + def class_result(self, i): + return self.metric_box.class_result(i) + self.metric_mask.class_result(i) + + def get_maps(self, nc): + return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc) + + @property + def ap_class_index(self): + # boxes and masks have the same ap_class_index + return self.metric_box.ap_class_index + + +KEYS = [ + 'train/box_loss', + 'train/seg_loss', # train loss + 'train/obj_loss', + 'train/cls_loss', + 'metrics/precision(B)', + 'metrics/recall(B)', + 'metrics/mAP_0.5(B)', + 'metrics/mAP_0.5:0.95(B)', # metrics + 'metrics/precision(M)', + 'metrics/recall(M)', + 'metrics/mAP_0.5(M)', + 'metrics/mAP_0.5:0.95(M)', # metrics + 'val/box_loss', + 'val/seg_loss', # val loss + 'val/obj_loss', + 'val/cls_loss', + 'x/lr0', + 'x/lr1', + 'x/lr2',] + +BEST_KEYS = [ + 'best/epoch', + 'best/precision(B)', + 'best/recall(B)', + 'best/mAP_0.5(B)', + 'best/mAP_0.5:0.95(B)', + 'best/precision(M)', + 'best/recall(M)', + 'best/mAP_0.5(M)', + 'best/mAP_0.5:0.95(M)',] diff --git a/algorithm/yolov5-master/utils/segment/plots.py b/algorithm/yolov5-master/utils/segment/plots.py new file mode 100644 index 0000000..1b22ec8 --- /dev/null +++ b/algorithm/yolov5-master/utils/segment/plots.py @@ -0,0 +1,143 @@ +import contextlib +import math +from pathlib import Path + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch + +from .. import threaded +from ..general import xywh2xyxy +from ..plots import Annotator, colors + + +@threaded +def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None): + # Plot image grid with labels + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + if isinstance(masks, torch.Tensor): + masks = masks.cpu().numpy().astype(int) + + max_size = 1920 # max image size + max_subplots = 16 # max image subplots, i.e. 4x4 + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs ** 0.5) # number of subplots (square) + if np.max(images[0]) <= 1: + images *= 255 # de-normalise (optional) + + # Build Image + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, im in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + im = im.transpose(1, 2, 0) + mosaic[y:y + h, x:x + w, :] = im + + # Resize (optional) + scale = max_size / ns / max(h, w) + if scale < 1: + h = math.ceil(scale * h) + w = math.ceil(scale * w) + mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) + + # Annotate + fs = int((h + w) * ns * 0.01) # font size + annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) + for i in range(i + 1): + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders + if paths: + annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames + if len(targets) > 0: + idx = targets[:, 0] == i + ti = targets[idx] # image targets + + boxes = xywh2xyxy(ti[:, 2:6]).T + classes = ti[:, 1].astype('int') + labels = ti.shape[1] == 6 # labels if no conf column + conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale < 1: # absolute coords need scale if image scales + boxes *= scale + boxes[[0, 2]] += x + boxes[[1, 3]] += y + for j, box in enumerate(boxes.T.tolist()): + cls = classes[j] + color = colors(cls) + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' + annotator.box_label(box, label, color=color) + + # Plot masks + if len(masks): + if masks.max() > 1.0: # mean that masks are overlap + image_masks = masks[[i]] # (1, 640, 640) + nl = len(ti) + index = np.arange(nl).reshape(nl, 1, 1) + 1 + image_masks = np.repeat(image_masks, nl, axis=0) + image_masks = np.where(image_masks == index, 1.0, 0.0) + else: + image_masks = masks[idx] + + im = np.asarray(annotator.im).copy() + for j, box in enumerate(boxes.T.tolist()): + if labels or conf[j] > 0.25: # 0.25 conf thresh + color = colors(classes[j]) + mh, mw = image_masks[j].shape + if mh != h or mw != w: + mask = image_masks[j].astype(np.uint8) + mask = cv2.resize(mask, (w, h)) + mask = mask.astype(bool) + else: + mask = image_masks[j].astype(bool) + with contextlib.suppress(Exception): + im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6 + annotator.fromarray(im) + annotator.im.save(fname) # save + + +def plot_results_with_masks(file='path/to/results.csv', dir='', best=True): + # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') + save_dir = Path(file).parent if file else Path(dir) + fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) + ax = ax.ravel() + files = list(save_dir.glob('results*.csv')) + assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' + for f in files: + try: + data = pd.read_csv(f) + index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + + 0.1 * data.values[:, 11]) + s = [x.strip() for x in data.columns] + x = data.values[:, 0] + for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]): + y = data.values[:, j] + # y[y == 0] = np.nan # don't show zero values + ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=2) + if best: + # best + ax[i].scatter(index, y[index], color='r', label=f'best:{index}', marker='*', linewidth=3) + ax[i].set_title(s[j] + f'\n{round(y[index], 5)}') + else: + # last + ax[i].scatter(x[-1], y[-1], color='r', label='last', marker='*', linewidth=3) + ax[i].set_title(s[j] + f'\n{round(y[-1], 5)}') + # if j in [8, 9, 10]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print(f'Warning: Plotting error for {f}: {e}') + ax[1].legend() + fig.savefig(save_dir / 'results.png', dpi=200) + plt.close() diff --git a/algorithm/yolov5-master/utils/torch_utils.py b/algorithm/yolov5-master/utils/torch_utils.py new file mode 100644 index 0000000..5b67b3f --- /dev/null +++ b/algorithm/yolov5-master/utils/torch_utils.py @@ -0,0 +1,432 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +PyTorch utils +""" + +import math +import os +import platform +import subprocess +import time +import warnings +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.parallel import DistributedDataParallel as DDP + +from utils.general import LOGGER, check_version, colorstr, file_date, git_describe + +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) + +try: + import thop # for FLOPs computation +except ImportError: + thop = None + +# Suppress PyTorch warnings +warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling') +warnings.filterwarnings('ignore', category=UserWarning) + + +def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')): + # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator + def decorate(fn): + return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) + + return decorate + + +def smartCrossEntropyLoss(label_smoothing=0.0): + # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0 + if check_version(torch.__version__, '1.10.0'): + return nn.CrossEntropyLoss(label_smoothing=label_smoothing) + if label_smoothing > 0: + LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0') + return nn.CrossEntropyLoss() + + +def smart_DDP(model): + # Model DDP creation with checks + assert not check_version(torch.__version__, '1.12.0', pinned=True), \ + 'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \ + 'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395' + if check_version(torch.__version__, '1.11.0'): + return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) + else: + return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) + + +def reshape_classifier_output(model, n=1000): + # Update a TorchVision classification model to class count 'n' if required + from models.common import Classify + name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module + if isinstance(m, Classify): # YOLOv5 Classify() head + if m.linear.out_features != n: + m.linear = nn.Linear(m.linear.in_features, n) + elif isinstance(m, nn.Linear): # ResNet, EfficientNet + if m.out_features != n: + setattr(model, name, nn.Linear(m.in_features, n)) + elif isinstance(m, nn.Sequential): + types = [type(x) for x in m] + if nn.Linear in types: + i = types.index(nn.Linear) # nn.Linear index + if m[i].out_features != n: + m[i] = nn.Linear(m[i].in_features, n) + elif nn.Conv2d in types: + i = types.index(nn.Conv2d) # nn.Conv2d index + if m[i].out_channels != n: + m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + # Decorator to make all processes in distributed training wait for each local_master to do something + if local_rank not in [-1, 0]: + dist.barrier(device_ids=[local_rank]) + yield + if local_rank == 0: + dist.barrier(device_ids=[0]) + + +def device_count(): + # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows + assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows' + try: + cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' # Windows + return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) + except Exception: + return 0 + + +def select_device(device='', batch_size=0, newline=True): + # device = None or 'cpu' or 0 or '0' or '0,1,2,3' + s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} ' + device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0' + cpu = device == 'cpu' + mps = device == 'mps' # Apple Metal Performance Shaders (MPS) + if cpu or mps: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available() + assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \ + f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" + + if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available + devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7 + n = len(devices) # device count + if n > 1 and batch_size > 0: # check batch_size is divisible by device_count + assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' + space = ' ' * (len(s) + 1) + for i, d in enumerate(devices): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB + arg = 'cuda:0' + elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available + s += 'MPS\n' + arg = 'mps' + else: # revert to CPU + s += 'CPU\n' + arg = 'cpu' + + if not newline: + s = s.rstrip() + LOGGER.info(s) + return torch.device(arg) + + +def time_sync(): + # PyTorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(input, ops, n=10, device=None): + """ YOLOv5 speed/memory/FLOPs profiler + Usage: + input = torch.randn(16, 3, 640, 640) + m1 = lambda x: x * torch.sigmoid(x) + m2 = nn.SiLU() + profile(input, [m1, m2], n=100) # profile over 100 iterations + """ + results = [] + if not isinstance(device, torch.device): + device = select_device(device) + print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" + f"{'input':>24s}{'output':>24s}") + + for x in input if isinstance(input, list) else [input]: + x = x.to(device) + x.requires_grad = True + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, 'to') else m # device + m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m + tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs + except Exception: + flops = 0 + + try: + for _ in range(n): + t[0] = time_sync() + y = m(x) + t[1] = time_sync() + try: + _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() + t[2] = time_sync() + except Exception: # no backward method + # print(e) # for debug + t[2] = float('nan') + tf += (t[1] - t[0]) * 1000 / n # ms per op forward + tb += (t[2] - t[1]) * 1000 / n # ms per op backward + mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB) + s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes + p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters + print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}') + results.append([p, flops, mem, tf, tb, s_in, s_out]) + except Exception as e: + print(e) + results.append(None) + torch.cuda.empty_cache() + return results + + +def is_parallel(model): + # Returns True if model is of type DP or DDP + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def de_parallel(model): + # De-parallelize a model: returns single-GPU model if model is of type DP or DDP + return model.module if is_parallel(model) else model + + +def initialize_weights(model): + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + # Finds layer indices matching module class 'mclass' + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + # Return global model sparsity + a, b = 0, 0 + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + # Prune model to requested global sparsity + import torch.nn.utils.prune as prune + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name='weight', amount=amount) # prune + prune.remove(m, 'weight') # make permanent + LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity') + + +def fuse_conv_and_bn(conv, bn): + # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + fusedconv = nn.Conv2d(conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + dilation=conv.dilation, + groups=conv.groups, + bias=True).requires_grad_(False).to(conv.weight.device) + + # Prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) + + # Prepare spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, imgsz=640): + # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace('module_list.', '') + print('%5g %40s %9s %12g %20s %10.3g %10.3g' % + (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + + try: # FLOPs + p = next(model.parameters()) + stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride + im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format + flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs + imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float + fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs + except Exception: + fs = '' + + name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model' + LOGGER.info(f'{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}') + + +def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) + # Scales img(bs,3,y,x) by ratio constrained to gs-multiple + if ratio == 1.0: + return img + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize + if not same_shape: # pad/crop img + h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) + return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith('_') or k in exclude: + continue + else: + setattr(a, k, v) + + +def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5): + # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay + g = [], [], [] # optimizer parameter groups + bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d() + for v in model.modules(): + for p_name, p in v.named_parameters(recurse=0): + if p_name == 'bias': # bias (no decay) + g[2].append(p) + elif p_name == 'weight' and isinstance(v, bn): # weight (no decay) + g[1].append(p) + else: + g[0].append(p) # weight (with decay) + + if name == 'Adam': + optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum + elif name == 'AdamW': + optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) + elif name == 'RMSProp': + optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) + elif name == 'SGD': + optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) + else: + raise NotImplementedError(f'Optimizer {name} not implemented.') + + optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay + optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights) + LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " + f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias') + return optimizer + + +def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs): + # YOLOv5 torch.hub.load() wrapper with smart error/issue handling + if check_version(torch.__version__, '1.9.1'): + kwargs['skip_validation'] = True # validation causes GitHub API rate limit errors + if check_version(torch.__version__, '1.12.0'): + kwargs['trust_repo'] = True # argument required starting in torch 0.12 + try: + return torch.hub.load(repo, model, **kwargs) + except Exception: + return torch.hub.load(repo, model, force_reload=True, **kwargs) + + +def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True): + # Resume training from a partially trained checkpoint + best_fitness = 0.0 + start_epoch = ckpt['epoch'] + 1 + if ckpt['optimizer'] is not None: + optimizer.load_state_dict(ckpt['optimizer']) # optimizer + best_fitness = ckpt['best_fitness'] + if ema and ckpt.get('ema'): + ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA + ema.updates = ckpt['updates'] + if resume: + assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \ + f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" + LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs') + if epochs < start_epoch: + LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") + epochs += ckpt['epoch'] # finetune additional epochs + return best_fitness, start_epoch, epochs + + +class EarlyStopping: + # YOLOv5 simple early stopper + def __init__(self, patience=30): + self.best_fitness = 0.0 # i.e. mAP + self.best_epoch = 0 + self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop + self.possible_stop = False # possible stop may occur next epoch + + def __call__(self, epoch, fitness): + if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training + self.best_epoch = epoch + self.best_fitness = fitness + delta = epoch - self.best_epoch # epochs without improvement + self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch + stop = delta >= self.patience # stop training if patience exceeded + if stop: + LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. ' + f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n' + f'To update EarlyStopping(patience={self.patience}) pass a new patience value, ' + f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.') + return stop + + +class ModelEMA: + """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models + Keeps a moving average of everything in the model state_dict (parameters and buffers) + For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + """ + + def __init__(self, model, decay=0.9999, tau=2000, updates=0): + # Create EMA + self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + self.updates += 1 + d = self.decay(self.updates) + + msd = de_parallel(model).state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: # true for FP16 and FP32 + v *= d + v += (1 - d) * msd[k].detach() + # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' + + def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) diff --git a/algorithm/yolov5-master/utils/triton.py b/algorithm/yolov5-master/utils/triton.py new file mode 100644 index 0000000..2592802 --- /dev/null +++ b/algorithm/yolov5-master/utils/triton.py @@ -0,0 +1,85 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" Utils to interact with the Triton Inference Server +""" + +import typing +from urllib.parse import urlparse + +import torch + + +class TritonRemoteModel: + """ A wrapper over a model served by the Triton Inference Server. It can + be configured to communicate over GRPC or HTTP. It accepts Torch Tensors + as input and returns them as outputs. + """ + + def __init__(self, url: str): + """ + Keyword arguments: + url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000 + """ + + parsed_url = urlparse(url) + if parsed_url.scheme == 'grpc': + from tritonclient.grpc import InferenceServerClient, InferInput + + self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client + model_repository = self.client.get_model_repository_index() + self.model_name = model_repository.models[0].name + self.metadata = self.client.get_model_metadata(self.model_name, as_json=True) + + def create_input_placeholders() -> typing.List[InferInput]: + return [ + InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']] + + else: + from tritonclient.http import InferenceServerClient, InferInput + + self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client + model_repository = self.client.get_model_repository_index() + self.model_name = model_repository[0]['name'] + self.metadata = self.client.get_model_metadata(self.model_name) + + def create_input_placeholders() -> typing.List[InferInput]: + return [ + InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']] + + self._create_input_placeholders_fn = create_input_placeholders + + @property + def runtime(self): + """Returns the model runtime""" + return self.metadata.get('backend', self.metadata.get('platform')) + + def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]: + """ Invokes the model. Parameters can be provided via args or kwargs. + args, if provided, are assumed to match the order of inputs of the model. + kwargs are matched with the model input names. + """ + inputs = self._create_inputs(*args, **kwargs) + response = self.client.infer(model_name=self.model_name, inputs=inputs) + result = [] + for output in self.metadata['outputs']: + tensor = torch.as_tensor(response.as_numpy(output['name'])) + result.append(tensor) + return result[0] if len(result) == 1 else result + + def _create_inputs(self, *args, **kwargs): + args_len, kwargs_len = len(args), len(kwargs) + if not args_len and not kwargs_len: + raise RuntimeError('No inputs provided.') + if args_len and kwargs_len: + raise RuntimeError('Cannot specify args and kwargs at the same time') + + placeholders = self._create_input_placeholders_fn() + if args_len: + if args_len != len(placeholders): + raise RuntimeError(f'Expected {len(placeholders)} inputs, got {args_len}.') + for input, value in zip(placeholders, args): + input.set_data_from_numpy(value.cpu().numpy()) + else: + for input in placeholders: + value = kwargs[input.name] + input.set_data_from_numpy(value.cpu().numpy()) + return placeholders diff --git a/algorithm/yolov5-master/val.py b/algorithm/yolov5-master/val.py new file mode 100644 index 0000000..d4073b4 --- /dev/null +++ b/algorithm/yolov5-master/val.py @@ -0,0 +1,409 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Validate a trained YOLOv5 detection model on a detection dataset + +Usage: + $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640 + +Usage - formats: + $ python val.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path + +import numpy as np +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.callbacks import Callbacks +from utils.dataloaders import create_dataloader +from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements, + check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, + print_args, scale_boxes, xywh2xyxy, xyxy2xywh) +from utils.metrics import ConfusionMatrix, ap_per_class, box_iou +from utils.plots import output_to_target, plot_images, plot_val_study +from utils.torch_utils import select_device, smart_inference_mode + + +def save_one_txt(predn, save_conf, shape, file): + # Save one txt result + gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh + for *xyxy, conf, cls in predn.tolist(): + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(file, 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + +def save_one_json(predn, jdict, path, class_map): + # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} + image_id = int(path.stem) if path.stem.isnumeric() else path.stem + box = xyxy2xywh(predn[:, :4]) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + for p, b in zip(predn.tolist(), box.tolist()): + jdict.append({ + 'image_id': image_id, + 'category_id': class_map[int(p[5])], + 'bbox': [round(x, 3) for x in b], + 'score': round(p[4], 5)}) + + +def process_batch(detections, labels, iouv): + """ + Return correct prediction matrix + Arguments: + detections (array[N, 6]), x1, y1, x2, y2, conf, class + labels (array[M, 5]), class, x1, y1, x2, y2 + Returns: + correct (array[N, 10]), for 10 IoU levels + """ + correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) + iou = box_iou(labels[:, 1:], detections[:, :4]) + correct_class = labels[:, 0:1] == detections[:, 5] + for i in range(len(iouv)): + x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + # matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True + return torch.tensor(correct, dtype=torch.bool, device=iouv.device) + + +@smart_inference_mode() +def run( + data, + weights=None, # model.pt path(s) + batch_size=32, # batch size + imgsz=640, # inference size (pixels) + conf_thres=0.001, # confidence threshold + iou_thres=0.6, # NMS IoU threshold + max_det=300, # maximum detections per image + task='val', # train, val, test, speed or study + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + single_cls=False, # treat as single-class dataset + augment=False, # augmented inference + verbose=False, # verbose output + save_txt=False, # save results to *.txt + save_hybrid=False, # save label+prediction hybrid results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_json=False, # save a COCO-JSON results file + project=ROOT / 'runs/val', # save to project/name + name='exp', # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + save_dir=Path(''), + plots=True, + callbacks=Callbacks(), + compute_loss=None, +): + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != 'cpu' # half precision only supported on CUDA + model.half() if half else model.float() + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + + # Data + data = check_dataset(data) # check + + # Configure + model.eval() + cuda = device.type != 'cpu' + is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset + nc = 1 if single_cls else int(data['nc']) # number of classes + iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Dataloader + if not training: + if pt and not single_cls: # check --weights are trained on --data + ncm = model.model.nc + assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ + f'classes). Pass correct combination of --weights and --data that are trained together.' + model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup + pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks + task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images + dataloader = create_dataloader(data[task], + imgsz, + batch_size, + stride, + single_cls, + pad=pad, + rect=rect, + workers=workers, + prefix=colorstr(f'{task}: '))[0] + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = model.names if hasattr(model, 'names') else model.module.names # get class names + if isinstance(names, (list, tuple)): # old format + names = dict(enumerate(names)) + class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) + s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95') + tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 + dt = Profile(), Profile(), Profile() # profiling times + loss = torch.zeros(3, device=device) + jdict, stats, ap, ap_class = [], [], [], [] + callbacks.run('on_val_start') + pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar + for batch_i, (im, targets, paths, shapes) in enumerate(pbar): + callbacks.run('on_val_batch_start') + with dt[0]: + if cuda: + im = im.to(device, non_blocking=True) + targets = targets.to(device) + im = im.half() if half else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + nb, _, height, width = im.shape # batch size, channels, height, width + + # Inference + with dt[1]: + preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None) + + # Loss + if compute_loss: + loss += compute_loss(train_out, targets)[1] # box, obj, cls + + # NMS + targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + with dt[2]: + preds = non_max_suppression(preds, + conf_thres, + iou_thres, + labels=lb, + multi_label=True, + agnostic=single_cls, + max_det=max_det) + + # Metrics + for si, pred in enumerate(preds): + labels = targets[targets[:, 0] == si, 1:] + nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions + path, shape = Path(paths[si]), shapes[si][0] + correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + seen += 1 + + if npr == 0: + if nl: + stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0])) + if plots: + confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) + continue + + # Predictions + if single_cls: + pred[:, 5] = 0 + predn = pred.clone() + scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred + + # Evaluate + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + correct = process_batch(predn, labelsn, iouv) + if plots: + confusion_matrix.process_batch(predn, labelsn) + stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls) + + # Save/log + if save_txt: + save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') + if save_json: + save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary + callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) + + # Plot images + if plots and batch_i < 3: + plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels + plot_images(im, output_to_target(preds), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred + + callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds) + + # Compute metrics + stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) + ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 + mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() + nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class + + # Print results + pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format + LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) + if nt.sum() == 0: + LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels') + + # Print results per class + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): + for i, c in enumerate(ap_class): + LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) + + # Print speeds + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + if not training: + shape = (batch_size, 3, imgsz, imgsz) + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) + + # Save JSON + if save_json and len(jdict): + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights + anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations + pred_json = str(save_dir / f'{w}_predictions.json') # predictions + LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...') + with open(pred_json, 'w') as f: + json.dump(jdict, f) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + check_requirements('pycocotools>=2.0.6') + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + anno = COCO(anno_json) # init annotations api + pred = anno.loadRes(pred_json) # init predictions api + eval = COCOeval(anno, pred, 'bbox') + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) + except Exception as e: + LOGGER.info(f'pycocotools unable to run: {e}') + + # Return results + model.float() # for training + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + maps = np.zeros(nc) + map + for i, c in enumerate(ap_class): + maps[c] = ap[i] + return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)') + parser.add_argument('--batch-size', type=int, default=32, help='batch size') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image') + parser.add_argument('--task', default='val', help='train, val, test, speed or study') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--verbose', action='store_true', help='report mAP by class') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file') + parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + opt.save_json |= opt.data.endswith('coco.yaml') + opt.save_txt |= opt.save_hybrid + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + + if opt.task in ('train', 'val', 'test'): # run normally + if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 + LOGGER.info(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results') + if opt.save_hybrid: + LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone') + run(**vars(opt)) + + else: + weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] + opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results + if opt.task == 'speed': # speed benchmarks + # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... + opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False + for opt.weights in weights: + run(**vars(opt), plots=False) + + elif opt.task == 'study': # speed vs mAP benchmarks + # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... + for opt.weights in weights: + f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to + x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis + for opt.imgsz in x: # img-size + LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...') + r, _, t = run(**vars(opt), plots=False) + y.append(r + t) # results and times + np.savetxt(f, y, fmt='%10.4g') # save + subprocess.run(['zip', '-r', 'study.zip', 'study_*.txt']) + plot_val_study(x=x) # plot + else: + raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/.dockerignore b/algorithm/yolov5/.dockerignore new file mode 100644 index 0000000..3b66925 --- /dev/null +++ b/algorithm/yolov5/.dockerignore @@ -0,0 +1,222 @@ +# Repo-specific DockerIgnore ------------------------------------------------------------------------------------------- +.git +.cache +.idea +runs +output +coco +storage.googleapis.com + +data/samples/* +**/results*.csv +*.jpg + +# Neural Network weights ----------------------------------------------------------------------------------------------- +**/*.pt +**/*.pth +**/*.onnx +**/*.engine +**/*.mlmodel +**/*.torchscript +**/*.torchscript.pt +**/*.tflite +**/*.h5 +**/*.pb +*_saved_model/ +*_web_model/ +*_openvino_model/ + +# Below Copied From .gitignore ----------------------------------------------------------------------------------------- +# Below Copied From .gitignore ----------------------------------------------------------------------------------------- + + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +wandb/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/algorithm/yolov5/.gitattributes b/algorithm/yolov5/.gitattributes new file mode 100644 index 0000000..dad4239 --- /dev/null +++ b/algorithm/yolov5/.gitattributes @@ -0,0 +1,2 @@ +# this drop notebooks from GitHub language stats +*.ipynb linguist-vendored diff --git a/algorithm/yolov5/.github/ISSUE_TEMPLATE/bug-report.yml b/algorithm/yolov5/.github/ISSUE_TEMPLATE/bug-report.yml new file mode 100644 index 0000000..fcb6413 --- /dev/null +++ b/algorithm/yolov5/.github/ISSUE_TEMPLATE/bug-report.yml @@ -0,0 +1,85 @@ +name: 🐛 Bug Report +# title: " " +description: Problems with YOLOv5 +labels: [bug, triage] +body: + - type: markdown + attributes: + value: | + Thank you for submitting a YOLOv5 🐛 Bug Report! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar bug report already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report. + required: true + + - type: dropdown + attributes: + label: YOLOv5 Component + description: | + Please select the part of YOLOv5 where you found the bug. + multiple: true + options: + - "Training" + - "Validation" + - "Detection" + - "Export" + - "PyTorch Hub" + - "Multi-GPU" + - "Evolution" + - "Integrations" + - "Other" + validations: + required: false + + - type: textarea + attributes: + label: Bug + description: Provide console output with error messages and/or screenshots of the bug. + placeholder: | + 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. + validations: + required: true + + - type: textarea + attributes: + label: Environment + description: Please specify the software and hardware you used to produce the bug. + placeholder: | + - YOLO: YOLOv5 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB) + - OS: Ubuntu 20.04 + - Python: 3.9.0 + validations: + required: false + + - type: textarea + attributes: + label: Minimal Reproducible Example + description: > + When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem. + This is referred to by community members as creating a [minimal reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). + placeholder: | + ``` + # Code to reproduce your issue here + ``` + validations: + required: false + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? + + - type: checkboxes + attributes: + label: Are you willing to submit a PR? + description: > + (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature. + See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started. + options: + - label: Yes I'd like to help by submitting a PR! diff --git a/algorithm/yolov5/.github/ISSUE_TEMPLATE/config.yml b/algorithm/yolov5/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..4db7cef --- /dev/null +++ b/algorithm/yolov5/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,8 @@ +blank_issues_enabled: true +contact_links: + - name: 💬 Forum + url: https://community.ultralytics.com/ + about: Ask on Ultralytics Community Forum + - name: Stack Overflow + url: https://stackoverflow.com/search?q=YOLOv5 + about: Ask on Stack Overflow with 'YOLOv5' tag diff --git a/algorithm/yolov5/.github/ISSUE_TEMPLATE/feature-request.yml b/algorithm/yolov5/.github/ISSUE_TEMPLATE/feature-request.yml new file mode 100644 index 0000000..68ef985 --- /dev/null +++ b/algorithm/yolov5/.github/ISSUE_TEMPLATE/feature-request.yml @@ -0,0 +1,50 @@ +name: 🚀 Feature Request +description: Suggest a YOLOv5 idea +# title: " " +labels: [enhancement] +body: + - type: markdown + attributes: + value: | + Thank you for submitting a YOLOv5 🚀 Feature Request! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar feature request already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar feature requests. + required: true + + - type: textarea + attributes: + label: Description + description: A short description of your feature. + placeholder: | + What new feature would you like to see in YOLOv5? + validations: + required: true + + - type: textarea + attributes: + label: Use case + description: | + Describe the use case of your feature request. It will help us understand and prioritize the feature request. + placeholder: | + How would this feature be used, and who would use it? + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? + + - type: checkboxes + attributes: + label: Are you willing to submit a PR? + description: > + (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature. + See the YOLOv5 [Contributing Guide](https://github.com/ultralytics/yolov5/blob/master/CONTRIBUTING.md) to get started. + options: + - label: Yes I'd like to help by submitting a PR! diff --git a/algorithm/yolov5/.github/ISSUE_TEMPLATE/question.yml b/algorithm/yolov5/.github/ISSUE_TEMPLATE/question.yml new file mode 100644 index 0000000..8e0993c --- /dev/null +++ b/algorithm/yolov5/.github/ISSUE_TEMPLATE/question.yml @@ -0,0 +1,33 @@ +name: ❓ Question +description: Ask a YOLOv5 question +# title: " " +labels: [question] +body: + - type: markdown + attributes: + value: | + Thank you for asking a YOLOv5 ❓ Question! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) to see if a similar question already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions. + required: true + + - type: textarea + attributes: + label: Question + description: What is your question? + placeholder: | + 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. + validations: + required: true + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? diff --git a/algorithm/yolov5/.github/PULL_REQUEST_TEMPLATE.md b/algorithm/yolov5/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 0000000..f25b017 --- /dev/null +++ b/algorithm/yolov5/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,9 @@ + diff --git a/algorithm/yolov5/.github/dependabot.yml b/algorithm/yolov5/.github/dependabot.yml new file mode 100644 index 0000000..c1b3d5d --- /dev/null +++ b/algorithm/yolov5/.github/dependabot.yml @@ -0,0 +1,23 @@ +version: 2 +updates: + - package-ecosystem: pip + directory: "/" + schedule: + interval: weekly + time: "04:00" + open-pull-requests-limit: 10 + reviewers: + - glenn-jocher + labels: + - dependencies + + - package-ecosystem: github-actions + directory: "/" + schedule: + interval: weekly + time: "04:00" + open-pull-requests-limit: 5 + reviewers: + - glenn-jocher + labels: + - dependencies diff --git a/algorithm/yolov5/.github/workflows/ci-testing.yml b/algorithm/yolov5/.github/workflows/ci-testing.yml new file mode 100644 index 0000000..a6f47bb --- /dev/null +++ b/algorithm/yolov5/.github/workflows/ci-testing.yml @@ -0,0 +1,153 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# YOLOv5 Continuous Integration (CI) GitHub Actions tests + +name: YOLOv5 CI + +on: + push: + branches: [ master ] + pull_request: + branches: [ master ] + schedule: + - cron: '0 0 * * *' # runs at 00:00 UTC every day + +jobs: + Benchmarks: + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest ] + python-version: [ '3.10' ] # requires python<=3.10 + model: [ yolov5n ] + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' # caching pip dependencies + - name: Install requirements + run: | + python -m pip install --upgrade pip wheel + pip install -r requirements.txt coremltools openvino-dev tensorflow-cpu --extra-index-url https://download.pytorch.org/whl/cpu + python --version + pip --version + pip list + - name: Benchmark DetectionModel + run: | + python benchmarks.py --data coco128.yaml --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29 + - name: Benchmark SegmentationModel + run: | + python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320 --hard-fail 0.22 + - name: Test predictions + run: | + python export.py --weights ${{ matrix.model }}-cls.pt --include onnx --img 224 + python detect.py --weights ${{ matrix.model }}.onnx --img 320 + python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320 + python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224 + + Tests: + timeout-minutes: 60 + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ ubuntu-latest, windows-latest ] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049 + python-version: [ '3.10' ] + model: [ yolov5n ] + include: + - os: ubuntu-latest + python-version: '3.7' # '3.6.8' min + model: yolov5n + - os: ubuntu-latest + python-version: '3.8' + model: yolov5n + - os: ubuntu-latest + python-version: '3.9' + model: yolov5n + - os: ubuntu-latest + python-version: '3.8' # torch 1.7.0 requires python >=3.6, <=3.8 + model: yolov5n + torch: '1.7.0' # min torch version CI https://pypi.org/project/torchvision/ + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + cache: 'pip' # caching pip dependencies + - name: Install requirements + run: | + python -m pip install --upgrade pip wheel + if [ "${{ matrix.torch }}" == "1.7.0" ]; then + pip install -r requirements.txt torch==1.7.0 torchvision==0.8.1 --extra-index-url https://download.pytorch.org/whl/cpu + else + pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu + fi + shell: bash # for Windows compatibility + - name: Check environment + run: | + python -c "import utils; utils.notebook_init()" + echo "RUNNER_OS is ${{ runner.os }}" + echo "GITHUB_EVENT_NAME is ${{ github.event_name }}" + echo "GITHUB_WORKFLOW is ${{ github.workflow }}" + echo "GITHUB_ACTOR is ${{ github.actor }}" + echo "GITHUB_REPOSITORY is ${{ github.repository }}" + echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}" + python --version + pip --version + pip list + - name: Test detection + shell: bash # for Windows compatibility + run: | + # export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories + m=${{ matrix.model }} # official weights + b=runs/train/exp/weights/best # best.pt checkpoint + python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train + for d in cpu; do # devices + for w in $m $b; do # weights + python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val + python detect.py --imgsz 64 --weights $w.pt --device $d # detect + done + done + python hubconf.py --model $m # hub + # python models/tf.py --weights $m.pt # build TF model + python models/yolo.py --cfg $m.yaml # build PyTorch model + python export.py --weights $m.pt --img 64 --include torchscript # export + python - <=3.7.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). To get started: + ```bash + git clone https://github.com/ultralytics/yolov5 # clone + cd yolov5 + pip install -r requirements.txt # install + ``` + + ## Environments + + YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): + + - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle + - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) + - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart) + - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) Docker Pulls + + ## Status + + YOLOv5 CI + + If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. + + ## Introducing YOLOv8 🚀 + + We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀! + + Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects. + + Check out our [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with: + ```bash + pip install ultralytics + ``` diff --git a/algorithm/yolov5/.github/workflows/stale.yml b/algorithm/yolov5/.github/workflows/stale.yml new file mode 100644 index 0000000..b21e9c0 --- /dev/null +++ b/algorithm/yolov5/.github/workflows/stale.yml @@ -0,0 +1,40 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +name: Close stale issues +on: + schedule: + - cron: '0 0 * * *' # Runs at 00:00 UTC every day + +jobs: + stale: + runs-on: ubuntu-latest + steps: + - uses: actions/stale@v7 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + stale-issue-message: | + 👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs. + + Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources: + - **Wiki** – https://github.com/ultralytics/yolov5/wiki + - **Tutorials** – https://github.com/ultralytics/yolov5#tutorials + - **Docs** – https://docs.ultralytics.com + + Access additional [Ultralytics](https://ultralytics.com) ⚡ resources: + - **Ultralytics HUB** – https://ultralytics.com/hub + - **Vision API** – https://ultralytics.com/yolov5 + - **About Us** – https://ultralytics.com/about + - **Join Our Team** – https://ultralytics.com/work + - **Contact Us** – https://ultralytics.com/contact + + Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed! + + Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐! + + stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.' + days-before-issue-stale: 30 + days-before-issue-close: 10 + days-before-pr-stale: 90 + days-before-pr-close: 30 + exempt-issue-labels: 'documentation,tutorial,TODO' + operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting. diff --git a/algorithm/yolov5/.github/workflows/translate-readme.yml b/algorithm/yolov5/.github/workflows/translate-readme.yml new file mode 100644 index 0000000..2bb351e --- /dev/null +++ b/algorithm/yolov5/.github/workflows/translate-readme.yml @@ -0,0 +1,26 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# README translation action to translate README.md to Chinese as README.zh-CN.md on any change to README.md + +name: Translate README + +on: + push: + branches: + - translate_readme # replace with 'master' to enable action + paths: + - README.md + +jobs: + Translate: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - name: Setup Node.js + uses: actions/setup-node@v3 + with: + node-version: 16 + # ISO Language Codes: https://cloud.google.com/translate/docs/languages + - name: Adding README - Chinese Simplified + uses: dephraiim/translate-readme@main + with: + LANG: zh-CN diff --git a/algorithm/yolov5/.gitignore b/algorithm/yolov5/.gitignore new file mode 100644 index 0000000..6bcedfa --- /dev/null +++ b/algorithm/yolov5/.gitignore @@ -0,0 +1,257 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.jpeg +*.png +*.bmp +*.tif +*.tiff +*.heic +*.JPG +*.JPEG +*.PNG +*.BMP +*.TIF +*.TIFF +*.HEIC +*.mp4 +*.mov +*.MOV +*.avi +*.data +*.json +*.cfg +!setup.cfg +!cfg/yolov3*.cfg + +storage.googleapis.com +runs/* +data/* +data/images/* +!data/*.yaml +!data/hyps +!data/scripts +!data/images +!data/images/zidane.jpg +!data/images/bus.jpg +!data/*.sh + +results*.csv + +# Datasets ------------------------------------------------------------------------------------------------------------- +coco/ +coco128/ +VOC/ + +# MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- +*.m~ +*.mat +!targets*.mat + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.pb +*.onnx +*.engine +*.mlmodel +*.torchscript +*.tflite +*.h5 +*_saved_model/ +*_web_model/ +*_openvino_model/ +*_paddle_model/ +darknet53.conv.74 +yolov3-tiny.conv.15 + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +/wandb/ +.installed.cfg +*.egg + + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/algorithm/yolov5/.pre-commit-config.yaml b/algorithm/yolov5/.pre-commit-config.yaml new file mode 100644 index 0000000..c516237 --- /dev/null +++ b/algorithm/yolov5/.pre-commit-config.yaml @@ -0,0 +1,69 @@ +# Ultralytics YOLO 🚀, GPL-3.0 license +# Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md + +exclude: 'docs/' +# Define bot property if installed via https://github.com/marketplace/pre-commit-ci +ci: + autofix_prs: true + autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions' + autoupdate_schedule: monthly + # submodules: true + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.4.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-case-conflict + - id: check-yaml + - id: check-docstring-first + - id: double-quote-string-fixer + - id: detect-private-key + + - repo: https://github.com/asottile/pyupgrade + rev: v3.3.1 + hooks: + - id: pyupgrade + name: Upgrade code + args: [--py37-plus] + + - repo: https://github.com/PyCQA/isort + rev: 5.12.0 + hooks: + - id: isort + name: Sort imports + + - repo: https://github.com/google/yapf + rev: v0.32.0 + hooks: + - id: yapf + name: YAPF formatting + + - repo: https://github.com/executablebooks/mdformat + rev: 0.7.16 + hooks: + - id: mdformat + name: MD formatting + additional_dependencies: + - mdformat-gfm + - mdformat-black + # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md" + + - repo: https://github.com/PyCQA/flake8 + rev: 6.0.0 + hooks: + - id: flake8 + name: PEP8 + + - repo: https://github.com/codespell-project/codespell + rev: v2.2.2 + hooks: + - id: codespell + args: + - --ignore-words-list=crate,nd,strack,dota + + #- repo: https://github.com/asottile/yesqa + # rev: v1.4.0 + # hooks: + # - id: yesqa diff --git a/algorithm/yolov5/CITATION.cff b/algorithm/yolov5/CITATION.cff new file mode 100644 index 0000000..8e2cf11 --- /dev/null +++ b/algorithm/yolov5/CITATION.cff @@ -0,0 +1,14 @@ +cff-version: 1.2.0 +preferred-citation: + type: software + message: If you use YOLOv5, please cite it as below. + authors: + - family-names: Jocher + given-names: Glenn + orcid: "https://orcid.org/0000-0001-5950-6979" + title: "YOLOv5 by Ultralytics" + version: 7.0 + doi: 10.5281/zenodo.3908559 + date-released: 2020-5-29 + license: GPL-3.0 + url: "https://github.com/ultralytics/yolov5" diff --git a/algorithm/yolov5/CONTRIBUTING.md b/algorithm/yolov5/CONTRIBUTING.md new file mode 100644 index 0000000..71857fa --- /dev/null +++ b/algorithm/yolov5/CONTRIBUTING.md @@ -0,0 +1,93 @@ +## Contributing to YOLOv5 🚀 + +We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's: + +- Reporting a bug +- Discussing the current state of the code +- Submitting a fix +- Proposing a new feature +- Becoming a maintainer + +YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be +helping push the frontiers of what's possible in AI 😃! + +## Submitting a Pull Request (PR) 🛠️ + +Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps: + +### 1. Select File to Update + +Select `requirements.txt` to update by clicking on it in GitHub. + +

PR_step1

+ +### 2. Click 'Edit this file' + +The button is in the top-right corner. + +

PR_step2

+ +### 3. Make Changes + +Change the `matplotlib` version from `3.2.2` to `3.3`. + +

PR_step3

+ +### 4. Preview Changes and Submit PR + +Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** +for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose +changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃! + +

PR_step4

+ +### PR recommendations + +To allow your work to be integrated as seamlessly as possible, we advise you to: + +- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update + your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally. + +

Screenshot 2022-08-29 at 22 47 15

+ +- ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**. + +

Screenshot 2022-08-29 at 22 47 03

+ +- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase + but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee + +## Submitting a Bug Report 🐛 + +If you spot a problem with YOLOv5 please submit a Bug Report! + +For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few +short guidelines below to help users provide what we need to get started. + +When asking a question, people will be better able to provide help if you provide **code** that they can easily +understand and use to **reproduce** the problem. This is referred to by community members as creating +a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces +the problem should be: + +- ✅ **Minimal** – Use as little code as possible that still produces the same problem +- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself +- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem + +In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code +should be: + +- ✅ **Current** – Verify that your code is up-to-date with the current + GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new + copy to ensure your problem has not already been resolved by previous commits. +- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this + repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. + +If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 +**Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and provide +a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better +understand and diagnose your problem. + +## License + +By contributing, you agree that your contributions will be licensed under +the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/) diff --git a/algorithm/yolov5/LICENSE b/algorithm/yolov5/LICENSE new file mode 100644 index 0000000..92b370f --- /dev/null +++ b/algorithm/yolov5/LICENSE @@ -0,0 +1,674 @@ +GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/algorithm/yolov5/README.md b/algorithm/yolov5/README.md new file mode 100644 index 0000000..16dfd9f --- /dev/null +++ b/algorithm/yolov5/README.md @@ -0,0 +1,493 @@ +
+

+ + +

+ +[English](README.md) | [简体中文](README.zh-CN.md) +
+ +
+ YOLOv5 CI + YOLOv5 Citation + Docker Pulls +
+ Run on Gradient + Open In Colab + Open In Kaggle +
+
+ +YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. + +To request an Enterprise License please complete the form at Ultralytics Licensing. + +
+ + + + + + + + + + + + + + + + + + + + +
+
+
+ +##
YOLOv8 🚀 NEW
+ +We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model +released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. +YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of +object detection, image segmentation and image classification tasks. + +See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with: + +```commandline +pip install ultralytics +``` + +
+ + +
+ +##
Documentation
+ +See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. See below for quickstart examples. + +
+Install + +Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a +[**Python>=3.7.0**](https://www.python.org/) environment, including +[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). + +```bash +git clone https://github.com/ultralytics/yolov5 # clone +cd yolov5 +pip install -r requirements.txt # install +``` + +
+ +
+Inference + +YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest +YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). + +```python +import torch + +# Model +model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom + +# Images +img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list + +# Inference +results = model(img) + +# Results +results.print() # or .show(), .save(), .crop(), .pandas(), etc. +``` + +
+ +
+Inference with detect.py + +`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from +the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. + +```bash +python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream +``` + +
+ +
+Training + +The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) +results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) +and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest +YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are +1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the +largest `--batch-size` possible, or pass `--batch-size -1` for +YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. + +```bash +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 + yolov5s 64 + yolov5m 40 + yolov5l 24 + yolov5x 16 +``` + + + +
+ +
+Tutorials + +- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 RECOMMENDED +- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️ + RECOMMENDED +- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) +- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 NEW +- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 +- [NVIDIA Jetson Nano Deployment](https://github.com/ultralytics/yolov5/issues/9627) 🌟 NEW +- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) +- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) +- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) +- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) +- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) +- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) 🌟 NEW +- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)  🌟 NEW +- [ClearML Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 NEW +- [YOLOv5 with Neural Magic's Deepsparse](https://bit.ly/yolov5-neuralmagic) 🌟 NEW +- [Comet Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet) 🌟 NEW + +
+ +##
Integrations
+ +
+ + +
+
+ +
+ + + + + + + + + + + +
+ +| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | +| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | +| Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions | Run YOLOv5 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | + +##
Ultralytics HUB
+ +Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now! + + + + +##
Why YOLOv5
+ +YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results. + +

+
+ YOLOv5-P5 640 Figure + +

+
+
+ Figure Notes + +- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536. +- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32. +- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8. +- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` + +
+ +### Pretrained Checkpoints + +| Model | size
(pixels) | mAPval
50-95 | mAPval
50 | Speed
CPU b1
(ms) | Speed
V100 b1
(ms) | Speed
V100 b32
(ms) | params
(M) | FLOPs
@640 (B) | +| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- | +| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | +| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | +| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | +| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | +| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | +| | | | | | | | | | +| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | +| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | +| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | +| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | +| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)
+ [TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | + +
+ Table Notes + +- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). +- **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` +- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.
Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1` +- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` + +
+ +##
Segmentation
+ +Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials. + +
+ Segmentation Checkpoints + +
+ + +
+ +We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility. + +| Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Train time
300 epochs
A100 (hours) | Speed
ONNX CPU
(ms) | Speed
TRT A100
(ms) | params
(M) | FLOPs
@640 (B) | +| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- | +| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | +| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | +| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | +| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | +| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | + +- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official +- **Accuracy** values are for single-model single-scale on COCO dataset.
Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` +- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image).
Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` +- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`.
Reproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` + +
+ +
+ Segmentation Usage Examples  Open In Colab + +### Train + +YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`. + +```bash +# Single-GPU +python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 + +# Multi-GPU DDP +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 +``` + +### Val + +Validate YOLOv5s-seg mask mAP on COCO dataset: + +```bash +bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images) +python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate +``` + +### Predict + +Use pretrained YOLOv5m-seg.pt to predict bus.jpg: + +```bash +python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5m-seg.pt" +) # load from PyTorch Hub (WARNING: inference not yet supported) +``` + +| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | +| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | + +### Export + +Export YOLOv5s-seg model to ONNX and TensorRT: + +```bash +python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 +``` + +
+ +##
Classification
+ +YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials. + +
+ Classification Checkpoints + +
+ +We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility. + +| Model | size
(pixels) | acc
top1 | acc
top5 | Training
90 epochs
4xA100 (hours) | Speed
ONNX CPU
(ms) | Speed
TensorRT V100
(ms) | params
(M) | FLOPs
@224 (B) | +| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- | +| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | +| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | +| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | +| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | +| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | +| | | | | | | | | | +| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | +| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | +| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | +| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | +| | | | | | | | | | +| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | +| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | +| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | +| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 | + +
+ Table Notes (click to expand) + +- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 +- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.
Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224` +- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.
Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` +- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`.
Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` + +
+
+ +
+ Classification Usage Examples  Open In Colab + +### Train + +YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`. + +```bash +# Single-GPU +python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 + +# Multi-GPU DDP +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 +``` + +### Val + +Validate YOLOv5m-cls accuracy on ImageNet-1k dataset: + +```bash +bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) +python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate +``` + +### Predict + +Use pretrained YOLOv5s-cls.pt to predict bus.jpg: + +```bash +python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5s-cls.pt" +) # load from PyTorch Hub +``` + +### Export + +Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT: + +```bash +python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 +``` + +
+ +##
Environments
+ +Get started in seconds with our verified environments. Click each icon below for details. + +
+ + + + + + + + + + + + + + + + + +
+ +##
Contribute
+ +We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors! + + + + + + +##
License
+ +YOLOv5 is available under two different licenses: + +- **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details. +- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license). + +##
Contact
+ +For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues) or the [Ultralytics Community Forum](https://community.ultralytics.com/). + +
+
+ + + + + + + + + + + + + + + + + + + + +
+ +[tta]: https://github.com/ultralytics/yolov5/issues/303 diff --git a/algorithm/yolov5/README.zh-CN.md b/algorithm/yolov5/README.zh-CN.md new file mode 100644 index 0000000..800a670 --- /dev/null +++ b/algorithm/yolov5/README.zh-CN.md @@ -0,0 +1,488 @@ +
+

+ + +

+ +[英文](README.md)|[简体中文](README.zh-CN.md)
+ +
+ YOLOv5 CI + YOLOv5 Citation + Docker Pulls +
+ Run on Gradient + Open In Colab + Open In Kaggle +
+
+ +YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表 Ultralytics 对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。 + +如果要申请企业许可证，请填写表格Ultralytics 许可. + +
+ + + + + + + + + + + + + + + + + + + + +
+
+ +##
YOLOv8 🚀 NEW
+ +We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model +released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. +YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of +object detection, image segmentation and image classification tasks. + +See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with: + +```commandline +pip install ultralytics +``` + +
+ + +
+ +##
文档
+ +有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com)。请参阅下面的快速入门示例。 + +
+安装 + +克隆 repo，并要求在 [**Python>=3.7.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/) 。 + +```bash +git clone https://github.com/ultralytics/yolov5 # clone +cd yolov5 +pip install -r requirements.txt # install +``` + +
+ +
+推理 + +使用 YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 +YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 + +```python +import torch + +# Model +model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom + +# Images +img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list + +# Inference +results = model(img) + +# Results +results.print() # or .show(), .save(), .crop(), .pandas(), etc. +``` + +
+ +
+使用 detect.py 推理 + +`detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 +最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。 + +```bash +python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream +``` + +
+ +
+训练 + +下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 +最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) +将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 +YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://github.com/ultralytics/yolov5/issues/475) 训练速度更快）。 +尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 +YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 + +```bash +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 + yolov5s 64 + yolov5m 40 + yolov5l 24 + yolov5x 16 +``` + + + +
+ +
+教程 + +- [训练自定义数据](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)🚀 推荐 +- [获得最佳训练结果的技巧](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)☘️ 推荐 +- [多 GPU 训练](https://github.com/ultralytics/yolov5/issues/475) +- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)🌟 新 +- [TFLite、ONNX、CoreML、TensorRT 导出](https://github.com/ultralytics/yolov5/issues/251)🚀 +- [NVIDIA Jetson Nano 部署](https://github.com/ultralytics/yolov5/issues/9627)🌟 新 +- [测试时数据增强 (TTA)](https://github.com/ultralytics/yolov5/issues/303) +- [模型集成](https://github.com/ultralytics/yolov5/issues/318) +- [模型修剪/稀疏度](https://github.com/ultralytics/yolov5/issues/304) +- [超参数进化](https://github.com/ultralytics/yolov5/issues/607) +- [使用冻结层进行迁移学习](https://github.com/ultralytics/yolov5/issues/1314) +- [架构总结](https://github.com/ultralytics/yolov5/issues/6998)🌟 新 +- [用于数据集、标签和主动学习的 Roboflow](https://github.com/ultralytics/yolov5/issues/4975)🌟 新 +- [ClearML 记录](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml)🌟 新 +- [Deci 平台](https://github.com/ultralytics/yolov5/wiki/Deci-Platform)🌟 新 +- [Comet Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet)🌟 新 + +
+ +##
模块集成
+ +
+ + +
+
+ +
+ + + + + + + + + + + +
+ +| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 | +| :--------------------------------------------------------------------------------: | :-------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: | +| 将您的自定义数据集进行标注并直接导出到 YOLOv5 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv5 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv5 推理的速度最高可提高6倍 | + +##
Ultralytics HUB
+ +[Ultralytics HUB](https://bit.ly/ultralytics_hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！ + + + + +##
为什么选择 YOLOv5
+ +YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结果。 + +

+
+ YOLOv5-P5 640 图 + +

+
+
+ 图表笔记 + +- **COCO AP val** 表示 mAP@0.5:0.95 指标，在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得， 图像包含 256 到 1536 各种推理大小。 +- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例，batchsize 为 32 。 +- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ， batchsize 为32。 +- **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` + +
+ +### 预训练模型 + +| 模型 | 尺寸
（像素） | mAPval
50-95 | mAPval
50 | 推理速度
CPU b1
（ms） | 推理速度
V100 b1
（ms） | 速度
V100 b32
（ms） | 参数量
(M) | FLOPs
@640 (B) | +| ---------------------------------------------------------------------------------------------- | --------------- | -------------------- | ----------------- | --------------------------- | ---------------------------- | --------------------------- | --------------- | ---------------------- | +| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | +| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | +| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | +| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | +| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | +| | | | | | | | | | +| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | +| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | +| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | +| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | +| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)
+[TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | + +
+ 笔记 + +- 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。 +- \*\*mAPval\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。
复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` +- **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。
复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1` +- **TTA** [测试时数据增强](https://github.com/ultralytics/yolov5/issues/303) 包括反射和尺度变换。
复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` + +
+ +##
实例分割模型 ⭐ 新
+ +我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型，击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。 + +
+ 实例分割模型列表 + +
+ +
+ + +
+ +我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现，我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。 + +| 模型 | 尺寸
（像素） | mAPbox
50-95 | mAPmask
50-95 | 训练时长
300 epochs
A100 GPU（小时） | 推理速度
ONNX CPU
（ms） | 推理速度
TRT A100
（ms） | 参数量
(M) | FLOPs
@640 (B) | +| ------------------------------------------------------------------------------------------ | --------------- | -------------------- | --------------------- | --------------------------------------- | ----------------------------- | ----------------------------- | --------------- | ---------------------- | +| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | +| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | +| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | +| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | +| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | + +- 所有模型使用 SGD 优化器训练， 都使用 `lr0=0.01` 和 `weight_decay=5e-5` 参数， 图像大小为 640 。
训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official +- **准确性**结果都在 COCO 数据集上，使用单模型单尺度测试得到。
复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` +- **推理速度**是使用 100 张图像推理时间进行平均得到，测试环境使用 [Colab Pro](https://colab.research.google.com/signup) 上 A100 高 RAM 实例。结果仅表示推理速度（NMS 每张图像增加约 1 毫秒）。
复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` +- **模型转换**到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 `export.py`.
运行命令 `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` + +
+ +
+ 分割模型使用示例  Open In Colab + +### 训练 + +YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集，用户仅需在启动指令中包含 `--data coco128-seg.yaml` 参数。 若要手动下载，使用命令 `bash data/scripts/get_coco.sh --train --val --segments`， 在下载完毕后，使用命令 `python train.py --data coco.yaml` 开启训练。 + +```bash +# 单 GPU +python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 + +# 多 GPU， DDP 模式 +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 +``` + +### 验证 + +在 COCO 数据集上验证 YOLOv5s-seg mask mAP： + +```bash +bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images) +python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证 +``` + +### 预测 + +使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg： + +```bash +python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5m-seg.pt" +) # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持) +``` + +| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | +| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | + +### 模型导出 + +将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT： + +```bash +python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 +``` + +
+ +##
分类网络 ⭐ 新
+ +YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对分类模型训练、验证和部署的支持！详情请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v6.2) 或访问我们的 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) 以快速入门。 + +
+ 分类网络模型 + +
+ +我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现，我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。 + +| 模型 | 尺寸
（像素） | acc
top1 | acc
top5 | 训练时长
90 epochs
4xA100（小时） | 推理速度
ONNX CPU
（ms） | 推理速度
TensorRT V100
（ms） | 参数
(M) | FLOPs
@640 (B) | +| -------------------------------------------------------------------------------------------------- | --------------- | ---------------- | ---------------- | ------------------------------------ | ----------------------------- | ---------------------------------- | -------------- | ---------------------- | +| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | +| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | +| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | +| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | +| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | +| | | | | | | | | | +| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | +| [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | +| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | +| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | +| | | | | | | | | | +| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | +| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | +| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | +| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 | + +
+ Table Notes (点击以展开) + +- 所有模型都使用 SGD 优化器训练 90 个 epochs，都使用 `lr0=0.001` 和 `weight_decay=5e-5` 参数， 图像大小为 224 ，且都使用默认设置。
训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 +- **准确性**都在单模型单尺度上计算，数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。
复现命令 `python classify/val.py --data ../datasets/imagenet --img 224` +- **推理速度**是使用 100 个推理图像进行平均得到，测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。
复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` +- **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。
复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` +
+
+ +
+ 分类训练示例  Open In Colab + +### 训练 + +YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集，命令中使用 `--data` 即可。 MNIST 示例 `--data mnist` 。 + +```bash +# 单 GPU +python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 + +# 多 GPU， DDP 模式 +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 +``` + +### 验证 + +在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性： + +```bash +bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) +python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate +``` + +### 预测 + +使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg： + +```bash +python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5s-cls.pt" +) # load from PyTorch Hub +``` + +### 模型导出 + +将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT： + +```bash +python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 +``` + +
+ +##
环境
+ +使用下面我们经过验证的环境，在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。 + +
+ + + + + + + + + + + + + + + + + +
+ +##
贡献
+ +我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 [投稿指南](CONTRIBUTING.md)，并填写 [YOLOv5调查](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！ + + + + + + +##
License
+ +YOLOv5 在两种不同的 License 下可用： + +- **GPL-3.0 License**： 查看 [License](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件的详细信息。 +- **企业License**：在没有 GPL-3.0 开源要求的情况下为商业产品开发提供更大的灵活性。典型用例是将 Ultralytics 软件和 AI 模型嵌入到商业产品和应用程序中。在以下位置申请企业许可证 [Ultralytics 许可](https://ultralytics.com/license) 。 + +##
联系我们
+ +请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues) 或 [Ultralytics Community Forum](https://community.ultralytis.com) 以报告 YOLOv5 错误和请求功能。 + +
+
+ + + + + + + + + + + + + + + + + + + + +
+ +[tta]: https://github.com/ultralytics/yolov5/issues/303 diff --git a/algorithm/yolov5/benchmarks.py b/algorithm/yolov5/benchmarks.py new file mode 100644 index 0000000..e6c940f --- /dev/null +++ b/algorithm/yolov5/benchmarks.py @@ -0,0 +1,169 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 benchmarks on all supported export formats + +Format | `export.py --include` | Model +--- | --- | --- +PyTorch | - | yolov5s.pt +TorchScript | `torchscript` | yolov5s.torchscript +ONNX | `onnx` | yolov5s.onnx +OpenVINO | `openvino` | yolov5s_openvino_model/ +TensorRT | `engine` | yolov5s.engine +CoreML | `coreml` | yolov5s.mlmodel +TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ +TensorFlow GraphDef | `pb` | yolov5s.pb +TensorFlow Lite | `tflite` | yolov5s.tflite +TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov5s_web_model/ + +Requirements: + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU + $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT + +Usage: + $ python benchmarks.py --weights yolov5s.pt --img 640 +""" + +import argparse +import platform +import sys +import time +from pathlib import Path + +import pandas as pd + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +# ROOT = ROOT.relative_to(Path.cwd()) # relative + +import export +from models.experimental import attempt_load +from models.yolo import SegmentationModel +from segment.val import run as val_seg +from algorithm.yolov5.utils import notebook_init +from algorithm.yolov5.utils.general import LOGGER, check_yaml, file_size, print_args +from algorithm.yolov5.utils.torch_utils import select_device +from val import run as val_det + + +def run( + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure +): + y, t = [], time.time() + device = select_device(device) + model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc. + for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU) + try: + assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported + assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML + if 'cpu' in device.type: + assert cpu, 'inference not supported on CPU' + if 'cuda' in device.type: + assert gpu, 'inference not supported on GPU' + + # Export + if f == '-': + w = weights # PyTorch format + else: + w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # all others + assert suffix in str(w), 'export failed' + + # Validate + if model_type == SegmentationModel: + result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half) + metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls)) + else: # DetectionModel: + result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half) + metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls)) + speed = result[2][1] # times (preprocess, inference, postprocess) + y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference + except Exception as e: + if hard_fail: + assert type(e) is AssertionError, f'Benchmark --hard-fail for {name}: {e}' + LOGGER.warning(f'WARNING ⚠️ Benchmark failure for {name}: {e}') + y.append([name, None, None, None]) # mAP, t_inference + if pt_only and i == 0: + break # break after PyTorch + + # Print results + LOGGER.info('\n') + parse_opt() + notebook_init() # print system info + c = ['Format', 'Size (MB)', 'mAP50-95', 'Inference time (ms)'] if map else ['Format', 'Export', '', ''] + py = pd.DataFrame(y, columns=c) + LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)') + LOGGER.info(str(py if map else py.iloc[:, :2])) + if hard_fail and isinstance(hard_fail, str): + metrics = py['mAP50-95'].array # values to compare to floor + floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n + assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: mAP50-95 < floor {floor}' + return py + + +def test( + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure +): + y, t = [], time.time() + device = select_device(device) + for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable) + try: + w = weights if f == '-' else \ + export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights + assert suffix in str(w), 'export failed' + y.append([name, True]) + except Exception: + y.append([name, False]) # mAP, t_inference + + # Print results + LOGGER.info('\n') + parse_opt() + notebook_init() # print system info + py = pd.DataFrame(y, columns=['Format', 'Export']) + LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)') + LOGGER.info(str(py)) + return py + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--test', action='store_true', help='test exports only') + parser.add_argument('--pt-only', action='store_true', help='test PyTorch only') + parser.add_argument('--hard-fail', nargs='?', const=True, default=False, help='Exception on error or < min metric') + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + print_args(vars(opt)) + return opt + + +def main(opt): + test(**vars(opt)) if opt.test else run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/classify/predict.py b/algorithm/yolov5/classify/predict.py new file mode 100644 index 0000000..5f0d407 --- /dev/null +++ b/algorithm/yolov5/classify/predict.py @@ -0,0 +1,226 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python classify/predict.py --weights yolov5s-cls.pt # PyTorch + yolov5s-cls.torchscript # TorchScript + yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-cls_openvino_model # OpenVINO + yolov5s-cls.engine # TensorRT + yolov5s-cls.mlmodel # CoreML (macOS-only) + yolov5s-cls_saved_model # TensorFlow SavedModel + yolov5s-cls.pb # TensorFlow GraphDef + yolov5s-cls.tflite # TensorFlow Lite + yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-cls_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch +import torch.nn.functional as F + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.augmentations import classify_transforms +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, print_args, strip_optimizer) +from utils.plots import Annotator +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(224, 224), # inference size (height, width) + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + nosave=False, # do not save images/videos + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/predict-cls', # save results to project/name + name='exp', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.Tensor(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + results = model(im) + + # Post-process + with dt[2]: + pred = F.softmax(results, dim=1) # probabilities + + # Process predictions + for i, prob in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + + s += '%gx%g ' % im.shape[2:] # print string + annotator = Annotator(im0, example=str(names), pil=True) + + # Print results + top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices + s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, " + + # Write results + text = '\n'.join(f'{prob[j]:.2f} {names[j]}' for j in top5i) + if save_img or view_img: # Add bbox to image + annotator.text((32, 32), text, txt_color=(255, 255, 255)) + if save_txt: # Write to file + with open(f'{txt_path}.txt', 'a') as f: + f.write(text + '\n') + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f'{s}{dt[1].dt * 1E3:.1f}ms') + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model path(s)') + parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[224], help='inference size h,w') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/predict-cls', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/classify/train.py b/algorithm/yolov5/classify/train.py new file mode 100644 index 0000000..ae2363c --- /dev/null +++ b/algorithm/yolov5/classify/train.py @@ -0,0 +1,333 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Train a YOLOv5 classifier model on a classification dataset + +Usage - Single-GPU training: + $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 + +Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data' +YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt +Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html +""" + +import argparse +import os +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.hub as hub +import torch.optim.lr_scheduler as lr_scheduler +import torchvision +from torch.cuda import amp +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from classify import val as validate +from models.experimental import attempt_load +from models.yolo import ClassificationModel, DetectionModel +from utils.dataloaders import create_classification_dataloader +from utils.general import (DATASETS_DIR, LOGGER, TQDM_BAR_FORMAT, WorkingDirectory, check_git_info, check_git_status, + check_requirements, colorstr, download, increment_path, init_seeds, print_args, yaml_save) +from utils.loggers import GenericLogger +from utils.plots import imshow_cls +from utils.torch_utils import (ModelEMA, de_parallel, model_info, reshape_classifier_output, select_device, smart_DDP, + smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first) + +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +GIT_INFO = check_git_info() + + +def train(opt, device): + init_seeds(opt.seed + 1 + RANK, deterministic=True) + save_dir, data, bs, epochs, nw, imgsz, pretrained = \ + opt.save_dir, Path(opt.data), opt.batch_size, opt.epochs, min(os.cpu_count() - 1, opt.workers), \ + opt.imgsz, str(opt.pretrained).lower() == 'true' + cuda = device.type != 'cpu' + + # Directories + wdir = save_dir / 'weights' + wdir.mkdir(parents=True, exist_ok=True) # make dir + last, best = wdir / 'last.pt', wdir / 'best.pt' + + # Save run settings + yaml_save(save_dir / 'opt.yaml', vars(opt)) + + # Logger + logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None + + # Download Dataset + with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): + data_dir = data if data.is_dir() else (DATASETS_DIR / data) + if not data_dir.is_dir(): + LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...') + t = time.time() + if str(data) == 'imagenet': + subprocess.run(['bash', str(ROOT / 'data/scripts/get_imagenet.sh')], shell=True, check=True) + else: + url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip' + download(url, dir=data_dir.parent) + s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n" + LOGGER.info(s) + + # Dataloaders + nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()]) # number of classes + trainloader = create_classification_dataloader(path=data_dir / 'train', + imgsz=imgsz, + batch_size=bs // WORLD_SIZE, + augment=True, + cache=opt.cache, + rank=LOCAL_RANK, + workers=nw) + + test_dir = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val + if RANK in {-1, 0}: + testloader = create_classification_dataloader(path=test_dir, + imgsz=imgsz, + batch_size=bs // WORLD_SIZE * 2, + augment=False, + cache=opt.cache, + rank=-1, + workers=nw) + + # Model + with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): + if Path(opt.model).is_file() or opt.model.endswith('.pt'): + model = attempt_load(opt.model, device='cpu', fuse=False) + elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0 + model = torchvision.models.__dict__[opt.model](weights='IMAGENET1K_V1' if pretrained else None) + else: + m = hub.list('ultralytics/yolov5') # + hub.list('pytorch/vision') # models + raise ModuleNotFoundError(f'--model {opt.model} not found. Available models are: \n' + '\n'.join(m)) + if isinstance(model, DetectionModel): + LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'") + model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model + reshape_classifier_output(model, nc) # update class count + for m in model.modules(): + if not pretrained and hasattr(m, 'reset_parameters'): + m.reset_parameters() + if isinstance(m, torch.nn.Dropout) and opt.dropout is not None: + m.p = opt.dropout # set dropout + for p in model.parameters(): + p.requires_grad = True # for training + model = model.to(device) + + # Info + if RANK in {-1, 0}: + model.names = trainloader.dataset.classes # attach class names + model.transforms = testloader.dataset.torch_transforms # attach inference transforms + model_info(model) + if opt.verbose: + LOGGER.info(model) + images, labels = next(iter(trainloader)) + file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / 'train_images.jpg') + logger.log_images(file, name='Train Examples') + logger.log_graph(model, imgsz) # log model + + # Optimizer + optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay) + + # Scheduler + lrf = 0.01 # final lr (fraction of lr0) + # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine + lf = lambda x: (1 - x / epochs) * (1 - lrf) + lrf # linear + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1, + # final_div_factor=1 / 25 / lrf) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Train + t0 = time.time() + criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function + best_fitness = 0.0 + scaler = amp.GradScaler(enabled=cuda) + val = test_dir.stem # 'val' or 'test' + LOGGER.info(f'Image sizes {imgsz} train, {imgsz} test\n' + f'Using {nw * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n' + f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}") + for epoch in range(epochs): # loop over the dataset multiple times + tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness + model.train() + if RANK != -1: + trainloader.sampler.set_epoch(epoch) + pbar = enumerate(trainloader) + if RANK in {-1, 0}: + pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT) + for i, (images, labels) in pbar: # progress bar + images, labels = images.to(device, non_blocking=True), labels.to(device) + + # Forward + with amp.autocast(enabled=cuda): # stability issues when enabled + loss = criterion(model(images), labels) + + # Backward + scaler.scale(loss).backward() + + # Optimize + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + if RANK in {-1, 0}: + # Print + tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses + mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) + pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + ' ' * 36 + + # Test + if i == len(pbar) - 1: # last batch + top1, top5, vloss = validate.run(model=ema.ema, + dataloader=testloader, + criterion=criterion, + pbar=pbar) # test accuracy, loss + fitness = top1 # define fitness as top1 accuracy + + # Scheduler + scheduler.step() + + # Log metrics + if RANK in {-1, 0}: + # Best fitness + if fitness > best_fitness: + best_fitness = fitness + + # Log + metrics = { + 'train/loss': tloss, + f'{val}/loss': vloss, + 'metrics/accuracy_top1': top1, + 'metrics/accuracy_top5': top5, + 'lr/0': optimizer.param_groups[0]['lr']} # learning rate + logger.log_metrics(metrics, epoch) + + # Save model + final_epoch = epoch + 1 == epochs + if (not opt.nosave) or final_epoch: + ckpt = { + 'epoch': epoch, + 'best_fitness': best_fitness, + 'model': deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(), + 'ema': None, # deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': None, # optimizer.state_dict(), + 'opt': vars(opt), + 'git': GIT_INFO, # {remote, branch, commit} if a git repo + 'date': datetime.now().isoformat()} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fitness: + torch.save(ckpt, best) + del ckpt + + # Train complete + if RANK in {-1, 0} and final_epoch: + LOGGER.info(f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)' + f"\nResults saved to {colorstr('bold', save_dir)}" + f'\nPredict: python classify/predict.py --weights {best} --source im.jpg' + f'\nValidate: python classify/val.py --weights {best} --data {data_dir}' + f'\nExport: python export.py --weights {best} --include onnx' + f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')" + f'\nVisualize: https://netron.app\n') + + # Plot examples + images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels + pred = torch.max(ema.ema(images.to(device)), 1)[1] + file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / 'test_images.jpg') + + # Log results + meta = {'epochs': epochs, 'top1_acc': best_fitness, 'date': datetime.now().isoformat()} + logger.log_images(file, name='Test Examples (true-predicted)', epoch=epoch) + logger.log_model(best, epochs, metadata=meta) + + +def parse_opt(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default='yolov5s-cls.pt', help='initial weights path') + parser.add_argument('--data', type=str, default='imagenette160', help='cifar10, cifar100, mnist, imagenet, ...') + parser.add_argument('--epochs', type=int, default=10, help='total training epochs') + parser.add_argument('--batch-size', type=int, default=64, help='total batch size for all GPUs') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='train, val image size (pixels)') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--project', default=ROOT / 'runs/train-cls', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--pretrained', nargs='?', const=True, default=True, help='start from i.e. --pretrained False') + parser.add_argument('--optimizer', choices=['SGD', 'Adam', 'AdamW', 'RMSProp'], default='Adam', help='optimizer') + parser.add_argument('--lr0', type=float, default=0.001, help='initial learning rate') + parser.add_argument('--decay', type=float, default=5e-5, help='weight decay') + parser.add_argument('--label-smoothing', type=float, default=0.1, help='Label smoothing epsilon') + parser.add_argument('--cutoff', type=int, default=None, help='Model layer cutoff index for Classify() head') + parser.add_argument('--dropout', type=float, default=None, help='Dropout (fraction)') + parser.add_argument('--verbose', action='store_true', help='Verbose mode') + parser.add_argument('--seed', type=int, default=0, help='Global training seed') + parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt): + # Checks + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements() + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + assert opt.batch_size != -1, 'AutoBatch is coming soon for classification, please pass a valid --batch-size' + assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' + assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + torch.cuda.set_device(LOCAL_RANK) + device = torch.device('cuda', LOCAL_RANK) + dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + + # Parameters + opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run + + # Train + train(opt, device) + + +def run(**kwargs): + # Usage: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m') + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/classify/tutorial.ipynb b/algorithm/yolov5/classify/tutorial.ipynb new file mode 100644 index 0000000..5872360 --- /dev/null +++ b/algorithm/yolov5/classify/tutorial.ipynb @@ -0,0 +1,1480 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "
\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wbvMlHd_QwMG", + "outputId": "0806e375-610d-4ec0-c867-763dbb518279" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + ] + } + ], + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Predict\n", + "\n", + "`classify/predict.py` runs YOLOv5 Classification inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict-cls`. Example inference sources are:\n", + "\n", + "```shell\n", + "python classify/predict.py --source 0 # webcam\n", + " img.jpg # image \n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zR9ZbuQCH7FX", + "outputId": "50504ef7-aa3e-4281-a4e3-d0c7df3c0ffe" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['yolov5s-cls.pt'], source=data/images, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt...\n", + "100% 10.5M/10.5M [00:00<00:00, 12.3MB/s]\n", + "\n", + "Fusing layers... \n", + "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 224x224 minibus 0.39, police van 0.24, amphibious vehicle 0.05, recreational vehicle 0.04, trolleybus 0.03, 3.9ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 224x224 suit 0.38, bow tie 0.19, bridegroom 0.18, rugby ball 0.04, stage 0.02, 4.6ms\n", + "Speed: 0.3ms pre-process, 4.3ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224)\n", + "Results saved to \u001b[1mruns/predict-cls/exp\u001b[0m\n" + ] + } + ], + "source": [ + "!python classify/predict.py --weights yolov5s-cls.pt --img 224 --source data/images\n", + "# display.Image(filename='runs/predict-cls/exp/zidane.jpg', width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [Imagenet](https://image-net.org/) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WQPtK1QYVaD_", + "outputId": "20fc0630-141e-4a90-ea06-342cbd7ce496" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n", + "Resolving image-net.org (image-net.org)... 171.64.68.16\n", + "Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6744924160 (6.3G) [application/x-tar]\n", + "Saving to: ‘ILSVRC2012_img_val.tar’\n", + "\n", + "ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s \n", + "\n", + "2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n", + "\n" + ] + } + ], + "source": [ + "# Download Imagenet val (6.3G, 50000 images)\n", + "!bash data/scripts/get_imagenet.sh --val" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X58w8JLpMnjH", + "outputId": "41843132-98e2-4c25-d474-4cd7b246fb8e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=../datasets/imagenet, weights=['yolov5s-cls.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=True, dnn=False\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Fusing layers... \n", + "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n", + "validating: 100% 391/391 [04:57<00:00, 1.31it/s]\n", + " Class Images top1_acc top5_acc\n", + " all 50000 0.715 0.902\n", + " tench 50 0.94 0.98\n", + " goldfish 50 0.88 0.92\n", + " great white shark 50 0.78 0.96\n", + " tiger shark 50 0.68 0.96\n", + " hammerhead shark 50 0.82 0.92\n", + " electric ray 50 0.76 0.9\n", + " stingray 50 0.7 0.9\n", + " cock 50 0.78 0.92\n", + " hen 50 0.84 0.96\n", + " ostrich 50 0.98 1\n", + " brambling 50 0.9 0.96\n", + " goldfinch 50 0.92 0.98\n", + " house finch 50 0.88 0.96\n", + " junco 50 0.94 0.98\n", + " indigo bunting 50 0.86 0.88\n", + " American robin 50 0.9 0.96\n", + " bulbul 50 0.84 0.96\n", + " jay 50 0.9 0.96\n", + " magpie 50 0.84 0.96\n", + " chickadee 50 0.9 1\n", + " American dipper 50 0.82 0.92\n", + " kite 50 0.76 0.94\n", + " bald eagle 50 0.92 1\n", + " vulture 50 0.96 1\n", + " great grey owl 50 0.94 0.98\n", + " fire salamander 50 0.96 0.98\n", + " smooth newt 50 0.58 0.94\n", + " newt 50 0.74 0.9\n", + " spotted salamander 50 0.86 0.94\n", + " axolotl 50 0.86 0.96\n", + " American bullfrog 50 0.78 0.92\n", + " tree frog 50 0.84 0.96\n", + " tailed frog 50 0.48 0.8\n", + " loggerhead sea turtle 50 0.68 0.94\n", + " leatherback sea turtle 50 0.5 0.8\n", + " mud turtle 50 0.64 0.84\n", + " terrapin 50 0.52 0.98\n", + " box turtle 50 0.84 0.98\n", + " banded gecko 50 0.7 0.88\n", + " green iguana 50 0.76 0.94\n", + " Carolina anole 50 0.58 0.96\n", + "desert grassland whiptail lizard 50 0.82 0.94\n", + " agama 50 0.74 0.92\n", + " frilled-necked lizard 50 0.84 0.86\n", + " alligator lizard 50 0.58 0.78\n", + " Gila monster 50 0.72 0.8\n", + " European green lizard 50 0.42 0.9\n", + " chameleon 50 0.76 0.84\n", + " Komodo dragon 50 0.86 0.96\n", + " Nile crocodile 50 0.7 0.84\n", + " American alligator 50 0.76 0.96\n", + " triceratops 50 0.9 0.94\n", + " worm snake 50 0.76 0.88\n", + " ring-necked snake 50 0.8 0.92\n", + " eastern hog-nosed snake 50 0.58 0.88\n", + " smooth green snake 50 0.6 0.94\n", + " kingsnake 50 0.82 0.9\n", + " garter snake 50 0.88 0.94\n", + " water snake 50 0.7 0.94\n", + " vine snake 50 0.66 0.76\n", + " night snake 50 0.34 0.82\n", + " boa constrictor 50 0.8 0.96\n", + " African rock python 50 0.48 0.76\n", + " Indian cobra 50 0.82 0.94\n", + " green mamba 50 0.54 0.86\n", + " sea snake 50 0.62 0.9\n", + " Saharan horned viper 50 0.56 0.86\n", + "eastern diamondback rattlesnake 50 0.6 0.86\n", + " sidewinder 50 0.28 0.86\n", + " trilobite 50 0.98 0.98\n", + " harvestman 50 0.86 0.94\n", + " scorpion 50 0.86 0.94\n", + " yellow garden spider 50 0.92 0.96\n", + " barn spider 50 0.38 0.98\n", + " European garden spider 50 0.62 0.98\n", + " southern black widow 50 0.88 0.94\n", + " tarantula 50 0.94 1\n", + " wolf spider 50 0.82 0.92\n", + " tick 50 0.74 0.84\n", + " centipede 50 0.68 0.82\n", + " black grouse 50 0.88 0.98\n", + " ptarmigan 50 0.78 0.94\n", + " ruffed grouse 50 0.88 1\n", + " prairie grouse 50 0.92 1\n", + " peacock 50 0.88 0.9\n", + " quail 50 0.9 0.94\n", + " partridge 50 0.74 0.96\n", + " grey parrot 50 0.9 0.96\n", + " macaw 50 0.88 0.98\n", + "sulphur-crested cockatoo 50 0.86 0.92\n", + " lorikeet 50 0.96 1\n", + " coucal 50 0.82 0.88\n", + " bee eater 50 0.96 0.98\n", + " hornbill 50 0.9 0.96\n", + " hummingbird 50 0.88 0.96\n", + " jacamar 50 0.92 0.94\n", + " toucan 50 0.84 0.94\n", + " duck 50 0.76 0.94\n", + " red-breasted merganser 50 0.86 0.96\n", + " goose 50 0.74 0.96\n", + " black swan 50 0.94 0.98\n", + " tusker 50 0.54 0.92\n", + " echidna 50 0.98 1\n", + " platypus 50 0.72 0.84\n", + " wallaby 50 0.78 0.88\n", + " koala 50 0.84 0.92\n", + " wombat 50 0.78 0.84\n", + " jellyfish 50 0.88 0.96\n", + " sea anemone 50 0.72 0.9\n", + " brain coral 50 0.88 0.96\n", + " flatworm 50 0.8 0.98\n", + " nematode 50 0.86 0.9\n", + " conch 50 0.74 0.88\n", + " snail 50 0.78 0.88\n", + " slug 50 0.74 0.82\n", + " sea slug 50 0.88 0.98\n", + " chiton 50 0.88 0.98\n", + " chambered nautilus 50 0.88 0.92\n", + " Dungeness crab 50 0.78 0.94\n", + " rock crab 50 0.68 0.86\n", + " fiddler crab 50 0.64 0.86\n", + " red king crab 50 0.76 0.96\n", + " American lobster 50 0.78 0.96\n", + " spiny lobster 50 0.74 0.88\n", + " crayfish 50 0.56 0.86\n", + " hermit crab 50 0.78 0.96\n", + " isopod 50 0.66 0.78\n", + " white stork 50 0.88 0.96\n", + " black stork 50 0.84 0.98\n", + " spoonbill 50 0.96 1\n", + " flamingo 50 0.94 1\n", + " little blue heron 50 0.92 0.98\n", + " great egret 50 0.9 0.96\n", + " bittern 50 0.86 0.94\n", + " crane (bird) 50 0.62 0.9\n", + " limpkin 50 0.98 1\n", + " common gallinule 50 0.92 0.96\n", + " American coot 50 0.9 0.98\n", + " bustard 50 0.92 0.96\n", + " ruddy turnstone 50 0.94 1\n", + " dunlin 50 0.86 0.94\n", + " common redshank 50 0.9 0.96\n", + " dowitcher 50 0.84 0.96\n", + " oystercatcher 50 0.86 0.94\n", + " pelican 50 0.92 0.96\n", + " king penguin 50 0.88 0.96\n", + " albatross 50 0.9 1\n", + " grey whale 50 0.84 0.92\n", + " killer whale 50 0.92 1\n", + " dugong 50 0.84 0.96\n", + " sea lion 50 0.82 0.92\n", + " Chihuahua 50 0.66 0.84\n", + " Japanese Chin 50 0.72 0.98\n", + " Maltese 50 0.76 0.94\n", + " Pekingese 50 0.84 0.94\n", + " Shih Tzu 50 0.74 0.96\n", + " King Charles Spaniel 50 0.88 0.98\n", + " Papillon 50 0.86 0.94\n", + " toy terrier 50 0.48 0.94\n", + " Rhodesian Ridgeback 50 0.76 0.98\n", + " Afghan Hound 50 0.84 1\n", + " Basset Hound 50 0.8 0.92\n", + " Beagle 50 0.82 0.96\n", + " Bloodhound 50 0.48 0.72\n", + " Bluetick Coonhound 50 0.86 0.94\n", + " Black and Tan Coonhound 50 0.54 0.8\n", + "Treeing Walker Coonhound 50 0.66 0.98\n", + " English foxhound 50 0.32 0.84\n", + " Redbone Coonhound 50 0.62 0.94\n", + " borzoi 50 0.92 1\n", + " Irish Wolfhound 50 0.48 0.88\n", + " Italian Greyhound 50 0.76 0.98\n", + " Whippet 50 0.74 0.92\n", + " Ibizan Hound 50 0.6 0.86\n", + " Norwegian Elkhound 50 0.88 0.98\n", + " Otterhound 50 0.62 0.9\n", + " Saluki 50 0.72 0.92\n", + " Scottish Deerhound 50 0.86 0.98\n", + " Weimaraner 50 0.88 0.94\n", + "Staffordshire Bull Terrier 50 0.66 0.98\n", + "American Staffordshire Terrier 50 0.64 0.92\n", + " Bedlington Terrier 50 0.9 0.92\n", + " Border Terrier 50 0.86 0.92\n", + " Kerry Blue Terrier 50 0.78 0.98\n", + " Irish Terrier 50 0.7 0.96\n", + " Norfolk Terrier 50 0.68 0.9\n", + " Norwich Terrier 50 0.72 1\n", + " Yorkshire Terrier 50 0.66 0.9\n", + " Wire Fox Terrier 50 0.64 0.98\n", + " Lakeland Terrier 50 0.74 0.92\n", + " Sealyham Terrier 50 0.76 0.9\n", + " Airedale Terrier 50 0.82 0.92\n", + " Cairn Terrier 50 0.76 0.9\n", + " Australian Terrier 50 0.48 0.84\n", + " Dandie Dinmont Terrier 50 0.82 0.92\n", + " Boston Terrier 50 0.92 1\n", + " Miniature Schnauzer 50 0.68 0.9\n", + " Giant Schnauzer 50 0.72 0.98\n", + " Standard Schnauzer 50 0.74 1\n", + " Scottish Terrier 50 0.76 0.96\n", + " Tibetan Terrier 50 0.48 1\n", + "Australian Silky Terrier 50 0.66 0.96\n", + "Soft-coated Wheaten Terrier 50 0.74 0.96\n", + "West Highland White Terrier 50 0.88 0.96\n", + " Lhasa Apso 50 0.68 0.96\n", + " Flat-Coated Retriever 50 0.72 0.94\n", + " Curly-coated Retriever 50 0.82 0.94\n", + " Golden Retriever 50 0.86 0.94\n", + " Labrador Retriever 50 0.82 0.94\n", + "Chesapeake Bay Retriever 50 0.76 0.96\n", + "German Shorthaired Pointer 50 0.8 0.96\n", + " Vizsla 50 0.68 0.96\n", + " English Setter 50 0.7 1\n", + " Irish Setter 50 0.8 0.9\n", + " Gordon Setter 50 0.84 0.92\n", + " Brittany 50 0.84 0.96\n", + " Clumber Spaniel 50 0.92 0.96\n", + "English Springer Spaniel 50 0.88 1\n", + " Welsh Springer Spaniel 50 0.92 1\n", + " Cocker Spaniels 50 0.7 0.94\n", + " Sussex Spaniel 50 0.72 0.92\n", + " Irish Water Spaniel 50 0.88 0.98\n", + " Kuvasz 50 0.66 0.9\n", + " Schipperke 50 0.9 0.98\n", + " Groenendael 50 0.8 0.94\n", + " Malinois 50 0.86 0.98\n", + " Briard 50 0.52 0.8\n", + " Australian Kelpie 50 0.6 0.88\n", + " Komondor 50 0.88 0.94\n", + " Old English Sheepdog 50 0.94 0.98\n", + " Shetland Sheepdog 50 0.74 0.9\n", + " collie 50 0.6 0.96\n", + " Border Collie 50 0.74 0.96\n", + " Bouvier des Flandres 50 0.78 0.94\n", + " Rottweiler 50 0.88 0.96\n", + " German Shepherd Dog 50 0.8 0.98\n", + " Dobermann 50 0.68 0.96\n", + " Miniature Pinscher 50 0.76 0.88\n", + "Greater Swiss Mountain Dog 50 0.68 0.94\n", + " Bernese Mountain Dog 50 0.96 1\n", + " Appenzeller Sennenhund 50 0.22 1\n", + " Entlebucher Sennenhund 50 0.64 0.98\n", + " Boxer 50 0.7 0.92\n", + " Bullmastiff 50 0.78 0.98\n", + " Tibetan Mastiff 50 0.88 0.96\n", + " French Bulldog 50 0.84 0.94\n", + " Great Dane 50 0.54 0.9\n", + " St. Bernard 50 0.92 1\n", + " husky 50 0.46 0.98\n", + " Alaskan Malamute 50 0.76 0.96\n", + " Siberian Husky 50 0.46 0.98\n", + " Dalmatian 50 0.94 0.98\n", + " Affenpinscher 50 0.78 0.9\n", + " Basenji 50 0.92 0.94\n", + " pug 50 0.94 0.98\n", + " Leonberger 50 1 1\n", + " Newfoundland 50 0.78 0.96\n", + " Pyrenean Mountain Dog 50 0.78 0.96\n", + " Samoyed 50 0.96 1\n", + " Pomeranian 50 0.98 1\n", + " Chow Chow 50 0.9 0.96\n", + " Keeshond 50 0.88 0.94\n", + " Griffon Bruxellois 50 0.84 0.98\n", + " Pembroke Welsh Corgi 50 0.82 0.94\n", + " Cardigan Welsh Corgi 50 0.66 0.98\n", + " Toy Poodle 50 0.52 0.88\n", + " Miniature Poodle 50 0.52 0.92\n", + " Standard Poodle 50 0.8 1\n", + " Mexican hairless dog 50 0.88 0.98\n", + " grey wolf 50 0.82 0.92\n", + " Alaskan tundra wolf 50 0.78 0.98\n", + " red wolf 50 0.48 0.9\n", + " coyote 50 0.64 0.86\n", + " dingo 50 0.76 0.88\n", + " dhole 50 0.9 0.98\n", + " African wild dog 50 0.98 1\n", + " hyena 50 0.88 0.96\n", + " red fox 50 0.54 0.92\n", + " kit fox 50 0.72 0.98\n", + " Arctic fox 50 0.94 1\n", + " grey fox 50 0.7 0.94\n", + " tabby cat 50 0.54 0.92\n", + " tiger cat 50 0.22 0.94\n", + " Persian cat 50 0.9 0.98\n", + " Siamese cat 50 0.96 1\n", + " Egyptian Mau 50 0.54 0.8\n", + " cougar 50 0.9 1\n", + " lynx 50 0.72 0.88\n", + " leopard 50 0.78 0.98\n", + " snow leopard 50 0.9 0.98\n", + " jaguar 50 0.7 0.94\n", + " lion 50 0.9 0.98\n", + " tiger 50 0.92 0.98\n", + " cheetah 50 0.94 0.98\n", + " brown bear 50 0.94 0.98\n", + " American black bear 50 0.8 1\n", + " polar bear 50 0.84 0.96\n", + " sloth bear 50 0.72 0.92\n", + " mongoose 50 0.7 0.92\n", + " meerkat 50 0.82 0.92\n", + " tiger beetle 50 0.92 0.94\n", + " ladybug 50 0.86 0.94\n", + " ground beetle 50 0.64 0.94\n", + " longhorn beetle 50 0.62 0.88\n", + " leaf beetle 50 0.64 0.98\n", + " dung beetle 50 0.86 0.98\n", + " rhinoceros beetle 50 0.86 0.94\n", + " weevil 50 0.9 1\n", + " fly 50 0.78 0.94\n", + " bee 50 0.68 0.94\n", + " ant 50 0.68 0.78\n", + " grasshopper 50 0.5 0.92\n", + " cricket 50 0.64 0.92\n", + " stick insect 50 0.64 0.92\n", + " cockroach 50 0.72 0.8\n", + " mantis 50 0.64 0.86\n", + " cicada 50 0.9 0.96\n", + " leafhopper 50 0.88 0.94\n", + " lacewing 50 0.78 0.92\n", + " dragonfly 50 0.82 0.98\n", + " damselfly 50 0.82 1\n", + " red admiral 50 0.94 0.96\n", + " ringlet 50 0.86 0.98\n", + " monarch butterfly 50 0.9 0.92\n", + " small white 50 0.9 1\n", + " sulphur butterfly 50 0.92 1\n", + "gossamer-winged butterfly 50 0.88 1\n", + " starfish 50 0.88 0.92\n", + " sea urchin 50 0.84 0.94\n", + " sea cucumber 50 0.66 0.84\n", + " cottontail rabbit 50 0.72 0.94\n", + " hare 50 0.84 0.96\n", + " Angora rabbit 50 0.94 0.98\n", + " hamster 50 0.96 1\n", + " porcupine 50 0.88 0.98\n", + " fox squirrel 50 0.76 0.94\n", + " marmot 50 0.92 0.96\n", + " beaver 50 0.78 0.94\n", + " guinea pig 50 0.78 0.94\n", + " common sorrel 50 0.96 0.98\n", + " zebra 50 0.94 0.96\n", + " pig 50 0.5 0.76\n", + " wild boar 50 0.84 0.96\n", + " warthog 50 0.84 0.96\n", + " hippopotamus 50 0.88 0.96\n", + " ox 50 0.48 0.94\n", + " water buffalo 50 0.78 0.94\n", + " bison 50 0.88 0.96\n", + " ram 50 0.58 0.92\n", + " bighorn sheep 50 0.66 1\n", + " Alpine ibex 50 0.92 0.98\n", + " hartebeest 50 0.94 1\n", + " impala 50 0.82 0.96\n", + " gazelle 50 0.7 0.96\n", + " dromedary 50 0.9 1\n", + " llama 50 0.82 0.94\n", + " weasel 50 0.44 0.92\n", + " mink 50 0.78 0.96\n", + " European polecat 50 0.46 0.9\n", + " black-footed ferret 50 0.68 0.96\n", + " otter 50 0.66 0.88\n", + " skunk 50 0.96 0.96\n", + " badger 50 0.86 0.92\n", + " armadillo 50 0.88 0.9\n", + " three-toed sloth 50 0.96 1\n", + " orangutan 50 0.78 0.92\n", + " gorilla 50 0.82 0.94\n", + " chimpanzee 50 0.84 0.94\n", + " gibbon 50 0.76 0.86\n", + " siamang 50 0.68 0.94\n", + " guenon 50 0.8 0.94\n", + " patas monkey 50 0.62 0.82\n", + " baboon 50 0.9 0.98\n", + " macaque 50 0.8 0.86\n", + " langur 50 0.6 0.82\n", + " black-and-white colobus 50 0.86 0.9\n", + " proboscis monkey 50 1 1\n", + " marmoset 50 0.74 0.98\n", + " white-headed capuchin 50 0.72 0.9\n", + " howler monkey 50 0.86 0.94\n", + " titi 50 0.5 0.9\n", + "Geoffroy's spider monkey 50 0.42 0.8\n", + " common squirrel monkey 50 0.76 0.92\n", + " ring-tailed lemur 50 0.72 0.94\n", + " indri 50 0.9 0.96\n", + " Asian elephant 50 0.58 0.92\n", + " African bush elephant 50 0.7 0.98\n", + " red panda 50 0.94 0.94\n", + " giant panda 50 0.94 0.98\n", + " snoek 50 0.74 0.9\n", + " eel 50 0.6 0.84\n", + " coho salmon 50 0.84 0.96\n", + " rock beauty 50 0.88 0.98\n", + " clownfish 50 0.78 0.98\n", + " sturgeon 50 0.68 0.94\n", + " garfish 50 0.62 0.8\n", + " lionfish 50 0.96 0.96\n", + " pufferfish 50 0.88 0.96\n", + " abacus 50 0.74 0.88\n", + " abaya 50 0.84 0.92\n", + " academic gown 50 0.42 0.86\n", + " accordion 50 0.8 0.9\n", + " acoustic guitar 50 0.5 0.76\n", + " aircraft carrier 50 0.8 0.96\n", + " airliner 50 0.92 1\n", + " airship 50 0.76 0.82\n", + " altar 50 0.64 0.98\n", + " ambulance 50 0.88 0.98\n", + " amphibious vehicle 50 0.64 0.94\n", + " analog clock 50 0.52 0.92\n", + " apiary 50 0.82 0.96\n", + " apron 50 0.7 0.84\n", + " waste container 50 0.4 0.8\n", + " assault rifle 50 0.42 0.84\n", + " backpack 50 0.34 0.64\n", + " bakery 50 0.4 0.68\n", + " balance beam 50 0.8 0.98\n", + " balloon 50 0.86 0.96\n", + " ballpoint pen 50 0.52 0.96\n", + " Band-Aid 50 0.7 0.9\n", + " banjo 50 0.84 1\n", + " baluster 50 0.68 0.94\n", + " barbell 50 0.56 0.9\n", + " barber chair 50 0.7 0.92\n", + " barbershop 50 0.54 0.86\n", + " barn 50 0.96 0.96\n", + " barometer 50 0.84 0.98\n", + " barrel 50 0.56 0.88\n", + " wheelbarrow 50 0.66 0.88\n", + " baseball 50 0.74 0.98\n", + " basketball 50 0.88 0.98\n", + " bassinet 50 0.66 0.92\n", + " bassoon 50 0.74 0.98\n", + " swimming cap 50 0.62 0.88\n", + " bath towel 50 0.54 0.78\n", + " bathtub 50 0.4 0.88\n", + " station wagon 50 0.66 0.84\n", + " lighthouse 50 0.78 0.94\n", + " beaker 50 0.52 0.68\n", + " military cap 50 0.84 0.96\n", + " beer bottle 50 0.66 0.88\n", + " beer glass 50 0.6 0.84\n", + " bell-cot 50 0.56 0.96\n", + " bib 50 0.58 0.82\n", + " tandem bicycle 50 0.86 0.96\n", + " bikini 50 0.56 0.88\n", + " ring binder 50 0.64 0.84\n", + " binoculars 50 0.54 0.78\n", + " birdhouse 50 0.86 0.94\n", + " boathouse 50 0.74 0.92\n", + " bobsleigh 50 0.92 0.96\n", + " bolo tie 50 0.8 0.94\n", + " poke bonnet 50 0.64 0.86\n", + " bookcase 50 0.66 0.92\n", + " bookstore 50 0.62 0.88\n", + " bottle cap 50 0.58 0.7\n", + " bow 50 0.72 0.86\n", + " bow tie 50 0.7 0.9\n", + " brass 50 0.92 0.96\n", + " bra 50 0.5 0.7\n", + " breakwater 50 0.62 0.86\n", + " breastplate 50 0.4 0.9\n", + " broom 50 0.6 0.86\n", + " bucket 50 0.66 0.8\n", + " buckle 50 0.5 0.68\n", + " bulletproof vest 50 0.5 0.78\n", + " high-speed train 50 0.94 0.96\n", + " butcher shop 50 0.74 0.94\n", + " taxicab 50 0.64 0.86\n", + " cauldron 50 0.44 0.66\n", + " candle 50 0.48 0.74\n", + " cannon 50 0.88 0.94\n", + " canoe 50 0.94 1\n", + " can opener 50 0.66 0.86\n", + " cardigan 50 0.68 0.8\n", + " car mirror 50 0.94 0.96\n", + " carousel 50 0.94 0.98\n", + " tool kit 50 0.56 0.78\n", + " carton 50 0.42 0.7\n", + " car wheel 50 0.38 0.74\n", + "automated teller machine 50 0.76 0.94\n", + " cassette 50 0.52 0.8\n", + " cassette player 50 0.28 0.9\n", + " castle 50 0.78 0.88\n", + " catamaran 50 0.78 1\n", + " CD player 50 0.52 0.82\n", + " cello 50 0.82 1\n", + " mobile phone 50 0.68 0.86\n", + " chain 50 0.38 0.66\n", + " chain-link fence 50 0.7 0.84\n", + " chain mail 50 0.64 0.9\n", + " chainsaw 50 0.84 0.92\n", + " chest 50 0.68 0.92\n", + " chiffonier 50 0.26 0.64\n", + " chime 50 0.62 0.84\n", + " china cabinet 50 0.82 0.96\n", + " Christmas stocking 50 0.92 0.94\n", + " church 50 0.62 0.9\n", + " movie theater 50 0.58 0.88\n", + " cleaver 50 0.32 0.62\n", + " cliff dwelling 50 0.88 1\n", + " cloak 50 0.32 0.64\n", + " clogs 50 0.58 0.88\n", + " cocktail shaker 50 0.62 0.7\n", + " coffee mug 50 0.44 0.72\n", + " coffeemaker 50 0.64 0.92\n", + " coil 50 0.66 0.84\n", + " combination lock 50 0.64 0.84\n", + " computer keyboard 50 0.7 0.82\n", + " confectionery store 50 0.54 0.86\n", + " container ship 50 0.82 0.98\n", + " convertible 50 0.78 0.98\n", + " corkscrew 50 0.82 0.92\n", + " cornet 50 0.46 0.88\n", + " cowboy boot 50 0.64 0.8\n", + " cowboy hat 50 0.64 0.82\n", + " cradle 50 0.38 0.8\n", + " crane (machine) 50 0.78 0.94\n", + " crash helmet 50 0.92 0.96\n", + " crate 50 0.52 0.82\n", + " infant bed 50 0.74 1\n", + " Crock Pot 50 0.78 0.9\n", + " croquet ball 50 0.9 0.96\n", + " crutch 50 0.46 0.7\n", + " cuirass 50 0.54 0.86\n", + " dam 50 0.74 0.92\n", + " desk 50 0.6 0.86\n", + " desktop computer 50 0.54 0.94\n", + " rotary dial telephone 50 0.88 0.94\n", + " diaper 50 0.68 0.84\n", + " digital clock 50 0.54 0.76\n", + " digital watch 50 0.58 0.86\n", + " dining table 50 0.76 0.9\n", + " dishcloth 50 0.94 1\n", + " dishwasher 50 0.44 0.78\n", + " disc brake 50 0.98 1\n", + " dock 50 0.54 0.94\n", + " dog sled 50 0.84 1\n", + " dome 50 0.72 0.92\n", + " doormat 50 0.56 0.82\n", + " drilling rig 50 0.84 0.96\n", + " drum 50 0.38 0.68\n", + " drumstick 50 0.56 0.72\n", + " dumbbell 50 0.62 0.9\n", + " Dutch oven 50 0.7 0.84\n", + " electric fan 50 0.82 0.86\n", + " electric guitar 50 0.62 0.84\n", + " electric locomotive 50 0.92 0.98\n", + " entertainment center 50 0.9 0.98\n", + " envelope 50 0.44 0.86\n", + " espresso machine 50 0.72 0.94\n", + " face powder 50 0.7 0.92\n", + " feather boa 50 0.7 0.84\n", + " filing cabinet 50 0.88 0.98\n", + " fireboat 50 0.94 0.98\n", + " fire engine 50 0.84 0.9\n", + " fire screen sheet 50 0.62 0.76\n", + " flagpole 50 0.74 0.88\n", + " flute 50 0.36 0.72\n", + " folding chair 50 0.62 0.84\n", + " football helmet 50 0.86 0.94\n", + " forklift 50 0.8 0.92\n", + " fountain 50 0.84 0.94\n", + " fountain pen 50 0.76 0.92\n", + " four-poster bed 50 0.78 0.94\n", + " freight car 50 0.96 1\n", + " French horn 50 0.76 0.92\n", + " frying pan 50 0.36 0.78\n", + " fur coat 50 0.84 0.96\n", + " garbage truck 50 0.9 0.98\n", + " gas mask 50 0.84 0.92\n", + " gas pump 50 0.9 0.98\n", + " goblet 50 0.68 0.82\n", + " go-kart 50 0.9 1\n", + " golf ball 50 0.84 0.9\n", + " golf cart 50 0.78 0.86\n", + " gondola 50 0.98 0.98\n", + " gong 50 0.74 0.92\n", + " gown 50 0.62 0.96\n", + " grand piano 50 0.7 0.96\n", + " greenhouse 50 0.8 0.98\n", + " grille 50 0.72 0.9\n", + " grocery store 50 0.66 0.94\n", + " guillotine 50 0.86 0.92\n", + " barrette 50 0.52 0.66\n", + " hair spray 50 0.5 0.74\n", + " half-track 50 0.78 0.9\n", + " hammer 50 0.56 0.76\n", + " hamper 50 0.64 0.84\n", + " hair dryer 50 0.56 0.74\n", + " hand-held computer 50 0.42 0.86\n", + " handkerchief 50 0.78 0.94\n", + " hard disk drive 50 0.76 0.84\n", + " harmonica 50 0.7 0.88\n", + " harp 50 0.88 0.96\n", + " harvester 50 0.78 1\n", + " hatchet 50 0.54 0.74\n", + " holster 50 0.66 0.84\n", + " home theater 50 0.64 0.94\n", + " honeycomb 50 0.56 0.88\n", + " hook 50 0.3 0.6\n", + " hoop skirt 50 0.64 0.86\n", + " horizontal bar 50 0.68 0.98\n", + " horse-drawn vehicle 50 0.88 0.94\n", + " hourglass 50 0.88 0.96\n", + " iPod 50 0.76 0.94\n", + " clothes iron 50 0.82 0.88\n", + " jack-o'-lantern 50 0.98 0.98\n", + " jeans 50 0.68 0.84\n", + " jeep 50 0.72 0.9\n", + " T-shirt 50 0.72 0.96\n", + " jigsaw puzzle 50 0.84 0.94\n", + " pulled rickshaw 50 0.86 0.94\n", + " joystick 50 0.8 0.9\n", + " kimono 50 0.84 0.96\n", + " knee pad 50 0.62 0.88\n", + " knot 50 0.66 0.8\n", + " lab coat 50 0.8 0.96\n", + " ladle 50 0.36 0.64\n", + " lampshade 50 0.48 0.84\n", + " laptop computer 50 0.26 0.88\n", + " lawn mower 50 0.78 0.96\n", + " lens cap 50 0.46 0.72\n", + " paper knife 50 0.26 0.5\n", + " library 50 0.54 0.9\n", + " lifeboat 50 0.92 0.98\n", + " lighter 50 0.56 0.78\n", + " limousine 50 0.76 0.92\n", + " ocean liner 50 0.88 0.94\n", + " lipstick 50 0.74 0.9\n", + " slip-on shoe 50 0.74 0.92\n", + " lotion 50 0.5 0.86\n", + " speaker 50 0.52 0.68\n", + " loupe 50 0.32 0.52\n", + " sawmill 50 0.72 0.9\n", + " magnetic compass 50 0.52 0.82\n", + " mail bag 50 0.68 0.92\n", + " mailbox 50 0.82 0.92\n", + " tights 50 0.22 0.94\n", + " tank suit 50 0.24 0.9\n", + " manhole cover 50 0.96 0.98\n", + " maraca 50 0.74 0.9\n", + " marimba 50 0.84 0.94\n", + " mask 50 0.44 0.82\n", + " match 50 0.66 0.9\n", + " maypole 50 0.96 1\n", + " maze 50 0.8 0.96\n", + " measuring cup 50 0.54 0.76\n", + " medicine chest 50 0.6 0.84\n", + " megalith 50 0.8 0.92\n", + " microphone 50 0.52 0.7\n", + " microwave oven 50 0.48 0.72\n", + " military uniform 50 0.62 0.84\n", + " milk can 50 0.68 0.82\n", + " minibus 50 0.7 1\n", + " miniskirt 50 0.46 0.76\n", + " minivan 50 0.38 0.8\n", + " missile 50 0.4 0.84\n", + " mitten 50 0.76 0.88\n", + " mixing bowl 50 0.8 0.92\n", + " mobile home 50 0.54 0.78\n", + " Model T 50 0.92 0.96\n", + " modem 50 0.58 0.86\n", + " monastery 50 0.44 0.9\n", + " monitor 50 0.4 0.86\n", + " moped 50 0.56 0.94\n", + " mortar 50 0.68 0.94\n", + " square academic cap 50 0.5 0.84\n", + " mosque 50 0.9 1\n", + " mosquito net 50 0.9 0.98\n", + " scooter 50 0.9 0.98\n", + " mountain bike 50 0.78 0.96\n", + " tent 50 0.88 0.96\n", + " computer mouse 50 0.42 0.82\n", + " mousetrap 50 0.76 0.88\n", + " moving van 50 0.4 0.72\n", + " muzzle 50 0.5 0.72\n", + " nail 50 0.68 0.74\n", + " neck brace 50 0.56 0.68\n", + " necklace 50 0.86 1\n", + " nipple 50 0.7 0.88\n", + " notebook computer 50 0.34 0.84\n", + " obelisk 50 0.8 0.92\n", + " oboe 50 0.6 0.84\n", + " ocarina 50 0.8 0.86\n", + " odometer 50 0.96 1\n", + " oil filter 50 0.58 0.82\n", + " organ 50 0.82 0.9\n", + " oscilloscope 50 0.9 0.96\n", + " overskirt 50 0.2 0.7\n", + " bullock cart 50 0.7 0.94\n", + " oxygen mask 50 0.46 0.84\n", + " packet 50 0.5 0.78\n", + " paddle 50 0.56 0.94\n", + " paddle wheel 50 0.86 0.96\n", + " padlock 50 0.74 0.78\n", + " paintbrush 50 0.62 0.8\n", + " pajamas 50 0.56 0.92\n", + " palace 50 0.64 0.96\n", + " pan flute 50 0.84 0.86\n", + " paper towel 50 0.66 0.84\n", + " parachute 50 0.92 0.94\n", + " parallel bars 50 0.62 0.96\n", + " park bench 50 0.74 0.9\n", + " parking meter 50 0.84 0.92\n", + " passenger car 50 0.5 0.82\n", + " patio 50 0.58 0.84\n", + " payphone 50 0.74 0.92\n", + " pedestal 50 0.52 0.9\n", + " pencil case 50 0.64 0.92\n", + " pencil sharpener 50 0.52 0.78\n", + " perfume 50 0.7 0.9\n", + " Petri dish 50 0.6 0.8\n", + " photocopier 50 0.88 0.98\n", + " plectrum 50 0.7 0.84\n", + " Pickelhaube 50 0.72 0.86\n", + " picket fence 50 0.84 0.94\n", + " pickup truck 50 0.64 0.92\n", + " pier 50 0.52 0.82\n", + " piggy bank 50 0.82 0.94\n", + " pill bottle 50 0.76 0.86\n", + " pillow 50 0.76 0.9\n", + " ping-pong ball 50 0.84 0.88\n", + " pinwheel 50 0.76 0.88\n", + " pirate ship 50 0.76 0.94\n", + " pitcher 50 0.46 0.84\n", + " hand plane 50 0.84 0.94\n", + " planetarium 50 0.88 0.98\n", + " plastic bag 50 0.36 0.62\n", + " plate rack 50 0.52 0.78\n", + " plow 50 0.78 0.88\n", + " plunger 50 0.42 0.7\n", + " Polaroid camera 50 0.84 0.92\n", + " pole 50 0.38 0.74\n", + " police van 50 0.76 0.94\n", + " poncho 50 0.58 0.86\n", + " billiard table 50 0.8 0.88\n", + " soda bottle 50 0.56 0.94\n", + " pot 50 0.78 0.92\n", + " potter's wheel 50 0.9 0.94\n", + " power drill 50 0.42 0.72\n", + " prayer rug 50 0.7 0.86\n", + " printer 50 0.54 0.86\n", + " prison 50 0.7 0.9\n", + " projectile 50 0.28 0.9\n", + " projector 50 0.62 0.84\n", + " hockey puck 50 0.92 0.96\n", + " punching bag 50 0.6 0.68\n", + " purse 50 0.42 0.78\n", + " quill 50 0.68 0.84\n", + " quilt 50 0.64 0.9\n", + " race car 50 0.72 0.92\n", + " racket 50 0.72 0.9\n", + " radiator 50 0.66 0.76\n", + " radio 50 0.64 0.92\n", + " radio telescope 50 0.9 0.96\n", + " rain barrel 50 0.8 0.98\n", + " recreational vehicle 50 0.84 0.94\n", + " reel 50 0.72 0.82\n", + " reflex camera 50 0.72 0.92\n", + " refrigerator 50 0.7 0.9\n", + " remote control 50 0.7 0.88\n", + " restaurant 50 0.5 0.66\n", + " revolver 50 0.82 1\n", + " rifle 50 0.38 0.7\n", + " rocking chair 50 0.62 0.84\n", + " rotisserie 50 0.88 0.92\n", + " eraser 50 0.54 0.76\n", + " rugby ball 50 0.86 0.94\n", + " ruler 50 0.68 0.86\n", + " running shoe 50 0.78 0.94\n", + " safe 50 0.82 0.92\n", + " safety pin 50 0.4 0.62\n", + " salt shaker 50 0.66 0.9\n", + " sandal 50 0.66 0.86\n", + " sarong 50 0.64 0.86\n", + " saxophone 50 0.66 0.88\n", + " scabbard 50 0.76 0.92\n", + " weighing scale 50 0.58 0.78\n", + " school bus 50 0.92 1\n", + " schooner 50 0.84 1\n", + " scoreboard 50 0.9 0.96\n", + " CRT screen 50 0.14 0.7\n", + " screw 50 0.9 0.98\n", + " screwdriver 50 0.3 0.58\n", + " seat belt 50 0.88 0.94\n", + " sewing machine 50 0.76 0.9\n", + " shield 50 0.56 0.82\n", + " shoe store 50 0.78 0.96\n", + " shoji 50 0.8 0.92\n", + " shopping basket 50 0.52 0.88\n", + " shopping cart 50 0.76 0.92\n", + " shovel 50 0.62 0.84\n", + " shower cap 50 0.7 0.84\n", + " shower curtain 50 0.64 0.82\n", + " ski 50 0.74 0.92\n", + " ski mask 50 0.72 0.88\n", + " sleeping bag 50 0.68 0.8\n", + " slide rule 50 0.72 0.88\n", + " sliding door 50 0.44 0.78\n", + " slot machine 50 0.94 0.98\n", + " snorkel 50 0.86 0.98\n", + " snowmobile 50 0.88 1\n", + " snowplow 50 0.84 0.98\n", + " soap dispenser 50 0.56 0.86\n", + " soccer ball 50 0.86 0.96\n", + " sock 50 0.62 0.76\n", + " solar thermal collector 50 0.72 0.96\n", + " sombrero 50 0.6 0.84\n", + " soup bowl 50 0.56 0.94\n", + " space bar 50 0.34 0.88\n", + " space heater 50 0.52 0.74\n", + " space shuttle 50 0.82 0.96\n", + " spatula 50 0.3 0.6\n", + " motorboat 50 0.86 1\n", + " spider web 50 0.7 0.9\n", + " spindle 50 0.86 0.98\n", + " sports car 50 0.6 0.94\n", + " spotlight 50 0.26 0.6\n", + " stage 50 0.68 0.86\n", + " steam locomotive 50 0.94 1\n", + " through arch bridge 50 0.84 0.96\n", + " steel drum 50 0.82 0.9\n", + " stethoscope 50 0.6 0.82\n", + " scarf 50 0.5 0.92\n", + " stone wall 50 0.76 0.9\n", + " stopwatch 50 0.58 0.9\n", + " stove 50 0.46 0.74\n", + " strainer 50 0.64 0.84\n", + " tram 50 0.88 0.96\n", + " stretcher 50 0.6 0.8\n", + " couch 50 0.8 0.96\n", + " stupa 50 0.88 0.88\n", + " submarine 50 0.72 0.92\n", + " suit 50 0.4 0.78\n", + " sundial 50 0.58 0.74\n", + " sunglass 50 0.14 0.58\n", + " sunglasses 50 0.28 0.58\n", + " sunscreen 50 0.32 0.7\n", + " suspension bridge 50 0.6 0.94\n", + " mop 50 0.74 0.92\n", + " sweatshirt 50 0.28 0.66\n", + " swimsuit 50 0.52 0.82\n", + " swing 50 0.76 0.84\n", + " switch 50 0.56 0.76\n", + " syringe 50 0.62 0.82\n", + " table lamp 50 0.6 0.88\n", + " tank 50 0.8 0.96\n", + " tape player 50 0.46 0.76\n", + " teapot 50 0.84 1\n", + " teddy bear 50 0.82 0.94\n", + " television 50 0.6 0.9\n", + " tennis ball 50 0.7 0.94\n", + " thatched roof 50 0.88 0.9\n", + " front curtain 50 0.8 0.92\n", + " thimble 50 0.6 0.8\n", + " threshing machine 50 0.56 0.88\n", + " throne 50 0.72 0.82\n", + " tile roof 50 0.72 0.94\n", + " toaster 50 0.66 0.84\n", + " tobacco shop 50 0.42 0.7\n", + " toilet seat 50 0.62 0.88\n", + " torch 50 0.64 0.84\n", + " totem pole 50 0.92 0.98\n", + " tow truck 50 0.62 0.88\n", + " toy store 50 0.6 0.94\n", + " tractor 50 0.76 0.98\n", + " semi-trailer truck 50 0.78 0.92\n", + " tray 50 0.46 0.64\n", + " trench coat 50 0.54 0.72\n", + " tricycle 50 0.72 0.94\n", + " trimaran 50 0.7 0.98\n", + " tripod 50 0.58 0.86\n", + " triumphal arch 50 0.92 0.98\n", + " trolleybus 50 0.9 1\n", + " trombone 50 0.54 0.88\n", + " tub 50 0.24 0.82\n", + " turnstile 50 0.84 0.94\n", + " typewriter keyboard 50 0.68 0.98\n", + " umbrella 50 0.52 0.7\n", + " unicycle 50 0.74 0.96\n", + " upright piano 50 0.76 0.9\n", + " vacuum cleaner 50 0.62 0.9\n", + " vase 50 0.5 0.78\n", + " vault 50 0.76 0.92\n", + " velvet 50 0.2 0.42\n", + " vending machine 50 0.9 1\n", + " vestment 50 0.54 0.82\n", + " viaduct 50 0.78 0.86\n", + " violin 50 0.68 0.78\n", + " volleyball 50 0.86 1\n", + " waffle iron 50 0.72 0.88\n", + " wall clock 50 0.54 0.88\n", + " wallet 50 0.52 0.9\n", + " wardrobe 50 0.68 0.88\n", + " military aircraft 50 0.9 0.98\n", + " sink 50 0.72 0.96\n", + " washing machine 50 0.78 0.94\n", + " water bottle 50 0.54 0.74\n", + " water jug 50 0.22 0.74\n", + " water tower 50 0.9 0.96\n", + " whiskey jug 50 0.64 0.74\n", + " whistle 50 0.72 0.84\n", + " wig 50 0.84 0.9\n", + " window screen 50 0.68 0.8\n", + " window shade 50 0.52 0.76\n", + " Windsor tie 50 0.22 0.66\n", + " wine bottle 50 0.42 0.82\n", + " wing 50 0.54 0.96\n", + " wok 50 0.46 0.82\n", + " wooden spoon 50 0.58 0.8\n", + " wool 50 0.32 0.82\n", + " split-rail fence 50 0.74 0.9\n", + " shipwreck 50 0.84 0.96\n", + " yawl 50 0.78 0.96\n", + " yurt 50 0.84 1\n", + " website 50 0.98 1\n", + " comic book 50 0.62 0.9\n", + " crossword 50 0.84 0.88\n", + " traffic sign 50 0.78 0.9\n", + " traffic light 50 0.8 0.94\n", + " dust jacket 50 0.72 0.94\n", + " menu 50 0.82 0.96\n", + " plate 50 0.44 0.88\n", + " guacamole 50 0.8 0.92\n", + " consomme 50 0.54 0.88\n", + " hot pot 50 0.86 0.98\n", + " trifle 50 0.92 0.98\n", + " ice cream 50 0.68 0.94\n", + " ice pop 50 0.62 0.84\n", + " baguette 50 0.62 0.88\n", + " bagel 50 0.64 0.92\n", + " pretzel 50 0.72 0.88\n", + " cheeseburger 50 0.9 1\n", + " hot dog 50 0.74 0.94\n", + " mashed potato 50 0.74 0.9\n", + " cabbage 50 0.84 0.96\n", + " broccoli 50 0.9 0.96\n", + " cauliflower 50 0.82 1\n", + " zucchini 50 0.74 0.9\n", + " spaghetti squash 50 0.8 0.96\n", + " acorn squash 50 0.82 0.96\n", + " butternut squash 50 0.7 0.94\n", + " cucumber 50 0.6 0.96\n", + " artichoke 50 0.84 0.94\n", + " bell pepper 50 0.84 0.98\n", + " cardoon 50 0.88 0.94\n", + " mushroom 50 0.38 0.92\n", + " Granny Smith 50 0.9 0.96\n", + " strawberry 50 0.6 0.88\n", + " orange 50 0.7 0.92\n", + " lemon 50 0.78 0.98\n", + " fig 50 0.82 0.96\n", + " pineapple 50 0.86 0.96\n", + " banana 50 0.84 0.96\n", + " jackfruit 50 0.9 0.98\n", + " custard apple 50 0.86 0.96\n", + " pomegranate 50 0.82 0.98\n", + " hay 50 0.8 0.92\n", + " carbonara 50 0.88 0.94\n", + " chocolate syrup 50 0.46 0.84\n", + " dough 50 0.4 0.6\n", + " meatloaf 50 0.58 0.84\n", + " pizza 50 0.84 0.96\n", + " pot pie 50 0.68 0.9\n", + " burrito 50 0.8 0.98\n", + " red wine 50 0.54 0.82\n", + " espresso 50 0.64 0.88\n", + " cup 50 0.38 0.7\n", + " eggnog 50 0.38 0.7\n", + " alp 50 0.54 0.88\n", + " bubble 50 0.8 0.96\n", + " cliff 50 0.64 1\n", + " coral reef 50 0.72 0.96\n", + " geyser 50 0.94 1\n", + " lakeshore 50 0.54 0.88\n", + " promontory 50 0.58 0.94\n", + " shoal 50 0.6 0.96\n", + " seashore 50 0.44 0.78\n", + " valley 50 0.72 0.94\n", + " volcano 50 0.78 0.96\n", + " baseball player 50 0.72 0.94\n", + " bridegroom 50 0.72 0.88\n", + " scuba diver 50 0.8 1\n", + " rapeseed 50 0.94 0.98\n", + " daisy 50 0.96 0.98\n", + " yellow lady's slipper 50 1 1\n", + " corn 50 0.4 0.88\n", + " acorn 50 0.92 0.98\n", + " rose hip 50 0.92 0.98\n", + " horse chestnut seed 50 0.94 0.98\n", + " coral fungus 50 0.96 0.96\n", + " agaric 50 0.82 0.94\n", + " gyromitra 50 0.98 1\n", + " stinkhorn mushroom 50 0.8 0.94\n", + " earth star 50 0.98 1\n", + " hen-of-the-woods 50 0.8 0.96\n", + " bolete 50 0.74 0.94\n", + " ear 50 0.48 0.94\n", + " toilet paper 50 0.36 0.68\n", + "Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\n", + "Results saved to \u001b[1mruns/val-cls/exp\u001b[0m\n" + ] + } + ], + "source": [ + "# Validate YOLOv5s on Imagenet val\n", + "!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZY2VXXXu74w5" + }, + "source": [ + "# 3. Train\n", + "\n", + "

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s Classification model on the [Imagenette](https://image-net.org/) dataset with `--data imagenet`, starting from pretrained `--pretrained yolov5s-cls.pt`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **Training Results** are saved to `runs/train-cls/` with incrementing run directories, i.e. `runs/train-cls/exp2`, `runs/train-cls/exp3` etc.\n", + "

\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Train on Custom Data with Roboflow 🌟 NEW\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", + "\n", + "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-classification-custom-data/](https://blog.roboflow.com/train-yolov5-classification-custom-data/?ref=ultralytics)\n", + "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KZiKUAjtARHAfZCXbJRv14-pOnIsBLPV?usp=sharing)\n", + "
\n", + "\n", + "

Label images lightning fast (including with model-assisted labeling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "outputs": [], + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML']\n", + "\n", + "if logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train\n", + "elif logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'ClearML':\n", + " import clearml; clearml.browser_login()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1NcFxRcFdJ_O", + "outputId": "77c8d487-16db-4073-b3ea-06cabf2e7766" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download...\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenette160.zip to /content/datasets/imagenette160.zip...\n", + "100% 103M/103M [00:00<00:00, 347MB/s] \n", + "Unzipping /content/datasets/imagenette160.zip...\n", + "Dataset download success ✅ (3.3s), saved to \u001b[1m/content/datasets/imagenette160\u001b[0m\n", + "\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n", + "Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\n", + "Image sizes 224 train, 224 test\n", + "Using 1 dataloader workers\n", + "Logging results to \u001b[1mruns/train-cls/exp\u001b[0m\n", + "Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs...\n", + "\n", + " Epoch GPU_mem train_loss val_loss top1_acc top5_acc\n", + " 1/5 1.47G 1.05 0.974 0.828 0.975: 100% 148/148 [00:38<00:00, 3.82it/s]\n", + " 2/5 1.73G 0.895 0.766 0.911 0.994: 100% 148/148 [00:36<00:00, 4.03it/s]\n", + " 3/5 1.73G 0.82 0.704 0.934 0.996: 100% 148/148 [00:35<00:00, 4.20it/s]\n", + " 4/5 1.73G 0.766 0.664 0.951 0.998: 100% 148/148 [00:36<00:00, 4.05it/s]\n", + " 5/5 1.73G 0.724 0.634 0.959 0.997: 100% 148/148 [00:37<00:00, 3.94it/s]\n", + "\n", + "Training complete (0.052 hours)\n", + "Results saved to \u001b[1mruns/train-cls/exp\u001b[0m\n", + "Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\n", + "Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160\n", + "Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx\n", + "PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt')\n", + "Visualize: https://netron.app\n", + "\n" + ] + } + ], + "source": [ + "# Train YOLOv5s Classification on Imagenette160 for 3 epochs\n", + "!python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 --cache" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15glLzbQx5u0" + }, + "source": [ + "# 4. Visualize" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nWOsI5wJR1o3" + }, + "source": [ + "## Comet Logging and Visualization 🌟 NEW\n", + "\n", + "[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n", + "\n", + "Getting started is easy:\n", + "```shell\n", + "pip install comet_ml # 1. install\n", + "export COMET_API_KEY= # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lay2WsTjNJzP" + }, + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "\n", + "\n", + "\"ClearML" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GMusP4OAxFu6" + }, + "outputs": [], + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "YOLOv5 Classification Tutorial", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/algorithm/yolov5/classify/val.py b/algorithm/yolov5/classify/val.py new file mode 100644 index 0000000..4edd5a1 --- /dev/null +++ b/algorithm/yolov5/classify/val.py @@ -0,0 +1,170 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Validate a trained YOLOv5 classification model on a classification dataset + +Usage: + $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) + $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet + +Usage - formats: + $ python classify/val.py --weights yolov5s-cls.pt # PyTorch + yolov5s-cls.torchscript # TorchScript + yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-cls_openvino_model # OpenVINO + yolov5s-cls.engine # TensorRT + yolov5s-cls.mlmodel # CoreML (macOS-only) + yolov5s-cls_saved_model # TensorFlow SavedModel + yolov5s-cls.pb # TensorFlow GraphDef + yolov5s-cls.tflite # TensorFlow Lite + yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-cls_paddle_model # PaddlePaddle +""" + +import argparse +import os +import sys +from pathlib import Path + +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import create_classification_dataloader +from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr, + increment_path, print_args) +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + data=ROOT / '../datasets/mnist', # dataset dir + weights=ROOT / 'yolov5s-cls.pt', # model.pt path(s) + batch_size=128, # batch size + imgsz=224, # inference size (pixels) + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + verbose=False, # verbose output + project=ROOT / 'runs/val-cls', # save to project/name + name='exp', # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + criterion=None, + pbar=None, +): + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != 'cpu' # half precision only supported on CUDA + model.half() if half else model.float() + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + save_dir.mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + + # Dataloader + data = Path(data) + test_dir = data / 'test' if (data / 'test').exists() else data / 'val' # data/test or data/val + dataloader = create_classification_dataloader(path=test_dir, + imgsz=imgsz, + batch_size=batch_size, + augment=False, + rank=-1, + workers=workers) + + model.eval() + pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile()) + n = len(dataloader) # number of batches + action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing' + desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}' + bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0) + with torch.cuda.amp.autocast(enabled=device.type != 'cpu'): + for images, labels in bar: + with dt[0]: + images, labels = images.to(device, non_blocking=True), labels.to(device) + + with dt[1]: + y = model(images) + + with dt[2]: + pred.append(y.argsort(1, descending=True)[:, :5]) + targets.append(labels) + if criterion: + loss += criterion(y, labels) + + loss /= n + pred, targets = torch.cat(pred), torch.cat(targets) + correct = (targets[:, None] == pred).float() + acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy + top1, top5 = acc.mean(0).tolist() + + if pbar: + pbar.desc = f'{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}' + if verbose: # all classes + LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}") + LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}") + for i, c in model.names.items(): + acc_i = acc[targets == i] + top1i, top5i = acc_i.mean(0).tolist() + LOGGER.info(f'{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}') + + # Print results + t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image + shape = (1, 3, imgsz, imgsz) + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}' % t) + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") + + return top1, top5, loss + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default=ROOT / '../datasets/mnist', help='dataset path') + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-cls.pt', help='model.pt path(s)') + parser.add_argument('--batch-size', type=int, default=128, help='batch size') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=224, help='inference size (pixels)') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--verbose', nargs='?', const=True, default=True, help='verbose output') + parser.add_argument('--project', default=ROOT / 'runs/val-cls', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + opt = parser.parse_args() + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/data/Argoverse.yaml b/algorithm/yolov5/data/Argoverse.yaml new file mode 100644 index 0000000..558151d --- /dev/null +++ b/algorithm/yolov5/data/Argoverse.yaml @@ -0,0 +1,74 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI +# Example usage: python train.py --data Argoverse.yaml +# parent +# ├── yolov5 +# └── datasets +# └── Argoverse ← downloads here (31.3 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/Argoverse # dataset root dir +train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images +val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images +test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: bus + 5: truck + 6: traffic_light + 7: stop_sign + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import json + + from tqdm import tqdm + from utils.general import download, Path + + + def argoverse2yolo(set): + labels = {} + a = json.load(open(set, "rb")) + for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): + img_id = annot['image_id'] + img_name = a['images'][img_id]['name'] + img_label_name = f'{img_name[:-3]}txt' + + cls = annot['category_id'] # instance class id + x_center, y_center, width, height = annot['bbox'] + x_center = (x_center + width / 2) / 1920.0 # offset and scale + y_center = (y_center + height / 2) / 1200.0 # offset and scale + width /= 1920.0 # scale + height /= 1200.0 # scale + + img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] + if not img_dir.exists(): + img_dir.mkdir(parents=True, exist_ok=True) + + k = str(img_dir / img_label_name) + if k not in labels: + labels[k] = [] + labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") + + for k in labels: + with open(k, "w") as f: + f.writelines(labels[k]) + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] + download(urls, dir=dir, delete=False) + + # Convert + annotations_dir = 'Argoverse-HD/annotations/' + (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' + for d in "train.json", "val.json": + argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels diff --git a/algorithm/yolov5/data/GlobalWheat2020.yaml b/algorithm/yolov5/data/GlobalWheat2020.yaml new file mode 100644 index 0000000..01812d0 --- /dev/null +++ b/algorithm/yolov5/data/GlobalWheat2020.yaml @@ -0,0 +1,54 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan +# Example usage: python train.py --data GlobalWheat2020.yaml +# parent +# ├── yolov5 +# └── datasets +# └── GlobalWheat2020 ← downloads here (7.0 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/GlobalWheat2020 # dataset root dir +train: # train images (relative to 'path') 3422 images + - images/arvalis_1 + - images/arvalis_2 + - images/arvalis_3 + - images/ethz_1 + - images/rres_1 + - images/inrae_1 + - images/usask_1 +val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1) + - images/ethz_1 +test: # test images (optional) 1276 images + - images/utokyo_1 + - images/utokyo_2 + - images/nau_1 + - images/uq_1 + +# Classes +names: + 0: wheat_head + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from utils.general import download, Path + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] + download(urls, dir=dir) + + # Make Directories + for p in 'annotations', 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + + # Move + for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ + 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': + (dir / p).rename(dir / 'images' / p) # move to /images + f = (dir / p).with_suffix('.json') # json file + if f.exists(): + f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations diff --git a/algorithm/yolov5/data/ImageNet.yaml b/algorithm/yolov5/data/ImageNet.yaml new file mode 100644 index 0000000..14f1295 --- /dev/null +++ b/algorithm/yolov5/data/ImageNet.yaml @@ -0,0 +1,1022 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet ← downloads here (144 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + 10: brambling + 11: goldfinch + 12: house finch + 13: junco + 14: indigo bunting + 15: American robin + 16: bulbul + 17: jay + 18: magpie + 19: chickadee + 20: American dipper + 21: kite + 22: bald eagle + 23: vulture + 24: great grey owl + 25: fire salamander + 26: smooth newt + 27: newt + 28: spotted salamander + 29: axolotl + 30: American bullfrog + 31: tree frog + 32: tailed frog + 33: loggerhead sea turtle + 34: leatherback sea turtle + 35: mud turtle + 36: terrapin + 37: box turtle + 38: banded gecko + 39: green iguana + 40: Carolina anole + 41: desert grassland whiptail lizard + 42: agama + 43: frilled-necked lizard + 44: alligator lizard + 45: Gila monster + 46: European green lizard + 47: chameleon + 48: Komodo dragon + 49: Nile crocodile + 50: American alligator + 51: triceratops + 52: worm snake + 53: ring-necked snake + 54: eastern hog-nosed snake + 55: smooth green snake + 56: kingsnake + 57: garter snake + 58: water snake + 59: vine snake + 60: night snake + 61: boa constrictor + 62: African rock python + 63: Indian cobra + 64: green mamba + 65: sea snake + 66: Saharan horned viper + 67: eastern diamondback rattlesnake + 68: sidewinder + 69: trilobite + 70: harvestman + 71: scorpion + 72: yellow garden spider + 73: barn spider + 74: European garden spider + 75: southern black widow + 76: tarantula + 77: wolf spider + 78: tick + 79: centipede + 80: black grouse + 81: ptarmigan + 82: ruffed grouse + 83: prairie grouse + 84: peacock + 85: quail + 86: partridge + 87: grey parrot + 88: macaw + 89: sulphur-crested cockatoo + 90: lorikeet + 91: coucal + 92: bee eater + 93: hornbill + 94: hummingbird + 95: jacamar + 96: toucan + 97: duck + 98: red-breasted merganser + 99: goose + 100: black swan + 101: tusker + 102: echidna + 103: platypus + 104: wallaby + 105: koala + 106: wombat + 107: jellyfish + 108: sea anemone + 109: brain coral + 110: flatworm + 111: nematode + 112: conch + 113: snail + 114: slug + 115: sea slug + 116: chiton + 117: chambered nautilus + 118: Dungeness crab + 119: rock crab + 120: fiddler crab + 121: red king crab + 122: American lobster + 123: spiny lobster + 124: crayfish + 125: hermit crab + 126: isopod + 127: white stork + 128: black stork + 129: spoonbill + 130: flamingo + 131: little blue heron + 132: great egret + 133: bittern + 134: crane (bird) + 135: limpkin + 136: common gallinule + 137: American coot + 138: bustard + 139: ruddy turnstone + 140: dunlin + 141: common redshank + 142: dowitcher + 143: oystercatcher + 144: pelican + 145: king penguin + 146: albatross + 147: grey whale + 148: killer whale + 149: dugong + 150: sea lion + 151: Chihuahua + 152: Japanese Chin + 153: Maltese + 154: Pekingese + 155: Shih Tzu + 156: King Charles Spaniel + 157: Papillon + 158: toy terrier + 159: Rhodesian Ridgeback + 160: Afghan Hound + 161: Basset Hound + 162: Beagle + 163: Bloodhound + 164: Bluetick Coonhound + 165: Black and Tan Coonhound + 166: Treeing Walker Coonhound + 167: English foxhound + 168: Redbone Coonhound + 169: borzoi + 170: Irish Wolfhound + 171: Italian Greyhound + 172: Whippet + 173: Ibizan Hound + 174: Norwegian Elkhound + 175: Otterhound + 176: Saluki + 177: Scottish Deerhound + 178: Weimaraner + 179: Staffordshire Bull Terrier + 180: American Staffordshire Terrier + 181: Bedlington Terrier + 182: Border Terrier + 183: Kerry Blue Terrier + 184: Irish Terrier + 185: Norfolk Terrier + 186: Norwich Terrier + 187: Yorkshire Terrier + 188: Wire Fox Terrier + 189: Lakeland Terrier + 190: Sealyham Terrier + 191: Airedale Terrier + 192: Cairn Terrier + 193: Australian Terrier + 194: Dandie Dinmont Terrier + 195: Boston Terrier + 196: Miniature Schnauzer + 197: Giant Schnauzer + 198: Standard Schnauzer + 199: Scottish Terrier + 200: Tibetan Terrier + 201: Australian Silky Terrier + 202: Soft-coated Wheaten Terrier + 203: West Highland White Terrier + 204: Lhasa Apso + 205: Flat-Coated Retriever + 206: Curly-coated Retriever + 207: Golden Retriever + 208: Labrador Retriever + 209: Chesapeake Bay Retriever + 210: German Shorthaired Pointer + 211: Vizsla + 212: English Setter + 213: Irish Setter + 214: Gordon Setter + 215: Brittany + 216: Clumber Spaniel + 217: English Springer Spaniel + 218: Welsh Springer Spaniel + 219: Cocker Spaniels + 220: Sussex Spaniel + 221: Irish Water Spaniel + 222: Kuvasz + 223: Schipperke + 224: Groenendael + 225: Malinois + 226: Briard + 227: Australian Kelpie + 228: Komondor + 229: Old English Sheepdog + 230: Shetland Sheepdog + 231: collie + 232: Border Collie + 233: Bouvier des Flandres + 234: Rottweiler + 235: German Shepherd Dog + 236: Dobermann + 237: Miniature Pinscher + 238: Greater Swiss Mountain Dog + 239: Bernese Mountain Dog + 240: Appenzeller Sennenhund + 241: Entlebucher Sennenhund + 242: Boxer + 243: Bullmastiff + 244: Tibetan Mastiff + 245: French Bulldog + 246: Great Dane + 247: St. Bernard + 248: husky + 249: Alaskan Malamute + 250: Siberian Husky + 251: Dalmatian + 252: Affenpinscher + 253: Basenji + 254: pug + 255: Leonberger + 256: Newfoundland + 257: Pyrenean Mountain Dog + 258: Samoyed + 259: Pomeranian + 260: Chow Chow + 261: Keeshond + 262: Griffon Bruxellois + 263: Pembroke Welsh Corgi + 264: Cardigan Welsh Corgi + 265: Toy Poodle + 266: Miniature Poodle + 267: Standard Poodle + 268: Mexican hairless dog + 269: grey wolf + 270: Alaskan tundra wolf + 271: red wolf + 272: coyote + 273: dingo + 274: dhole + 275: African wild dog + 276: hyena + 277: red fox + 278: kit fox + 279: Arctic fox + 280: grey fox + 281: tabby cat + 282: tiger cat + 283: Persian cat + 284: Siamese cat + 285: Egyptian Mau + 286: cougar + 287: lynx + 288: leopard + 289: snow leopard + 290: jaguar + 291: lion + 292: tiger + 293: cheetah + 294: brown bear + 295: American black bear + 296: polar bear + 297: sloth bear + 298: mongoose + 299: meerkat + 300: tiger beetle + 301: ladybug + 302: ground beetle + 303: longhorn beetle + 304: leaf beetle + 305: dung beetle + 306: rhinoceros beetle + 307: weevil + 308: fly + 309: bee + 310: ant + 311: grasshopper + 312: cricket + 313: stick insect + 314: cockroach + 315: mantis + 316: cicada + 317: leafhopper + 318: lacewing + 319: dragonfly + 320: damselfly + 321: red admiral + 322: ringlet + 323: monarch butterfly + 324: small white + 325: sulphur butterfly + 326: gossamer-winged butterfly + 327: starfish + 328: sea urchin + 329: sea cucumber + 330: cottontail rabbit + 331: hare + 332: Angora rabbit + 333: hamster + 334: porcupine + 335: fox squirrel + 336: marmot + 337: beaver + 338: guinea pig + 339: common sorrel + 340: zebra + 341: pig + 342: wild boar + 343: warthog + 344: hippopotamus + 345: ox + 346: water buffalo + 347: bison + 348: ram + 349: bighorn sheep + 350: Alpine ibex + 351: hartebeest + 352: impala + 353: gazelle + 354: dromedary + 355: llama + 356: weasel + 357: mink + 358: European polecat + 359: black-footed ferret + 360: otter + 361: skunk + 362: badger + 363: armadillo + 364: three-toed sloth + 365: orangutan + 366: gorilla + 367: chimpanzee + 368: gibbon + 369: siamang + 370: guenon + 371: patas monkey + 372: baboon + 373: macaque + 374: langur + 375: black-and-white colobus + 376: proboscis monkey + 377: marmoset + 378: white-headed capuchin + 379: howler monkey + 380: titi + 381: Geoffroy's spider monkey + 382: common squirrel monkey + 383: ring-tailed lemur + 384: indri + 385: Asian elephant + 386: African bush elephant + 387: red panda + 388: giant panda + 389: snoek + 390: eel + 391: coho salmon + 392: rock beauty + 393: clownfish + 394: sturgeon + 395: garfish + 396: lionfish + 397: pufferfish + 398: abacus + 399: abaya + 400: academic gown + 401: accordion + 402: acoustic guitar + 403: aircraft carrier + 404: airliner + 405: airship + 406: altar + 407: ambulance + 408: amphibious vehicle + 409: analog clock + 410: apiary + 411: apron + 412: waste container + 413: assault rifle + 414: backpack + 415: bakery + 416: balance beam + 417: balloon + 418: ballpoint pen + 419: Band-Aid + 420: banjo + 421: baluster + 422: barbell + 423: barber chair + 424: barbershop + 425: barn + 426: barometer + 427: barrel + 428: wheelbarrow + 429: baseball + 430: basketball + 431: bassinet + 432: bassoon + 433: swimming cap + 434: bath towel + 435: bathtub + 436: station wagon + 437: lighthouse + 438: beaker + 439: military cap + 440: beer bottle + 441: beer glass + 442: bell-cot + 443: bib + 444: tandem bicycle + 445: bikini + 446: ring binder + 447: binoculars + 448: birdhouse + 449: boathouse + 450: bobsleigh + 451: bolo tie + 452: poke bonnet + 453: bookcase + 454: bookstore + 455: bottle cap + 456: bow + 457: bow tie + 458: brass + 459: bra + 460: breakwater + 461: breastplate + 462: broom + 463: bucket + 464: buckle + 465: bulletproof vest + 466: high-speed train + 467: butcher shop + 468: taxicab + 469: cauldron + 470: candle + 471: cannon + 472: canoe + 473: can opener + 474: cardigan + 475: car mirror + 476: carousel + 477: tool kit + 478: carton + 479: car wheel + 480: automated teller machine + 481: cassette + 482: cassette player + 483: castle + 484: catamaran + 485: CD player + 486: cello + 487: mobile phone + 488: chain + 489: chain-link fence + 490: chain mail + 491: chainsaw + 492: chest + 493: chiffonier + 494: chime + 495: china cabinet + 496: Christmas stocking + 497: church + 498: movie theater + 499: cleaver + 500: cliff dwelling + 501: cloak + 502: clogs + 503: cocktail shaker + 504: coffee mug + 505: coffeemaker + 506: coil + 507: combination lock + 508: computer keyboard + 509: confectionery store + 510: container ship + 511: convertible + 512: corkscrew + 513: cornet + 514: cowboy boot + 515: cowboy hat + 516: cradle + 517: crane (machine) + 518: crash helmet + 519: crate + 520: infant bed + 521: Crock Pot + 522: croquet ball + 523: crutch + 524: cuirass + 525: dam + 526: desk + 527: desktop computer + 528: rotary dial telephone + 529: diaper + 530: digital clock + 531: digital watch + 532: dining table + 533: dishcloth + 534: dishwasher + 535: disc brake + 536: dock + 537: dog sled + 538: dome + 539: doormat + 540: drilling rig + 541: drum + 542: drumstick + 543: dumbbell + 544: Dutch oven + 545: electric fan + 546: electric guitar + 547: electric locomotive + 548: entertainment center + 549: envelope + 550: espresso machine + 551: face powder + 552: feather boa + 553: filing cabinet + 554: fireboat + 555: fire engine + 556: fire screen sheet + 557: flagpole + 558: flute + 559: folding chair + 560: football helmet + 561: forklift + 562: fountain + 563: fountain pen + 564: four-poster bed + 565: freight car + 566: French horn + 567: frying pan + 568: fur coat + 569: garbage truck + 570: gas mask + 571: gas pump + 572: goblet + 573: go-kart + 574: golf ball + 575: golf cart + 576: gondola + 577: gong + 578: gown + 579: grand piano + 580: greenhouse + 581: grille + 582: grocery store + 583: guillotine + 584: barrette + 585: hair spray + 586: half-track + 587: hammer + 588: hamper + 589: hair dryer + 590: hand-held computer + 591: handkerchief + 592: hard disk drive + 593: harmonica + 594: harp + 595: harvester + 596: hatchet + 597: holster + 598: home theater + 599: honeycomb + 600: hook + 601: hoop skirt + 602: horizontal bar + 603: horse-drawn vehicle + 604: hourglass + 605: iPod + 606: clothes iron + 607: jack-o'-lantern + 608: jeans + 609: jeep + 610: T-shirt + 611: jigsaw puzzle + 612: pulled rickshaw + 613: joystick + 614: kimono + 615: knee pad + 616: knot + 617: lab coat + 618: ladle + 619: lampshade + 620: laptop computer + 621: lawn mower + 622: lens cap + 623: paper knife + 624: library + 625: lifeboat + 626: lighter + 627: limousine + 628: ocean liner + 629: lipstick + 630: slip-on shoe + 631: lotion + 632: speaker + 633: loupe + 634: sawmill + 635: magnetic compass + 636: mail bag + 637: mailbox + 638: tights + 639: tank suit + 640: manhole cover + 641: maraca + 642: marimba + 643: mask + 644: match + 645: maypole + 646: maze + 647: measuring cup + 648: medicine chest + 649: megalith + 650: microphone + 651: microwave oven + 652: military uniform + 653: milk can + 654: minibus + 655: miniskirt + 656: minivan + 657: missile + 658: mitten + 659: mixing bowl + 660: mobile home + 661: Model T + 662: modem + 663: monastery + 664: monitor + 665: moped + 666: mortar + 667: square academic cap + 668: mosque + 669: mosquito net + 670: scooter + 671: mountain bike + 672: tent + 673: computer mouse + 674: mousetrap + 675: moving van + 676: muzzle + 677: nail + 678: neck brace + 679: necklace + 680: nipple + 681: notebook computer + 682: obelisk + 683: oboe + 684: ocarina + 685: odometer + 686: oil filter + 687: organ + 688: oscilloscope + 689: overskirt + 690: bullock cart + 691: oxygen mask + 692: packet + 693: paddle + 694: paddle wheel + 695: padlock + 696: paintbrush + 697: pajamas + 698: palace + 699: pan flute + 700: paper towel + 701: parachute + 702: parallel bars + 703: park bench + 704: parking meter + 705: passenger car + 706: patio + 707: payphone + 708: pedestal + 709: pencil case + 710: pencil sharpener + 711: perfume + 712: Petri dish + 713: photocopier + 714: plectrum + 715: Pickelhaube + 716: picket fence + 717: pickup truck + 718: pier + 719: piggy bank + 720: pill bottle + 721: pillow + 722: ping-pong ball + 723: pinwheel + 724: pirate ship + 725: pitcher + 726: hand plane + 727: planetarium + 728: plastic bag + 729: plate rack + 730: plow + 731: plunger + 732: Polaroid camera + 733: pole + 734: police van + 735: poncho + 736: billiard table + 737: soda bottle + 738: pot + 739: potter's wheel + 740: power drill + 741: prayer rug + 742: printer + 743: prison + 744: projectile + 745: projector + 746: hockey puck + 747: punching bag + 748: purse + 749: quill + 750: quilt + 751: race car + 752: racket + 753: radiator + 754: radio + 755: radio telescope + 756: rain barrel + 757: recreational vehicle + 758: reel + 759: reflex camera + 760: refrigerator + 761: remote control + 762: restaurant + 763: revolver + 764: rifle + 765: rocking chair + 766: rotisserie + 767: eraser + 768: rugby ball + 769: ruler + 770: running shoe + 771: safe + 772: safety pin + 773: salt shaker + 774: sandal + 775: sarong + 776: saxophone + 777: scabbard + 778: weighing scale + 779: school bus + 780: schooner + 781: scoreboard + 782: CRT screen + 783: screw + 784: screwdriver + 785: seat belt + 786: sewing machine + 787: shield + 788: shoe store + 789: shoji + 790: shopping basket + 791: shopping cart + 792: shovel + 793: shower cap + 794: shower curtain + 795: ski + 796: ski mask + 797: sleeping bag + 798: slide rule + 799: sliding door + 800: slot machine + 801: snorkel + 802: snowmobile + 803: snowplow + 804: soap dispenser + 805: soccer ball + 806: sock + 807: solar thermal collector + 808: sombrero + 809: soup bowl + 810: space bar + 811: space heater + 812: space shuttle + 813: spatula + 814: motorboat + 815: spider web + 816: spindle + 817: sports car + 818: spotlight + 819: stage + 820: steam locomotive + 821: through arch bridge + 822: steel drum + 823: stethoscope + 824: scarf + 825: stone wall + 826: stopwatch + 827: stove + 828: strainer + 829: tram + 830: stretcher + 831: couch + 832: stupa + 833: submarine + 834: suit + 835: sundial + 836: sunglass + 837: sunglasses + 838: sunscreen + 839: suspension bridge + 840: mop + 841: sweatshirt + 842: swimsuit + 843: swing + 844: switch + 845: syringe + 846: table lamp + 847: tank + 848: tape player + 849: teapot + 850: teddy bear + 851: television + 852: tennis ball + 853: thatched roof + 854: front curtain + 855: thimble + 856: threshing machine + 857: throne + 858: tile roof + 859: toaster + 860: tobacco shop + 861: toilet seat + 862: torch + 863: totem pole + 864: tow truck + 865: toy store + 866: tractor + 867: semi-trailer truck + 868: tray + 869: trench coat + 870: tricycle + 871: trimaran + 872: tripod + 873: triumphal arch + 874: trolleybus + 875: trombone + 876: tub + 877: turnstile + 878: typewriter keyboard + 879: umbrella + 880: unicycle + 881: upright piano + 882: vacuum cleaner + 883: vase + 884: vault + 885: velvet + 886: vending machine + 887: vestment + 888: viaduct + 889: violin + 890: volleyball + 891: waffle iron + 892: wall clock + 893: wallet + 894: wardrobe + 895: military aircraft + 896: sink + 897: washing machine + 898: water bottle + 899: water jug + 900: water tower + 901: whiskey jug + 902: whistle + 903: wig + 904: window screen + 905: window shade + 906: Windsor tie + 907: wine bottle + 908: wing + 909: wok + 910: wooden spoon + 911: wool + 912: split-rail fence + 913: shipwreck + 914: yawl + 915: yurt + 916: website + 917: comic book + 918: crossword + 919: traffic sign + 920: traffic light + 921: dust jacket + 922: menu + 923: plate + 924: guacamole + 925: consomme + 926: hot pot + 927: trifle + 928: ice cream + 929: ice pop + 930: baguette + 931: bagel + 932: pretzel + 933: cheeseburger + 934: hot dog + 935: mashed potato + 936: cabbage + 937: broccoli + 938: cauliflower + 939: zucchini + 940: spaghetti squash + 941: acorn squash + 942: butternut squash + 943: cucumber + 944: artichoke + 945: bell pepper + 946: cardoon + 947: mushroom + 948: Granny Smith + 949: strawberry + 950: orange + 951: lemon + 952: fig + 953: pineapple + 954: banana + 955: jackfruit + 956: custard apple + 957: pomegranate + 958: hay + 959: carbonara + 960: chocolate syrup + 961: dough + 962: meatloaf + 963: pizza + 964: pot pie + 965: burrito + 966: red wine + 967: espresso + 968: cup + 969: eggnog + 970: alp + 971: bubble + 972: cliff + 973: coral reef + 974: geyser + 975: lakeshore + 976: promontory + 977: shoal + 978: seashore + 979: valley + 980: volcano + 981: baseball player + 982: bridegroom + 983: scuba diver + 984: rapeseed + 985: daisy + 986: yellow lady's slipper + 987: corn + 988: acorn + 989: rose hip + 990: horse chestnut seed + 991: coral fungus + 992: agaric + 993: gyromitra + 994: stinkhorn mushroom + 995: earth star + 996: hen-of-the-woods + 997: bolete + 998: ear + 999: toilet paper + + +# Download script/URL (optional) +download: data/scripts/get_imagenet.sh diff --git a/algorithm/yolov5/data/Objects365.yaml b/algorithm/yolov5/data/Objects365.yaml new file mode 100644 index 0000000..05b26a1 --- /dev/null +++ b/algorithm/yolov5/data/Objects365.yaml @@ -0,0 +1,438 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Objects365 dataset https://www.objects365.org/ by Megvii +# Example usage: python train.py --data Objects365.yaml +# parent +# ├── yolov5 +# └── datasets +# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/Objects365 # dataset root dir +train: images/train # train images (relative to 'path') 1742289 images +val: images/val # val images (relative to 'path') 80000 images +test: # test images (optional) + +# Classes +names: + 0: Person + 1: Sneakers + 2: Chair + 3: Other Shoes + 4: Hat + 5: Car + 6: Lamp + 7: Glasses + 8: Bottle + 9: Desk + 10: Cup + 11: Street Lights + 12: Cabinet/shelf + 13: Handbag/Satchel + 14: Bracelet + 15: Plate + 16: Picture/Frame + 17: Helmet + 18: Book + 19: Gloves + 20: Storage box + 21: Boat + 22: Leather Shoes + 23: Flower + 24: Bench + 25: Potted Plant + 26: Bowl/Basin + 27: Flag + 28: Pillow + 29: Boots + 30: Vase + 31: Microphone + 32: Necklace + 33: Ring + 34: SUV + 35: Wine Glass + 36: Belt + 37: Monitor/TV + 38: Backpack + 39: Umbrella + 40: Traffic Light + 41: Speaker + 42: Watch + 43: Tie + 44: Trash bin Can + 45: Slippers + 46: Bicycle + 47: Stool + 48: Barrel/bucket + 49: Van + 50: Couch + 51: Sandals + 52: Basket + 53: Drum + 54: Pen/Pencil + 55: Bus + 56: Wild Bird + 57: High Heels + 58: Motorcycle + 59: Guitar + 60: Carpet + 61: Cell Phone + 62: Bread + 63: Camera + 64: Canned + 65: Truck + 66: Traffic cone + 67: Cymbal + 68: Lifesaver + 69: Towel + 70: Stuffed Toy + 71: Candle + 72: Sailboat + 73: Laptop + 74: Awning + 75: Bed + 76: Faucet + 77: Tent + 78: Horse + 79: Mirror + 80: Power outlet + 81: Sink + 82: Apple + 83: Air Conditioner + 84: Knife + 85: Hockey Stick + 86: Paddle + 87: Pickup Truck + 88: Fork + 89: Traffic Sign + 90: Balloon + 91: Tripod + 92: Dog + 93: Spoon + 94: Clock + 95: Pot + 96: Cow + 97: Cake + 98: Dinning Table + 99: Sheep + 100: Hanger + 101: Blackboard/Whiteboard + 102: Napkin + 103: Other Fish + 104: Orange/Tangerine + 105: Toiletry + 106: Keyboard + 107: Tomato + 108: Lantern + 109: Machinery Vehicle + 110: Fan + 111: Green Vegetables + 112: Banana + 113: Baseball Glove + 114: Airplane + 115: Mouse + 116: Train + 117: Pumpkin + 118: Soccer + 119: Skiboard + 120: Luggage + 121: Nightstand + 122: Tea pot + 123: Telephone + 124: Trolley + 125: Head Phone + 126: Sports Car + 127: Stop Sign + 128: Dessert + 129: Scooter + 130: Stroller + 131: Crane + 132: Remote + 133: Refrigerator + 134: Oven + 135: Lemon + 136: Duck + 137: Baseball Bat + 138: Surveillance Camera + 139: Cat + 140: Jug + 141: Broccoli + 142: Piano + 143: Pizza + 144: Elephant + 145: Skateboard + 146: Surfboard + 147: Gun + 148: Skating and Skiing shoes + 149: Gas stove + 150: Donut + 151: Bow Tie + 152: Carrot + 153: Toilet + 154: Kite + 155: Strawberry + 156: Other Balls + 157: Shovel + 158: Pepper + 159: Computer Box + 160: Toilet Paper + 161: Cleaning Products + 162: Chopsticks + 163: Microwave + 164: Pigeon + 165: Baseball + 166: Cutting/chopping Board + 167: Coffee Table + 168: Side Table + 169: Scissors + 170: Marker + 171: Pie + 172: Ladder + 173: Snowboard + 174: Cookies + 175: Radiator + 176: Fire Hydrant + 177: Basketball + 178: Zebra + 179: Grape + 180: Giraffe + 181: Potato + 182: Sausage + 183: Tricycle + 184: Violin + 185: Egg + 186: Fire Extinguisher + 187: Candy + 188: Fire Truck + 189: Billiards + 190: Converter + 191: Bathtub + 192: Wheelchair + 193: Golf Club + 194: Briefcase + 195: Cucumber + 196: Cigar/Cigarette + 197: Paint Brush + 198: Pear + 199: Heavy Truck + 200: Hamburger + 201: Extractor + 202: Extension Cord + 203: Tong + 204: Tennis Racket + 205: Folder + 206: American Football + 207: earphone + 208: Mask + 209: Kettle + 210: Tennis + 211: Ship + 212: Swing + 213: Coffee Machine + 214: Slide + 215: Carriage + 216: Onion + 217: Green beans + 218: Projector + 219: Frisbee + 220: Washing Machine/Drying Machine + 221: Chicken + 222: Printer + 223: Watermelon + 224: Saxophone + 225: Tissue + 226: Toothbrush + 227: Ice cream + 228: Hot-air balloon + 229: Cello + 230: French Fries + 231: Scale + 232: Trophy + 233: Cabbage + 234: Hot dog + 235: Blender + 236: Peach + 237: Rice + 238: Wallet/Purse + 239: Volleyball + 240: Deer + 241: Goose + 242: Tape + 243: Tablet + 244: Cosmetics + 245: Trumpet + 246: Pineapple + 247: Golf Ball + 248: Ambulance + 249: Parking meter + 250: Mango + 251: Key + 252: Hurdle + 253: Fishing Rod + 254: Medal + 255: Flute + 256: Brush + 257: Penguin + 258: Megaphone + 259: Corn + 260: Lettuce + 261: Garlic + 262: Swan + 263: Helicopter + 264: Green Onion + 265: Sandwich + 266: Nuts + 267: Speed Limit Sign + 268: Induction Cooker + 269: Broom + 270: Trombone + 271: Plum + 272: Rickshaw + 273: Goldfish + 274: Kiwi fruit + 275: Router/modem + 276: Poker Card + 277: Toaster + 278: Shrimp + 279: Sushi + 280: Cheese + 281: Notepaper + 282: Cherry + 283: Pliers + 284: CD + 285: Pasta + 286: Hammer + 287: Cue + 288: Avocado + 289: Hamimelon + 290: Flask + 291: Mushroom + 292: Screwdriver + 293: Soap + 294: Recorder + 295: Bear + 296: Eggplant + 297: Board Eraser + 298: Coconut + 299: Tape Measure/Ruler + 300: Pig + 301: Showerhead + 302: Globe + 303: Chips + 304: Steak + 305: Crosswalk Sign + 306: Stapler + 307: Camel + 308: Formula 1 + 309: Pomegranate + 310: Dishwasher + 311: Crab + 312: Hoverboard + 313: Meat ball + 314: Rice Cooker + 315: Tuba + 316: Calculator + 317: Papaya + 318: Antelope + 319: Parrot + 320: Seal + 321: Butterfly + 322: Dumbbell + 323: Donkey + 324: Lion + 325: Urinal + 326: Dolphin + 327: Electric Drill + 328: Hair Dryer + 329: Egg tart + 330: Jellyfish + 331: Treadmill + 332: Lighter + 333: Grapefruit + 334: Game board + 335: Mop + 336: Radish + 337: Baozi + 338: Target + 339: French + 340: Spring Rolls + 341: Monkey + 342: Rabbit + 343: Pencil Case + 344: Yak + 345: Red Cabbage + 346: Binoculars + 347: Asparagus + 348: Barbell + 349: Scallop + 350: Noddles + 351: Comb + 352: Dumpling + 353: Oyster + 354: Table Tennis paddle + 355: Cosmetics Brush/Eyeliner Pencil + 356: Chainsaw + 357: Eraser + 358: Lobster + 359: Durian + 360: Okra + 361: Lipstick + 362: Cosmetics Mirror + 363: Curling + 364: Table Tennis + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from tqdm import tqdm + + from utils.general import Path, check_requirements, download, np, xyxy2xywhn + + check_requirements(('pycocotools>=2.0',)) + from pycocotools.coco import COCO + + # Make Directories + dir = Path(yaml['path']) # dataset root dir + for p in 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + for q in 'train', 'val': + (dir / p / q).mkdir(parents=True, exist_ok=True) + + # Train, Val Splits + for split, patches in [('train', 50 + 1), ('val', 43 + 1)]: + print(f"Processing {split} in {patches} patches ...") + images, labels = dir / 'images' / split, dir / 'labels' / split + + # Download + url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/" + if split == 'train': + download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json + download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8) + elif split == 'val': + download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json + download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8) + download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8) + + # Move + for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'): + f.rename(images / f.name) # move to /images/{split} + + # Labels + coco = COCO(dir / f'zhiyuan_objv2_{split}.json') + names = [x["name"] for x in coco.loadCats(coco.getCatIds())] + for cid, cat in enumerate(names): + catIds = coco.getCatIds(catNms=[cat]) + imgIds = coco.getImgIds(catIds=catIds) + for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'): + width, height = im["width"], im["height"] + path = Path(im["file_name"]) # image filename + try: + with open(labels / path.with_suffix('.txt').name, 'a') as file: + annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None) + for a in coco.loadAnns(annIds): + x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner) + xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4) + x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped + file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n") + except Exception as e: + print(e) diff --git a/algorithm/yolov5/data/SKU-110K.yaml b/algorithm/yolov5/data/SKU-110K.yaml new file mode 100644 index 0000000..edae717 --- /dev/null +++ b/algorithm/yolov5/data/SKU-110K.yaml @@ -0,0 +1,53 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail +# Example usage: python train.py --data SKU-110K.yaml +# parent +# ├── yolov5 +# └── datasets +# └── SKU-110K ← downloads here (13.6 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/SKU-110K # dataset root dir +train: train.txt # train images (relative to 'path') 8219 images +val: val.txt # val images (relative to 'path') 588 images +test: test.txt # test images (optional) 2936 images + +# Classes +names: + 0: object + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import shutil + from tqdm import tqdm + from utils.general import np, pd, Path, download, xyxy2xywh + + + # Download + dir = Path(yaml['path']) # dataset root dir + parent = Path(dir.parent) # download dir + urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz'] + download(urls, dir=parent, delete=False) + + # Rename directories + if dir.exists(): + shutil.rmtree(dir) + (parent / 'SKU110K_fixed').rename(dir) # rename dir + (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir + + # Convert labels + names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names + for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv': + x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations + images, unique_images = x[:, 0], np.unique(x[:, 0]) + with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f: + f.writelines(f'./images/{s}\n' for s in unique_images) + for im in tqdm(unique_images, desc=f'Converting {dir / d}'): + cls = 0 # single-class dataset + with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f: + for r in x[images == im]: + w, h = r[6], r[7] # image width, height + xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance + f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label diff --git a/algorithm/yolov5/data/VOC.yaml b/algorithm/yolov5/data/VOC.yaml new file mode 100644 index 0000000..27d3810 --- /dev/null +++ b/algorithm/yolov5/data/VOC.yaml @@ -0,0 +1,100 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford +# Example usage: python train.py --data VOC.yaml +# parent +# ├── yolov5 +# └── datasets +# └── VOC ← downloads here (2.8 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/VOC +train: # train images (relative to 'path') 16551 images + - images/train2012 + - images/train2007 + - images/val2012 + - images/val2007 +val: # val images (relative to 'path') 4952 images + - images/test2007 +test: # test images (optional) + - images/test2007 + +# Classes +names: + 0: aeroplane + 1: bicycle + 2: bird + 3: boat + 4: bottle + 5: bus + 6: car + 7: cat + 8: chair + 9: cow + 10: diningtable + 11: dog + 12: horse + 13: motorbike + 14: person + 15: pottedplant + 16: sheep + 17: sofa + 18: train + 19: tvmonitor + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import xml.etree.ElementTree as ET + + from tqdm import tqdm + from utils.general import download, Path + + + def convert_label(path, lb_path, year, image_id): + def convert_box(size, box): + dw, dh = 1. / size[0], 1. / size[1] + x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2] + return x * dw, y * dh, w * dw, h * dh + + in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') + out_file = open(lb_path, 'w') + tree = ET.parse(in_file) + root = tree.getroot() + size = root.find('size') + w = int(size.find('width').text) + h = int(size.find('height').text) + + names = list(yaml['names'].values()) # names list + for obj in root.iter('object'): + cls = obj.find('name').text + if cls in names and int(obj.find('difficult').text) != 1: + xmlbox = obj.find('bndbox') + bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) + cls_id = names.index(cls) # class id + out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') + + + # Download + dir = Path(yaml['path']) # dataset root dir + url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' + urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images + f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images + f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images + download(urls, dir=dir / 'images', delete=False, curl=True, threads=3) + + # Convert + path = dir / 'images/VOCdevkit' + for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): + imgs_path = dir / 'images' / f'{image_set}{year}' + lbs_path = dir / 'labels' / f'{image_set}{year}' + imgs_path.mkdir(exist_ok=True, parents=True) + lbs_path.mkdir(exist_ok=True, parents=True) + + with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f: + image_ids = f.read().strip().split() + for id in tqdm(image_ids, desc=f'{image_set}{year}'): + f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path + lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path + f.rename(imgs_path / f.name) # move image + convert_label(path, lb_path, year, id) # convert labels to YOLO format diff --git a/algorithm/yolov5/data/VisDrone.yaml b/algorithm/yolov5/data/VisDrone.yaml new file mode 100644 index 0000000..a8bcf8e --- /dev/null +++ b/algorithm/yolov5/data/VisDrone.yaml @@ -0,0 +1,70 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University +# Example usage: python train.py --data VisDrone.yaml +# parent +# ├── yolov5 +# └── datasets +# └── VisDrone ← downloads here (2.3 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/VisDrone # dataset root dir +train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images +val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images +test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images + +# Classes +names: + 0: pedestrian + 1: people + 2: bicycle + 3: car + 4: van + 5: truck + 6: tricycle + 7: awning-tricycle + 8: bus + 9: motor + + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from utils.general import download, os, Path + + def visdrone2yolo(dir): + from PIL import Image + from tqdm import tqdm + + def convert_box(size, box): + # Convert VisDrone box to YOLO xywh box + dw = 1. / size[0] + dh = 1. / size[1] + return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh + + (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory + pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') + for f in pbar: + img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size + lines = [] + with open(f, 'r') as file: # read annotation.txt + for row in [x.split(',') for x in file.read().strip().splitlines()]: + if row[4] == '0': # VisDrone 'ignored regions' class 0 + continue + cls = int(row[5]) - 1 + box = convert_box(img_size, tuple(map(int, row[:4]))) + lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") + with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: + fl.writelines(lines) # write label.txt + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip'] + download(urls, dir=dir, curl=True, threads=4) + + # Convert + for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': + visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels diff --git a/algorithm/yolov5/data/coco.yaml b/algorithm/yolov5/data/coco.yaml new file mode 100644 index 0000000..d64dfc7 --- /dev/null +++ b/algorithm/yolov5/data/coco.yaml @@ -0,0 +1,116 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# COCO 2017 dataset http://cocodataset.org by Microsoft +# Example usage: python train.py --data coco.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco ← downloads here (20.1 GB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco # dataset root dir +train: train2017.txt # train images (relative to 'path') 118287 images +val: val2017.txt # val images (relative to 'path') 5000 images +test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + + +# Download script/URL (optional) +download: | + from utils.general import download, Path + + + # Download labels + segments = False # segment or box labels + dir = Path(yaml['path']) # dataset root dir + url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' + urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels + download(urls, dir=dir.parent) + + # Download data + urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images + 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images + 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) + download(urls, dir=dir / 'images', threads=3) diff --git a/algorithm/yolov5/data/coco128-seg.yaml b/algorithm/yolov5/data/coco128-seg.yaml new file mode 100644 index 0000000..5e81910 --- /dev/null +++ b/algorithm/yolov5/data/coco128-seg.yaml @@ -0,0 +1,101 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# Example usage: python train.py --data coco128.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco128-seg ← downloads here (7 MB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco128-seg # dataset root dir +train: images/train2017 # train images (relative to 'path') 128 images +val: images/train2017 # val images (relative to 'path') 128 images +test: # test images (optional) + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + + +# Download script/URL (optional) +download: https://ultralytics.com/assets/coco128-seg.zip diff --git a/algorithm/yolov5/data/coco128.yaml b/algorithm/yolov5/data/coco128.yaml new file mode 100644 index 0000000..1255673 --- /dev/null +++ b/algorithm/yolov5/data/coco128.yaml @@ -0,0 +1,101 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# Example usage: python train.py --data coco128.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco128 ← downloads here (7 MB) + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco128 # dataset root dir +train: images/train2017 # train images (relative to 'path') 128 images +val: images/train2017 # val images (relative to 'path') 128 images +test: # test images (optional) + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + + +# Download script/URL (optional) +download: https://ultralytics.com/assets/coco128.zip diff --git a/algorithm/yolov5/data/hyps/hyp.Objects365.yaml b/algorithm/yolov5/data/hyps/hyp.Objects365.yaml new file mode 100644 index 0000000..7497174 --- /dev/null +++ b/algorithm/yolov5/data/hyps/hyp.Objects365.yaml @@ -0,0 +1,34 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for Objects365 training +# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve +# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.00258 +lrf: 0.17 +momentum: 0.779 +weight_decay: 0.00058 +warmup_epochs: 1.33 +warmup_momentum: 0.86 +warmup_bias_lr: 0.0711 +box: 0.0539 +cls: 0.299 +cls_pw: 0.825 +obj: 0.632 +obj_pw: 1.0 +iou_t: 0.2 +anchor_t: 3.44 +anchors: 3.2 +fl_gamma: 0.0 +hsv_h: 0.0188 +hsv_s: 0.704 +hsv_v: 0.36 +degrees: 0.0 +translate: 0.0902 +scale: 0.491 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.0 +copy_paste: 0.0 diff --git a/algorithm/yolov5/data/hyps/hyp.VOC.yaml b/algorithm/yolov5/data/hyps/hyp.VOC.yaml new file mode 100644 index 0000000..0aa4e7d --- /dev/null +++ b/algorithm/yolov5/data/hyps/hyp.VOC.yaml @@ -0,0 +1,40 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for VOC training +# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve +# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials + +# YOLOv5 Hyperparameter Evolution Results +# Best generation: 467 +# Last generation: 996 +# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss +# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865 + +lr0: 0.00334 +lrf: 0.15135 +momentum: 0.74832 +weight_decay: 0.00025 +warmup_epochs: 3.3835 +warmup_momentum: 0.59462 +warmup_bias_lr: 0.18657 +box: 0.02 +cls: 0.21638 +cls_pw: 0.5 +obj: 0.51728 +obj_pw: 0.67198 +iou_t: 0.2 +anchor_t: 3.3744 +fl_gamma: 0.0 +hsv_h: 0.01041 +hsv_s: 0.54703 +hsv_v: 0.27739 +degrees: 0.0 +translate: 0.04591 +scale: 0.75544 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 0.85834 +mixup: 0.04266 +copy_paste: 0.0 +anchors: 3.412 diff --git a/algorithm/yolov5/data/hyps/hyp.no-augmentation.yaml b/algorithm/yolov5/data/hyps/hyp.no-augmentation.yaml new file mode 100644 index 0000000..8fbd5b2 --- /dev/null +++ b/algorithm/yolov5/data/hyps/hyp.no-augmentation.yaml @@ -0,0 +1,35 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters when using Albumentations frameworks +# python train.py --hyp hyp.no-augmentation.yaml +# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +# this parameters are all zero since we want to use albumentation framework +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0 # image HSV-Hue augmentation (fraction) +hsv_s: 00 # image HSV-Saturation augmentation (fraction) +hsv_v: 0 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0 # image translation (+/- fraction) +scale: 0 # image scale (+/- gain) +shear: 0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.0 # image flip left-right (probability) +mosaic: 0.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/algorithm/yolov5/data/hyps/hyp.scratch-high.yaml b/algorithm/yolov5/data/hyps/hyp.scratch-high.yaml new file mode 100644 index 0000000..123cc84 --- /dev/null +++ b/algorithm/yolov5/data/hyps/hyp.scratch-high.yaml @@ -0,0 +1,34 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for high-augmentation COCO training from scratch +# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.1 # image mixup (probability) +copy_paste: 0.1 # segment copy-paste (probability) diff --git a/algorithm/yolov5/data/hyps/hyp.scratch-low.yaml b/algorithm/yolov5/data/hyps/hyp.scratch-low.yaml new file mode 100644 index 0000000..b9ef1d5 --- /dev/null +++ b/algorithm/yolov5/data/hyps/hyp.scratch-low.yaml @@ -0,0 +1,34 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for low-augmentation COCO training from scratch +# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.5 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 1.0 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/algorithm/yolov5/data/hyps/hyp.scratch-med.yaml b/algorithm/yolov5/data/hyps/hyp.scratch-med.yaml new file mode 100644 index 0000000..d6867d7 --- /dev/null +++ b/algorithm/yolov5/data/hyps/hyp.scratch-med.yaml @@ -0,0 +1,34 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Hyperparameters for medium-augmentation COCO training from scratch +# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.1 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/algorithm/yolov5/data/images/bus.jpg b/algorithm/yolov5/data/images/bus.jpg new file mode 100644 index 0000000..b43e311 Binary files /dev/null and b/algorithm/yolov5/data/images/bus.jpg differ diff --git a/algorithm/yolov5/data/images/zidane.jpg b/algorithm/yolov5/data/images/zidane.jpg new file mode 100644 index 0000000..92d72ea Binary files /dev/null and b/algorithm/yolov5/data/images/zidane.jpg differ diff --git a/algorithm/yolov5/data/scripts/download_weights.sh b/algorithm/yolov5/data/scripts/download_weights.sh new file mode 100644 index 0000000..31e0a15 --- /dev/null +++ b/algorithm/yolov5/data/scripts/download_weights.sh @@ -0,0 +1,22 @@ +#!/bin/bash +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Download latest models from https://github.com/ultralytics/yolov5/releases +# Example usage: bash data/scripts/download_weights.sh +# parent +# └── yolov5 +# ├── yolov5s.pt ← downloads here +# ├── yolov5m.pt +# └── ... + +python - <= cls >= 0, f'incorrect class index {cls}' + + # Write YOLO label + if id not in shapes: + shapes[id] = Image.open(file).size + box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True) + with open((labels / id).with_suffix('.txt'), 'a') as f: + f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt + except Exception as e: + print(f'WARNING: skipping one label for {file}: {e}') + + + # Download manually from https://challenge.xviewdataset.org + dir = Path(yaml['path']) # dataset root dir + # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels + # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images + # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels) + # download(urls, dir=dir, delete=False) + + # Convert labels + convert_labels(dir / 'xView_train.geojson') + + # Move images + images = Path(dir / 'images') + images.mkdir(parents=True, exist_ok=True) + Path(dir / 'train_images').rename(dir / 'images' / 'train') + Path(dir / 'val_images').rename(dir / 'images' / 'val') + + # Split + autosplit(dir / 'images' / 'train') diff --git a/algorithm/yolov5/detect.py b/algorithm/yolov5/detect.py new file mode 100644 index 0000000..bc9c620 --- /dev/null +++ b/algorithm/yolov5/detect.py @@ -0,0 +1,261 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from algorithm.yolov5.models.common import DetectMultiBackend +from algorithm.yolov5.utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from algorithm.yolov5.utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) +from algorithm.yolov5.utils.plots import Annotator, colors, save_one_box +from algorithm.yolov5.utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s.pt', # model path or triton URL + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/detect', # save results to project/name + name='exp', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + pred = model(im, augment=augment, visualize=visualize) + + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + s += '%gx%g ' % im.shape[2:] # print string + gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh + imc = im0.copy() if save_crop else im0 # for save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Write results + for *xyxy, conf, cls in reversed(det): + if save_txt: # Write to file + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(f'{txt_path}.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + annotator.box_label(xyxy, label, color=colors(c, True)) + if save_crop: + save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path or triton URL') + parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') + parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') + parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/export.py b/algorithm/yolov5/export.py new file mode 100644 index 0000000..e167b20 --- /dev/null +++ b/algorithm/yolov5/export.py @@ -0,0 +1,672 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit + +Format | `export.py --include` | Model +--- | --- | --- +PyTorch | - | yolov5s.pt +TorchScript | `torchscript` | yolov5s.torchscript +ONNX | `onnx` | yolov5s.onnx +OpenVINO | `openvino` | yolov5s_openvino_model/ +TensorRT | `engine` | yolov5s.engine +CoreML | `coreml` | yolov5s.mlmodel +TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ +TensorFlow GraphDef | `pb` | yolov5s.pb +TensorFlow Lite | `tflite` | yolov5s.tflite +TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov5s_web_model/ +PaddlePaddle | `paddle` | yolov5s_paddle_model/ + +Requirements: + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU + +Usage: + $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ... + +Inference: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle + +TensorFlow.js: + $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example + $ npm install + $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model + $ npm start +""" + +import argparse +import contextlib +import json +import os +import platform +import re +import subprocess +import sys +import time +import warnings +from pathlib import Path + +import pandas as pd +import torch +from torch.utils.mobile_optimizer import optimize_for_mobile + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +if platform.system() != 'Windows': + ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.experimental import attempt_load +from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel +from utils.dataloaders import LoadImages +from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version, + check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save) +from utils.torch_utils import select_device, smart_inference_mode + +MACOS = platform.system() == 'Darwin' # macOS environment + + +def export_formats(): + # YOLOv5 export formats + x = [ + ['PyTorch', '-', '.pt', True, True], + ['TorchScript', 'torchscript', '.torchscript', True, True], + ['ONNX', 'onnx', '.onnx', True, True], + ['OpenVINO', 'openvino', '_openvino_model', True, False], + ['TensorRT', 'engine', '.engine', False, True], + ['CoreML', 'coreml', '.mlmodel', True, False], + ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True], + ['TensorFlow GraphDef', 'pb', '.pb', True, True], + ['TensorFlow Lite', 'tflite', '.tflite', True, False], + ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False], + ['TensorFlow.js', 'tfjs', '_web_model', False, False], + ['PaddlePaddle', 'paddle', '_paddle_model', True, True],] + return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU']) + + +def try_export(inner_func): + # YOLOv5 export decorator, i..e @try_export + inner_args = get_default_args(inner_func) + + def outer_func(*args, **kwargs): + prefix = inner_args['prefix'] + try: + with Profile() as dt: + f, model = inner_func(*args, **kwargs) + LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)') + return f, model + except Exception as e: + LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}') + return None, None + + return outer_func + + +@try_export +def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')): + # YOLOv5 TorchScript model export + LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...') + f = file.with_suffix('.torchscript') + + ts = torch.jit.trace(model, im, strict=False) + d = {'shape': im.shape, 'stride': int(max(model.stride)), 'names': model.names} + extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap() + if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html + optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) + else: + ts.save(str(f), _extra_files=extra_files) + return f, None + + +@try_export +def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')): + # YOLOv5 ONNX export + check_requirements('onnx>=1.12.0') + import onnx + + LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...') + f = file.with_suffix('.onnx') + + output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0'] + if dynamic: + dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640) + if isinstance(model, SegmentationModel): + dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85) + dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160) + elif isinstance(model, DetectionModel): + dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85) + + torch.onnx.export( + model.cpu() if dynamic else model, # --dynamic only compatible with cpu + im.cpu() if dynamic else im, + f, + verbose=False, + opset_version=opset, + do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False + input_names=['images'], + output_names=output_names, + dynamic_axes=dynamic or None) + + # Checks + model_onnx = onnx.load(f) # load onnx model + onnx.checker.check_model(model_onnx) # check onnx model + + # Metadata + d = {'stride': int(max(model.stride)), 'names': model.names} + for k, v in d.items(): + meta = model_onnx.metadata_props.add() + meta.key, meta.value = k, str(v) + onnx.save(model_onnx, f) + + # Simplify + if simplify: + try: + cuda = torch.cuda.is_available() + check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1')) + import onnxsim + + LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') + model_onnx, check = onnxsim.simplify(model_onnx) + assert check, 'assert check failed' + onnx.save(model_onnx, f) + except Exception as e: + LOGGER.info(f'{prefix} simplifier failure: {e}') + return f, model_onnx + + +@try_export +def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')): + # YOLOv5 OpenVINO export + check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/ + import openvino.inference_engine as ie + + LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...') + f = str(file).replace('.pt', f'_openvino_model{os.sep}') + + args = [ + 'mo', + '--input_model', + str(file.with_suffix('.onnx')), + '--output_dir', + f, + '--data_type', + ('FP16' if half else 'FP32'),] + subprocess.run(args, check=True, env=os.environ) # export + yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml + return f, None + + +@try_export +def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')): + # YOLOv5 Paddle export + check_requirements(('paddlepaddle', 'x2paddle')) + import x2paddle + from x2paddle.convert import pytorch2paddle + + LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...') + f = str(file).replace('.pt', f'_paddle_model{os.sep}') + + pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im]) # export + yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml + return f, None + + +@try_export +def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')): + # YOLOv5 CoreML export + check_requirements('coremltools') + import coremltools as ct + + LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...') + f = file.with_suffix('.mlmodel') + + ts = torch.jit.trace(model, im, strict=False) # TorchScript model + ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])]) + bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None) + if bits < 32: + if MACOS: # quantization only supported on macOS + with warnings.catch_warnings(): + warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress numpy==1.20 float warning + ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) + else: + print(f'{prefix} quantization only supported on macOS, skipping...') + ct_model.save(f) + return f, ct_model + + +@try_export +def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')): + # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt + assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`' + try: + import tensorrt as trt + except Exception: + if platform.system() == 'Linux': + check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com') + import tensorrt as trt + + if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 + grid = model.model[-1].anchor_grid + model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] + export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 + model.model[-1].anchor_grid = grid + else: # TensorRT >= 8 + check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0 + export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 + onnx = file.with_suffix('.onnx') + + LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...') + assert onnx.exists(), f'failed to export ONNX file: {onnx}' + f = file.with_suffix('.engine') # TensorRT engine file + logger = trt.Logger(trt.Logger.INFO) + if verbose: + logger.min_severity = trt.Logger.Severity.VERBOSE + + builder = trt.Builder(logger) + config = builder.create_builder_config() + config.max_workspace_size = workspace * 1 << 30 + # config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice + + flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) + network = builder.create_network(flag) + parser = trt.OnnxParser(network, logger) + if not parser.parse_from_file(str(onnx)): + raise RuntimeError(f'failed to load ONNX file: {onnx}') + + inputs = [network.get_input(i) for i in range(network.num_inputs)] + outputs = [network.get_output(i) for i in range(network.num_outputs)] + for inp in inputs: + LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}') + for out in outputs: + LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}') + + if dynamic: + if im.shape[0] <= 1: + LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument') + profile = builder.create_optimization_profile() + for inp in inputs: + profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape) + config.add_optimization_profile(profile) + + LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}') + if builder.platform_has_fast_fp16 and half: + config.set_flag(trt.BuilderFlag.FP16) + with builder.build_engine(network, config) as engine, open(f, 'wb') as t: + t.write(engine.serialize()) + return f, None + + +@try_export +def export_saved_model(model, + im, + file, + dynamic, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25, + keras=False, + prefix=colorstr('TensorFlow SavedModel:')): + # YOLOv5 TensorFlow SavedModel export + try: + import tensorflow as tf + except Exception: + check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}") + import tensorflow as tf + from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 + + from models.tf import TFModel + + LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') + f = str(file).replace('.pt', '_saved_model') + batch_size, ch, *imgsz = list(im.shape) # BCHW + + tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) + im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow + _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) + inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size) + outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) + keras_model = tf.keras.Model(inputs=inputs, outputs=outputs) + keras_model.trainable = False + keras_model.summary() + if keras: + keras_model.save(f, save_format='tf') + else: + spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype) + m = tf.function(lambda x: keras_model(x)) # full model + m = m.get_concrete_function(spec) + frozen_func = convert_variables_to_constants_v2(m) + tfm = tf.Module() + tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec]) + tfm.__call__(im) + tf.saved_model.save(tfm, + f, + options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version( + tf.__version__, '2.6') else tf.saved_model.SaveOptions()) + return f, keras_model + + +@try_export +def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')): + # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow + import tensorflow as tf + from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 + + LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') + f = file.with_suffix('.pb') + + m = tf.function(lambda x: keras_model(x)) # full model + m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) + frozen_func = convert_variables_to_constants_v2(m) + frozen_func.graph.as_graph_def() + tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False) + return f, None + + +@try_export +def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')): + # YOLOv5 TensorFlow Lite export + import tensorflow as tf + + LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') + batch_size, ch, *imgsz = list(im.shape) # BCHW + f = str(file).replace('.pt', '-fp16.tflite') + + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + converter.target_spec.supported_types = [tf.float16] + converter.optimizations = [tf.lite.Optimize.DEFAULT] + if int8: + from models.tf import representative_dataset_gen + dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False) + converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100) + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] + converter.target_spec.supported_types = [] + converter.inference_input_type = tf.uint8 # or tf.int8 + converter.inference_output_type = tf.uint8 # or tf.int8 + converter.experimental_new_quantizer = True + f = str(file).replace('.pt', '-int8.tflite') + if nms or agnostic_nms: + converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS) + + tflite_model = converter.convert() + open(f, 'wb').write(tflite_model) + return f, None + + +@try_export +def export_edgetpu(file, prefix=colorstr('Edge TPU:')): + # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/ + cmd = 'edgetpu_compiler --version' + help_url = 'https://coral.ai/docs/edgetpu/compiler/' + assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}' + if subprocess.run(f'{cmd} > /dev/null 2>&1', shell=True).returncode != 0: + LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}') + sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system + for c in ( + 'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -', + 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', + 'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'): + subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True) + ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1] + + LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...') + f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model + f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model + + subprocess.run([ + 'edgetpu_compiler', + '-s', + '-d', + '-k', + '10', + '--out_dir', + str(file.parent), + f_tfl,], check=True) + return f, None + + +@try_export +def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')): + # YOLOv5 TensorFlow.js export + check_requirements('tensorflowjs') + import tensorflowjs as tfjs + + LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...') + f = str(file).replace('.pt', '_web_model') # js dir + f_pb = file.with_suffix('.pb') # *.pb path + f_json = f'{f}/model.json' # *.json path + + args = [ + 'tensorflowjs_converter', + '--input_format=tf_frozen_model', + '--quantize_uint8' if int8 else '', + '--output_node_names=Identity,Identity_1,Identity_2,Identity_3', + str(f_pb), + str(f),] + subprocess.run([arg for arg in args if arg], check=True) + + json = Path(f_json).read_text() + with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order + subst = re.sub( + r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, ' + r'"Identity_1": {"name": "Identity_1"}, ' + r'"Identity_2": {"name": "Identity_2"}, ' + r'"Identity_3": {"name": "Identity_3"}}}', json) + j.write(subst) + return f, None + + +def add_tflite_metadata(file, metadata, num_outputs): + # Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata + with contextlib.suppress(ImportError): + # check_requirements('tflite_support') + from tflite_support import flatbuffers + from tflite_support import metadata as _metadata + from tflite_support import metadata_schema_py_generated as _metadata_fb + + tmp_file = Path('/tmp/meta.txt') + with open(tmp_file, 'w') as meta_f: + meta_f.write(str(metadata)) + + model_meta = _metadata_fb.ModelMetadataT() + label_file = _metadata_fb.AssociatedFileT() + label_file.name = tmp_file.name + model_meta.associatedFiles = [label_file] + + subgraph = _metadata_fb.SubGraphMetadataT() + subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()] + subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs + model_meta.subgraphMetadata = [subgraph] + + b = flatbuffers.Builder(0) + b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER) + metadata_buf = b.Output() + + populator = _metadata.MetadataPopulator.with_model_file(file) + populator.load_metadata_buffer(metadata_buf) + populator.load_associated_files([str(tmp_file)]) + populator.populate() + tmp_file.unlink() + + +@smart_inference_mode() +def run( + data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path' + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=(640, 640), # image (height, width) + batch_size=1, # batch size + device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu + include=('torchscript', 'onnx'), # include formats + half=False, # FP16 half-precision export + inplace=False, # set YOLOv5 Detect() inplace=True + keras=False, # use Keras + optimize=False, # TorchScript: optimize for mobile + int8=False, # CoreML/TF INT8 quantization + dynamic=False, # ONNX/TF/TensorRT: dynamic axes + simplify=False, # ONNX: simplify model + opset=12, # ONNX: opset version + verbose=False, # TensorRT: verbose log + workspace=4, # TensorRT: workspace size (GB) + nms=False, # TF: add NMS to model + agnostic_nms=False, # TF: add agnostic NMS to model + topk_per_class=100, # TF.js NMS: topk per class to keep + topk_all=100, # TF.js NMS: topk for all classes to keep + iou_thres=0.45, # TF.js NMS: IoU threshold + conf_thres=0.25, # TF.js NMS: confidence threshold +): + t = time.time() + include = [x.lower() for x in include] # to lowercase + fmts = tuple(export_formats()['Argument'][1:]) # --include arguments + flags = [x in include for x in fmts] + assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}' + jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans + file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights + + # Load PyTorch model + device = select_device(device) + if half: + assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0' + assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both' + model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model + + # Checks + imgsz *= 2 if len(imgsz) == 1 else 1 # expand + if optimize: + assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu' + + # Input + gs = int(max(model.stride)) # grid size (max stride) + imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples + im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection + + # Update model + model.eval() + for k, m in model.named_modules(): + if isinstance(m, Detect): + m.inplace = inplace + m.dynamic = dynamic + m.export = True + + for _ in range(2): + y = model(im) # dry runs + if half and not coreml: + im, model = im.half(), model.half() # to FP16 + shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape + metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata + LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)") + + # Exports + f = [''] * len(fmts) # exported filenames + warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning + if jit: # TorchScript + f[0], _ = export_torchscript(model, im, file, optimize) + if engine: # TensorRT required before ONNX + f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose) + if onnx or xml: # OpenVINO requires ONNX + f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify) + if xml: # OpenVINO + f[3], _ = export_openvino(file, metadata, half) + if coreml: # CoreML + f[4], _ = export_coreml(model, im, file, int8, half) + if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats + assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.' + assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.' + f[5], s_model = export_saved_model(model.cpu(), + im, + file, + dynamic, + tf_nms=nms or agnostic_nms or tfjs, + agnostic_nms=agnostic_nms or tfjs, + topk_per_class=topk_per_class, + topk_all=topk_all, + iou_thres=iou_thres, + conf_thres=conf_thres, + keras=keras) + if pb or tfjs: # pb prerequisite to tfjs + f[6], _ = export_pb(s_model, file) + if tflite or edgetpu: + f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms) + if edgetpu: + f[8], _ = export_edgetpu(file) + add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) + if tfjs: + f[9], _ = export_tfjs(file, int8) + if paddle: # PaddlePaddle + f[10], _ = export_paddle(model, im, file, metadata) + + # Finish + f = [str(x) for x in f if x] # filter out '' and None + if any(f): + cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type + det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel) + dir = Path('segment' if seg else 'classify' if cls else '') + h = '--half' if half else '' # --half FP16 inference arg + s = '# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference' if cls else \ + '# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference' if seg else '' + LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)' + f"\nResults saved to {colorstr('bold', file.parent.resolve())}" + f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}" + f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}" + f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}" + f'\nVisualize: https://netron.app') + return f # return list of exported files/dirs + + +def parse_opt(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--half', action='store_true', help='FP16 half-precision export') + parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True') + parser.add_argument('--keras', action='store_true', help='TF: use Keras') + parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile') + parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization') + parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes') + parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model') + parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version') + parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log') + parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)') + parser.add_argument('--nms', action='store_true', help='TF: add NMS to model') + parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model') + parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep') + parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep') + parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold') + parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold') + parser.add_argument( + '--include', + nargs='+', + default=['torchscript'], + help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle') + opt = parser.parse_known_args()[0] if known else parser.parse_args() + print_args(vars(opt)) + return opt + + +def main(opt): + for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]): + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/hubconf.py b/algorithm/yolov5/hubconf.py new file mode 100644 index 0000000..41af8e3 --- /dev/null +++ b/algorithm/yolov5/hubconf.py @@ -0,0 +1,169 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5 + +Usage: + import torch + model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model + model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch + model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model + model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo +""" + +import torch + + +def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): + """Creates or loads a YOLOv5 model + + Arguments: + name (str): model name 'yolov5s' or path 'path/to/best.pt' + pretrained (bool): load pretrained weights into the model + channels (int): number of input channels + classes (int): number of model classes + autoshape (bool): apply YOLOv5 .autoshape() wrapper to model + verbose (bool): print all information to screen + device (str, torch.device, None): device to use for model parameters + + Returns: + YOLOv5 model + """ + from pathlib import Path + + from models.common import AutoShape, DetectMultiBackend + from models.experimental import attempt_load + from models.yolo import ClassificationModel, DetectionModel, SegmentationModel + from utils.downloads import attempt_download + from utils.general import LOGGER, check_requirements, intersect_dicts, logging + from utils.torch_utils import select_device + + if not verbose: + LOGGER.setLevel(logging.WARNING) + check_requirements(exclude=('opencv-python', 'tensorboard', 'thop')) + name = Path(name) + path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name # checkpoint path + try: + device = select_device(device) + if pretrained and channels == 3 and classes == 80: + try: + model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model + if autoshape: + if model.pt and isinstance(model.model, ClassificationModel): + LOGGER.warning('WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. ' + 'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).') + elif model.pt and isinstance(model.model, SegmentationModel): + LOGGER.warning('WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. ' + 'You will not be able to run inference with this model.') + else: + model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS + except Exception: + model = attempt_load(path, device=device, fuse=False) # arbitrary model + else: + cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path + model = DetectionModel(cfg, channels, classes) # create model + if pretrained: + ckpt = torch.load(attempt_download(path), map_location=device) # load + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect + model.load_state_dict(csd, strict=False) # load + if len(ckpt['model'].names) == classes: + model.names = ckpt['model'].names # set class names attribute + if not verbose: + LOGGER.setLevel(logging.INFO) # reset to default + return model.to(device) + + except Exception as e: + help_url = 'https://github.com/ultralytics/yolov5/issues/36' + s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.' + raise Exception(s) from e + + +def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None): + # YOLOv5 custom or local model + return _create(path, autoshape=autoshape, verbose=_verbose, device=device) + + +def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-nano model https://github.com/ultralytics/yolov5 + return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-small model https://github.com/ultralytics/yolov5 + return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-medium model https://github.com/ultralytics/yolov5 + return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-large model https://github.com/ultralytics/yolov5 + return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 + return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5 + return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device) + + +if __name__ == '__main__': + import argparse + from pathlib import Path + + import numpy as np + from PIL import Image + + from utils.general import cv2, print_args + + # Argparser + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default='yolov5s', help='model name') + opt = parser.parse_args() + print_args(vars(opt)) + + # Model + model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) + # model = custom(path='path/to/model.pt') # custom + + # Images + imgs = [ + 'data/images/zidane.jpg', # filename + Path('data/images/zidane.jpg'), # Path + 'https://ultralytics.com/images/zidane.jpg', # URI + cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV + Image.open('data/images/bus.jpg'), # PIL + np.zeros((320, 640, 3))] # numpy + + # Inference + results = model(imgs, size=320) # batched inference + + # Results + results.print() + results.save() diff --git a/algorithm/yolov5/models/__init__.py b/algorithm/yolov5/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5/models/common.py b/algorithm/yolov5/models/common.py new file mode 100644 index 0000000..f00e899 --- /dev/null +++ b/algorithm/yolov5/models/common.py @@ -0,0 +1,1021 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Common modules +""" + +import ast +import contextlib +import json +import math +import platform +import warnings +import zipfile +from collections import OrderedDict, namedtuple +from copy import copy +from pathlib import Path +from urllib.parse import urlparse + +import cv2 +import numpy as np +import pandas as pd +import requests +import torch +import torch.nn as nn +from PIL import Image +from torch.cuda import amp + +from algorithm.yolov5.utils import TryExcept +from algorithm.yolov5.utils.dataloaders import exif_transpose, letterbox +from algorithm.yolov5.utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr, + increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy, + xyxy2xywh, yaml_load) +from algorithm.yolov5.utils.plots import Annotator, colors, save_one_box +from algorithm.yolov5.utils.torch_utils import copy_attr, smart_inference_mode + + +def autopad(k, p=None, d=1): # kernel, padding, dilation + # Pad to 'same' shape outputs + if d > 1: + k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + +def channel_shuffle(x, groups): + batchsize, num_channels, height, width = x.data.size() + channels_per_group = num_channels // groups + + # reshape + x = x.view(batchsize, groups, channels_per_group, height, width) + x = torch.transpose(x, 1, 2).contiguous() + + # flatten + x = x.view(batchsize, -1, height, width) + return x + +def DWConv(c1, c2, k=1, s=1, act=True): + # Depthwise convolution + return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) + +class Conv(nn.Module): + # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation) + default_act = nn.SiLU() # default activation + + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): + super().__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def forward_fuse(self, x): + # print(x) + return self.act(self.conv(x)) + + +class DWConv(Conv): + # Depth-wise convolution + def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation + super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) + + +class DWConvTranspose2d(nn.ConvTranspose2d): + # Depth-wise transpose convolution + def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out + super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) + + +class TransformerLayer(nn.Module): + # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) + def __init__(self, c, num_heads): + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + +class StemBlock(nn.Module): + def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True): + super(StemBlock, self).__init__() + self.stem_1 = Conv(c1, c2, k, s, p, g, act) + self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0) + self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1) + self.stem_2p = nn.MaxPool2d(kernel_size=2,stride=2,ceil_mode=True) + self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0) + + def forward(self, x): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + x = x.to(device) + stem_1_out = self.stem_1(x) + stem_2a_out = self.stem_2a(stem_1_out) + stem_2b_out = self.stem_2b(stem_2a_out) + stem_2p_out = self.stem_2p(stem_1_out) + out = self.stem_3(torch.cat((stem_2b_out,stem_2p_out),1)) + return out + + + +class TransformerBlock(nn.Module): + # Vision Transformer https://arxiv.org/abs/2010.11929 + def __init__(self, c1, c2, num_heads, num_layers): + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) + self.c2 = c2 + + def forward(self, x): + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2).permute(2, 0, 1) + return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) + + +class Bottleneck(nn.Module): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2, 3, 1, g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class BottleneckCSP(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) + self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) + self.cv4 = Conv(2 * c_, c2, 1, 1) + self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) + self.act = nn.SiLU() + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) + + +class CrossConv(nn.Module): + # Cross Convolution Downsample + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): + # ch_in, ch_out, kernel, stride, groups, expansion, shortcut + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, (1, k), (1, s)) + self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class C3(nn.Module): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) + + +class C3x(C3): + # C3 module with cross-convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n))) + + +class C3TR(C3): + # C3 module with TransformerBlock() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = TransformerBlock(c_, c_, 4, n) + + +class C3SPP(C3): + # C3 module with SPP() + def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = SPP(c_, c_, k) + + +class C3Ghost(C3): + # C3 module with GhostBottleneck() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) + + +class SPP(nn.Module): + # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 + def __init__(self, c1, c2, k=(5, 9, 13)): + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + + def forward(self, x): + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + + +class SPPF(nn.Module): + # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher + def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * 4, c2, 1, 1) + self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) + + def forward(self, x): + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) + + +class Focus(nn.Module): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act) + # self.contract = Contract(gain=2) + + def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) + return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1)) + # return self.conv(self.contract(x)) + + +class GhostConv(nn.Module): + # Ghost Convolution https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups + super().__init__() + c_ = c2 // 2 # hidden channels + self.cv1 = Conv(c1, c_, k, s, None, g, act=act) + self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act) + + def forward(self, x): + y = self.cv1(x) + return torch.cat((y, self.cv2(y)), 1) + + +class GhostBottleneck(nn.Module): + # Ghost Bottleneck https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride + super().__init__() + c_ = c2 // 2 + self.conv = nn.Sequential( + GhostConv(c1, c_, 1, 1), # pw + DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw + GhostConv(c_, c2, 1, 1, act=False)) # pw-linear + self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, + act=False)) if s == 2 else nn.Identity() + + def forward(self, x): + return self.conv(x) + self.shortcut(x) + + +class Contract(nn.Module): + # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' + s = self.gain + x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) + x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) + return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) + + +class Expand(nn.Module): + # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) + def __init__(self, gain=2): + super().__init__() + self.gain = gain + + def forward(self, x): + b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' + s = self.gain + x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) + x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) + return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) + + +class Concat(nn.Module): + # Concatenate a list of tensors along dimension + def __init__(self, dimension=1): + super().__init__() + self.d = dimension + + def forward(self, x): + return torch.cat(x, self.d) + + +class DetectMultiBackend(nn.Module): + # YOLOv5 MultiBackend class for python inference on various backends + def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True, rotation=False): + # Usage: + # PyTorch: weights = *.pt + # TorchScript: *.torchscript + # ONNX Runtime: *.onnx + # ONNX OpenCV DNN: *.onnx --dnn + # OpenVINO: *_openvino_model + # CoreML: *.mlmodel + # TensorRT: *.engine + # TensorFlow SavedModel: *_saved_model + # TensorFlow GraphDef: *.pb + # TensorFlow Lite: *.tflite + # TensorFlow Edge TPU: *_edgetpu.tflite + # PaddlePaddle: *_paddle_model + from algorithm.yolov5.models.experimental import attempt_download, attempt_load # scoped to avoid circular import + + super().__init__() + w = str(weights[0] if isinstance(weights, list) else weights) + pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w) + fp16 &= pt or jit or onnx or engine # FP16 + nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) + stride = 32 # default stride + cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA + if not (pt or triton): + w = attempt_download(w) # download if not local + + if pt: # PyTorch + model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse, rotation=rotation) + stride = max(int(model.stride.max()), 32) # model stride + names = model.module.names if hasattr(model, 'module') else model.names # get class names + model.half() if fp16 else model.float() + self.model = model # explicitly assign for to(), cpu(), cuda(), half() + elif jit: # TorchScript + LOGGER.info(f'Loading {w} for TorchScript inference...') + extra_files = {'config.txt': ''} # model metadata + model = torch.jit.load(w, _extra_files=extra_files, map_location=device) + model.half() if fp16 else model.float() + if extra_files['config.txt']: # load metadata dict + d = json.loads(extra_files['config.txt'], + object_hook=lambda d: {int(k) if k.isdigit() else k: v + for k, v in d.items()}) + stride, names = int(d['stride']), d['names'] + elif dnn: # ONNX OpenCV DNN + LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') + check_requirements('opencv-python>=4.5.4') + net = cv2.dnn.readNetFromONNX(w) + elif onnx: # ONNX Runtime + LOGGER.info(f'Loading {w} for ONNX Runtime inference...') + check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) + import onnxruntime + providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] + session = onnxruntime.InferenceSession(w, providers=providers) + output_names = [x.name for x in session.get_outputs()] + meta = session.get_modelmeta().custom_metadata_map # metadata + if 'stride' in meta: + stride, names = int(meta['stride']), eval(meta['names']) + elif xml: # OpenVINO + LOGGER.info(f'Loading {w} for OpenVINO inference...') + check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/ + from openvino.runtime import Core, Layout, get_batch + ie = Core() + if not Path(w).is_file(): # if not *.xml + w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir + network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin')) + if network.get_parameters()[0].get_layout().empty: + network.get_parameters()[0].set_layout(Layout('NCHW')) + batch_dim = get_batch(network) + if batch_dim.is_static: + batch_size = batch_dim.get_length() + executable_network = ie.compile_model(network, device_name='CPU') # device_name="MYRIAD" for Intel NCS2 + stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata + elif engine: # TensorRT + LOGGER.info(f'Loading {w} for TensorRT inference...') + import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download + check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 + if device.type == 'cpu': + device = torch.device('cuda:0') + Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) + logger = trt.Logger(trt.Logger.INFO) + with open(w, 'rb') as f, trt.Runtime(logger) as runtime: + model = runtime.deserialize_cuda_engine(f.read()) + context = model.create_execution_context() + bindings = OrderedDict() + output_names = [] + fp16 = False # default updated below + dynamic = False + for i in range(model.num_bindings): + name = model.get_binding_name(i) + dtype = trt.nptype(model.get_binding_dtype(i)) + if model.binding_is_input(i): + if -1 in tuple(model.get_binding_shape(i)): # dynamic + dynamic = True + context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2])) + if dtype == np.float16: + fp16 = True + else: # output + output_names.append(name) + shape = tuple(context.get_binding_shape(i)) + im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) + bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) + binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) + batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size + elif coreml: # CoreML + LOGGER.info(f'Loading {w} for CoreML inference...') + import coremltools as ct + model = ct.models.MLModel(w) + elif saved_model: # TF SavedModel + LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') + import tensorflow as tf + keras = False # assume TF1 saved_model + model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) + elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') + import tensorflow as tf + + def wrap_frozen_graph(gd, inputs, outputs): + x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), []) # wrapped + ge = x.graph.as_graph_element + return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) + + def gd_outputs(gd): + name_list, input_list = [], [] + for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef + name_list.append(node.name) + input_list.extend(node.input) + return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp')) + + gd = tf.Graph().as_graph_def() # TF GraphDef + with open(w, 'rb') as f: + gd.ParseFromString(f.read()) + frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd)) + elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python + try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu + from tflite_runtime.interpreter import Interpreter, load_delegate + except ImportError: + import tensorflow as tf + Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate, + if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime + LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') + delegate = { + 'Linux': 'libedgetpu.so.1', + 'Darwin': 'libedgetpu.1.dylib', + 'Windows': 'edgetpu.dll'}[platform.system()] + interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) + else: # TFLite + LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') + interpreter = Interpreter(model_path=w) # load TFLite model + interpreter.allocate_tensors() # allocate + input_details = interpreter.get_input_details() # inputs + output_details = interpreter.get_output_details() # outputs + # load metadata + with contextlib.suppress(zipfile.BadZipFile): + with zipfile.ZipFile(w, 'r') as model: + meta_file = model.namelist()[0] + meta = ast.literal_eval(model.read(meta_file).decode('utf-8')) + stride, names = int(meta['stride']), meta['names'] + elif tfjs: # TF.js + raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported') + elif paddle: # PaddlePaddle + LOGGER.info(f'Loading {w} for PaddlePaddle inference...') + check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle') + import paddle.inference as pdi + if not Path(w).is_file(): # if not *.pdmodel + w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir + weights = Path(w).with_suffix('.pdiparams') + config = pdi.Config(str(w), str(weights)) + if cuda: + config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) + predictor = pdi.create_predictor(config) + input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) + output_names = predictor.get_output_names() + elif triton: # NVIDIA Triton Inference Server + LOGGER.info(f'Using {w} as Triton Inference Server...') + check_requirements('tritonclient[all]') + from utils.triton import TritonRemoteModel + model = TritonRemoteModel(url=w) + nhwc = model.runtime.startswith('tensorflow') + else: + raise NotImplementedError(f'ERROR: {w} is not a supported format') + + # class names + if 'names' not in locals(): + names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)} + if names[0] == 'n01440764' and len(names) == 1000: # ImageNet + names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names + + self.__dict__.update(locals()) # assign all variables to self + + def forward(self, im, augment=False, visualize=False): + # YOLOv5 MultiBackend inference + b, ch, h, w = im.shape # batch, channel, height, width + if self.fp16 and im.dtype != torch.float16: + im = im.half() # to FP16 + if self.nhwc: + im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) + + if self.pt: # PyTorch + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + elif self.jit: # TorchScript + y = self.model(im) + elif self.dnn: # ONNX OpenCV DNN + im = im.cpu().numpy() # torch to numpy + self.net.setInput(im) + y = self.net.forward() + elif self.onnx: # ONNX Runtime + im = im.cpu().numpy() # torch to numpy + y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) + elif self.xml: # OpenVINO + im = im.cpu().numpy() # FP32 + y = list(self.executable_network([im]).values()) + elif self.engine: # TensorRT + if self.dynamic and im.shape != self.bindings['images'].shape: + i = self.model.get_binding_index('images') + self.context.set_binding_shape(i, im.shape) # reshape if dynamic + self.bindings['images'] = self.bindings['images']._replace(shape=im.shape) + for name in self.output_names: + i = self.model.get_binding_index(name) + self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) + s = self.bindings['images'].shape + assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" + self.binding_addrs['images'] = int(im.data_ptr()) + self.context.execute_v2(list(self.binding_addrs.values())) + y = [self.bindings[x].data for x in sorted(self.output_names)] + elif self.coreml: # CoreML + im = im.cpu().numpy() + im = Image.fromarray((im[0] * 255).astype('uint8')) + # im = im.resize((192, 320), Image.ANTIALIAS) + y = self.model.predict({'image': im}) # coordinates are xywh normalized + if 'confidence' in y: + box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels + conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) + y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) + else: + y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) + elif self.paddle: # PaddlePaddle + im = im.cpu().numpy().astype(np.float32) + self.input_handle.copy_from_cpu(im) + self.predictor.run() + y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] + elif self.triton: # NVIDIA Triton Inference Server + y = self.model(im) + else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) + im = im.cpu().numpy() + if self.saved_model: # SavedModel + y = self.model(im, training=False) if self.keras else self.model(im) + elif self.pb: # GraphDef + y = self.frozen_func(x=self.tf.constant(im)) + else: # Lite or Edge TPU + input = self.input_details[0] + int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model + if int8: + scale, zero_point = input['quantization'] + im = (im / scale + zero_point).astype(np.uint8) # de-scale + self.interpreter.set_tensor(input['index'], im) + self.interpreter.invoke() + y = [] + for output in self.output_details: + x = self.interpreter.get_tensor(output['index']) + if int8: + scale, zero_point = output['quantization'] + x = (x.astype(np.float32) - zero_point) * scale # re-scale + y.append(x) + y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] + y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels + + if isinstance(y, (list, tuple)): + return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] + else: + return self.from_numpy(y) + + def from_numpy(self, x): + return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x + + def warmup(self, imgsz=(1, 3, 640, 640)): + # Warmup model by running inference once + warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton + if any(warmup_types) and (self.device.type != 'cpu' or self.triton): + im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input + for _ in range(2 if self.jit else 1): # + self.forward(im) # warmup + + @staticmethod + def _model_type(p='path/to/model.pt'): + # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx + # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] + from algorithm.yolov5.export import export_formats + from utils.downloads import is_url + sf = list(export_formats().Suffix) # export suffixes + if not is_url(p, check=False): + check_suffix(p, sf) # checks + url = urlparse(p) # if url may be Triton inference server + types = [s in Path(p).name for s in sf] + types[8] &= not types[9] # tflite &= not edgetpu + triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc]) + return types + [triton] + + @staticmethod + def _load_metadata(f=Path('path/to/meta.yaml')): + # Load metadata from meta.yaml if it exists + if f.exists(): + d = yaml_load(f) + return d['stride'], d['names'] # assign stride, names + return None, None + + +class AutoShape(nn.Module): + # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS + conf = 0.25 # NMS confidence threshold + iou = 0.45 # NMS IoU threshold + agnostic = False # NMS class-agnostic + multi_label = False # NMS multiple labels per box + classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs + max_det = 1000 # maximum number of detections per image + amp = False # Automatic Mixed Precision (AMP) inference + + def __init__(self, model, verbose=True): + super().__init__() + if verbose: + LOGGER.info('Adding AutoShape... ') + copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes + self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance + self.pt = not self.dmb or model.pt # PyTorch model + self.model = model.eval() + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.inplace = False # Detect.inplace=False for safe multithread inference + m.export = True # do not output loss values + + def _apply(self, fn): + # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers + self = super()._apply(fn) + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + @smart_inference_mode() + def forward(self, ims, size=640, augment=False, profile=False): + # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are: + # file: ims = 'data/images/zidane.jpg' # str or PosixPath + # URI: = 'https://ultralytics.com/images/zidane.jpg' + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) + # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + + dt = (Profile(), Profile(), Profile()) + with dt[0]: + if isinstance(size, int): # expand + size = (size, size) + p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param + autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference + if isinstance(ims, torch.Tensor): # torch + with amp.autocast(autocast): + return self.model(ims.to(p.device).type_as(p), augment=augment) # inference + + # Pre-process + n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images + shape0, shape1, files = [], [], [] # image and inference shapes, filenames + for i, im in enumerate(ims): + f = f'image{i}' # filename + if isinstance(im, (str, Path)): # filename or uri + im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im + im = np.asarray(exif_transpose(im)) + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f + files.append(Path(f).with_suffix('.jpg').name) + if im.shape[0] < 5: # image in CHW + im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) + im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input + s = im.shape[:2] # HWC + shape0.append(s) # image shape + g = max(size) / max(s) # gain + shape1.append([int(y * g) for y in s]) + ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update + shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape + x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad + x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW + x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 + + with amp.autocast(autocast): + # Inference + with dt[1]: + y = self.model(x, augment=augment) # forward + + # Post-process + with dt[2]: + y = non_max_suppression(y if self.dmb else y[0], + self.conf, + self.iou, + self.classes, + self.agnostic, + self.multi_label, + max_det=self.max_det) # NMS + for i in range(n): + scale_boxes(shape1, y[i][:, :4], shape0[i]) + + return Detections(ims, y, files, dt, self.names, x.shape) + + +class Detections: + # YOLOv5 detections class for inference results + def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): + super().__init__() + d = pred[0].device # device + gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations + self.ims = ims # list of images as numpy arrays + self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) + self.names = names # class names + self.files = files # image filenames + self.times = times # profiling times + self.xyxy = pred # xyxy pixels + self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels + self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized + self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized + self.n = len(self.pred) # number of images (batch size) + self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms) + self.s = tuple(shape) # inference BCHW shape + + def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')): + s, crops = '', [] + for i, (im, pred) in enumerate(zip(self.ims, self.pred)): + s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string + if pred.shape[0]: + for c in pred[:, -1].unique(): + n = (pred[:, -1] == c).sum() # detections per class + s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + s = s.rstrip(', ') + if show or save or render or crop: + annotator = Annotator(im, example=str(self.names)) + for *box, conf, cls in reversed(pred): # xyxy, confidence, class + label = f'{self.names[int(cls)]} {conf:.2f}' + if crop: + file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None + crops.append({ + 'box': box, + 'conf': conf, + 'cls': cls, + 'label': label, + 'im': save_one_box(box, im, file=file, save=save)}) + else: # all others + annotator.box_label(box, label if labels else '', color=colors(cls)) + im = annotator.im + else: + s += '(no detections)' + + im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np + if show: + if is_jupyter(): + from IPython.display import display + display(im) + else: + im.show(self.files[i]) + if save: + f = self.files[i] + im.save(save_dir / f) # save + if i == self.n - 1: + LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") + if render: + self.ims[i] = np.asarray(im) + if pprint: + s = s.lstrip('\n') + return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t + if crop: + if save: + LOGGER.info(f'Saved results to {save_dir}\n') + return crops + + @TryExcept('Showing images is not supported in this environment') + def show(self, labels=True): + self._run(show=True, labels=labels) # show results + + def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): + save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir + self._run(save=True, labels=labels, save_dir=save_dir) # save results + + def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): + save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None + return self._run(crop=True, save=save, save_dir=save_dir) # crop results + + def render(self, labels=True): + self._run(render=True, labels=labels) # render results + return self.ims + + def pandas(self): + # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) + new = copy(self) # return copy + ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns + cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns + for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): + a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new + + def tolist(self): + # return a list of Detections objects, i.e. 'for result in results.tolist():' + r = range(self.n) # iterable + x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] + # for d in x: + # for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: + # setattr(d, k, getattr(d, k)[0]) # pop out of list + return x + + def print(self): + LOGGER.info(self.__str__()) + + def __len__(self): # override len(results) + return self.n + + def __str__(self): # override print(results) + return self._run(pprint=True) # print results + + def __repr__(self): + return f'YOLOv5 {self.__class__} instance\n' + self.__str__() + + +class Proto(nn.Module): + # YOLOv5 mask Proto module for segmentation models + def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks + super().__init__() + self.cv1 = Conv(c1, c_, k=3) + self.upsample = nn.Upsample(scale_factor=2, mode='nearest') + self.cv2 = Conv(c_, c_, k=3) + self.cv3 = Conv(c_, c2) + + def forward(self, x): + return self.cv3(self.cv2(self.upsample(self.cv1(x)))) + + +class Classify(nn.Module): + # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2) + def __init__(self, + c1, + c2, + k=1, + s=1, + p=None, + g=1, + dropout_p=0.0): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability + super().__init__() + c_ = 1280 # efficientnet_b0 size + self.conv = Conv(c1, c_, k, s, autopad(k, p), g) + self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1) + self.drop = nn.Dropout(p=dropout_p, inplace=True) + self.linear = nn.Linear(c_, c2) # to x(b,c2) + + def forward(self, x): + if isinstance(x, list): + x = torch.cat(x, 1) + return self.linear(self.drop(self.pool(self.conv(x)).flatten(1))) + + +class ShuffleV2Block(nn.Module): + def __init__(self, inp, oup, stride): + super(ShuffleV2Block, self).__init__() + + if not (1 <= stride <= 3): + raise ValueError('illegal stride value') + self.stride = stride + + branch_features = oup // 2 + assert (self.stride != 1) or (inp == branch_features << 1) + + if self.stride > 1: + self.branch1 = nn.Sequential( + self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1), + nn.BatchNorm2d(inp), + nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(branch_features), + nn.SiLU(), + ) + else: + self.branch1 = nn.Sequential() + + self.branch2 = nn.Sequential( + nn.Conv2d(inp if (self.stride > 1) else branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(branch_features), + nn.SiLU(), + self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1), + nn.BatchNorm2d(branch_features), + nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False), + nn.BatchNorm2d(branch_features), + nn.SiLU(), + ) + + @staticmethod + def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False): + return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i) + + def forward(self, x): + if self.stride == 1: + x1, x2 = x.chunk(2, dim=1) + out = torch.cat((x1, self.branch2(x2)), dim=1) + else: + out = torch.cat((self.branch1(x), self.branch2(x)), dim=1) + out = channel_shuffle(out, 2) + return out + +class BlazeBlock(nn.Module): + def __init__(self, in_channels,out_channels,mid_channels=None,stride=1): + super(BlazeBlock, self).__init__() + mid_channels = mid_channels or in_channels + assert stride in [1, 2] + if stride>1: + self.use_pool = True + else: + self.use_pool = False + + self.branch1 = nn.Sequential( + nn.Conv2d(in_channels=in_channels,out_channels=mid_channels,kernel_size=5,stride=stride,padding=2,groups=in_channels), + nn.BatchNorm2d(mid_channels), + nn.Conv2d(in_channels=mid_channels,out_channels=out_channels,kernel_size=1,stride=1), + nn.BatchNorm2d(out_channels), + ) + + if self.use_pool: + self.shortcut = nn.Sequential( + nn.MaxPool2d(kernel_size=stride, stride=stride), + nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1), + nn.BatchNorm2d(out_channels), + ) + + self.relu = nn.SiLU(inplace=True) + + def forward(self, x): + branch1 = self.branch1(x) + out = (branch1+self.shortcut(x)) if self.use_pool else (branch1+x) + return self.relu(out) + +class DoubleBlazeBlock(nn.Module): + def __init__(self,in_channels,out_channels,mid_channels=None,stride=1): + super(DoubleBlazeBlock, self).__init__() + mid_channels = mid_channels or in_channels + assert stride in [1, 2] + if stride > 1: + self.use_pool = True + else: + self.use_pool = False + + self.branch1 = nn.Sequential( + nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=5, stride=stride,padding=2,groups=in_channels), + nn.BatchNorm2d(in_channels), + nn.Conv2d(in_channels=in_channels, out_channels=mid_channels, kernel_size=1, stride=1), + nn.BatchNorm2d(mid_channels), + nn.SiLU(inplace=True), + nn.Conv2d(in_channels=mid_channels, out_channels=mid_channels, kernel_size=5, stride=1,padding=2), + nn.BatchNorm2d(mid_channels), + nn.Conv2d(in_channels=mid_channels, out_channels=out_channels, kernel_size=1, stride=1), + nn.BatchNorm2d(out_channels), + ) + + if self.use_pool: + self.shortcut = nn.Sequential( + nn.MaxPool2d(kernel_size=stride, stride=stride), + nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1), + nn.BatchNorm2d(out_channels), + ) + + self.relu = nn.SiLU(inplace=True) + + def forward(self, x): + branch1 = self.branch1(x) + out = (branch1 + self.shortcut(x)) if self.use_pool else (branch1 + x) + return self.relu(out) + \ No newline at end of file diff --git a/algorithm/yolov5/models/experimental.py b/algorithm/yolov5/models/experimental.py new file mode 100644 index 0000000..583c0d6 --- /dev/null +++ b/algorithm/yolov5/models/experimental.py @@ -0,0 +1,115 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Experimental modules +""" +import math + +import numpy as np +import torch +import torch.nn as nn + +from algorithm.yolov5.utils.downloads import attempt_download + + +class Sum(nn.Module): + # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 + def __init__(self, n, weight=False): # n: number of inputs + super().__init__() + self.weight = weight # apply weights boolean + self.iter = range(n - 1) # iter object + if weight: + self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights + + def forward(self, x): + y = x[0] # no weight + if self.weight: + w = torch.sigmoid(self.w) * 2 + for i in self.iter: + y = y + x[i + 1] * w[i] + else: + for i in self.iter: + y = y + x[i + 1] + return y + + +class MixConv2d(nn.Module): + # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595 + def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy + super().__init__() + n = len(k) # number of convolutions + if equal_ch: # equal c_ per group + i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices + c_ = [(i == g).sum() for g in range(n)] # intermediate channels + else: # equal weight.numel() per group + b = [c2] + [0] * n + a = np.eye(n + 1, n, k=-1) + a -= np.roll(a, 1, axis=1) + a *= np.array(k) ** 2 + a[0] = 1 + c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b + + self.m = nn.ModuleList([ + nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.SiLU() + + def forward(self, x): + return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) + + +class Ensemble(nn.ModuleList): + # Ensemble of models + def __init__(self): + super().__init__() + + def forward(self, x, augment=False, profile=False, visualize=False): + y = [module(x, augment, profile, visualize)[0] for module in self] + # y = torch.stack(y).max(0)[0] # max ensemble + # y = torch.stack(y).mean(0) # mean ensemble + y = torch.cat(y, 1) # nms ensemble + return y, None # inference, train output + + +def attempt_load(weights, device=None, inplace=True, fuse=True, rotation=False): + # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a + from models.yolo import Detect, Model + + model = Ensemble() + for w in weights if isinstance(weights, list) else [weights]: + ckpt = torch.load(attempt_download(w), map_location='cpu') # load + ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model + + # Model compatibility updates + if not hasattr(ckpt, 'stride'): + ckpt.stride = torch.tensor([32.]) + if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)): + ckpt.names = dict(enumerate(ckpt.names)) # convert to dict + + model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode + + # Module compatibility updates + for m in model.modules(): + t = type(m) + if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): + m.inplace = inplace # torch 1.7.0 compatibility + # print(11111111111) + if t is Detect: + m.rotation = rotation + # print("进行旋转",rotation) + if not isinstance(m.anchor_grid, list): + delattr(m, 'anchor_grid') + setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) + elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): + m.recompute_scale_factor = None # torch 1.11.0 compatibility + + # Return model + if len(model) == 1: + return model[-1] + + # Return detection ensemble + print(f'Ensemble created with {weights}\n') + for k in 'names', 'nc', 'yaml': + setattr(model, k, getattr(model[0], k)) + model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride + assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}' + return model diff --git a/algorithm/yolov5/models/hub/anchors.yaml b/algorithm/yolov5/models/hub/anchors.yaml new file mode 100644 index 0000000..e4d7beb --- /dev/null +++ b/algorithm/yolov5/models/hub/anchors.yaml @@ -0,0 +1,59 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Default anchors for COCO data + + +# P5 ------------------------------------------------------------------------------------------------------------------- +# P5-640: +anchors_p5_640: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + + +# P6 ------------------------------------------------------------------------------------------------------------------- +# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 +anchors_p6_640: + - [9,11, 21,19, 17,41] # P3/8 + - [43,32, 39,70, 86,64] # P4/16 + - [65,131, 134,130, 120,265] # P5/32 + - [282,180, 247,354, 512,387] # P6/64 + +# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 +anchors_p6_1280: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 +anchors_p6_1920: + - [28,41, 67,59, 57,141] # P3/8 + - [144,103, 129,227, 270,205] # P4/16 + - [209,452, 455,396, 358,812] # P5/32 + - [653,922, 1109,570, 1387,1187] # P6/64 + + +# P7 ------------------------------------------------------------------------------------------------------------------- +# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 +anchors_p7_640: + - [11,11, 13,30, 29,20] # P3/8 + - [30,46, 61,38, 39,92] # P4/16 + - [78,80, 146,66, 79,163] # P5/32 + - [149,150, 321,143, 157,303] # P6/64 + - [257,402, 359,290, 524,372] # P7/128 + +# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 +anchors_p7_1280: + - [19,22, 54,36, 32,77] # P3/8 + - [70,83, 138,71, 75,173] # P4/16 + - [165,159, 148,334, 375,151] # P5/32 + - [334,317, 251,626, 499,474] # P6/64 + - [750,326, 534,814, 1079,818] # P7/128 + +# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 +anchors_p7_1920: + - [29,34, 81,55, 47,115] # P3/8 + - [105,124, 207,107, 113,259] # P4/16 + - [247,238, 222,500, 563,227] # P5/32 + - [501,476, 376,939, 749,711] # P6/64 + - [1126,489, 801,1222, 1618,1227] # P7/128 diff --git a/algorithm/yolov5/models/hub/yolov3-spp.yaml b/algorithm/yolov5/models/hub/yolov3-spp.yaml new file mode 100644 index 0000000..c669821 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov3-spp.yaml @@ -0,0 +1,51 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3-SPP head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, SPP, [512, [5, 9, 13]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov3-tiny.yaml b/algorithm/yolov5/models/hub/yolov3-tiny.yaml new file mode 100644 index 0000000..b28b443 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov3-tiny.yaml @@ -0,0 +1,41 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,14, 23,27, 37,58] # P4/16 + - [81,82, 135,169, 344,319] # P5/32 + +# YOLOv3-tiny backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [16, 3, 1]], # 0 + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 + [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 + ] + +# YOLOv3-tiny head +head: + [[-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) + + [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov3.yaml b/algorithm/yolov5/models/hub/yolov3.yaml new file mode 100644 index 0000000..d1ef912 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov3.yaml @@ -0,0 +1,51 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3 head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov5-bifpn.yaml b/algorithm/yolov5/models/hub/yolov5-bifpn.yaml new file mode 100644 index 0000000..504815f --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5-bifpn.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 BiFPN head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov5-fpn.yaml b/algorithm/yolov5/models/hub/yolov5-fpn.yaml new file mode 100644 index 0000000..a23e9c6 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5-fpn.yaml @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 FPN head +head: + [[-1, 3, C3, [1024, False]], # 10 (P5/32-large) + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [512, 1, 1]], + [-1, 3, C3, [512, False]], # 14 (P4/16-medium) + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Conv, [256, 1, 1]], + [-1, 3, C3, [256, False]], # 18 (P3/8-small) + + [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov5-p2.yaml b/algorithm/yolov5/models/hub/yolov5-p2.yaml new file mode 100644 index 0000000..554117d --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5-p2.yaml @@ -0,0 +1,54 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 2], 1, Concat, [1]], # cat backbone P2 + [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P3 + [-1, 3, C3, [256, False]], # 24 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 27 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 30 (P5/32-large) + + [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov5-p34.yaml b/algorithm/yolov5/models/hub/yolov5-p34.yaml new file mode 100644 index 0000000..dbf0f85 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5-p34.yaml @@ -0,0 +1,41 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ], # 0-P1/2 + [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 + [ -1, 3, C3, [ 128 ] ], + [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 + [ -1, 6, C3, [ 256 ] ], + [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 + [ -1, 9, C3, [ 512 ] ], + [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 + [ -1, 3, C3, [ 1024 ] ], + [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 + ] + +# YOLOv5 v6.0 head with (P3, P4) outputs +head: + [ [ -1, 1, Conv, [ 512, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 + [ -1, 3, C3, [ 512, False ] ], # 13 + + [ -1, 1, Conv, [ 256, 1, 1 ] ], + [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], + [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 + [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) + + [ -1, 1, Conv, [ 256, 3, 2 ] ], + [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 + [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) + + [ [ 17, 20 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4) + ] diff --git a/algorithm/yolov5/models/hub/yolov5-p6.yaml b/algorithm/yolov5/models/hub/yolov5-p6.yaml new file mode 100644 index 0000000..a17202f --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5-p6.yaml @@ -0,0 +1,56 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5/models/hub/yolov5-p7.yaml b/algorithm/yolov5/models/hub/yolov5-p7.yaml new file mode 100644 index 0000000..edd7d13 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5-p7.yaml @@ -0,0 +1,67 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 + [-1, 3, C3, [1280]], + [-1, 1, SPPF, [1280, 5]], # 13 + ] + +# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs +head: + [[-1, 1, Conv, [1024, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 10], 1, Concat, [1]], # cat backbone P6 + [-1, 3, C3, [1024, False]], # 17 + + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 21 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 25 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 29 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 26], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 32 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 35 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) + + [-1, 1, Conv, [1024, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P7 + [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) + + [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) + ] diff --git a/algorithm/yolov5/models/hub/yolov5-panet.yaml b/algorithm/yolov5/models/hub/yolov5-panet.yaml new file mode 100644 index 0000000..ccfbf90 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5-panet.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 PANet head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov5l6.yaml b/algorithm/yolov5/models/hub/yolov5l6.yaml new file mode 100644 index 0000000..632c2cb --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5l6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5/models/hub/yolov5m6.yaml b/algorithm/yolov5/models/hub/yolov5m6.yaml new file mode 100644 index 0000000..ecc53fd --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5m6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5/models/hub/yolov5n6.yaml b/algorithm/yolov5/models/hub/yolov5n6.yaml new file mode 100644 index 0000000..0c0c71d --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5n6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5/models/hub/yolov5s-LeakyReLU.yaml b/algorithm/yolov5/models/hub/yolov5s-LeakyReLU.yaml new file mode 100644 index 0000000..3a179bf --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5s-LeakyReLU.yaml @@ -0,0 +1,49 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +activation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov5s-ghost.yaml b/algorithm/yolov5/models/hub/yolov5s-ghost.yaml new file mode 100644 index 0000000..ff9519c --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5s-ghost.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3Ghost, [128]], + [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3Ghost, [256]], + [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3Ghost, [512]], + [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3Ghost, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, GhostConv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3Ghost, [512, False]], # 13 + + [-1, 1, GhostConv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) + + [-1, 1, GhostConv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) + + [-1, 1, GhostConv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov5s-transformer.yaml b/algorithm/yolov5/models/hub/yolov5s-transformer.yaml new file mode 100644 index 0000000..100d7c4 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5s-transformer.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/hub/yolov5s6.yaml b/algorithm/yolov5/models/hub/yolov5s6.yaml new file mode 100644 index 0000000..a28fb55 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5s6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5/models/hub/yolov5x6.yaml b/algorithm/yolov5/models/hub/yolov5x6.yaml new file mode 100644 index 0000000..ba795c4 --- /dev/null +++ b/algorithm/yolov5/models/hub/yolov5x6.yaml @@ -0,0 +1,60 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [19,27, 44,40, 38,94] # P3/8 + - [96,68, 86,152, 180,137] # P4/16 + - [140,301, 303,264, 238,542] # P5/32 + - [436,615, 739,380, 925,792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/algorithm/yolov5/models/segment/yolov5l-seg.yaml b/algorithm/yolov5/models/segment/yolov5l-seg.yaml new file mode 100644 index 0000000..4782de1 --- /dev/null +++ b/algorithm/yolov5/models/segment/yolov5l-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/segment/yolov5m-seg.yaml b/algorithm/yolov5/models/segment/yolov5m-seg.yaml new file mode 100644 index 0000000..07ec25b --- /dev/null +++ b/algorithm/yolov5/models/segment/yolov5m-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/segment/yolov5n-seg.yaml b/algorithm/yolov5/models/segment/yolov5n-seg.yaml new file mode 100644 index 0000000..c28225a --- /dev/null +++ b/algorithm/yolov5/models/segment/yolov5n-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/segment/yolov5s-seg.yaml b/algorithm/yolov5/models/segment/yolov5s-seg.yaml new file mode 100644 index 0000000..a827814 --- /dev/null +++ b/algorithm/yolov5/models/segment/yolov5s-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.5 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/segment/yolov5x-seg.yaml b/algorithm/yolov5/models/segment/yolov5x-seg.yaml new file mode 100644 index 0000000..5d0c452 --- /dev/null +++ b/algorithm/yolov5/models/segment/yolov5x-seg.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/tf.py b/algorithm/yolov5/models/tf.py new file mode 100644 index 0000000..8290cf2 --- /dev/null +++ b/algorithm/yolov5/models/tf.py @@ -0,0 +1,608 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +TensorFlow, Keras and TFLite versions of YOLOv5 +Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127 + +Usage: + $ python models/tf.py --weights yolov5s.pt + +Export: + $ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs +""" + +import argparse +import sys +from copy import deepcopy +from pathlib import Path + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +# ROOT = ROOT.relative_to(Path.cwd()) # relative + +import numpy as np +import tensorflow as tf +import torch +import torch.nn as nn +from tensorflow import keras + +from models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv, + DWConvTranspose2d, Focus, autopad) +from models.experimental import MixConv2d, attempt_load +from models.yolo import Detect, Segment +from utils.activations import SiLU +from utils.general import LOGGER, make_divisible, print_args + + +class TFBN(keras.layers.Layer): + # TensorFlow BatchNormalization wrapper + def __init__(self, w=None): + super().__init__() + self.bn = keras.layers.BatchNormalization( + beta_initializer=keras.initializers.Constant(w.bias.numpy()), + gamma_initializer=keras.initializers.Constant(w.weight.numpy()), + moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), + moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), + epsilon=w.eps) + + def call(self, inputs): + return self.bn(inputs) + + +class TFPad(keras.layers.Layer): + # Pad inputs in spatial dimensions 1 and 2 + def __init__(self, pad): + super().__init__() + if isinstance(pad, int): + self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) + else: # tuple/list + self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]]) + + def call(self, inputs): + return tf.pad(inputs, self.pad, mode='constant', constant_values=0) + + +class TFConv(keras.layers.Layer): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + # ch_in, ch_out, weights, kernel, stride, padding, groups + super().__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) + # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch + conv = keras.layers.Conv2D( + filters=c2, + kernel_size=k, + strides=s, + padding='SAME' if s == 1 else 'VALID', + use_bias=not hasattr(w, 'bn'), + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) + self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) + self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity + self.act = activations(w.act) if act else tf.identity + + def call(self, inputs): + return self.act(self.bn(self.conv(inputs))) + + +class TFDWConv(keras.layers.Layer): + # Depthwise convolution + def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None): + # ch_in, ch_out, weights, kernel, stride, padding, groups + super().__init__() + assert c2 % c1 == 0, f'TFDWConv() output={c2} must be a multiple of input={c1} channels' + conv = keras.layers.DepthwiseConv2D( + kernel_size=k, + depth_multiplier=c2 // c1, + strides=s, + padding='SAME' if s == 1 else 'VALID', + use_bias=not hasattr(w, 'bn'), + depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) + self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) + self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity + self.act = activations(w.act) if act else tf.identity + + def call(self, inputs): + return self.act(self.bn(self.conv(inputs))) + + +class TFDWConvTranspose2d(keras.layers.Layer): + # Depthwise ConvTranspose2d + def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None): + # ch_in, ch_out, weights, kernel, stride, padding, groups + super().__init__() + assert c1 == c2, f'TFDWConv() output={c2} must be equal to input={c1} channels' + assert k == 4 and p1 == 1, 'TFDWConv() only valid for k=4 and p1=1' + weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy() + self.c1 = c1 + self.conv = [ + keras.layers.Conv2DTranspose(filters=1, + kernel_size=k, + strides=s, + padding='VALID', + output_padding=p2, + use_bias=True, + kernel_initializer=keras.initializers.Constant(weight[..., i:i + 1]), + bias_initializer=keras.initializers.Constant(bias[i])) for i in range(c1)] + + def call(self, inputs): + return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1] + + +class TFFocus(keras.layers.Layer): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + # ch_in, ch_out, kernel, stride, padding, groups + super().__init__() + self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) + + def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) + # inputs = inputs / 255 # normalize 0-255 to 0-1 + inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]] + return self.conv(tf.concat(inputs, 3)) + + +class TFBottleneck(keras.layers.Layer): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class TFCrossConv(keras.layers.Layer): + # Cross Convolution + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None): + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1) + self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class TFConv2d(keras.layers.Layer): + # Substitution for PyTorch nn.Conv2D + def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): + super().__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + self.conv = keras.layers.Conv2D(filters=c2, + kernel_size=k, + strides=s, + padding='VALID', + use_bias=bias, + kernel_initializer=keras.initializers.Constant( + w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None) + + def call(self, inputs): + return self.conv(inputs) + + +class TFBottleneckCSP(keras.layers.Layer): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) + self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3) + self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4) + self.bn = TFBN(w.bn) + self.act = lambda x: keras.activations.swish(x) + self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + y1 = self.cv3(self.m(self.cv1(inputs))) + y2 = self.cv2(inputs) + return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) + + +class TFC3(keras.layers.Layer): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + +class TFC3x(keras.layers.Layer): + # 3 module with cross-convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + # ch_in, ch_out, number, shortcut, groups, expansion + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential([ + TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + +class TFSPP(keras.layers.Layer): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, k=(5, 9, 13), w=None): + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) + self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k] + + def call(self, inputs): + x = self.cv1(inputs) + return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) + + +class TFSPPF(keras.layers.Layer): + # Spatial pyramid pooling-Fast layer + def __init__(self, c1, c2, k=5, w=None): + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) + self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME') + + def call(self, inputs): + x = self.cv1(inputs) + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3)) + + +class TFDetect(keras.layers.Layer): + # TF YOLOv5 Detect layer + def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer + super().__init__() + self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [tf.zeros(1)] * self.nl # init grid + self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) + self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2]) + self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] + self.training = False # set to False after building model + self.imgsz = imgsz + for i in range(self.nl): + ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] + self.grid[i] = self._make_grid(nx, ny) + + def call(self, inputs): + z = [] # inference output + x = [] + for i in range(self.nl): + x.append(self.m[i](inputs[i])) + # x(bs,20,20,255) to x(bs,3,20,20,85) + ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] + x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no]) + + if not self.training: # inference + y = x[i] + grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5 + anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4 + xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy + wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid + # Normalize xywh to 0-1 to reduce calibration error + xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) + wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) + y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1) + z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) + + return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),) + + @staticmethod + def _make_grid(nx=20, ny=20): + # yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) + return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) + + +class TFSegment(TFDetect): + # YOLOv5 Segment head for segmentation models + def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None): + super().__init__(nc, anchors, ch, imgsz, w) + self.nm = nm # number of masks + self.npr = npr # number of protos + self.no = 5 + nc + self.nm # number of outputs per anchor + self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv + self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos + self.detect = TFDetect.call + + def call(self, x): + p = self.proto(x[0]) + # p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos + p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160) + x = self.detect(self, x) + return (x, p) if self.training else (x[0], p) + + +class TFProto(keras.layers.Layer): + + def __init__(self, c1, c_=256, c2=32, w=None): + super().__init__() + self.cv1 = TFConv(c1, c_, k=3, w=w.cv1) + self.upsample = TFUpsample(None, scale_factor=2, mode='nearest') + self.cv2 = TFConv(c_, c_, k=3, w=w.cv2) + self.cv3 = TFConv(c_, c2, w=w.cv3) + + def call(self, inputs): + return self.cv3(self.cv2(self.upsample(self.cv1(inputs)))) + + +class TFUpsample(keras.layers.Layer): + # TF version of torch.nn.Upsample() + def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w' + super().__init__() + assert scale_factor % 2 == 0, 'scale_factor must be multiple of 2' + self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode) + # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) + # with default arguments: align_corners=False, half_pixel_centers=False + # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x, + # size=(x.shape[1] * 2, x.shape[2] * 2)) + + def call(self, inputs): + return self.upsample(inputs) + + +class TFConcat(keras.layers.Layer): + # TF version of torch.concat() + def __init__(self, dimension=1, w=None): + super().__init__() + assert dimension == 1, 'convert only NCHW to NHWC concat' + self.d = 3 + + def call(self, inputs): + return tf.concat(inputs, self.d) + + +def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") + anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + m_str = m + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except NameError: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [ + nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv, + BottleneckCSP, C3, C3x]: + c1, c2 = ch[f], args[0] + c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 + + args = [c1, c2, *args[1:]] + if m in [BottleneckCSP, C3, C3x]: + args.insert(2, n) + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) + elif m in [Detect, Segment]: + args.append([ch[x + 1] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + if m is Segment: + args[3] = make_divisible(args[3] * gw, 8) + args.append(imgsz) + else: + c2 = ch[f] + + tf_m = eval('TF' + m_str.replace('nn.', '')) + m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ + else tf_m(*args, w=model.model[i]) # module + + torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum(x.numel() for x in torch_m_.parameters()) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + ch.append(c2) + return keras.Sequential(layers), sorted(save) + + +class TFModel: + # TF YOLOv5 model + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes + super().__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + + # Define model + if nc and nc != self.yaml['nc']: + LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") + self.yaml['nc'] = nc # override yaml value + self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) + + def predict(self, + inputs, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25): + y = [] # outputs + x = inputs + for m in self.model.layers: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + x = m(x) # run + y.append(x if m.i in self.savelist else None) # save output + + # Add TensorFlow NMS + if tf_nms: + boxes = self._xywh2xyxy(x[0][..., :4]) + probs = x[0][:, :, 4:5] + classes = x[0][:, :, 5:] + scores = probs * classes + if agnostic_nms: + nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres) + else: + boxes = tf.expand_dims(boxes, 2) + nms = tf.image.combined_non_max_suppression(boxes, + scores, + topk_per_class, + topk_all, + iou_thres, + conf_thres, + clip_boxes=False) + return (nms,) + return x # output [1,6300,85] = [xywh, conf, class0, class1, ...] + # x = x[0] # [x(1,6300,85), ...] to x(6300,85) + # xywh = x[..., :4] # x(6300,4) boxes + # conf = x[..., 4:5] # x(6300,1) confidences + # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes + # return tf.concat([conf, cls, xywh], 1) + + @staticmethod + def _xywh2xyxy(xywh): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) + return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) + + +class AgnosticNMS(keras.layers.Layer): + # TF Agnostic NMS + def call(self, input, topk_all, iou_thres, conf_thres): + # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450 + return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), + input, + fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), + name='agnostic_nms') + + @staticmethod + def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS + boxes, classes, scores = x + class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) + scores_inp = tf.reduce_max(scores, -1) + selected_inds = tf.image.non_max_suppression(boxes, + scores_inp, + max_output_size=topk_all, + iou_threshold=iou_thres, + score_threshold=conf_thres) + selected_boxes = tf.gather(boxes, selected_inds) + padded_boxes = tf.pad(selected_boxes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], + mode='CONSTANT', + constant_values=0.0) + selected_scores = tf.gather(scores_inp, selected_inds) + padded_scores = tf.pad(selected_scores, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode='CONSTANT', + constant_values=-1.0) + selected_classes = tf.gather(class_inds, selected_inds) + padded_classes = tf.pad(selected_classes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode='CONSTANT', + constant_values=-1.0) + valid_detections = tf.shape(selected_inds)[0] + return padded_boxes, padded_scores, padded_classes, valid_detections + + +def activations(act=nn.SiLU): + # Returns TF activation from input PyTorch activation + if isinstance(act, nn.LeakyReLU): + return lambda x: keras.activations.relu(x, alpha=0.1) + elif isinstance(act, nn.Hardswish): + return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667 + elif isinstance(act, (nn.SiLU, SiLU)): + return lambda x: keras.activations.swish(x) + else: + raise Exception(f'no matching TensorFlow activation found for PyTorch activation {act}') + + +def representative_dataset_gen(dataset, ncalib=100): + # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays + for n, (path, img, im0s, vid_cap, string) in enumerate(dataset): + im = np.transpose(img, [1, 2, 0]) + im = np.expand_dims(im, axis=0).astype(np.float32) + im /= 255 + yield [im] + if n >= ncalib: + break + + +def run( + weights=ROOT / 'yolov5s.pt', # weights path + imgsz=(640, 640), # inference size h,w + batch_size=1, # batch size + dynamic=False, # dynamic batch size +): + # PyTorch model + im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image + model = attempt_load(weights, device=torch.device('cpu'), inplace=True, fuse=False) + _ = model(im) # inference + model.info() + + # TensorFlow model + im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image + tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) + _ = tf_model.predict(im) # inference + + # Keras model + im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) + keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) + keras_model.summary() + + LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.') + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--dynamic', action='store_true', help='dynamic batch size') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/models/yolo.py b/algorithm/yolov5/models/yolo.py new file mode 100644 index 0000000..5775b32 --- /dev/null +++ b/algorithm/yolov5/models/yolo.py @@ -0,0 +1,518 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +YOLO-specific modules + +Usage: + $ python models/yolo.py --cfg yolov5s.yaml +""" + +import argparse +import contextlib +import os +import platform +import sys +from copy import deepcopy +from pathlib import Path + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +if platform.system() != 'Windows': + ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import * +from models.experimental import * +from utils.autoanchor import check_anchor_order +from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args +from utils.plots import feature_visualization +from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device, + time_sync) + +try: + import thop # for FLOPs computation +except ImportError: + thop = None + + +class Detect(nn.Module): + # YOLOv5 Detect head for detection models + stride = None # strides computed during build + dynamic = False # force grid reconstruction + export = False # export mode + + def __init__(self, nc=80, anchors=(), ch=(), inplace=True, rotation=False): # detection layer + super().__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid + self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid + self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + self.inplace = inplace # use inplace ops (e.g. slice assignment) + self.rotation = rotation + + + def forward(self, x): + z = [] # inference output + + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + + if not self.training : # inference + if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) + + if isinstance(self, Segment): # 判断是否为Segment类型 + xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) + xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # 计算xy坐标 + wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # 计算wh宽高 + y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) # 将xy, wh, conf, mask拼接在一起 + elif self.rotation: # 旋转专用 + + y = x[i].sigmoid() + y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + + else: # Detect (boxes only) + #原有的 + xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) + xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy + wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh + y = torch.cat((xy, wh, conf), 4) + + + z.append(y.view(bs, self.na * nx * ny, self.no)) + + + return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) + + def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')): + d = self.anchors[i].device + t = self.anchors[i].dtype + shape = 1, self.na, ny, nx, 2 # grid shape + y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) + yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility + grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 + anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) + return grid, anchor_grid + + + +class Segment(Detect): + # YOLOv5 Segment head for segmentation models + def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True): + super().__init__(nc, anchors, ch, inplace) + self.nm = nm # number of masks + self.npr = npr # number of protos + self.no = 5 + nc + self.nm # number of outputs per anchor + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + self.proto = Proto(ch[0], self.npr, self.nm) # protos + self.detect = Detect.forward + + def forward(self, x): + p = self.proto(x[0]) + x = self.detect(self, x) + return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) + + + +class BaseModel(nn.Module): + # YOLOv5 base model + def forward(self, x, profile=False, visualize=False): + return self._forward_once(x, profile, visualize) # single-scale inference, train + + def _forward_once(self, x, profile=False, visualize=False): + y, dt = [], [] # outputs + for m in self.model: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + if profile: + self._profile_one_layer(m, x, dt) + x = m(x) # run + y.append(x if m.i in self.save else None) # save output + if visualize: + feature_visualization(x, m.type, m.i, save_dir=visualize) + return x + + def _profile_one_layer(self, m, x, dt): + c = m == self.model[-1] # is final layer, copy input as inplace fix + o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs + t = time_sync() + for _ in range(10): + m(x.copy() if c else x) + dt.append((time_sync() - t) * 100) + if m == self.model[0]: + LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module") + LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') + if c: + LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") + + def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers + LOGGER.info('Fusing layers... ') + for m in self.model.modules(): + if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'): + m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv + delattr(m, 'bn') # remove batchnorm + m.forward = m.forward_fuse # update forward + self.info() + return self + + def info(self, verbose=False, img_size=640): # print model information + model_info(self, verbose, img_size) + + def _apply(self, fn): + # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers + self = super()._apply(fn) + m = self.model[-1] # Detect() + if isinstance(m, (Detect, Segment)): + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + +class DetectionModel(BaseModel): + # YOLOv5 detection model + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes + super().__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + self.yaml_file = Path(cfg).name + with open(cfg, encoding='ascii', errors='ignore') as f: + self.yaml = yaml.safe_load(f) # model dict + + # print(self.cfg) + # Define model + ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels + if nc and nc != self.yaml['nc']: + LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") + self.yaml['nc'] = nc # override yaml value + if anchors: + LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') + self.yaml['anchors'] = round(anchors) # override yaml value + self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist + self.names = [str(i) for i in range(self.yaml['nc'])] # default names + self.inplace = self.yaml.get('inplace', True) + + # Build strides, anchors + m = self.model[-1] # Detect() + if isinstance(m, (Detect, Segment)): + s = 256 # 2x min stride + m.inplace = self.inplace + forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) + m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases() # only run once + + # Init weights, biases + initialize_weights(self) + self.info() + LOGGER.info('') + + def forward(self, x, augment=False, profile=False, visualize=False): + if augment: + return self._forward_augment(x) # augmented inference, None + return self._forward_once(x, profile, visualize) # single-scale inference, train + + def _forward_augment(self, x): + img_size = x.shape[-2:] # height, width + s = [1, 0.83, 0.67] # scales + f = [None, 3, None] # flips (2-ud, 3-lr) + y = [] # outputs + for si, fi in zip(s, f): + xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) + yi = self._forward_once(xi)[0] # forward + # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + yi = self._descale_pred(yi, fi, si, img_size) + y.append(yi) + y = self._clip_augmented(y) # clip augmented tails + return torch.cat(y, 1), None # augmented inference, train + + def _descale_pred(self, p, flips, scale, img_size): + # de-scale predictions following augmented inference (inverse operation) + if self.inplace: + p[..., :4] /= scale # de-scale + if flips == 2: + p[..., 1] = img_size[0] - p[..., 1] # de-flip ud + elif flips == 3: + p[..., 0] = img_size[1] - p[..., 0] # de-flip lr + else: + x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale + if flips == 2: + y = img_size[0] - y # de-flip ud + elif flips == 3: + x = img_size[1] - x # de-flip lr + p = torch.cat((x, y, wh, p[..., 4:]), -1) + return p + + def _clip_augmented(self, y): + # Clip YOLOv5 augmented inference tails + nl = self.model[-1].nl # number of detection layers (P3-P5) + g = sum(4 ** x for x in range(nl)) # grid points + e = 1 # exclude layer count + i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices + y[0] = y[0][:, :-i] # large + i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices + y[-1] = y[-1][:, i:] # small + return y + + def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + +Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility + + +class SegmentationModel(DetectionModel): + # YOLOv5 segmentation model + def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None): + super().__init__(cfg, ch, nc, anchors) + + +class ClassificationModel(BaseModel): + # YOLOv5 classification model + def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index + super().__init__() + self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg) + + def _from_detection_model(self, model, nc=1000, cutoff=10): + # Create a YOLOv5 classification model from a YOLOv5 detection model + if isinstance(model, DetectMultiBackend): + model = model.model # unwrap DetectMultiBackend + model.model = model.model[:cutoff] # backbone + m = model.model[-1] # last layer + ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module + c = Classify(ch, nc) # Classify() + c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type + model.model[-1] = c # replace + self.model = model.model + self.stride = model.stride + self.save = [] + self.nc = nc + + def _from_yaml(self, cfg): + # Create a YOLOv5 classification model from a *.yaml file + self.model = None + +class CarDetect(nn.Module): + stride = None # strides computed during build + export_cat = False # onnx export cat output + + def __init__(self, nc=80, anchors=(), ch=()): # detection layer + super(Detect, self).__init__() + self.nc = nc # number of classes + #self.no = nc + 5 # number of outputs per anchor + self.no = nc + 5 + 8 # number of outputs per anchor + + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer('anchors', a) # shape(nl,na,2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + if self.export_cat: + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + + self.grid[i], self.anchor_grid[i] = self._make_grid_new(nx, ny,i) + + y = torch.full_like(x[i], 0) + y = y + torch.cat((x[i][:, :, :, :, 0:5].sigmoid(), torch.cat((x[i][:, :, :, :, 5:13], x[i][:, :, :, :, 13:13+self.nc].sigmoid()), 4)), 4) + + box_xy = (y[:, :, :, :, 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy + box_wh = (y[:, :, :, :, 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + # box_conf = torch.cat((box_xy, torch.cat((box_wh, y[:, :, :, :, 4:5]), 4)), 4) + + landm1 = y[:, :, :, :, 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] # landmark x1 y1 + landm2 = y[:, :, :, :, 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] # landmark x2 y2 + landm3 = y[:, :, :, :, 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] # landmark x3 y3 + landm4 = y[:, :, :, :, 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] # landmark x4 y4 + prob= y[:, :, :, :, 13:13+self.nc] + score,index_ = torch.max(prob,dim=-1,keepdim=True) + score=score.type(box_xy.dtype) + index_=index_.type(box_xy.dtype) + index =torch.argmax(prob,dim=-1,keepdim=True).type(box_xy.dtype) + # landm5 = y[:, :, :, :, 13:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] # landmark x5 y5 + # landm = torch.cat((landm1, torch.cat((landm2, torch.cat((landm3, torch.cat((landm4, landm5), 4)), 4)), 4)), 4) + # y = torch.cat((box_conf, torch.cat((landm, y[:, :, :, :, 13:13+self.nc]), 4)), 4) + y = torch.cat([box_xy, box_wh, y[:, :, :, :, 4:5], landm1, landm2, landm3, landm4, y[:, :, :, :, 13:13+self.nc]], -1) + + z.append(y.view(bs, -1, self.no)) + return torch.cat(z, 1) + + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = torch.full_like(x[i], 0) + class_range = list(range(5)) + list(range(13,13+self.nc)) + y[..., class_range] = x[i][..., class_range].sigmoid() + y[..., 5:13] = x[i][..., 5:13] + #y = x[i].sigmoid() + + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + + #y[..., 5:13] = y[..., 5:13] * 8 - 4 + y[..., 5:7] = y[..., 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i] # landmark x1 y1 + y[..., 7:9] = y[..., 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]# landmark x2 y2 + y[..., 9:11] = y[..., 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]# landmark x3 y3 + y[..., 11:13] = y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]# landmark x4 y4 + # y[..., 13:13] = y[..., 13:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]# landmark x5 y5 + + #y[..., 5:7] = (y[..., 5:7] * 2 -1) * self.anchor_grid[i] # landmark x1 y1 + #y[..., 7:9] = (y[..., 7:9] * 2 -1) * self.anchor_grid[i] # landmark x2 y2 + #y[..., 9:11] = (y[..., 9:11] * 2 -1) * self.anchor_grid[i] # landmark x3 y3 + #y[..., 11:13] = (y[..., 11:13] * 2 -1) * self.anchor_grid[i] # landmark x4 y4 + #y[..., 13:13] = (y[..., 13:13] * 2 -1) * self.anchor_grid[i] # landmark x5 y5 + + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + def _make_grid_new(self,nx=20, ny=20,i=0): + d = self.anchors[i].device + if '1.10.0' in torch.__version__: # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility + yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)], indexing='ij') + else: + yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)]) + grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float() + anchor_grid = (self.anchors[i].clone() * self.stride[i]).view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float() + return grid, anchor_grid + +def parse_model(d, ch): # model_dict, input_channels(3) + # Parse a YOLOv5 model.yaml dictionary + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") + anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation') + if act: + Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() + LOGGER.info(f"{colorstr('activation:')} {act}") # print + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + with contextlib.suppress(NameError): + args[j] = eval(a) if isinstance(a, str) else a # eval strings + + n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in { + Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, + ShuffleV2Block, StemBlock, BlazeBlock, DoubleBlazeBlock,BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}: + c1, c2 = ch[f], args[0] + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, 8) + + args = [c1, c2, *args[1:]] + if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}: + args.insert(2, n) # number of repeats + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum(ch[x] for x in f) + # TODO: channel, gw, gd + elif m in {Detect, Segment}: + args.append([ch[x] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + if m is Segment: + args[3] = make_divisible(args[3] * gw, 8) + elif m is Contract: + c2 = ch[f] * args[0] ** 2 + elif m is Expand: + c2 = ch[f] // args[0] ** 2 + + elif m is CarDetect: + args.append([ch[x + 1] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + else: + c2 = ch[f] + + m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum(x.numel() for x in m_.parameters()) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + if i == 0: + ch = [] + ch.append(c2) + return nn.Sequential(*layers), sorted(save) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') + parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--profile', action='store_true', help='profile model speed') + parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer') + parser.add_argument('--test', action='store_true', help='test all yolo*.yaml') + opt = parser.parse_args() + opt.cfg = check_yaml(opt.cfg) # check YAML + print_args(vars(opt)) + device = select_device(opt.device) + + # Create model + im = torch.rand(opt.batch_size, 3, 640, 640).to(device) + model = Model(opt.cfg).to(device) + + # Options + if opt.line_profile: # profile layer by layer + model(im, profile=True) + + elif opt.profile: # profile forward-backward + results = profile(input=im, ops=[model], n=3) + + elif opt.test: # test all models + for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'): + try: + _ = Model(cfg) + except Exception as e: + print(f'Error in {cfg}: {e}') + + else: # report fused model summary + model.fuse() diff --git a/algorithm/yolov5/models/yolov5l.yaml b/algorithm/yolov5/models/yolov5l.yaml new file mode 100644 index 0000000..ce8a5de --- /dev/null +++ b/algorithm/yolov5/models/yolov5l.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/yolov5m.yaml b/algorithm/yolov5/models/yolov5m.yaml new file mode 100644 index 0000000..ad13ab3 --- /dev/null +++ b/algorithm/yolov5/models/yolov5m.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/yolov5n-0.5.yaml b/algorithm/yolov5/models/yolov5n-0.5.yaml new file mode 100644 index 0000000..8f24276 --- /dev/null +++ b/algorithm/yolov5/models/yolov5n-0.5.yaml @@ -0,0 +1,46 @@ +# parameters +nc: 1 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 0.5 # layer channel multiple + +# anchors +anchors: + - [4,5, 8,10, 13,16] # P3/8 + - [23,29, 43,55, 73,105] # P4/16 + - [146,217, 231,300, 335,433] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, StemBlock, [32, 3, 2]], # 0-P2/4 + [-1, 1, ShuffleV2Block, [128, 2]], # 1-P3/8 + [-1, 3, ShuffleV2Block, [128, 1]], # 2 + [-1, 1, ShuffleV2Block, [256, 2]], # 3-P4/16 + [-1, 7, ShuffleV2Block, [256, 1]], # 4 + [-1, 1, ShuffleV2Block, [512, 2]], # 5-P5/32 + [-1, 3, ShuffleV2Block, [512, 1]], # 6 + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P4 + [-1, 1, C3, [128, False]], # 10 + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 2], 1, Concat, [1]], # cat backbone P3 + [-1, 1, C3, [128, False]], # 14 (P3/8-small) + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 11], 1, Concat, [1]], # cat head P4 + [-1, 1, C3, [128, False]], # 17 (P4/16-medium) + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 7], 1, Concat, [1]], # cat head P5 + [-1, 1, C3, [128, False]], # 20 (P5/32-large) + + [[14, 17, 20], 1, CarDetect, [nc, anchors]], # Detect(P3, P4, P5) + ] + diff --git a/algorithm/yolov5/models/yolov5n.yaml b/algorithm/yolov5/models/yolov5n.yaml new file mode 100644 index 0000000..8a28a40 --- /dev/null +++ b/algorithm/yolov5/models/yolov5n.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/yolov5s.yaml b/algorithm/yolov5/models/yolov5s.yaml new file mode 100644 index 0000000..f35beab --- /dev/null +++ b/algorithm/yolov5/models/yolov5s.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/models/yolov5x.yaml b/algorithm/yolov5/models/yolov5x.yaml new file mode 100644 index 0000000..f617a02 --- /dev/null +++ b/algorithm/yolov5/models/yolov5x.yaml @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/algorithm/yolov5/requirements.txt b/algorithm/yolov5/requirements.txt new file mode 100644 index 0000000..db4851e --- /dev/null +++ b/algorithm/yolov5/requirements.txt @@ -0,0 +1,50 @@ +# YOLOv5 requirements +# Usage: pip install -r requirements.txt + +# Base ------------------------------------------------------------------------ +gitpython>=3.1.30 +matplotlib>=3.2.2 +numpy>=1.18.5 +opencv-python>=4.1.1 +Pillow>=7.1.2 +psutil # system resources +PyYAML>=5.3.1 +requests>=2.23.0 +scipy>=1.4.1 +# thop>=0.1.1 # FLOPs computation +# torch>=1.7.0 # see https://pytorch.org/get-started/locally (recommended) +# torchvision>=0.8.1 +tqdm>=4.64.0 +# protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 + +# Logging --------------------------------------------------------------------- +tensorboard>=2.4.1 +# clearml>=1.2.0 +# comet + +# Plotting -------------------------------------------------------------------- +pandas>=1.1.4 +seaborn>=0.11.0 + +# Export ---------------------------------------------------------------------- +# coremltools>=6.0 # CoreML export +# onnx>=1.12.0 # ONNX export +# onnx-simplifier>=0.4.1 # ONNX simplifier +# nvidia-pyindex # TensorRT export +# nvidia-tensorrt # TensorRT export +# scikit-learn<=1.1.2 # CoreML quantization +# tensorflow>=2.4.1 # TF exports (-cpu, -aarch64, -macos) +# tensorflowjs>=3.9.0 # TF.js export +# openvino-dev # OpenVINO export + +# Deploy ---------------------------------------------------------------------- +setuptools>=65.5.1 # Snyk vulnerability fix +# tritonclient[all]~=2.24.0 + +# Extras ---------------------------------------------------------------------- +# ipython # interactive notebook +# mss # screenshots +# albumentations>=1.0.3 +# pycocotools>=2.0.6 # COCO mAP +# roboflow +# ultralytics # HUB https://hub.ultralytics.com diff --git a/algorithm/yolov5/segment/predict.py b/algorithm/yolov5/segment/predict.py new file mode 100644 index 0000000..d82df89 --- /dev/null +++ b/algorithm/yolov5/segment/predict.py @@ -0,0 +1,284 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run YOLOv5 segmentation inference on images, videos, directories, streams, etc. + +Usage - sources: + $ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/Zgi9g1ksQHc' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python segment/predict.py --weights yolov5s-seg.pt # PyTorch + yolov5s-seg.torchscript # TorchScript + yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-seg_openvino_model # OpenVINO + yolov5s-seg.engine # TensorRT + yolov5s-seg.mlmodel # CoreML (macOS-only) + yolov5s-seg_saved_model # TensorFlow SavedModel + yolov5s-seg.pb # TensorFlow GraphDef + yolov5s-seg.tflite # TensorFlow Lite + yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-seg_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, + increment_path, non_max_suppression, print_args, scale_boxes, scale_segments, + strip_optimizer) +from utils.plots import Annotator, colors, save_one_box +from utils.segment.general import masks2segments, process_mask, process_mask_native +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / 'yolov5s-seg.pt', # model.pt path(s) + source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) + data=ROOT / 'data/coco128.yaml', # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / 'runs/predict-seg', # save results to project/name + name='exp', # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride + retina_masks=False, +): + source = str(source) + save_img = not nosave and not source.endswith('.txt') # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) + webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file) + screenshot = source.lower().startswith('screen') + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + pred, proto = model(im, augment=augment, visualize=visualize)[:2] + + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32) + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f'{i}: ' + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt + s += '%gx%g ' % im.shape[2:] # print string + imc = im0.copy() if save_crop else im0 # for save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + if len(det): + if retina_masks: + # scale bbox first the crop masks + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size + masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC + else: + masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size + + # Segments + if save_txt: + segments = [ + scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True) + for x in reversed(masks2segments(masks))] + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Mask plotting + annotator.masks( + masks, + colors=[colors(x, True) for x in det[:, 5]], + im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() / + 255 if retina_masks else im[i]) + + # Write results + for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): + if save_txt: # Write to file + seg = segments[j].reshape(-1) # (n,2) to (n*2) + line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format + with open(f'{txt_path}.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') + annotator.box_label(xyxy, label, color=colors(c, True)) + # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3) + if save_crop: + save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == 'Linux' and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + if cv2.waitKey(1) == ord('q'): # 1 millisecond + exit() + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)') + parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') + parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') + parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='show results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') + parser.add_argument('--nosave', action='store_true', help='do not save images/videos') + parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--visualize', action='store_true', help='visualize features') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default=ROOT / 'runs/predict-seg', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') + parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') + parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') + parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution') + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + run(**vars(opt)) + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/segment/train.py b/algorithm/yolov5/segment/train.py new file mode 100644 index 0000000..8ed75ba --- /dev/null +++ b/algorithm/yolov5/segment/train.py @@ -0,0 +1,664 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Train a YOLOv5 segment model on a segment dataset +Models and datasets download automatically from the latest YOLOv5 release. + +Usage - Single-GPU training: + $ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended) + $ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 + +Models: https://github.com/ultralytics/yolov5/tree/master/models +Datasets: https://github.com/ultralytics/yolov5/tree/master/data +Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data +""" + +import argparse +import math +import os +import random +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +import yaml +from torch.optim import lr_scheduler +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import segment.val as validate # for end-of-epoch mAP +from models.experimental import attempt_load +from models.yolo import SegmentationModel +from utils.autoanchor import check_anchors +from utils.autobatch import check_train_batch_size +from utils.callbacks import Callbacks +from utils.downloads import attempt_download, is_url +from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, + check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, + get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, + labels_to_image_weights, one_cycle, print_args, print_mutation, strip_optimizer, yaml_save) +from utils.loggers import GenericLogger +from utils.plots import plot_evolve, plot_labels +from utils.segment.dataloaders import create_dataloader +from utils.segment.loss import ComputeLoss +from utils.segment.metrics import KEYS, fitness +from utils.segment.plots import plot_images_and_masks, plot_results_with_masks +from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, + smart_resume, torch_distributed_zero_first) + +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +GIT_INFO = check_git_info() + + +def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary + save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, mask_ratio = \ + Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ + opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze, opt.mask_ratio + # callbacks.run('on_pretrain_routine_start') + + # Directories + w = save_dir / 'weights' # weights dir + (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir + last, best = w / 'last.pt', w / 'best.pt' + + # Hyperparameters + if isinstance(hyp, str): + with open(hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) + opt.hyp = hyp.copy() # for saving hyps to checkpoints + + # Save run settings + if not evolve: + yaml_save(save_dir / 'hyp.yaml', hyp) + yaml_save(save_dir / 'opt.yaml', vars(opt)) + + # Loggers + data_dict = None + if RANK in {-1, 0}: + logger = GenericLogger(opt=opt, console_logger=LOGGER) + + # Config + plots = not evolve and not opt.noplots # create plots + overlap = not opt.no_overlap + cuda = device.type != 'cpu' + init_seeds(opt.seed + 1 + RANK, deterministic=True) + with torch_distributed_zero_first(LOCAL_RANK): + data_dict = data_dict or check_dataset(data) # check if None + train_path, val_path = data_dict['train'], data_dict['val'] + nc = 1 if single_cls else int(data_dict['nc']) # number of classes + names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names + is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset + + # Model + check_suffix(weights, '.pt') # check weights + pretrained = weights.endswith('.pt') + if pretrained: + with torch_distributed_zero_first(LOCAL_RANK): + weights = attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak + model = SegmentationModel(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) + exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(csd, strict=False) # load + LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report + else: + model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + amp = check_amp(model) # check AMP + + # Freeze + freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) + if any(x in k for x in freeze): + LOGGER.info(f'freezing {k}') + v.requires_grad = False + + # Image size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple + + # Batch size + if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size + batch_size = check_train_batch_size(model, imgsz, amp) + logger.update_params({'batch_size': batch_size}) + # loggers.on_params_update({"batch_size": batch_size}) + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay + optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) + + # Scheduler + if opt.cos_lr: + lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + else: + lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # Resume + best_fitness, start_epoch = 0.0, 0 + if pretrained: + if resume: + best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) + del ckpt, csd + + # DP mode + if cuda and RANK == -1 and torch.cuda.device_count() > 1: + LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' + 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and RANK != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + LOGGER.info('Using SyncBatchNorm()') + + # Trainloader + train_loader, dataset = create_dataloader( + train_path, + imgsz, + batch_size // WORLD_SIZE, + gs, + single_cls, + hyp=hyp, + augment=True, + cache=None if opt.cache == 'val' else opt.cache, + rect=opt.rect, + rank=LOCAL_RANK, + workers=workers, + image_weights=opt.image_weights, + quad=opt.quad, + prefix=colorstr('train: '), + shuffle=True, + mask_downsample_ratio=mask_ratio, + overlap_mask=overlap, + ) + labels = np.concatenate(dataset.labels, 0) + mlc = int(labels[:, 0].max()) # max label class + assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' + + # Process 0 + if RANK in {-1, 0}: + val_loader = create_dataloader(val_path, + imgsz, + batch_size // WORLD_SIZE * 2, + gs, + single_cls, + hyp=hyp, + cache=None if noval else opt.cache, + rect=True, + rank=-1, + workers=workers * 2, + pad=0.5, + mask_downsample_ratio=mask_ratio, + overlap_mask=overlap, + prefix=colorstr('val: '))[0] + + if not resume: + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor + model.half().float() # pre-reduce anchor precision + + if plots: + plot_labels(labels, names, save_dir) + # callbacks.run('on_pretrain_routine_end', labels, names) + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Model attributes + nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) + hyp['box'] *= 3 / nl # scale to layers + hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers + hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers + hyp['label_smoothing'] = opt.label_smoothing + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nb = len(train_loader) # number of batches + nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + last_opt_step = -1 + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = torch.cuda.amp.GradScaler(enabled=amp) + stopper, stop = EarlyStopping(patience=opt.patience), False + compute_loss = ComputeLoss(model, overlap=overlap) # init loss class + # callbacks.run('on_train_start') + LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' + f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting training for {epochs} epochs...') + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + # callbacks.run('on_train_epoch_start') + model.train() + + # Update image weights (optional, single-GPU only) + if opt.image_weights: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + + # Update mosaic border (optional) + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(4, device=device) # mean losses + if RANK != -1: + train_loader.sampler.set_epoch(epoch) + pbar = enumerate(train_loader) + LOGGER.info(('\n' + '%11s' * 8) % + ('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) + if RANK in {-1, 0}: + pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------ + # callbacks.run('on_train_batch_start') + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + with torch.cuda.amp.autocast(amp): + pred = model(imgs) # forward + loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float()) + if RANK != -1: + loss *= WORLD_SIZE # gradient averaged between devices in DDP mode + if opt.quad: + loss *= 4. + + # Backward + scaler.scale(loss).backward() + + # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html + if ni - last_opt_step >= accumulate: + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + last_opt_step = ni + + # Log + if RANK in {-1, 0}: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) + pbar.set_description(('%11s' * 2 + '%11.4g' * 6) % + (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) + # callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths) + # if callbacks.stop_training: + # return + + # Mosaic plots + if plots: + if ni < 3: + plot_images_and_masks(imgs, targets, masks, paths, save_dir / f'train_batch{ni}.jpg') + if ni == 10: + files = sorted(save_dir.glob('train*.jpg')) + logger.log_images(files, 'Mosaics', epoch) + # end batch ------------------------------------------------------------------------------------------------ + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for loggers + scheduler.step() + + if RANK in {-1, 0}: + # mAP + # callbacks.run('on_train_epoch_end', epoch=epoch) + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) + final_epoch = (epoch + 1 == epochs) or stopper.possible_stop + if not noval or final_epoch: # Calculate mAP + results, maps, _ = validate.run(data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + half=amp, + model=ema.ema, + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + plots=False, + callbacks=callbacks, + compute_loss=compute_loss, + mask_downsample_ratio=mask_ratio, + overlap=overlap) + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + stop = stopper(epoch=epoch, fitness=fi) # early stop check + if fi > best_fitness: + best_fitness = fi + log_vals = list(mloss) + list(results) + lr + # callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) + # Log val metrics and media + metrics_dict = dict(zip(KEYS, log_vals)) + logger.log_metrics(metrics_dict, epoch) + + # Save model + if (not nosave) or (final_epoch and not evolve): # if save + ckpt = { + 'epoch': epoch, + 'best_fitness': best_fitness, + 'model': deepcopy(de_parallel(model)).half(), + 'ema': deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': optimizer.state_dict(), + 'opt': vars(opt), + 'git': GIT_INFO, # {remote, branch, commit} if a git repo + 'date': datetime.now().isoformat()} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + if opt.save_period > 0 and epoch % opt.save_period == 0: + torch.save(ckpt, w / f'epoch{epoch}.pt') + logger.log_model(w / f'epoch{epoch}.pt') + del ckpt + # callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) + + # EarlyStopping + if RANK != -1: # if DDP training + broadcast_list = [stop if RANK == 0 else None] + dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks + if RANK != 0: + stop = broadcast_list[0] + if stop: + break # must break all DDP ranks + + # end epoch ---------------------------------------------------------------------------------------------------- + # end training ----------------------------------------------------------------------------------------------------- + if RANK in {-1, 0}: + LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if f is best: + LOGGER.info(f'\nValidating {f}...') + results, _, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + model=attempt_load(f, device).half(), + iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + save_json=is_coco, + verbose=True, + plots=plots, + callbacks=callbacks, + compute_loss=compute_loss, + mask_downsample_ratio=mask_ratio, + overlap=overlap) # val best model with plots + if is_coco: + # callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) + metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr)) + logger.log_metrics(metrics_dict, epoch) + + # callbacks.run('on_train_end', last, best, epoch, results) + # on train end callback using genericLogger + logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs) + if not opt.evolve: + logger.log_model(best, epoch) + if plots: + plot_results_with_masks(file=save_dir / 'results.csv') # save results.png + files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] + files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") + logger.log_images(files, 'Results', epoch + 1) + logger.log_images(sorted(save_dir.glob('val*.jpg')), 'Validation', epoch + 1) + torch.cuda.empty_cache() + return results + + +def parse_opt(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s-seg.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.yaml', help='dataset.yaml path') + parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=100, help='total training epochs') + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--noval', action='store_true', help='only validate final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') + parser.add_argument('--noplots', action='store_true', help='save no plot files') + parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--project', default=ROOT / 'runs/train-seg', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') + parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') + parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') + parser.add_argument('--seed', type=int, default=0, help='Global training seed') + parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + + # Instance Segmentation Args + parser.add_argument('--mask-ratio', type=int, default=4, help='Downsample the truth masks to saving memory') + parser.add_argument('--no-overlap', action='store_true', help='Overlap masks train faster at slightly less mAP') + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt, callbacks=Callbacks()): + # Checks + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements() + + # Resume + if opt.resume and not opt.evolve: # resume from specified or most recent last.pt + last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) + opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml + opt_data = opt.data # original dataset + if opt_yaml.is_file(): + with open(opt_yaml, errors='ignore') as f: + d = yaml.safe_load(f) + else: + d = torch.load(last, map_location='cpu')['opt'] + opt = argparse.Namespace(**d) # replace + opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate + if is_url(opt_data): + opt.data = check_file(opt_data) # avoid HUB resume auth timeout + else: + opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ + check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks + assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + if opt.evolve: + if opt.project == str(ROOT / 'runs/train-seg'): # if default project name, rename to runs/evolve-seg + opt.project = str(ROOT / 'runs/evolve-seg') + opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume + if opt.name == 'cfg': + opt.name = Path(opt.cfg).stem # use model.yaml as name + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' + assert not opt.image_weights, f'--image-weights {msg}' + assert not opt.evolve, f'--evolve {msg}' + assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' + assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' + assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + torch.cuda.set_device(LOCAL_RANK) + device = torch.device('cuda', LOCAL_RANK) + dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + + # Train + if not opt.evolve: + train(opt.hyp, opt, device, callbacks) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = { + 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr + 'box': (1, 0.02, 0.2), # box loss gain + 'cls': (1, 0.2, 4.0), # cls loss gain + 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (0, 0.1, 0.7), # IoU training threshold + 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (1, 0.0, 0.9), # image scale (+/- gain) + 'shear': (1, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (1, 0.0, 1.0), # image mixup (probability) + 'mixup': (1, 0.0, 1.0), # image mixup (probability) + 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) + + with open(opt.hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + if 'anchors' not in hyp: # anchors commented in hyp.yaml + hyp['anchors'] = 3 + if opt.noautoanchor: + del hyp['anchors'], meta['anchors'] + opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' + if opt.bucket: + # download evolve.csv if exists + subprocess.run([ + 'gsutil', + 'cp', + f'gs://{opt.bucket}/evolve.csv', + str(evolve_csv),]) + + for _ in range(opt.evolve): # generations to evolve + if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 12] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device, callbacks) + callbacks = Callbacks() + # Write mutation results + print_mutation(KEYS[4:16], results, hyp.copy(), save_dir, opt.bucket) + + # Plot results + plot_evolve(evolve_csv) + LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' + f"Results saved to {colorstr('bold', save_dir)}\n" + f'Usage example: $ python train.py --hyp {evolve_yaml}') + + +def run(**kwargs): + # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/segment/tutorial.ipynb b/algorithm/yolov5/segment/tutorial.ipynb new file mode 100644 index 0000000..cb52045 --- /dev/null +++ b/algorithm/yolov5/segment/tutorial.ipynb @@ -0,0 +1,594 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "
\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wbvMlHd_QwMG", + "outputId": "171b23f0-71b9-4cbf-b666-6fa2ecef70c8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + ] + } + ], + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Predict\n", + "\n", + "`segment/predict.py` runs YOLOv5 instance segmentation inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict`. Example inference sources are:\n", + "\n", + "```shell\n", + "python segment/predict.py --source 0 # webcam\n", + " img.jpg # image \n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zR9ZbuQCH7FX", + "outputId": "3f67f1c7-f15e-4fa5-d251-967c3b77eaad" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/predict: \u001b[0mweights=['yolov5s-seg.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\n", + "100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\n", + "\n", + "Fusing layers... \n", + "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\n", + "Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/predict-seg/exp\u001b[0m\n" + ] + } + ], + "source": [ + "!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\n", + "#display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WQPtK1QYVaD_", + "outputId": "9d751d8c-bee8-4339-cf30-9854ca530449" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip ...\n", + "Downloading http://images.cocodataset.org/zips/val2017.zip ...\n", + "######################################################################## 100.0%\n", + "######################################################################## 100.0%\n" + ] + } + ], + "source": [ + "# Download COCO val\n", + "!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X58w8JLpMnjH", + "outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Fusing layers... \n", + "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n", + " all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n", + "Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n" + ] + } + ], + "source": [ + "# Validate YOLOv5s-seg on COCO val\n", + "!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZY2VXXXu74w5" + }, + "source": [ + "# 3. Train\n", + "\n", + "

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n", + "- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\n", + "

\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Train on Custom Data with Roboflow 🌟 NEW\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", + "\n", + "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/](https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/?ref=ultralytics)\n", + "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JTz7kpmHsg-5qwVz2d2IH3AaenI1tv0N?usp=sharing)\n", + "
\n", + "\n", + "

Label images lightning fast (including with model-assisted labeling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "outputs": [], + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML']\n", + "\n", + "if logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train-seg\n", + "elif logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'ClearML':\n", + " import clearml; clearml.browser_login()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1NcFxRcFdJ_O", + "outputId": "3a3e0cf7-e79c-47a5-c8e7-2d26eeeab988" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n", + "Downloading https://ultralytics.com/assets/coco128-seg.zip to coco128-seg.zip...\n", + "100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n", + "Dataset download success ✅ (1.9s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", + " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", + " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 19 [-1, 14] 1 0 models.common.Concat [1] \n", + " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", + " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 22 [-1, 10] 1 0 models.common.Concat [1] \n", + " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", + " 24 [17, 20, 23] 1 615133 models.yolo.Segment [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 128, [128, 256, 512]]\n", + "Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n", + "\n", + "Transferred 367/367 items from yolov5s-seg.pt\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lay2WsTjNJzP" + }, + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "\n", + "\n", + "\"ClearML" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GMusP4OAxFu6" + }, + "outputs": [], + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s-seg') # yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "YOLOv5 Segmentation Tutorial", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/algorithm/yolov5/segment/val.py b/algorithm/yolov5/segment/val.py new file mode 100644 index 0000000..a7f95fe --- /dev/null +++ b/algorithm/yolov5/segment/val.py @@ -0,0 +1,473 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Validate a trained YOLOv5 segment model on a segment dataset + +Usage: + $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images) + $ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments + +Usage - formats: + $ python segment/val.py --weights yolov5s-seg.pt # PyTorch + yolov5s-seg.torchscript # TorchScript + yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-seg_openvino_label # OpenVINO + yolov5s-seg.engine # TensorRT + yolov5s-seg.mlmodel # CoreML (macOS-only) + yolov5s-seg_saved_model # TensorFlow SavedModel + yolov5s-seg.pb # TensorFlow GraphDef + yolov5s-seg.tflite # TensorFlow Lite + yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-seg_paddle_model # PaddlePaddle +""" + +import argparse +import json +import os +import subprocess +import sys +from multiprocessing.pool import ThreadPool +from pathlib import Path + +import numpy as np +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import torch.nn.functional as F + +from models.common import DetectMultiBackend +from models.yolo import SegmentationModel +from utils.callbacks import Callbacks +from utils.general import (LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, + check_requirements, check_yaml, coco80_to_coco91_class, colorstr, increment_path, + non_max_suppression, print_args, scale_boxes, xywh2xyxy, xyxy2xywh) +from utils.metrics import ConfusionMatrix, box_iou +from utils.plots import output_to_target, plot_val_study +from utils.segment.dataloaders import create_dataloader +from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image +from utils.segment.metrics import Metrics, ap_per_class_box_and_mask +from utils.segment.plots import plot_images_and_masks +from utils.torch_utils import de_parallel, select_device, smart_inference_mode + + +def save_one_txt(predn, save_conf, shape, file): + # Save one txt result + gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh + for *xyxy, conf, cls in predn.tolist(): + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(file, 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + +def save_one_json(predn, jdict, path, class_map, pred_masks): + # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} + from pycocotools.mask import encode + + def single_encode(x): + rle = encode(np.asarray(x[:, :, None], order='F', dtype='uint8'))[0] + rle['counts'] = rle['counts'].decode('utf-8') + return rle + + image_id = int(path.stem) if path.stem.isnumeric() else path.stem + box = xyxy2xywh(predn[:, :4]) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + pred_masks = np.transpose(pred_masks, (2, 0, 1)) + with ThreadPool(NUM_THREADS) as pool: + rles = pool.map(single_encode, pred_masks) + for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())): + jdict.append({ + 'image_id': image_id, + 'category_id': class_map[int(p[5])], + 'bbox': [round(x, 3) for x in b], + 'score': round(p[4], 5), + 'segmentation': rles[i]}) + + +def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False): + """ + Return correct prediction matrix + Arguments: + detections (array[N, 6]), x1, y1, x2, y2, conf, class + labels (array[M, 5]), class, x1, y1, x2, y2 + Returns: + correct (array[N, 10]), for 10 IoU levels + """ + if masks: + if overlap: + nl = len(labels) + index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1 + gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640) + gt_masks = torch.where(gt_masks == index, 1.0, 0.0) + if gt_masks.shape[1:] != pred_masks.shape[1:]: + gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode='bilinear', align_corners=False)[0] + gt_masks = gt_masks.gt_(0.5) + iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1)) + else: # boxes + iou = box_iou(labels[:, 1:], detections[:, :4]) + + correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) + correct_class = labels[:, 0:1] == detections[:, 5] + for i in range(len(iouv)): + x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + # matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True + return torch.tensor(correct, dtype=torch.bool, device=iouv.device) + + +@smart_inference_mode() +def run( + data, + weights=None, # model.pt path(s) + batch_size=32, # batch size + imgsz=640, # inference size (pixels) + conf_thres=0.001, # confidence threshold + iou_thres=0.6, # NMS IoU threshold + max_det=300, # maximum detections per image + task='val', # train, val, test, speed or study + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + single_cls=False, # treat as single-class dataset + augment=False, # augmented inference + verbose=False, # verbose output + save_txt=False, # save results to *.txt + save_hybrid=False, # save label+prediction hybrid results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_json=False, # save a COCO-JSON results file + project=ROOT / 'runs/val-seg', # save to project/name + name='exp', # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + save_dir=Path(''), + plots=True, + overlap=False, + mask_downsample_ratio=1, + compute_loss=None, + callbacks=Callbacks(), +): + if save_json: + check_requirements('pycocotools>=2.0.6') + process = process_mask_native # more accurate + else: + process = process_mask # faster + + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != 'cpu' # half precision only supported on CUDA + model.half() if half else model.float() + nm = de_parallel(model).model[-1].nm # number of masks + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + + # Data + data = check_dataset(data) # check + + # Configure + model.eval() + cuda = device.type != 'cpu' + is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset + nc = 1 if single_cls else int(data['nc']) # number of classes + iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Dataloader + if not training: + if pt and not single_cls: # check --weights are trained on --data + ncm = model.model.nc + assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ + f'classes). Pass correct combination of --weights and --data that are trained together.' + model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup + pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks + task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images + dataloader = create_dataloader(data[task], + imgsz, + batch_size, + stride, + single_cls, + pad=pad, + rect=rect, + workers=workers, + prefix=colorstr(f'{task}: '), + overlap_mask=overlap, + mask_downsample_ratio=mask_downsample_ratio)[0] + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = model.names if hasattr(model, 'names') else model.module.names # get class names + if isinstance(names, (list, tuple)): # old format + names = dict(enumerate(names)) + class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) + s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R', + 'mAP50', 'mAP50-95)') + dt = Profile(), Profile(), Profile() + metrics = Metrics() + loss = torch.zeros(4, device=device) + jdict, stats = [], [] + # callbacks.run('on_val_start') + pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar + for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar): + # callbacks.run('on_val_batch_start') + with dt[0]: + if cuda: + im = im.to(device, non_blocking=True) + targets = targets.to(device) + masks = masks.to(device) + masks = masks.float() + im = im.half() if half else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + nb, _, height, width = im.shape # batch size, channels, height, width + + # Inference + with dt[1]: + preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None) + + # Loss + if compute_loss: + loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls + + # NMS + targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + with dt[2]: + preds = non_max_suppression(preds, + conf_thres, + iou_thres, + labels=lb, + multi_label=True, + agnostic=single_cls, + max_det=max_det, + nm=nm) + + # Metrics + plot_masks = [] # masks for plotting + for si, (pred, proto) in enumerate(zip(preds, protos)): + labels = targets[targets[:, 0] == si, 1:] + nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions + path, shape = Path(paths[si]), shapes[si][0] + correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + seen += 1 + + if npr == 0: + if nl: + stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0])) + if plots: + confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) + continue + + # Masks + midx = [si] if overlap else targets[:, 0] == si + gt_masks = masks[midx] + pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:]) + + # Predictions + if single_cls: + pred[:, 5] = 0 + predn = pred.clone() + scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred + + # Evaluate + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + correct_bboxes = process_batch(predn, labelsn, iouv) + correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True) + if plots: + confusion_matrix.process_batch(predn, labelsn) + stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls) + + pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8) + if plots and batch_i < 3: + plot_masks.append(pred_masks[:15]) # filter top 15 to plot + + # Save/log + if save_txt: + save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') + if save_json: + pred_masks = scale_image(im[si].shape[1:], + pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]) + save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary + # callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) + + # Plot images + if plots and batch_i < 3: + if len(plot_masks): + plot_masks = torch.cat(plot_masks, dim=0) + plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) + plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths, + save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred + + # callbacks.run('on_val_batch_end') + + # Compute metrics + stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names) + metrics.update(results) + nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class + + # Print results + pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format + LOGGER.info(pf % ('all', seen, nt.sum(), *metrics.mean_results())) + if nt.sum() == 0: + LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels') + + # Print results per class + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): + for i, c in enumerate(metrics.ap_class_index): + LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i))) + + # Print speeds + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + if not training: + shape = (batch_size, 3, imgsz, imgsz) + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + # callbacks.run('on_val_end') + + mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results() + + # Save JSON + if save_json and len(jdict): + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights + anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations + pred_json = str(save_dir / f'{w}_predictions.json') # predictions + LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...') + with open(pred_json, 'w') as f: + json.dump(jdict, f) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + anno = COCO(anno_json) # init annotations api + pred = anno.loadRes(pred_json) # init predictions api + results = [] + for eval in COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm'): + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5) + map_bbox, map50_bbox, map_mask, map50_mask = results + except Exception as e: + LOGGER.info(f'pycocotools unable to run: {e}') + + # Return results + model.float() # for training + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask + return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.yaml', help='dataset.yaml path') + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)') + parser.add_argument('--batch-size', type=int, default=32, help='batch size') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image') + parser.add_argument('--task', default='val', help='train, val, test, speed or study') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--verbose', action='store_true', help='report mAP by class') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file') + parser.add_argument('--project', default=ROOT / 'runs/val-seg', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + # opt.save_json |= opt.data.endswith('coco.yaml') + opt.save_txt |= opt.save_hybrid + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) + + if opt.task in ('train', 'val', 'test'): # run normally + if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 + LOGGER.warning(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results') + if opt.save_hybrid: + LOGGER.warning('WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone') + run(**vars(opt)) + + else: + weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] + opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results + if opt.task == 'speed': # speed benchmarks + # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... + opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False + for opt.weights in weights: + run(**vars(opt), plots=False) + + elif opt.task == 'study': # speed vs mAP benchmarks + # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... + for opt.weights in weights: + f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to + x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis + for opt.imgsz in x: # img-size + LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...') + r, _, t = run(**vars(opt), plots=False) + y.append(r + t) # results and times + np.savetxt(f, y, fmt='%10.4g') # save + subprocess.run(['zip', '-r', 'study.zip', 'study_*.txt']) + plot_val_study(x=x) # plot + else: + raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/algorithm/yolov5/setup.cfg b/algorithm/yolov5/setup.cfg new file mode 100644 index 0000000..d7c4cb3 --- /dev/null +++ b/algorithm/yolov5/setup.cfg @@ -0,0 +1,54 @@ +# Project-wide configuration file, can be used for package metadata and other toll configurations +# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments +# Local usage: pip install pre-commit, pre-commit run --all-files + +[metadata] +license_file = LICENSE +description_file = README.md + +[tool:pytest] +norecursedirs = + .git + dist + build +addopts = + --doctest-modules + --durations=25 + --color=yes + +[flake8] +max-line-length = 120 +exclude = .tox,*.egg,build,temp +select = E,W,F +doctests = True +verbose = 2 +# https://pep8.readthedocs.io/en/latest/intro.html#error-codes +format = pylint +# see: https://www.flake8rules.com/ +ignore = E731,F405,E402,F401,W504,E127,E231,E501,F403 + # E731: Do not assign a lambda expression, use a def + # F405: name may be undefined, or defined from star imports: module + # E402: module level import not at top of file + # F401: module imported but unused + # W504: line break after binary operator + # E127: continuation line over-indented for visual indent + # E231: missing whitespace after ‘,’, ‘;’, or ‘:’ + # E501: line too long + # F403: ‘from module import *’ used; unable to detect undefined names + +[isort] +# https://pycqa.github.io/isort/docs/configuration/options.html +line_length = 120 +# see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html +multi_line_output = 0 + +[yapf] +based_on_style = pep8 +spaces_before_comment = 2 +COLUMN_LIMIT = 120 +COALESCE_BRACKETS = True +SPACES_AROUND_POWER_OPERATOR = True +SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False +SPLIT_BEFORE_CLOSING_BRACKET = False +SPLIT_BEFORE_FIRST_ARGUMENT = False +# EACH_DICT_ENTRY_ON_SEPARATE_LINE = False diff --git a/algorithm/yolov5/train.py b/algorithm/yolov5/train.py new file mode 100644 index 0000000..c4e3aac --- /dev/null +++ b/algorithm/yolov5/train.py @@ -0,0 +1,640 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Train a YOLOv5 model on a custom dataset. +Models and datasets download automatically from the latest YOLOv5 release. + +Usage - Single-GPU training: + $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) + $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3 + +Models: https://github.com/ultralytics/yolov5/tree/master/models +Datasets: https://github.com/ultralytics/yolov5/tree/master/data +Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data +""" + +import argparse +import math +import os +import random +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +import yaml +from torch.optim import lr_scheduler +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import val as validate # for end-of-epoch mAP +from models.experimental import attempt_load +from models.yolo import Model +from utils.autoanchor import check_anchors +from utils.autobatch import check_train_batch_size +from utils.callbacks import Callbacks +from utils.dataloaders import create_dataloader +from utils.downloads import attempt_download, is_url +from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info, + check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr, + get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights, + labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer, + yaml_save) +from utils.loggers import Loggers +from utils.loggers.comet.comet_utils import check_comet_resume +from utils.loss import ComputeLoss +from utils.metrics import fitness +from utils.plots import plot_evolve +from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer, + smart_resume, torch_distributed_zero_first) + +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) +GIT_INFO = check_git_info() + + +def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary + save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ + Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ + opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze + callbacks.run('on_pretrain_routine_start') + + # Directories + w = save_dir / 'weights' # weights dir + (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir + last, best = w / 'last.pt', w / 'best.pt' + + # Hyperparameters + if isinstance(hyp, str): + with open(hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) + opt.hyp = hyp.copy() # for saving hyps to checkpoints + + # Save run settings + if not evolve: + yaml_save(save_dir / 'hyp.yaml', hyp) + yaml_save(save_dir / 'opt.yaml', vars(opt)) + + # Loggers + data_dict = None + if RANK in {-1, 0}: + loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance + + # Register actions + for k in methods(loggers): + callbacks.register_action(k, callback=getattr(loggers, k)) + + # Process custom dataset artifact link + data_dict = loggers.remote_dataset + if resume: # If resuming runs from remote artifact + weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size + + # Config + plots = not evolve and not opt.noplots # create plots + cuda = device.type != 'cpu' + init_seeds(opt.seed + 1 + RANK, deterministic=True) + with torch_distributed_zero_first(LOCAL_RANK): + data_dict = data_dict or check_dataset(data) # check if None + train_path, val_path = data_dict['train'], data_dict['val'] + nc = 1 if single_cls else int(data_dict['nc']) # number of classes + names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names + is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset + + # Model + check_suffix(weights, '.pt') # check weights + pretrained = weights.endswith('.pt') + if pretrained: + with torch_distributed_zero_first(LOCAL_RANK): + weights = attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak + model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys + csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(csd, strict=False) # load + LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report + else: + model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create + amp = check_amp(model) # check AMP + + # Freeze + freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) + if any(x in k for x in freeze): + LOGGER.info(f'freezing {k}') + v.requires_grad = False + + # Image size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple + + # Batch size + if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size + batch_size = check_train_batch_size(model, imgsz, amp) + loggers.on_params_update({'batch_size': batch_size}) + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay + optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay']) + + # Scheduler + if opt.cos_lr: + lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] + else: + lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # Resume + best_fitness, start_epoch = 0.0, 0 + if pretrained: + if resume: + best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) + del ckpt, csd + + # DP mode + if cuda and RANK == -1 and torch.cuda.device_count() > 1: + LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' + 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and RANK != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + LOGGER.info('Using SyncBatchNorm()') + + # Trainloader + train_loader, dataset = create_dataloader(train_path, + imgsz, + batch_size // WORLD_SIZE, + gs, + single_cls, + hyp=hyp, + augment=True, + cache=None if opt.cache == 'val' else opt.cache, + rect=opt.rect, + rank=LOCAL_RANK, + workers=workers, + image_weights=opt.image_weights, + quad=opt.quad, + prefix=colorstr('train: '), + shuffle=True, + seed=opt.seed) + labels = np.concatenate(dataset.labels, 0) + mlc = int(labels[:, 0].max()) # max label class + assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' + + # Process 0 + if RANK in {-1, 0}: + val_loader = create_dataloader(val_path, + imgsz, + batch_size // WORLD_SIZE * 2, + gs, + single_cls, + hyp=hyp, + cache=None if noval else opt.cache, + rect=True, + rank=-1, + workers=workers * 2, + pad=0.5, + prefix=colorstr('val: '))[0] + + if not resume: + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor + model.half().float() # pre-reduce anchor precision + + callbacks.run('on_pretrain_routine_end', labels, names) + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Model attributes + nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) + hyp['box'] *= 3 / nl # scale to layers + hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers + hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers + hyp['label_smoothing'] = opt.label_smoothing + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nb = len(train_loader) # number of batches + nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + last_opt_step = -1 + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = torch.cuda.amp.GradScaler(enabled=amp) + stopper, stop = EarlyStopping(patience=opt.patience), False + compute_loss = ComputeLoss(model) # init loss class + callbacks.run('on_train_start') + LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' + f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting training for {epochs} epochs...') + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + callbacks.run('on_train_epoch_start') + model.train() + + # Update image weights (optional, single-GPU only) + if opt.image_weights: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + + # Update mosaic border (optional) + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(3, device=device) # mean losses + if RANK != -1: + train_loader.sampler.set_epoch(epoch) + pbar = enumerate(train_loader) + LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size')) + if RANK in {-1, 0}: + pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- + callbacks.run('on_train_batch_start') + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + with torch.cuda.amp.autocast(amp): + pred = model(imgs) # forward + loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size + if RANK != -1: + loss *= WORLD_SIZE # gradient averaged between devices in DDP mode + if opt.quad: + loss *= 4. + + # Backward + scaler.scale(loss).backward() + + # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html + if ni - last_opt_step >= accumulate: + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + last_opt_step = ni + + # Log + if RANK in {-1, 0}: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) + pbar.set_description(('%11s' * 2 + '%11.4g' * 5) % + (f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) + callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss)) + if callbacks.stop_training: + return + # end batch ------------------------------------------------------------------------------------------------ + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for loggers + scheduler.step() + + if RANK in {-1, 0}: + # mAP + callbacks.run('on_train_epoch_end', epoch=epoch) + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) + final_epoch = (epoch + 1 == epochs) or stopper.possible_stop + if not noval or final_epoch: # Calculate mAP + results, maps, _ = validate.run(data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + half=amp, + model=ema.ema, + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + plots=False, + callbacks=callbacks, + compute_loss=compute_loss) + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + stop = stopper(epoch=epoch, fitness=fi) # early stop check + if fi > best_fitness: + best_fitness = fi + log_vals = list(mloss) + list(results) + lr + callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) + + # Save model + if (not nosave) or (final_epoch and not evolve): # if save + ckpt = { + 'epoch': epoch, + 'best_fitness': best_fitness, + 'model': deepcopy(de_parallel(model)).half(), + 'ema': deepcopy(ema.ema).half(), + 'updates': ema.updates, + 'optimizer': optimizer.state_dict(), + 'opt': vars(opt), + 'git': GIT_INFO, # {remote, branch, commit} if a git repo + 'date': datetime.now().isoformat()} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + if opt.save_period > 0 and epoch % opt.save_period == 0: + torch.save(ckpt, w / f'epoch{epoch}.pt') + del ckpt + callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) + + # EarlyStopping + if RANK != -1: # if DDP training + broadcast_list = [stop if RANK == 0 else None] + dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks + if RANK != 0: + stop = broadcast_list[0] + if stop: + break # must break all DDP ranks + + # end epoch ---------------------------------------------------------------------------------------------------- + # end training ----------------------------------------------------------------------------------------------------- + if RANK in {-1, 0}: + LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if f is best: + LOGGER.info(f'\nValidating {f}...') + results, _, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + model=attempt_load(f, device).half(), + iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + save_json=is_coco, + verbose=True, + plots=plots, + callbacks=callbacks, + compute_loss=compute_loss) # val best model with plots + if is_coco: + callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) + + callbacks.run('on_train_end', last, best, epoch, results) + + torch.cuda.empty_cache() + return results + + +def parse_opt(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=100, help='total training epochs') + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--noval', action='store_true', help='only validate final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') + parser.add_argument('--noplots', action='store_true', help='save no plot files') + parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') + parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') + parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') + parser.add_argument('--seed', type=int, default=0, help='Global training seed') + parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + + # Logger arguments + parser.add_argument('--entity', default=None, help='Entity') + parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option') + parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval') + parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use') + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt, callbacks=Callbacks()): + # Checks + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements() + + # Resume (from specified or most recent last.pt) + if opt.resume and not check_comet_resume(opt) and not opt.evolve: + last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) + opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml + opt_data = opt.data # original dataset + if opt_yaml.is_file(): + with open(opt_yaml, errors='ignore') as f: + d = yaml.safe_load(f) + else: + d = torch.load(last, map_location='cpu')['opt'] + opt = argparse.Namespace(**d) # replace + opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate + if is_url(opt_data): + opt.data = check_file(opt_data) # avoid HUB resume auth timeout + else: + opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ + check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks + assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + if opt.evolve: + if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve + opt.project = str(ROOT / 'runs/evolve') + opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume + if opt.name == 'cfg': + opt.name = Path(opt.cfg).stem # use model.yaml as name + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + msg = 'is not compatible with YOLOv5 Multi-GPU DDP training' + assert not opt.image_weights, f'--image-weights {msg}' + assert not opt.evolve, f'--evolve {msg}' + assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size' + assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE' + assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' + torch.cuda.set_device(LOCAL_RANK) + device = torch.device('cuda', LOCAL_RANK) + dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo') + + # Train + if not opt.evolve: + train(opt.hyp, opt, device, callbacks) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = { + 'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr + 'box': (1, 0.02, 0.2), # box loss gain + 'cls': (1, 0.2, 4.0), # cls loss gain + 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (0, 0.1, 0.7), # IoU training threshold + 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (1, 0.0, 0.9), # image scale (+/- gain) + 'shear': (1, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (1, 0.0, 1.0), # image mixup (probability) + 'mixup': (1, 0.0, 1.0), # image mixup (probability) + 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) + + with open(opt.hyp, errors='ignore') as f: + hyp = yaml.safe_load(f) # load hyps dict + if 'anchors' not in hyp: # anchors commented in hyp.yaml + hyp['anchors'] = 3 + if opt.noautoanchor: + del hyp['anchors'], meta['anchors'] + opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' + if opt.bucket: + # download evolve.csv if exists + subprocess.run([ + 'gsutil', + 'cp', + f'gs://{opt.bucket}/evolve.csv', + str(evolve_csv),]) + + for _ in range(opt.evolve): # generations to evolve + if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() + 1E-6 # weights (sum > 0) + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 7] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device, callbacks) + callbacks = Callbacks() + # Write mutation results + keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss', + 'val/obj_loss', 'val/cls_loss') + print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) + + # Plot results + plot_evolve(evolve_csv) + LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n' + f"Results saved to {colorstr('bold', save_dir)}\n" + f'Usage example: $ python train.py --hyp {evolve_yaml}') + + +def run(**kwargs): + # Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git 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"1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "
\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wbvMlHd_QwMG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f9f016ad-3dcf-4bd2-e1c3-d5b79efc6f32" + }, + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Detect\n", + "\n", + "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n", + "\n", + "```shell\n", + "python detect.py --source 0 # webcam\n", + " img.jpg # image \n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zR9ZbuQCH7FX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b4db5c49-f501-4505-cf0d-a1d35236c485" + }, + "source": [ + "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n", + "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n", + "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n", + "100% 14.1M/14.1M [00:00<00:00, 116MB/s] \n", + "\n", + "Fusing layers... \n", + "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 17.0ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 14.3ms\n", + "Speed: 0.5ms pre-process, 15.7ms inference, 18.6ms NMS per image at shape (1, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WQPtK1QYVaD_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "1f7df330663048998adcf8a45bc8f69b", + "e896e6096dd244c59d7955e2035cd729", + "a6ff238c29984b24bf6d0bd175c19430", + "3c085ba3f3fd4c3c8a6bb41b41ce1479", + "16b0c8aa6e0f427e8a54d3791abb7504", + "c7b2dd0f78384cad8e400b282996cdf5", + "6a27e43b0e434edd82ee63f0a91036ca", + "cce0e6c0c4ec442cb47e65c674e02e92", + "c5b9f38e2f0d4f9aa97fe87265263743", + "df554fb955c7454696beac5a82889386", + "74e9112a87a242f4831b7d68c7da6333" + ] + }, + "outputId": "c7d0a0d2-abfb-44c3-d60d-f99d0e7aabad" + }, + "source": [ + "# Download COCO val\n", + "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n", + "!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0.00/780M [00:00

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n", + "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n", + "

\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Train on Custom Data with Roboflow 🌟 NEW\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", + "\n", + "- Custom Training Example: [https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/](https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/?ref=ultralytics)\n", + "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)\n", + "
\n", + "\n", + "

Label images lightning fast (including with model-assisted labeling)" + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'ClearML' #@param ['ClearML', 'Comet', 'TensorBoard']\n", + "\n", + "if logger == 'ClearML':\n", + " %pip install -q clearml\n", + " import clearml; clearml.browser_login()\n", + "elif logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train" + ], + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1NcFxRcFdJ_O", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "721b9028-767f-4a05-c964-692c245f7398" + }, + "source": [ + "# Train YOLOv5s on COCO128 for 3 epochs\n", + "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n", + "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n", + "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n", + "Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n", + "100% 6.66M/6.66M [00:00<00:00, 261MB/s]\n", + "Dataset download success ✅ (0.3s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", + " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", + " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 19 [-1, 14] 1 0 models.common.Concat [1] \n", + " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", + " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 22 [-1, 10] 1 0 models.common.Concat [1] \n", + " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", + " 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n", + "Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n", + "\n", + "Transferred 349/349 items from yolov5s.pt\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1911.57it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 229.69it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ], + "metadata": { + "id": "nWOsI5wJR1o3" + } + }, + { + "cell_type": "markdown", + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", + "\n", + "\n", + "\"ClearML" + ], + "metadata": { + "id": "Lay2WsTjNJzP" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GMusP4OAxFu6" + }, + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) # yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ], + "execution_count": null, + "outputs": [] + } + ] +} diff --git a/algorithm/yolov5/utils/__init__.py b/algorithm/yolov5/utils/__init__.py new file mode 100644 index 0000000..33db6b0 --- /dev/null +++ b/algorithm/yolov5/utils/__init__.py @@ -0,0 +1,82 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +utils/initialization +""" + +import contextlib +import platform +import threading + + +def emojis(str=''): + # Return platform-dependent emoji-safe version of string + return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str + + +class TryExcept(contextlib.ContextDecorator): + # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager + def __init__(self, msg=''): + self.msg = msg + + def __enter__(self): + pass + + def __exit__(self, exc_type, value, traceback): + if value: + print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}")) + return True + + +def threaded(func): + # Multi-threads a target function and returns thread. Usage: @threaded decorator + def wrapper(*args, **kwargs): + thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) + thread.start() + return thread + + return wrapper + + +def join_threads(verbose=False): + # Join all daemon threads, i.e. atexit.register(lambda: join_threads()) + main_thread = threading.current_thread() + for t in threading.enumerate(): + if t is not main_thread: + if verbose: + print(f'Joining thread {t.name}') + t.join() + + +def notebook_init(verbose=True): + # Check system software and hardware + print('Checking setup...') + + import os + import shutil + + from utils.general import check_font, check_requirements, is_colab + from utils.torch_utils import select_device # imports + + check_font() + + import psutil + + if is_colab(): + shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory + + # System info + display = None + if verbose: + gb = 1 << 30 # bytes to GiB (1024 ** 3) + ram = psutil.virtual_memory().total + total, used, free = shutil.disk_usage('/') + with contextlib.suppress(Exception): # clear display if ipython is installed + from IPython import display + display.clear_output() + s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)' + else: + s = '' + + select_device(newline=False) + print(emojis(f'Setup complete ✅ {s}')) + return display diff --git a/algorithm/yolov5/utils/activations.py b/algorithm/yolov5/utils/activations.py new file mode 100644 index 0000000..084ce8c --- /dev/null +++ b/algorithm/yolov5/utils/activations.py @@ -0,0 +1,103 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Activation functions +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class SiLU(nn.Module): + # SiLU activation https://arxiv.org/pdf/1606.08415.pdf + @staticmethod + def forward(x): + return x * torch.sigmoid(x) + + +class Hardswish(nn.Module): + # Hard-SiLU activation + @staticmethod + def forward(x): + # return x * F.hardsigmoid(x) # for TorchScript and CoreML + return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX + + +class Mish(nn.Module): + # Mish activation https://github.com/digantamisra98/Mish + @staticmethod + def forward(x): + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + # Mish activation memory-efficient + class F(torch.autograd.Function): + + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + return self.F.apply(x) + + +class FReLU(nn.Module): + # FReLU activation https://arxiv.org/abs/2007.11824 + def __init__(self, c1, k=3): # ch_in, kernel + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + return torch.max(x, self.bn(self.conv(x))) + + +class AconC(nn.Module): + r""" ACON activation (activate or not) + AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter + according to "Activate or Not: Learning Customized Activation" . + """ + + def __init__(self, c1): + super().__init__() + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) + + def forward(self, x): + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x + + +class MetaAconC(nn.Module): + r""" ACON activation (activate or not) + MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network + according to "Activate or Not: Learning Customized Activation" . + """ + + def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r + super().__init__() + c2 = max(r, c1 // r) + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) + self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) + # self.bn1 = nn.BatchNorm2d(c2) + # self.bn2 = nn.BatchNorm2d(c1) + + def forward(self, x): + y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) + # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 + # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable + beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(beta * dpx) + self.p2 * x diff --git a/algorithm/yolov5/utils/augmentations.py b/algorithm/yolov5/utils/augmentations.py new file mode 100644 index 0000000..9da0456 --- /dev/null +++ b/algorithm/yolov5/utils/augmentations.py @@ -0,0 +1,397 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Image augmentation functions +""" + +import math +import random + +import cv2 +import numpy as np +import torch +import torchvision.transforms as T +import torchvision.transforms.functional as TF + +from algorithm.yolov5.utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy +from algorithm.yolov5.utils.metrics import bbox_ioa + +IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean +IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation + + +class Albumentations: + # YOLOv5 Albumentations class (optional, only used if package is installed) + def __init__(self, size=640): + self.transform = None + prefix = colorstr('albumentations: ') + try: + import albumentations as A + check_version(A.__version__, '1.0.3', hard=True) # version requirement + + T = [ + A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), + A.Blur(p=0.01), + A.MedianBlur(p=0.01), + A.ToGray(p=0.01), + A.CLAHE(p=0.01), + A.RandomBrightnessContrast(p=0.0), + A.RandomGamma(p=0.0), + A.ImageCompression(quality_lower=75, p=0.0)] # transforms + self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) + + LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) + except ImportError: # package not installed, skip + pass + except Exception as e: + LOGGER.info(f'{prefix}{e}') + + def __call__(self, im, labels, p=1.0): + if self.transform and random.random() < p: + new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed + im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) + return im, labels + + +def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): + # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std + return TF.normalize(x, mean, std, inplace=inplace) + + +def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): + # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean + for i in range(3): + x[:, i] = x[:, i] * std[i] + mean[i] + return x + + +def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): + # HSV color-space augmentation + if hgain or sgain or vgain: + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) + dtype = im.dtype # uint8 + + x = np.arange(0, 256, dtype=r.dtype) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) + cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed + + +def hist_equalize(im, clahe=True, bgr=False): + # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 + yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB + + +def replicate(im, labels): + # Replicate labels + h, w = im.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return im, labels + + +def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + # Resize and pad image while meeting stride-multiple constraints + shape = im.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better val mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return im, ratio, (dw, dh) + + +def random_perspective(im, + targets=(), + segments=(), + degrees=10, + translate=.1, + scale=.1, + shear=10, + perspective=0.0, + border=(0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = im.shape[0] + border[0] * 2 # shape(h,w,c) + width = im.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -im.shape[1] / 2 # x translation (pixels) + C[1, 2] = -im.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(im[:, :, ::-1]) # base + # ax[1].imshow(im2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + if n: + use_segments = any(x.any() for x in segments) and len(segments) == n + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) + targets = targets[i] + targets[:, 1:5] = new[i] + + return im, targets + + +def copy_paste(im, labels, segments, p=0.5): + # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) + n = len(segments) + if p and n: + h, w, c = im.shape # height, width, channels + im_new = np.zeros(im.shape, np.uint8) + for j in random.sample(range(n), k=round(p * n)): + l, s = labels[j], segments[j] + box = w - l[3], l[2], w - l[1], l[4] + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + if (ioa < 0.30).all(): # allow 30% obscuration of existing labels + labels = np.concatenate((labels, [[l[0], *box]]), 0) + segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) + cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) + + result = cv2.flip(im, 1) # augment segments (flip left-right) + i = cv2.flip(im_new, 1).astype(bool) + im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug + + return im, labels, segments + + +def cutout(im, labels, p=0.5): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + if random.random() < p: + h, w = im.shape[:2] + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) # create random masks + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def mixup(im, labels, im2, labels2): + # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf + r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 + im = (im * r + im2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + return im, labels + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) + # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates + + +def classify_albumentations( + augment=True, + size=224, + scale=(0.08, 1.0), + ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 + hflip=0.5, + vflip=0.0, + jitter=0.4, + mean=IMAGENET_MEAN, + std=IMAGENET_STD, + auto_aug=False): + # YOLOv5 classification Albumentations (optional, only used if package is installed) + prefix = colorstr('albumentations: ') + try: + import albumentations as A + from albumentations.pytorch import ToTensorV2 + check_version(A.__version__, '1.0.3', hard=True) # version requirement + if augment: # Resize and crop + T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] + if auto_aug: + # TODO: implement AugMix, AutoAug & RandAug in albumentation + LOGGER.info(f'{prefix}auto augmentations are currently not supported') + else: + if hflip > 0: + T += [A.HorizontalFlip(p=hflip)] + if vflip > 0: + T += [A.VerticalFlip(p=vflip)] + if jitter > 0: + color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue + T += [A.ColorJitter(*color_jitter, 0)] + else: # Use fixed crop for eval set (reproducibility) + T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] + T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor + LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) + return A.Compose(T) + + except ImportError: # package not installed, skip + LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)') + except Exception as e: + LOGGER.info(f'{prefix}{e}') + + +def classify_transforms(size=224): + # Transforms to apply if albumentations not installed + assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' + # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) + return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) + + +class LetterBox: + # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) + def __init__(self, size=(640, 640), auto=False, stride=32): + super().__init__() + self.h, self.w = (size, size) if isinstance(size, int) else size + self.auto = auto # pass max size integer, automatically solve for short side using stride + self.stride = stride # used with auto + + def __call__(self, im): # im = np.array HWC + imh, imw = im.shape[:2] + r = min(self.h / imh, self.w / imw) # ratio of new/old + h, w = round(imh * r), round(imw * r) # resized image + hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w + top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) + im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) + im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) + return im_out + + +class CenterCrop: + # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) + def __init__(self, size=640): + super().__init__() + self.h, self.w = (size, size) if isinstance(size, int) else size + + def __call__(self, im): # im = np.array HWC + imh, imw = im.shape[:2] + m = min(imh, imw) # min dimension + top, left = (imh - m) // 2, (imw - m) // 2 + return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) + + +class ToTensor: + # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) + def __init__(self, half=False): + super().__init__() + self.half = half + + def __call__(self, im): # im = np.array HWC in BGR order + im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous + im = torch.from_numpy(im) # to torch + im = im.half() if self.half else im.float() # uint8 to fp16/32 + im /= 255.0 # 0-255 to 0.0-1.0 + return im diff --git a/algorithm/yolov5/utils/autoanchor.py b/algorithm/yolov5/utils/autoanchor.py new file mode 100644 index 0000000..bb5cf6e --- /dev/null +++ b/algorithm/yolov5/utils/autoanchor.py @@ -0,0 +1,169 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +AutoAnchor utils +""" + +import random + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +from utils import TryExcept +from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr + +PREFIX = colorstr('AutoAnchor: ') + + +def check_anchor_order(m): + # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary + a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da and (da.sign() != ds.sign()): # same order + LOGGER.info(f'{PREFIX}Reversing anchor order') + m.anchors[:] = m.anchors.flip(0) + + +@TryExcept(f'{PREFIX}ERROR') +def check_anchors(dataset, model, thr=4.0, imgsz=640): + # Check anchor fit to data, recompute if necessary + m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh + + def metric(k): # compute metric + r = wh[:, None] / k[None] + x = torch.min(r, 1 / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1 / thr).float().mean() # best possible recall + return bpr, aat + + stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides + anchors = m.anchors.clone() * stride # current anchors + bpr, aat = metric(anchors.cpu().view(-1, 2)) + s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). ' + if bpr > 0.98: # threshold to recompute + LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅') + else: + LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...') + na = m.anchors.numel() // 2 # number of anchors + anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + new_bpr = metric(anchors)[0] + if new_bpr > bpr: # replace anchors + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchors[:] = anchors.clone().view_as(m.anchors) + check_anchor_order(m) # must be in pixel-space (not grid-space) + m.anchors /= stride + s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)' + else: + s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)' + LOGGER.info(s) + + +def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ Creates kmeans-evolved anchors from training dataset + + Arguments: + dataset: path to data.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + from scipy.cluster.vq import kmeans + + npr = np.random + thr = 1 / thr + + def metric(k, wh): # compute metrics + r = wh[:, None] / k[None] + x = torch.min(r, 1 / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k, verbose=True): + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr + s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \ + f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \ + f'past_thr={x[x > thr].mean():.3f}-mean: ' + for x in k: + s += '%i,%i, ' % (round(x[0]), round(x[1])) + if verbose: + LOGGER.info(s[:-2]) + return k + + if isinstance(dataset, str): # *.yaml file + with open(dataset, errors='ignore') as f: + data_dict = yaml.safe_load(f) # model dict + from utils.dataloaders import LoadImagesAndLabels + dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + LOGGER.info(f'{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size') + wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels + # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 + + # Kmeans init + try: + LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...') + assert n <= len(wh) # apply overdetermined constraint + s = wh.std(0) # sigmas for whitening + k = kmeans(wh / s, n, iter=30)[0] * s # points + assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar + except Exception: + LOGGER.warning(f'{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init') + k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init + wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) + k = print_results(k, verbose=False) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma + pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' + if verbose: + print_results(k, verbose) + + return print_results(k).astype(np.float32) diff --git a/algorithm/yolov5/utils/autobatch.py b/algorithm/yolov5/utils/autobatch.py new file mode 100644 index 0000000..bdeb91c --- /dev/null +++ b/algorithm/yolov5/utils/autobatch.py @@ -0,0 +1,72 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Auto-batch utils +""" + +from copy import deepcopy + +import numpy as np +import torch + +from utils.general import LOGGER, colorstr +from utils.torch_utils import profile + + +def check_train_batch_size(model, imgsz=640, amp=True): + # Check YOLOv5 training batch size + with torch.cuda.amp.autocast(amp): + return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size + + +def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): + # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory + # Usage: + # import torch + # from utils.autobatch import autobatch + # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) + # print(autobatch(model)) + + # Check device + prefix = colorstr('AutoBatch: ') + LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}') + device = next(model.parameters()).device # get model device + if device.type == 'cpu': + LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}') + return batch_size + if torch.backends.cudnn.benchmark: + LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}') + return batch_size + + # Inspect CUDA memory + gb = 1 << 30 # bytes to GiB (1024 ** 3) + d = str(device).upper() # 'CUDA:0' + properties = torch.cuda.get_device_properties(device) # device properties + t = properties.total_memory / gb # GiB total + r = torch.cuda.memory_reserved(device) / gb # GiB reserved + a = torch.cuda.memory_allocated(device) / gb # GiB allocated + f = t - (r + a) # GiB free + LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') + + # Profile batch sizes + batch_sizes = [1, 2, 4, 8, 16] + try: + img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] + results = profile(img, model, n=3, device=device) + except Exception as e: + LOGGER.warning(f'{prefix}{e}') + + # Fit a solution + y = [x[2] for x in results if x] # memory [2] + p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit + b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) + if None in results: # some sizes failed + i = results.index(None) # first fail index + if b >= batch_sizes[i]: # y intercept above failure point + b = batch_sizes[max(i - 1, 0)] # select prior safe point + if b < 1 or b > 1024: # b outside of safe range + b = batch_size + LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.') + + fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted + LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅') + return b diff --git a/algorithm/yolov5/utils/aws/__init__.py b/algorithm/yolov5/utils/aws/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5/utils/aws/mime.sh b/algorithm/yolov5/utils/aws/mime.sh new file mode 100644 index 0000000..c319a83 --- /dev/null +++ b/algorithm/yolov5/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/algorithm/yolov5/utils/aws/resume.py b/algorithm/yolov5/utils/aws/resume.py new file mode 100644 index 0000000..b21731c --- /dev/null +++ b/algorithm/yolov5/utils/aws/resume.py @@ -0,0 +1,40 @@ +# Resume all interrupted trainings in yolov5/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[2] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +port = 0 # --master_port +path = Path('').resolve() +for last in path.rglob('*/**/last.pt'): + ckpt = torch.load(last) + if ckpt['optimizer'] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: + opt = yaml.safe_load(f) + + # Get device count + d = opt['device'].split(',') # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' + else: # single-GPU + cmd = f'python train.py --resume {last}' + + cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/algorithm/yolov5/utils/aws/userdata.sh b/algorithm/yolov5/utils/aws/userdata.sh new file mode 100644 index 0000000..5fc1332 --- /dev/null +++ b/algorithm/yolov5/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolov5 ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 + cd yolov5 + bash data/scripts/get_coco.sh && echo "COCO done." & + sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/algorithm/yolov5/utils/callbacks.py b/algorithm/yolov5/utils/callbacks.py new file mode 100644 index 0000000..166d893 --- /dev/null +++ b/algorithm/yolov5/utils/callbacks.py @@ -0,0 +1,76 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Callback utils +""" + +import threading + + +class Callbacks: + """" + Handles all registered callbacks for YOLOv5 Hooks + """ + + def __init__(self): + # Define the available callbacks + self._callbacks = { + 'on_pretrain_routine_start': [], + 'on_pretrain_routine_end': [], + 'on_train_start': [], + 'on_train_epoch_start': [], + 'on_train_batch_start': [], + 'optimizer_step': [], + 'on_before_zero_grad': [], + 'on_train_batch_end': [], + 'on_train_epoch_end': [], + 'on_val_start': [], + 'on_val_batch_start': [], + 'on_val_image_end': [], + 'on_val_batch_end': [], + 'on_val_end': [], + 'on_fit_epoch_end': [], # fit = train + val + 'on_model_save': [], + 'on_train_end': [], + 'on_params_update': [], + 'teardown': [],} + self.stop_training = False # set True to interrupt training + + def register_action(self, hook, name='', callback=None): + """ + Register a new action to a callback hook + + Args: + hook: The callback hook name to register the action to + name: The name of the action for later reference + callback: The callback to fire + """ + assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" + assert callable(callback), f"callback '{callback}' is not callable" + self._callbacks[hook].append({'name': name, 'callback': callback}) + + def get_registered_actions(self, hook=None): + """" + Returns all the registered actions by callback hook + + Args: + hook: The name of the hook to check, defaults to all + """ + return self._callbacks[hook] if hook else self._callbacks + + def run(self, hook, *args, thread=False, **kwargs): + """ + Loop through the registered actions and fire all callbacks on main thread + + Args: + hook: The name of the hook to check, defaults to all + args: Arguments to receive from YOLOv5 + thread: (boolean) Run callbacks in daemon thread + kwargs: Keyword Arguments to receive from YOLOv5 + """ + + assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" + for logger in self._callbacks[hook]: + if thread: + threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start() + else: + logger['callback'](*args, **kwargs) diff --git a/algorithm/yolov5/utils/dataloaders.py b/algorithm/yolov5/utils/dataloaders.py new file mode 100644 index 0000000..26e14d5 --- /dev/null +++ b/algorithm/yolov5/utils/dataloaders.py @@ -0,0 +1,1221 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Dataloaders and dataset utils +""" + +import contextlib +import glob +import hashlib +import json +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import Pool, ThreadPool +from pathlib import Path +from threading import Thread +from urllib.parse import urlparse + +import numpy as np +import psutil +import torch +import torch.nn.functional as F +import torchvision +import yaml +from PIL import ExifTags, Image, ImageOps +from torch.utils.data import DataLoader, Dataset, dataloader, distributed +from tqdm import tqdm + +from algorithm.yolov5.utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste, + letterbox, mixup, random_perspective) +from algorithm.yolov5.utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements, + check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy, + xywh2xyxy, xywhn2xyxy, xyxy2xywhn) +from algorithm.yolov5.utils.torch_utils import torch_distributed_zero_first + +# Parameters +HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes +VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == 'Orientation': + break + + +def get_hash(paths): + # Returns a single hash value of a list of paths (files or dirs) + size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes + h = hashlib.sha256(str(size).encode()) # hash sizes + h.update(''.join(paths).encode()) # hash paths + return h.hexdigest() # return hash + + +def exif_size(img): + # Returns exif-corrected PIL size + s = img.size # (width, height) + with contextlib.suppress(Exception): + rotation = dict(img._getexif().items())[orientation] + if rotation in [6, 8]: # rotation 270 or 90 + s = (s[1], s[0]) + return s + + +def exif_transpose(image): + """ + Transpose a PIL image accordingly if it has an EXIF Orientation tag. + Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() + + :param image: The image to transpose. + :return: An image. + """ + exif = image.getexif() + orientation = exif.get(0x0112, 1) # default 1 + if orientation > 1: + method = { + 2: Image.FLIP_LEFT_RIGHT, + 3: Image.ROTATE_180, + 4: Image.FLIP_TOP_BOTTOM, + 5: Image.TRANSPOSE, + 6: Image.ROTATE_270, + 7: Image.TRANSVERSE, + 8: Image.ROTATE_90}.get(orientation) + if method is not None: + image = image.transpose(method) + del exif[0x0112] + image.info['exif'] = exif.tobytes() + return image + + +def seed_worker(worker_id): + # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader + worker_seed = torch.initial_seed() % 2 ** 32 + np.random.seed(worker_seed) + random.seed(worker_seed) + + +def create_dataloader(path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix='', + shuffle=False, + seed=0): + if rect and shuffle: + LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') + shuffle = False + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = LoadImagesAndLabels( + path, + imgsz, + batch_size, + augment=augment, # augmentation + hyp=hyp, # hyperparameters + rect=rect, # rectangular batches + cache_images=cache, + single_cls=single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix) + + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() # number of CUDA devices + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + seed + RANK) + return loader(dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, + worker_init_fn=seed_worker, + generator=generator), dataset + + +class InfiniteDataLoader(dataloader.DataLoader): + """ Dataloader that reuses workers + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + return len(self.batch_sampler.sampler) + + def __iter__(self): + for _ in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler: + """ Sampler that repeats forever + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + self.sampler = sampler + + def __iter__(self): + while True: + yield from iter(self.sampler) + + +class LoadScreenshots: + # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` + def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): + # source = [screen_number left top width height] (pixels) + check_requirements('mss') + import mss + + source, *params = source.split() + self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0 + if len(params) == 1: + self.screen = int(params[0]) + elif len(params) == 4: + left, top, width, height = (int(x) for x in params) + elif len(params) == 5: + self.screen, left, top, width, height = (int(x) for x in params) + self.img_size = img_size + self.stride = stride + self.transforms = transforms + self.auto = auto + self.mode = 'stream' + self.frame = 0 + self.sct = mss.mss() + + # Parse monitor shape + monitor = self.sct.monitors[self.screen] + self.top = monitor['top'] if top is None else (monitor['top'] + top) + self.left = monitor['left'] if left is None else (monitor['left'] + left) + self.width = width or monitor['width'] + self.height = height or monitor['height'] + self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height} + + def __iter__(self): + return self + + def __next__(self): + # mss screen capture: get raw pixels from the screen as np array + im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR + s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: ' + + if self.transforms: + im = self.transforms(im0) # transforms + else: + im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize + im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + im = np.ascontiguousarray(im) # contiguous + self.frame += 1 + return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s + + +class LoadImages: + # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` + def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line + path = Path(path).read_text().rsplit() + files = [] + for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: + p = str(Path(p).resolve()) + if '*' in p: + files.extend(sorted(glob.glob(p, recursive=True))) # glob + elif os.path.isdir(p): + files.extend(sorted(glob.glob(os.path.join(p, '*.*')))) # dir + elif os.path.isfile(p): + files.append(p) # files + else: + raise FileNotFoundError(f'{p} does not exist') + + images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS] + videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.stride = stride + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = 'image' + self.auto = auto + self.transforms = transforms # optional + self.vid_stride = vid_stride # video frame-rate stride + if any(videos): + self._new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, f'No images or videos found in {p}. ' \ + f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}' + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = 'video' + for _ in range(self.vid_stride): + self.cap.grab() + ret_val, im0 = self.cap.retrieve() + while not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + path = self.files[self.count] + self._new_video(path) + ret_val, im0 = self.cap.read() + + self.frame += 1 + # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False + s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ' + + else: + # Read image + self.count += 1 + im0 = cv2.imread(path) # BGR + assert im0 is not None, f'Image Not Found {path}' + s = f'image {self.count}/{self.nf} {path}: ' + + if self.transforms: + im = self.transforms(im0) # transforms + else: + im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize + im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + im = np.ascontiguousarray(im) # contiguous + + return path, im, im0, self.cap, s + + def _new_video(self, path): + # Create a new video capture object + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) + self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees + # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 + + def _cv2_rotate(self, im): + # Rotate a cv2 video manually + if self.orientation == 0: + return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) + elif self.orientation == 180: + return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) + elif self.orientation == 90: + return cv2.rotate(im, cv2.ROTATE_180) + return im + + def __len__(self): + return self.nf # number of files + + +class LoadStreams: + # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` + def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + torch.backends.cudnn.benchmark = True # faster for fixed-size inference + self.mode = 'stream' + self.img_size = img_size + self.stride = stride + self.vid_stride = vid_stride # video frame-rate stride + sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources] + n = len(sources) + self.sources = [clean_str(x) for x in sources] # clean source names for later + self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n + for i, s in enumerate(sources): # index, source + # Start thread to read frames from video stream + st = f'{i + 1}/{n}: {s}... ' + if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video + # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc' + check_requirements(('pafy', 'youtube_dl==2020.12.2')) + import pafy + s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL + s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam + if s == 0: + assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.' + assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.' + cap = cv2.VideoCapture(s) + assert cap.isOpened(), f'{st}Failed to open {s}' + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan + self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback + self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback + + _, self.imgs[i] = cap.read() # guarantee first frame + self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) + LOGGER.info(f'{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)') + self.threads[i].start() + LOGGER.info('') # newline + + # check for common shapes + s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + self.auto = auto and self.rect + self.transforms = transforms # optional + if not self.rect: + LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.') + + def update(self, i, cap, stream): + # Read stream `i` frames in daemon thread + n, f = 0, self.frames[i] # frame number, frame array + while cap.isOpened() and n < f: + n += 1 + cap.grab() # .read() = .grab() followed by .retrieve() + if n % self.vid_stride == 0: + success, im = cap.retrieve() + if success: + self.imgs[i] = im + else: + LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.') + self.imgs[i] = np.zeros_like(self.imgs[i]) + cap.open(stream) # re-open stream if signal was lost + time.sleep(0.0) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + im0 = self.imgs.copy() + if self.transforms: + im = np.stack([self.transforms(x) for x in im0]) # transforms + else: + im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize + im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW + im = np.ascontiguousarray(im) # contiguous + + return self.sources, im, im0, None, '' + + def __len__(self): + return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + # Define label paths as a function of image paths + sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}' # /images/, /labels/ substrings + return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths] + + +class LoadImagesAndLabels(Dataset): + # YOLOv5 train_loader/val_loader, loads images and labels for training and validation + cache_version = 0.6 # dataset labels *.cache version + rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] + + def __init__(self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0.0, + min_items=0, + prefix=''): + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + self.path = path + self.albumentations = Albumentations(size=img_size) if augment else None + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / '**' / '*.*'), recursive=True) + # f = list(p.rglob('*.*')) # pathlib + elif p.is_file(): # file + with open(p) as t: + t = t.read().strip().splitlines() + parent = str(p.parent) + os.sep + f += [x.replace('./', parent, 1) if x.startswith('./') else x for x in t] # to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) + else: + raise FileNotFoundError(f'{prefix}{p} does not exist') + self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib + assert self.im_files, f'{prefix}No images found' + except Exception as e: + raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e + + # Check cache + self.label_files = img2label_paths(self.im_files) # labels + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') + try: + cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict + assert cache['version'] == self.cache_version # matches current version + assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash + except Exception: + cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops + + # Display cache + nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total + if exists and LOCAL_RANK in {-1, 0}: + d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt' + tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results + if cache['msgs']: + LOGGER.info('\n'.join(cache['msgs'])) # display warnings + assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}' + + # Read cache + [cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items + labels, shapes, self.segments = zip(*cache.values()) + nl = len(np.concatenate(labels, 0)) # number of labels + assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}' + self.labels = list(labels) + self.shapes = np.array(shapes) + self.im_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + + # Filter images + if min_items: + include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int) + LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset') + self.im_files = [self.im_files[i] for i in include] + self.label_files = [self.label_files[i] for i in include] + self.labels = [self.labels[i] for i in include] + self.segments = [self.segments[i] for i in include] + self.shapes = self.shapes[include] # wh + + # Create indices + n = len(self.shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = range(n) + + # Update labels + include_class = [] # filter labels to include only these classes (optional) + include_class_array = np.array(include_class).reshape(1, -1) + for i, (label, segment) in enumerate(zip(self.labels, self.segments)): + if include_class: + j = (label[:, 0:1] == include_class_array).any(1) + self.labels[i] = label[j] + if segment: + self.segments[i] = segment[j] + if single_cls: # single-class training, merge all classes into 0 + self.labels[i][:, 0] = 0 + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.im_files = [self.im_files[i] for i in irect] + self.label_files = [self.label_files[i] for i in irect] + self.labels = [self.labels[i] for i in irect] + self.segments = [self.segments[i] for i in irect] + self.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride + + # Cache images into RAM/disk for faster training + if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix): + cache_images = False + self.ims = [None] * n + self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files] + if cache_images: + b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes + self.im_hw0, self.im_hw = [None] * n, [None] * n + fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image + results = ThreadPool(NUM_THREADS).imap(fcn, range(n)) + pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) + for i, x in pbar: + if cache_images == 'disk': + b += self.npy_files[i].stat().st_size + else: # 'ram' + self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) + b += self.ims[i].nbytes + pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})' + pbar.close() + + def check_cache_ram(self, safety_margin=0.1, prefix=''): + # Check image caching requirements vs available memory + b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes + n = min(self.n, 30) # extrapolate from 30 random images + for _ in range(n): + im = cv2.imread(random.choice(self.im_files)) # sample image + ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio + b += im.nbytes * ratio ** 2 + mem_required = b * self.n / n # GB required to cache dataset into RAM + mem = psutil.virtual_memory() + cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question + if not cache: + LOGGER.info(f'{prefix}{mem_required / gb:.1f}GB RAM required, ' + f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, ' + f"{'caching images ✅' if cache else 'not caching images ⚠️'}") + return cache + + def cache_labels(self, path=Path('./labels.cache'), prefix=''): + # Cache dataset labels, check images and read shapes + x = {} # dict + nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages + desc = f'{prefix}Scanning {path.parent / path.stem}...' + with Pool(NUM_THREADS) as pool: + pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), + desc=desc, + total=len(self.im_files), + bar_format=TQDM_BAR_FORMAT) + for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: + nm += nm_f + nf += nf_f + ne += ne_f + nc += nc_f + if im_file: + x[im_file] = [lb, shape, segments] + if msg: + msgs.append(msg) + pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt' + + pbar.close() + if msgs: + LOGGER.info('\n'.join(msgs)) + if nf == 0: + LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}') + x['hash'] = get_hash(self.label_files + self.im_files) + x['results'] = nf, nm, ne, nc, len(self.im_files) + x['msgs'] = msgs # warnings + x['version'] = self.cache_version # cache version + try: + np.save(path, x) # save cache for next time + path.with_suffix('.cache.npy').rename(path) # remove .npy suffix + LOGGER.info(f'{prefix}New cache created: {path}') + except Exception as e: + LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}') # not writeable + return x + + def __len__(self): + return len(self.im_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + if mosaic: + # Load mosaic + img, labels = self.load_mosaic(index) + shapes = None + + # MixUp augmentation + if random.random() < hyp['mixup']: + img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1))) + + else: + # Load image + img, (h0, w0), (h, w) = self.load_image(index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + img, labels = random_perspective(img, + labels, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + nl = len(labels) # number of labels + if nl: + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3) + + if self.augment: + # Albumentations + img, labels = self.albumentations(img, labels) + nl = len(labels) # update after albumentations + + # HSV color-space + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Flip up-down + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nl: + labels[:, 2] = 1 - labels[:, 2] + + # Flip left-right + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nl: + labels[:, 1] = 1 - labels[:, 1] + + # Cutouts + # labels = cutout(img, labels, p=0.5) + # nl = len(labels) # update after cutout + + labels_out = torch.zeros((nl, 6)) + if nl: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.im_files[index], shapes + + def load_image(self, i): + # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw) + im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i], + if im is None: # not cached in RAM + if fn.exists(): # load npy + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + assert im is not None, f'Image Not Found {f}' + h0, w0 = im.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # ratio + if r != 1: # if sizes are not equal + interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA + im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp) + return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized + return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized + + def cache_images_to_disk(self, i): + # Saves an image as an *.npy file for faster loading + f = self.npy_files[i] + if not f.exists(): + np.save(f.as_posix(), cv2.imread(self.im_files[i])) + + def load_mosaic(self, index): + # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic + labels4, segments4 = [], [] + s = self.img_size + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + random.shuffle(indices) + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) + img4, labels4 = random_perspective(img4, + labels4, + segments4, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img4, labels4 + + def load_mosaic9(self, index): + # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic + labels9, segments9 = [], [] + s = self.img_size + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices + random.shuffle(indices) + hp, wp = -1, -1 # height, width previous + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img9 + if i == 0: # center + img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + h0, w0 = h, w + c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates + elif i == 1: # top + c = s, s - h, s + w, s + elif i == 2: # top right + c = s + wp, s - h, s + wp + w, s + elif i == 3: # right + c = s + w0, s, s + w0 + w, s + h + elif i == 4: # bottom right + c = s + w0, s + hp, s + w0 + w, s + hp + h + elif i == 5: # bottom + c = s + w0 - w, s + h0, s + w0, s + h0 + h + elif i == 6: # bottom left + c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h + elif i == 7: # left + c = s - w, s + h0 - h, s, s + h0 + elif i == 8: # top left + c = s - w, s + h0 - hp - h, s, s + h0 - hp + + padx, pady = c[:2] + x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] + labels9.append(labels) + segments9.extend(segments) + + # Image + img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] + hp, wp = h, w # height, width previous + + # Offset + yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y + img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] + + # Concat/clip labels + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] + + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate + + # Augment + img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste']) + img9, labels9 = random_perspective(img9, + labels9, + segments9, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + return img9, labels9 + + @staticmethod + def collate_fn(batch): + im, label, path, shapes = zip(*batch) # transposed + for i, lb in enumerate(label): + lb[:, 0] = i # add target image index for build_targets() + return torch.stack(im, 0), torch.cat(label, 0), path, shapes + + @staticmethod + def collate_fn4(batch): + im, label, path, shapes = zip(*batch) # transposed + n = len(shapes) // 4 + im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] + + ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) + wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) + s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale + for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW + i *= 4 + if random.random() < 0.5: + im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', + align_corners=False)[0].type(im[i].type()) + lb = label[i] + else: + im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2) + lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s + im4.append(im1) + label4.append(lb) + + for i, lb in enumerate(label4): + lb[:, 0] = i # add target image index for build_targets() + + return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def flatten_recursive(path=DATASETS_DIR / 'coco128'): + # Flatten a recursive directory by bringing all files to top level + new_path = Path(f'{str(path)}_flat') + if os.path.exists(new_path): + shutil.rmtree(new_path) # delete output folder + os.makedirs(new_path) # make new output folder + for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes(path=DATASETS_DIR / 'coco128'): # from utils.dataloaders import *; extract_boxes() + # Convert detection dataset into classification dataset, with one directory per class + path = Path(path) # images dir + shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None # remove existing + files = list(path.rglob('*.*')) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in IMG_FORMATS: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file) as f: + lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' + + +def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False): + """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + Usage: from utils.dataloaders import *; autosplit() + Arguments + path: Path to images directory + weights: Train, val, test weights (list, tuple) + annotated_only: Only use images with an annotated txt file + """ + path = Path(path) # images dir + files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only + n = len(files) # number of files + random.seed(0) # for reproducibility + indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + + txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files + for x in txt: + if (path.parent / x).exists(): + (path.parent / x).unlink() # remove existing + + print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) + for i, img in tqdm(zip(indices, files), total=n): + if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label + with open(path.parent / txt[i], 'a') as f: + f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n') # add image to txt file + + +def verify_image_label(args): + # Verify one image-label pair + im_file, lb_file, prefix = args + nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', [] # number (missing, found, empty, corrupt), message, segments + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' + assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}' + if im.format.lower() in ('jpg', 'jpeg'): + with open(im_file, 'rb') as f: + f.seek(-2, 2) + if f.read() != b'\xff\xd9': # corrupt JPEG + ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100) + msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved' + + # verify labels + if os.path.isfile(lb_file): + nf = 1 # label found + with open(lb_file) as f: + lb = [x.split() for x in f.read().strip().splitlines() if len(x)] + if any(len(x) > 6 for x in lb): # is segment + classes = np.array([x[0] for x in lb], dtype=np.float32) + segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...) + lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) + lb = np.array(lb, dtype=np.float32) + nl = len(lb) + if nl: + assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected' + assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}' + assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}' + _, i = np.unique(lb, axis=0, return_index=True) + if len(i) < nl: # duplicate row check + lb = lb[i] # remove duplicates + if segments: + segments = [segments[x] for x in i] + msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed' + else: + ne = 1 # label empty + lb = np.zeros((0, 5), dtype=np.float32) + else: + nm = 1 # label missing + lb = np.zeros((0, 5), dtype=np.float32) + return im_file, lb, shape, segments, nm, nf, ne, nc, msg + except Exception as e: + nc = 1 + msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}' + return [None, None, None, None, nm, nf, ne, nc, msg] + + +class HUBDatasetStats(): + """ Class for generating HUB dataset JSON and `-hub` dataset directory + + Arguments + path: Path to data.yaml or data.zip (with data.yaml inside data.zip) + autodownload: Attempt to download dataset if not found locally + + Usage + from utils.dataloaders import HUBDatasetStats + stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1 + stats = HUBDatasetStats('path/to/coco128.zip') # usage 2 + stats.get_json(save=False) + stats.process_images() + """ + + def __init__(self, path='coco128.yaml', autodownload=False): + # Initialize class + zipped, data_dir, yaml_path = self._unzip(Path(path)) + try: + with open(check_yaml(yaml_path), errors='ignore') as f: + data = yaml.safe_load(f) # data dict + if zipped: + data['path'] = data_dir + except Exception as e: + raise Exception('error/HUB/dataset_stats/yaml_load') from e + + check_dataset(data, autodownload) # download dataset if missing + self.hub_dir = Path(data['path'] + '-hub') + self.im_dir = self.hub_dir / 'images' + self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images + self.stats = {'nc': data['nc'], 'names': list(data['names'].values())} # statistics dictionary + self.data = data + + @staticmethod + def _find_yaml(dir): + # Return data.yaml file + files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml')) # try root level first and then recursive + assert files, f'No *.yaml file found in {dir}' + if len(files) > 1: + files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name + assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed' + assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}' + return files[0] + + def _unzip(self, path): + # Unzip data.zip + if not str(path).endswith('.zip'): # path is data.yaml + return False, None, path + assert Path(path).is_file(), f'Error unzipping {path}, file not found' + unzip_file(path, path=path.parent) + dir = path.with_suffix('') # dataset directory == zip name + assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/' + return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path + + def _hub_ops(self, f, max_dim=1920): + # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing + f_new = self.im_dir / Path(f).name # dataset-hub image filename + try: # use PIL + im = Image.open(f) + r = max_dim / max(im.height, im.width) # ratio + if r < 1.0: # image too large + im = im.resize((int(im.width * r), int(im.height * r))) + im.save(f_new, 'JPEG', quality=50, optimize=True) # save + except Exception as e: # use OpenCV + LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}') + im = cv2.imread(f) + im_height, im_width = im.shape[:2] + r = max_dim / max(im_height, im_width) # ratio + if r < 1.0: # image too large + im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) + cv2.imwrite(str(f_new), im) + + def get_json(self, save=False, verbose=False): + # Return dataset JSON for Ultralytics HUB + def _round(labels): + # Update labels to integer class and 6 decimal place floats + return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] + + for split in 'train', 'val', 'test': + if self.data.get(split) is None: + self.stats[split] = None # i.e. no test set + continue + dataset = LoadImagesAndLabels(self.data[split]) # load dataset + x = np.array([ + np.bincount(label[:, 0].astype(int), minlength=self.data['nc']) + for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics')]) # shape(128x80) + self.stats[split] = { + 'instance_stats': { + 'total': int(x.sum()), + 'per_class': x.sum(0).tolist()}, + 'image_stats': { + 'total': dataset.n, + 'unlabelled': int(np.all(x == 0, 1).sum()), + 'per_class': (x > 0).sum(0).tolist()}, + 'labels': [{ + str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]} + + # Save, print and return + if save: + stats_path = self.hub_dir / 'stats.json' + print(f'Saving {stats_path.resolve()}...') + with open(stats_path, 'w') as f: + json.dump(self.stats, f) # save stats.json + if verbose: + print(json.dumps(self.stats, indent=2, sort_keys=False)) + return self.stats + + def process_images(self): + # Compress images for Ultralytics HUB + for split in 'train', 'val', 'test': + if self.data.get(split) is None: + continue + dataset = LoadImagesAndLabels(self.data[split]) # load dataset + desc = f'{split} images' + for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc): + pass + print(f'Done. All images saved to {self.im_dir}') + return self.im_dir + + +# Classification dataloaders ------------------------------------------------------------------------------------------- +class ClassificationDataset(torchvision.datasets.ImageFolder): + """ + YOLOv5 Classification Dataset. + Arguments + root: Dataset path + transform: torchvision transforms, used by default + album_transform: Albumentations transforms, used if installed + """ + + def __init__(self, root, augment, imgsz, cache=False): + super().__init__(root=root) + self.torch_transforms = classify_transforms(imgsz) + self.album_transforms = classify_albumentations(augment, imgsz) if augment else None + self.cache_ram = cache is True or cache == 'ram' + self.cache_disk = cache == 'disk' + self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples] # file, index, npy, im + + def __getitem__(self, i): + f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image + if self.cache_ram and im is None: + im = self.samples[i][3] = cv2.imread(f) + elif self.cache_disk: + if not fn.exists(): # load npy + np.save(fn.as_posix(), cv2.imread(f)) + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + if self.album_transforms: + sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image'] + else: + sample = self.torch_transforms(im) + return sample, j + + +def create_classification_dataloader(path, + imgsz=224, + batch_size=16, + augment=True, + cache=False, + rank=-1, + workers=8, + shuffle=True): + # Returns Dataloader object to be used with YOLOv5 Classifier + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache) + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) + sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + RANK) + return InfiniteDataLoader(dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + worker_init_fn=seed_worker, + generator=generator) # or DataLoader(persistent_workers=True) diff --git a/algorithm/yolov5/utils/docker/Dockerfile b/algorithm/yolov5/utils/docker/Dockerfile new file mode 100644 index 0000000..b5d2af9 --- /dev/null +++ b/algorithm/yolov5/utils/docker/Dockerfile @@ -0,0 +1,75 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference + +# Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch +# FROM docker.io/pytorch/pytorch:latest +FROM pytorch/pytorch:latest + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg +# RUN alias python=python3 + +# Security updates +# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796 +RUN apt upgrade --no-install-recommends -y openssl + +# Create working directory +RUN rm -rf /usr/src/app && mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +# COPY . /usr/src/app (issues as not a .git directory) +RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \ + coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3' + # tensorflow tensorflowjs \ + +# Set environment variables +ENV OMP_NUM_THREADS=1 + +# Cleanup +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t + +# Pull and Run with local directory access +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t + +# Kill all +# sudo docker kill $(sudo docker ps -q) + +# Kill all image-based +# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) + +# DockerHub tag update +# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew + +# Clean up +# sudo docker system prune -a --volumes + +# Update Ubuntu drivers +# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ + +# DDP test +# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 + +# GCP VM from Image +# docker.io/ultralytics/yolov5:latest diff --git a/algorithm/yolov5/utils/docker/Dockerfile-arm64 b/algorithm/yolov5/utils/docker/Dockerfile-arm64 new file mode 100644 index 0000000..7023c6a --- /dev/null +++ b/algorithm/yolov5/utils/docker/Dockerfile-arm64 @@ -0,0 +1,41 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu +FROM arm64v8/ubuntu:rolling + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev +# RUN alias python=python3 + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ + coremltools onnx onnxruntime + # tensorflow-aarch64 tensorflowjs \ + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +# COPY . /usr/src/app (issues as not a .git directory) +RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/algorithm/yolov5/utils/docker/Dockerfile-cpu b/algorithm/yolov5/utils/docker/Dockerfile-cpu new file mode 100644 index 0000000..06bad9a --- /dev/null +++ b/algorithm/yolov5/utils/docker/Dockerfile-cpu @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu +FROM ubuntu:rolling + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg +# RUN alias python=python3 + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ + coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3' \ + # tensorflow tensorflowjs \ + --extra-index-url https://download.pytorch.org/whl/cpu + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +# COPY . /usr/src/app (issues as not a .git directory) +RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/algorithm/yolov5/utils/downloads.py b/algorithm/yolov5/utils/downloads.py new file mode 100644 index 0000000..643b529 --- /dev/null +++ b/algorithm/yolov5/utils/downloads.py @@ -0,0 +1,128 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Download utils +""" + +import logging +import os +import subprocess +import urllib +from pathlib import Path + +import requests +import torch + + +def is_url(url, check=True): + # Check if string is URL and check if URL exists + try: + url = str(url) + result = urllib.parse.urlparse(url) + assert all([result.scheme, result.netloc]) # check if is url + return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online + except (AssertionError, urllib.request.HTTPError): + return False + + +def gsutil_getsize(url=''): + # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du + output = subprocess.check_output(['gsutil', 'du', url], shell=True, encoding='utf-8') + if output: + return int(output.split()[0]) + return 0 + + +def url_getsize(url='https://ultralytics.com/images/bus.jpg'): + # Return downloadable file size in bytes + response = requests.head(url, allow_redirects=True) + return int(response.headers.get('content-length', -1)) + + +def curl_download(url, filename, *, silent: bool = False) -> bool: + """ + Download a file from a url to a filename using curl. + """ + silent_option = 'sS' if silent else '' # silent + proc = subprocess.run([ + 'curl', + '-#', + f'-{silent_option}L', + url, + '--output', + filename, + '--retry', + '9', + '-C', + '-',]) + return proc.returncode == 0 + + +def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): + # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes + from utils.general import LOGGER + + file = Path(file) + assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" + try: # url1 + LOGGER.info(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO) + assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check + except Exception as e: # url2 + if file.exists(): + file.unlink() # remove partial downloads + LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...') + # curl download, retry and resume on fail + curl_download(url2 or url, file) + finally: + if not file.exists() or file.stat().st_size < min_bytes: # check + if file.exists(): + file.unlink() # remove partial downloads + LOGGER.info(f'ERROR: {assert_msg}\n{error_msg}') + LOGGER.info('') + + +def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'): + # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc. + from utils.general import LOGGER + + def github_assets(repository, version='latest'): + # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...]) + if version != 'latest': + version = f'tags/{version}' # i.e. tags/v7.0 + response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api + return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets + + file = Path(str(file).strip().replace("'", '')) + if not file.exists(): + # URL specified + name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc. + if str(file).startswith(('http:/', 'https:/')): # download + url = str(file).replace(':/', '://') # Pathlib turns :// -> :/ + file = name.split('?')[0] # parse authentication https://url.com/file.txt?auth... + if Path(file).is_file(): + LOGGER.info(f'Found {url} locally at {file}') # file already exists + else: + safe_download(file=file, url=url, min_bytes=1E5) + return file + + # GitHub assets + assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] # default + try: + tag, assets = github_assets(repo, release) + except Exception: + try: + tag, assets = github_assets(repo) # latest release + except Exception: + try: + tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] + except Exception: + tag = release + + file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) + if name in assets: + safe_download(file, + url=f'https://github.com/{repo}/releases/download/{tag}/{name}', + min_bytes=1E5, + error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}') + + return str(file) diff --git a/algorithm/yolov5/utils/flask_rest_api/README.md b/algorithm/yolov5/utils/flask_rest_api/README.md new file mode 100644 index 0000000..a726acb --- /dev/null +++ b/algorithm/yolov5/utils/flask_rest_api/README.md @@ -0,0 +1,73 @@ +# Flask REST API + +[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are +commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API +created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). + +## Requirements + +[Flask](https://palletsprojects.com/p/flask/) is required. Install with: + +```shell +$ pip install Flask +``` + +## Run + +After Flask installation run: + +```shell +$ python3 restapi.py --port 5000 +``` + +Then use [curl](https://curl.se/) to perform a request: + +```shell +$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s' +``` + +The model inference results are returned as a JSON response: + +```json +[ + { + "class": 0, + "confidence": 0.8900438547, + "height": 0.9318675399, + "name": "person", + "width": 0.3264600933, + "xcenter": 0.7438579798, + "ycenter": 0.5207948685 + }, + { + "class": 0, + "confidence": 0.8440024257, + "height": 0.7155083418, + "name": "person", + "width": 0.6546785235, + "xcenter": 0.427829951, + "ycenter": 0.6334488392 + }, + { + "class": 27, + "confidence": 0.3771208823, + "height": 0.3902671337, + "name": "tie", + "width": 0.0696444362, + "xcenter": 0.3675483763, + "ycenter": 0.7991207838 + }, + { + "class": 27, + "confidence": 0.3527112305, + "height": 0.1540903747, + "name": "tie", + "width": 0.0336618312, + "xcenter": 0.7814827561, + "ycenter": 0.5065554976 + } +] +``` + +An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given +in `example_request.py` diff --git a/algorithm/yolov5/utils/flask_rest_api/example_request.py b/algorithm/yolov5/utils/flask_rest_api/example_request.py new file mode 100644 index 0000000..952e5dc --- /dev/null +++ b/algorithm/yolov5/utils/flask_rest_api/example_request.py @@ -0,0 +1,19 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Perform test request +""" + +import pprint + +import requests + +DETECTION_URL = 'http://localhost:5000/v1/object-detection/yolov5s' +IMAGE = 'zidane.jpg' + +# Read image +with open(IMAGE, 'rb') as f: + image_data = f.read() + +response = requests.post(DETECTION_URL, files={'image': image_data}).json() + +pprint.pprint(response) diff --git a/algorithm/yolov5/utils/flask_rest_api/restapi.py b/algorithm/yolov5/utils/flask_rest_api/restapi.py new file mode 100644 index 0000000..9258b1a --- /dev/null +++ b/algorithm/yolov5/utils/flask_rest_api/restapi.py @@ -0,0 +1,48 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Run a Flask REST API exposing one or more YOLOv5s models +""" + +import argparse +import io + +import torch +from flask import Flask, request +from PIL import Image + +app = Flask(__name__) +models = {} + +DETECTION_URL = '/v1/object-detection/' + + +@app.route(DETECTION_URL, methods=['POST']) +def predict(model): + if request.method != 'POST': + return + + if request.files.get('image'): + # Method 1 + # with request.files["image"] as f: + # im = Image.open(io.BytesIO(f.read())) + + # Method 2 + im_file = request.files['image'] + im_bytes = im_file.read() + im = Image.open(io.BytesIO(im_bytes)) + + if model in models: + results = models[model](im, size=640) # reduce size=320 for faster inference + return results.pandas().xyxy[0].to_json(orient='records') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Flask API exposing YOLOv5 model') + parser.add_argument('--port', default=5000, type=int, help='port number') + parser.add_argument('--model', nargs='+', default=['yolov5s'], help='model(s) to run, i.e. --model yolov5n yolov5s') + opt = parser.parse_args() + + for m in opt.model: + models[m] = torch.hub.load('ultralytics/yolov5', m, force_reload=True, skip_validation=True) + + app.run(host='0.0.0.0', port=opt.port) # debug=True causes Restarting with stat diff --git a/algorithm/yolov5/utils/general.py b/algorithm/yolov5/utils/general.py new file mode 100644 index 0000000..387b986 --- /dev/null +++ b/algorithm/yolov5/utils/general.py @@ -0,0 +1,1233 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +General utils +""" + +import contextlib +import glob +import inspect +import logging +import logging.config +import math +import os +import platform +import random +import re +import signal +import subprocess +import sys +import time +import urllib +from copy import deepcopy +from datetime import datetime +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from subprocess import check_output +from tarfile import is_tarfile +from typing import Optional +from zipfile import ZipFile, is_zipfile + +import cv2 +import numpy as np +import pandas as pd +import pkg_resources as pkg +import torch +import torchvision +import yaml + +from algorithm.yolov5.utils import TryExcept, emojis +from algorithm.yolov5.utils.downloads import curl_download, gsutil_getsize +from algorithm.yolov5.utils.metrics import box_iou, fitness +from algorithm.yolov5.utils.nms_rotated import obb_nms + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +RANK = int(os.getenv('RANK', -1)) + +# Settings +NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads +DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory +AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode +VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode +TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format +FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf + +torch.set_printoptions(linewidth=320, precision=5, profile='long') +np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +pd.options.display.max_columns = 10 +cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) +os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads +os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) + + +def is_ascii(s=''): + # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) + s = str(s) # convert list, tuple, None, etc. to str + return len(s.encode().decode('ascii', 'ignore')) == len(s) + + +def is_chinese(s='人工智能'): + # Is string composed of any Chinese characters? + return bool(re.search('[\u4e00-\u9fff]', str(s))) + + +def is_colab(): + # Is environment a Google Colab instance? + return 'google.colab' in sys.modules + + +def is_jupyter(): + """ + Check if the current script is running inside a Jupyter Notebook. + Verified on Colab, Jupyterlab, Kaggle, Paperspace. + + Returns: + bool: True if running inside a Jupyter Notebook, False otherwise. + """ + with contextlib.suppress(Exception): + from IPython import get_ipython + return get_ipython() is not None + return False + + +def is_kaggle(): + # Is environment a Kaggle Notebook? + return os.environ.get('PWD') == '/kaggle/working' and os.environ.get('KAGGLE_URL_BASE') == 'https://www.kaggle.com' + + +def is_docker() -> bool: + """Check if the process runs inside a docker container.""" + if Path('/.dockerenv').exists(): + return True + try: # check if docker is in control groups + with open('/proc/self/cgroup') as file: + return any('docker' in line for line in file) + except OSError: + return False + + +def is_writeable(dir, test=False): + # Return True if directory has write permissions, test opening a file with write permissions if test=True + if not test: + return os.access(dir, os.W_OK) # possible issues on Windows + file = Path(dir) / 'tmp.txt' + try: + with open(file, 'w'): # open file with write permissions + pass + file.unlink() # remove file + return True + except OSError: + return False + + +LOGGING_NAME = 'yolov5' + + +def set_logging(name=LOGGING_NAME, verbose=True): + # sets up logging for the given name + rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings + level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR + logging.config.dictConfig({ + 'version': 1, + 'disable_existing_loggers': False, + 'formatters': { + name: { + 'format': '%(message)s'}}, + 'handlers': { + name: { + 'class': 'logging.StreamHandler', + 'formatter': name, + 'level': level,}}, + 'loggers': { + name: { + 'level': level, + 'handlers': [name], + 'propagate': False,}}}) + + +set_logging(LOGGING_NAME) # run before defining LOGGER +LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.) +if platform.system() == 'Windows': + for fn in LOGGER.info, LOGGER.warning: + setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging + + +def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'): + # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required. + env = os.getenv(env_var) + if env: + path = Path(env) # use environment variable + else: + cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs + path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir + path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable + path.mkdir(exist_ok=True) # make if required + return path + + +CONFIG_DIR = user_config_dir() # Ultralytics settings dir + + +class Profile(contextlib.ContextDecorator): + # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager + def __init__(self, t=0.0): + self.t = t + self.cuda = torch.cuda.is_available() + + def __enter__(self): + self.start = self.time() + return self + + def __exit__(self, type, value, traceback): + self.dt = self.time() - self.start # delta-time + self.t += self.dt # accumulate dt + + def time(self): + if self.cuda: + torch.cuda.synchronize() + return time.time() + + +class Timeout(contextlib.ContextDecorator): + # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager + def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True): + self.seconds = int(seconds) + self.timeout_message = timeout_msg + self.suppress = bool(suppress_timeout_errors) + + def _timeout_handler(self, signum, frame): + raise TimeoutError(self.timeout_message) + + def __enter__(self): + if platform.system() != 'Windows': # not supported on Windows + signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM + signal.alarm(self.seconds) # start countdown for SIGALRM to be raised + + def __exit__(self, exc_type, exc_val, exc_tb): + if platform.system() != 'Windows': + signal.alarm(0) # Cancel SIGALRM if it's scheduled + if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError + return True + + +class WorkingDirectory(contextlib.ContextDecorator): + # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager + def __init__(self, new_dir): + self.dir = new_dir # new dir + self.cwd = Path.cwd().resolve() # current dir + + def __enter__(self): + os.chdir(self.dir) + + def __exit__(self, exc_type, exc_val, exc_tb): + os.chdir(self.cwd) + + +def methods(instance): + # Get class/instance methods + return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith('__')] + + +def print_args(args: Optional[dict] = None, show_file=True, show_func=False): + # Print function arguments (optional args dict) + x = inspect.currentframe().f_back # previous frame + file, _, func, _, _ = inspect.getframeinfo(x) + if args is None: # get args automatically + args, _, _, frm = inspect.getargvalues(x) + args = {k: v for k, v in frm.items() if k in args} + try: + file = Path(file).resolve().relative_to(ROOT).with_suffix('') + except ValueError: + file = Path(file).stem + s = (f'{file}: ' if show_file else '') + (f'{func}: ' if show_func else '') + LOGGER.info(colorstr(s) + ', '.join(f'{k}={v}' for k, v in args.items())) + + +def init_seeds(seed=0, deterministic=False): + # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe + # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 + if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213 + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.deterministic = True + os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8' + os.environ['PYTHONHASHSEED'] = str(seed) + + +def intersect_dicts(da, db, exclude=()): + # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values + return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape} + + +def get_default_args(func): + # Get func() default arguments + signature = inspect.signature(func) + return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty} + + +def get_latest_run(search_dir='.'): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) + return max(last_list, key=os.path.getctime) if last_list else '' + + +def file_age(path=__file__): + # Return days since last file update + dt = (datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime)) # delta + return dt.days # + dt.seconds / 86400 # fractional days + + +def file_date(path=__file__): + # Return human-readable file modification date, i.e. '2021-3-26' + t = datetime.fromtimestamp(Path(path).stat().st_mtime) + return f'{t.year}-{t.month}-{t.day}' + + +def file_size(path): + # Return file/dir size (MB) + mb = 1 << 20 # bytes to MiB (1024 ** 2) + path = Path(path) + if path.is_file(): + return path.stat().st_size / mb + elif path.is_dir(): + return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / mb + else: + return 0.0 + + +def check_online(): + # Check internet connectivity + import socket + + def run_once(): + # Check once + try: + socket.create_connection(('1.1.1.1', 443), 5) # check host accessibility + return True + except OSError: + return False + + return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues + + +def git_describe(path=ROOT): # path must be a directory + # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe + try: + assert (Path(path) / '.git').is_dir() + return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1] + except Exception: + return '' + + +@TryExcept() +@WorkingDirectory(ROOT) +def check_git_status(repo='ultralytics/yolov5', branch='master'): + # YOLOv5 status check, recommend 'git pull' if code is out of date + url = f'https://github.com/{repo}' + msg = f', for updates see {url}' + s = colorstr('github: ') # string + assert Path('.git').exists(), s + 'skipping check (not a git repository)' + msg + assert check_online(), s + 'skipping check (offline)' + msg + + splits = re.split(pattern=r'\s', string=check_output('git remote -v', shell=True).decode()) + matches = [repo in s for s in splits] + if any(matches): + remote = splits[matches.index(True) - 1] + else: + remote = 'ultralytics' + check_output(f'git remote add {remote} {url}', shell=True) + check_output(f'git fetch {remote}', shell=True, timeout=5) # git fetch + local_branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out + n = int(check_output(f'git rev-list {local_branch}..{remote}/{branch} --count', shell=True)) # commits behind + if n > 0: + pull = 'git pull' if remote == 'origin' else f'git pull {remote} {branch}' + s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update." + else: + s += f'up to date with {url} ✅' + LOGGER.info(s) + + +@WorkingDirectory(ROOT) +def check_git_info(path='.'): + # YOLOv5 git info check, return {remote, branch, commit} + check_requirements('gitpython') + import git + try: + repo = git.Repo(path) + remote = repo.remotes.origin.url.replace('.git', '') # i.e. 'https://github.com/ultralytics/yolov5' + commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d' + try: + branch = repo.active_branch.name # i.e. 'main' + except TypeError: # not on any branch + branch = None # i.e. 'detached HEAD' state + return {'remote': remote, 'branch': branch, 'commit': commit} + except git.exc.InvalidGitRepositoryError: # path is not a git dir + return {'remote': None, 'branch': None, 'commit': None} + + +def check_python(minimum='3.7.0'): + # Check current python version vs. required python version + check_version(platform.python_version(), minimum, name='Python ', hard=True) + + +def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False): + # Check version vs. required version + current, minimum = (pkg.parse_version(x) for x in (current, minimum)) + result = (current == minimum) if pinned else (current >= minimum) # bool + s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed' # string + if hard: + assert result, emojis(s) # assert min requirements met + if verbose and not result: + LOGGER.warning(s) + return result + + +@TryExcept() +def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True, cmds=''): + # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str) + prefix = colorstr('red', 'bold', 'requirements:') + check_python() # check python version + if isinstance(requirements, Path): # requirements.txt file + file = requirements.resolve() + assert file.exists(), f'{prefix} {file} not found, check failed.' + with file.open() as f: + requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude] + elif isinstance(requirements, str): + requirements = [requirements] + + s = '' + n = 0 + for r in requirements: + try: + pkg.require(r) + except (pkg.VersionConflict, pkg.DistributionNotFound): # exception if requirements not met + s += f'"{r}" ' + n += 1 + + if s and install and AUTOINSTALL: # check environment variable + LOGGER.info(f"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...") + try: + # assert check_online(), "AutoUpdate skipped (offline)" + LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode()) + source = file if 'file' in locals() else requirements + s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ + f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" + LOGGER.info(s) + except Exception as e: + LOGGER.warning(f'{prefix} ❌ {e}') + + +def check_img_size(imgsz, s=32, floor=0): + # Verify image size is a multiple of stride s in each dimension + if isinstance(imgsz, int): # integer i.e. img_size=640 + new_size = max(make_divisible(imgsz, int(s)), floor) + else: # list i.e. img_size=[640, 480] + imgsz = list(imgsz) # convert to list if tuple + new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] + if new_size != imgsz: + LOGGER.warning(f'WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}') + return new_size + + +def check_imshow(warn=False): + # Check if environment supports image displays + try: + assert not is_jupyter() + assert not is_docker() + cv2.imshow('test', np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + if warn: + LOGGER.warning(f'WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}') + return False + + +def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''): + # Check file(s) for acceptable suffix + if file and suffix: + if isinstance(suffix, str): + suffix = [suffix] + for f in file if isinstance(file, (list, tuple)) else [file]: + s = Path(f).suffix.lower() # file suffix + if len(s): + assert s in suffix, f'{msg}{f} acceptable suffix is {suffix}' + + +def check_yaml(file, suffix=('.yaml', '.yml')): + # Search/download YAML file (if necessary) and return path, checking suffix + return check_file(file, suffix) + + +def check_file(file, suffix=''): + # Search/download file (if necessary) and return path + check_suffix(file, suffix) # optional + file = str(file) # convert to str() + if os.path.isfile(file) or not file: # exists + return file + elif file.startswith(('http:/', 'https:/')): # download + url = file # warning: Pathlib turns :// -> :/ + file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth + if os.path.isfile(file): + LOGGER.info(f'Found {url} locally at {file}') # file already exists + else: + LOGGER.info(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, file) + assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check + return file + elif file.startswith('clearml://'): # ClearML Dataset ID + assert 'clearml' in sys.modules, "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." + return file + else: # search + files = [] + for d in 'data', 'models', 'utils': # search directories + files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file + assert len(files), f'File not found: {file}' # assert file was found + assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique + return files[0] # return file + + +def check_font(font=FONT, progress=False): + # Download font to CONFIG_DIR if necessary + font = Path(font) + file = CONFIG_DIR / font.name + if not font.exists() and not file.exists(): + url = f'https://ultralytics.com/assets/{font.name}' + LOGGER.info(f'Downloading {url} to {file}...') + torch.hub.download_url_to_file(url, str(file), progress=progress) + + +def check_dataset(data, autodownload=True): + # Download, check and/or unzip dataset if not found locally + + # Download (optional) + extract_dir = '' + if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): + download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1) + data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml')) + extract_dir, autodownload = data.parent, False + + # Read yaml (optional) + if isinstance(data, (str, Path)): + data = yaml_load(data) # dictionary + + # Checks + for k in 'train', 'val', 'names': + assert k in data, emojis(f"data.yaml '{k}:' field missing ❌") + if isinstance(data['names'], (list, tuple)): # old array format + data['names'] = dict(enumerate(data['names'])) # convert to dict + assert all(isinstance(k, int) for k in data['names'].keys()), 'data.yaml names keys must be integers, i.e. 2: car' + data['nc'] = len(data['names']) + + # Resolve paths + path = Path(extract_dir or data.get('path') or '') # optional 'path' default to '.' + if not path.is_absolute(): + path = (ROOT / path).resolve() + data['path'] = path # download scripts + for k in 'train', 'val', 'test': + if data.get(k): # prepend path + if isinstance(data[k], str): + x = (path / data[k]).resolve() + if not x.exists() and data[k].startswith('../'): + x = (path / data[k][3:]).resolve() + data[k] = str(x) + else: + data[k] = [str((path / x).resolve()) for x in data[k]] + + # Parse yaml + train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download')) + if val: + val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path + if not all(x.exists() for x in val): + LOGGER.info('\nDataset not found ⚠️, missing paths %s' % [str(x) for x in val if not x.exists()]) + if not s or not autodownload: + raise Exception('Dataset not found ❌') + t = time.time() + if s.startswith('http') and s.endswith('.zip'): # URL + f = Path(s).name # filename + LOGGER.info(f'Downloading {s} to {f}...') + torch.hub.download_url_to_file(s, f) + Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root + unzip_file(f, path=DATASETS_DIR) # unzip + Path(f).unlink() # remove zip + r = None # success + elif s.startswith('bash '): # bash script + LOGGER.info(f'Running {s} ...') + r = subprocess.run(s, shell=True) + else: # python script + r = exec(s, {'yaml': data}) # return None + dt = f'({round(time.time() - t, 1)}s)' + s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f'failure {dt} ❌' + LOGGER.info(f'Dataset download {s}') + check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True) # download fonts + return data # dictionary + + +def check_amp(model): + # Check PyTorch Automatic Mixed Precision (AMP) functionality. Return True on correct operation + from models.common import AutoShape, DetectMultiBackend + + def amp_allclose(model, im): + # All close FP32 vs AMP results + m = AutoShape(model, verbose=False) # model + a = m(im).xywhn[0] # FP32 inference + m.amp = True + b = m(im).xywhn[0] # AMP inference + return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance + + prefix = colorstr('AMP: ') + device = next(model.parameters()).device # get model device + if device.type in ('cpu', 'mps'): + return False # AMP only used on CUDA devices + f = ROOT / 'data' / 'images' / 'bus.jpg' # image to check + im = f if f.exists() else 'https://ultralytics.com/images/bus.jpg' if check_online() else np.ones((640, 640, 3)) + try: + assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend('yolov5n.pt', device), im) + LOGGER.info(f'{prefix}checks passed ✅') + return True + except Exception: + help_url = 'https://github.com/ultralytics/yolov5/issues/7908' + LOGGER.warning(f'{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}') + return False + + +def yaml_load(file='data.yaml'): + # Single-line safe yaml loading + with open(file, errors='ignore') as f: + return yaml.safe_load(f) + + +def yaml_save(file='data.yaml', data={}): + # Single-line safe yaml saving + with open(file, 'w') as f: + yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False) + + +def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')): + # Unzip a *.zip file to path/, excluding files containing strings in exclude list + if path is None: + path = Path(file).parent # default path + with ZipFile(file) as zipObj: + for f in zipObj.namelist(): # list all archived filenames in the zip + if all(x not in f for x in exclude): + zipObj.extract(f, path=path) + + +def url2file(url): + # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt + url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/ + return Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth + + +def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1, retry=3): + # Multithreaded file download and unzip function, used in data.yaml for autodownload + def download_one(url, dir): + # Download 1 file + success = True + if os.path.isfile(url): + f = Path(url) # filename + else: # does not exist + f = dir / Path(url).name + LOGGER.info(f'Downloading {url} to {f}...') + for i in range(retry + 1): + if curl: + success = curl_download(url, f, silent=(threads > 1)) + else: + torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download + success = f.is_file() + if success: + break + elif i < retry: + LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...') + else: + LOGGER.warning(f'❌ Failed to download {url}...') + + if unzip and success and (f.suffix == '.gz' or is_zipfile(f) or is_tarfile(f)): + LOGGER.info(f'Unzipping {f}...') + if is_zipfile(f): + unzip_file(f, dir) # unzip + elif is_tarfile(f): + subprocess.run(['tar', 'xf', f, '--directory', f.parent], check=True) # unzip + elif f.suffix == '.gz': + subprocess.run(['tar', 'xfz', f, '--directory', f.parent], check=True) # unzip + if delete: + f.unlink() # remove zip + + dir = Path(dir) + dir.mkdir(parents=True, exist_ok=True) # make directory + if threads > 1: + pool = ThreadPool(threads) + pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded + pool.close() + pool.join() + else: + for u in [url] if isinstance(url, (str, Path)) else url: + download_one(u, dir) + + +def make_divisible(x, divisor): + # Returns nearest x divisible by divisor + if isinstance(divisor, torch.Tensor): + divisor = int(divisor.max()) # to int + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + # Cleans a string by replacing special characters with underscore _ + return re.sub(pattern='[|@#!¡·$€%&()=?¿^*;:,¨´><+]', repl='_', string=s) + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +def colorstr(*input): + # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') + *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string + colors = { + 'black': '\033[30m', # basic colors + 'red': '\033[31m', + 'green': '\033[32m', + 'yellow': '\033[33m', + 'blue': '\033[34m', + 'magenta': '\033[35m', + 'cyan': '\033[36m', + 'white': '\033[37m', + 'bright_black': '\033[90m', # bright colors + 'bright_red': '\033[91m', + 'bright_green': '\033[92m', + 'bright_yellow': '\033[93m', + 'bright_blue': '\033[94m', + 'bright_magenta': '\033[95m', + 'bright_cyan': '\033[96m', + 'bright_white': '\033[97m', + 'end': '\033[0m', # misc + 'bold': '\033[1m', + 'underline': '\033[4m'} + return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] + + +def labels_to_class_weights(labels, nc=80): + # Get class weights (inverse frequency) from training labels + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(int) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights).float() + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + # Produces image weights based on class_weights and image contents + # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample + class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels]) + return (class_weights.reshape(1, nc) * class_counts).sum(1) + + +def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) + # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + return [ + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] + + +def xyxy2xywh(x): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center + y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center + y[..., 2] = x[..., 2] - x[..., 0] # width + y[..., 3] = x[..., 3] - x[..., 1] # height + return y + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x + y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y + y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x + y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y + return y + + +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x + y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y + y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x + y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y + return y + + +def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right + if clip: + clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center + y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center + y[..., 2] = (x[..., 2] - x[..., 0]) / w # width + y[..., 3] = (x[..., 3] - x[..., 1]) / h # height + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + # Convert normalized segments into pixel segments, shape (n,2) + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = w * x[..., 0] + padw # top left x + y[..., 1] = h * x[..., 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + x, y, = x[inside], y[inside] + return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy + + +def segments2boxes(segments): + # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + # Up-sample an (n,2) segment + for i, s in enumerate(segments): + s = np.concatenate((s, s[0:1, :]), axis=0) + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy + return segments + + +def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): + # Rescale boxes (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + clip_boxes(boxes, img0_shape) + return boxes + + +def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + segments[:, 0] -= pad[0] # x padding + segments[:, 1] -= pad[1] # y padding + segments /= gain + clip_segments(segments, img0_shape) + if normalize: + segments[:, 0] /= img0_shape[1] # width + segments[:, 1] /= img0_shape[0] # height + return segments + + +def clip_boxes(boxes, shape): + # Clip boxes (xyxy) to image shape (height, width) + if isinstance(boxes, torch.Tensor): # faster individually + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 + else: # np.array (faster grouped) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + + +def clip_segments(segments, shape): + # Clip segments (xy1,xy2,...) to image shape (height, width) + if isinstance(segments, torch.Tensor): # faster individually + segments[:, 0].clamp_(0, shape[1]) # x + segments[:, 1].clamp_(0, shape[0]) # y + else: # np.array (faster grouped) + segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x + segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0, # number of masks +): + """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + # Checks + assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) + prediction = prediction[0] # select only inference output + + device = prediction.device + mps = 'mps' in device.type # Apple MPS + if mps: # MPS not fully supported yet, convert tensors to CPU before NMS + prediction = prediction.cpu() + bs = prediction.shape[0] # batch size + nc = prediction.shape[2] - nm - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + # min_wh = 2 # (pixels) minimum box width and height + max_wh = 7680 # (pixels) maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 0.5 + 0.05 * bs # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + mi = 5 + nc # mask start index + output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + lb = labels[xi] + v = torch.zeros((len(lb), nc + nm + 5), device=x.device) + v[:, :4] = lb[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box/Mask + box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) + mask = x[:, mi:] # zero columns if no masks + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) + else: # best class only + conf, j = x[:, 5:mi].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + i = i[:max_det] # limit detections + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if mps: + output[xi] = output[xi].to(device) + if (time.time() - t) > time_limit: + LOGGER.warning(f'WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded') + break # time limit exceeded + + return output + +def non_max_suppression_obb(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, + labels=(), max_det=1500): + """Runs Non-Maximum Suppression (NMS) on inference results_obb + Args: + prediction (tensor): (b, n_all_anchors, [cx cy l s obj num_cls theta_cls]) + agnostic (bool): True = NMS will be applied between elements of different categories + labels : () or + + Returns: + list of detections, len=batch_size, on (n,7) tensor per image [xylsθ, conf, cls] θ ∈ [-pi/2, pi/2) + """ + + nc = prediction.shape[2] - 5 - 180 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + class_index = nc + 5 + + # Checks + assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' + assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' + + # Settings + max_wh = 4096 # min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 30.0 # seconds to quit after + # redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + + t = time.time() + output = [torch.zeros((0, 7), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence, (tensor): (n_conf_thres, [cx cy l s obj num_cls theta_cls]) + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:class_index] *= x[:, 4:5] # conf = obj_conf * cls_conf + + _, theta_pred = torch.max(x[:, class_index:], 1, keepdim=True) # [n_conf_thres, 1] θ ∈ int[0, 179] + theta_pred = (theta_pred - 90) / 180 * pi # [n_conf_thres, 1] θ ∈ [-pi/2, pi/2) + + # Detections matrix nx7 (xyls, θ, conf, cls) θ ∈ [-pi/2, pi/2) + if multi_label: + i, j = (x[:, 5:class_index] > conf_thres).nonzero(as_tuple=False).T # () + x = torch.cat((x[i, :4], theta_pred[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:class_index].max(1, keepdim=True) + x = torch.cat((x[:, :4], theta_pred, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 6:7] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + x = x[x[:, 5].argsort(descending=True)[:max_nms]] # sort by confidence + + # Batched NMS + c = x[:, 6:7] * (0 if agnostic else max_wh) # classes + rboxes = x[:, :5].clone() + rboxes[:, :2] = rboxes[:, :2] + c # rboxes (offset by class) + scores = x[:, 5] # scores + _, i = obb_nms(rboxes, scores, iou_thres) + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f'WARNING: NMS time limit {time_limit}s exceeded') + break # time limit exceeded + + return output + + +def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() + # Strip optimizer from 'f' to finalize training, optionally save as 's' + x = torch.load(f, map_location=torch.device('cpu')) + if x.get('ema'): + x['model'] = x['ema'] # replace model with ema + for k in 'optimizer', 'best_fitness', 'ema', 'updates': # keys + x[k] = None + x['epoch'] = -1 + x['model'].half() # to FP16 + for p in x['model'].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1E6 # filesize + LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB") + + +def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')): + evolve_csv = save_dir / 'evolve.csv' + evolve_yaml = save_dir / 'hyp_evolve.yaml' + keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps] + keys = tuple(x.strip() for x in keys) + vals = results + tuple(hyp.values()) + n = len(keys) + + # Download (optional) + if bucket: + url = f'gs://{bucket}/evolve.csv' + if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0): + subprocess.run(['gsutil', 'cp', f'{url}', f'{save_dir}']) # download evolve.csv if larger than local + + # Log to evolve.csv + s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header + with open(evolve_csv, 'a') as f: + f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n') + + # Save yaml + with open(evolve_yaml, 'w') as f: + data = pd.read_csv(evolve_csv, skipinitialspace=True) + data = data.rename(columns=lambda x: x.strip()) # strip keys + i = np.argmax(fitness(data.values[:, :4])) # + generations = len(data) + f.write('# YOLOv5 Hyperparameter Evolution Results\n' + f'# Best generation: {i}\n' + + f'# Last generation: {generations - 1}\n' + '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + + '\n' + '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n') + yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False) + + # Print to screen + LOGGER.info(prefix + f'{generations} generations finished, current result:\n' + prefix + + ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' + prefix + ', '.join(f'{x:20.5g}' + for x in vals) + '\n\n') + + if bucket: + subprocess.run(['gsutil', 'cp', f'{evolve_csv}', f'{evolve_yaml}', f'gs://{bucket}']) # upload + + +def apply_classifier(x, model, img, im0): + # Apply a second stage classifier to YOLO outputs + # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_boxes(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for a in d: + cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + + im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction + x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections + + return x + + +def increment_path(path, exist_ok=False, sep='', mkdir=False): + # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. + path = Path(path) # os-agnostic + if path.exists() and not exist_ok: + path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '') + + # Method 1 + for n in range(2, 9999): + p = f'{path}{sep}{n}{suffix}' # increment path + if not os.path.exists(p): # + break + path = Path(p) + + # Method 2 (deprecated) + # dirs = glob.glob(f"{path}{sep}*") # similar paths + # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs] + # i = [int(m.groups()[0]) for m in matches if m] # indices + # n = max(i) + 1 if i else 2 # increment number + # path = Path(f"{path}{sep}{n}{suffix}") # increment path + + if mkdir: + path.mkdir(parents=True, exist_ok=True) # make directory + + return path + + +# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------ +imshow_ = cv2.imshow # copy to avoid recursion errors + + +def imread(path, flags=cv2.IMREAD_COLOR): + return cv2.imdecode(np.fromfile(path, np.uint8), flags) + + +def imwrite(path, im): + try: + cv2.imencode(Path(path).suffix, im)[1].tofile(path) + return True + except Exception: + return False + + +def imshow(path, im): + imshow_(path.encode('unicode_escape').decode(), im) + + +cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine + +# Variables ------------------------------------------------------------------------------------------------------------ diff --git a/algorithm/yolov5/utils/google_app_engine/Dockerfile b/algorithm/yolov5/utils/google_app_engine/Dockerfile new file mode 100644 index 0000000..0155618 --- /dev/null +++ b/algorithm/yolov5/utils/google_app_engine/Dockerfile @@ -0,0 +1,25 @@ +FROM gcr.io/google-appengine/python + +# Create a virtualenv for dependencies. This isolates these packages from +# system-level packages. +# Use -p python3 or -p python3.7 to select python version. Default is version 2. +RUN virtualenv /env -p python3 + +# Setting these environment variables are the same as running +# source /env/bin/activate. +ENV VIRTUAL_ENV /env +ENV PATH /env/bin:$PATH + +RUN apt-get update && apt-get install -y python-opencv + +# Copy the application's requirements.txt and run pip to install all +# dependencies into the virtualenv. +ADD requirements.txt /app/requirements.txt +RUN pip install -r /app/requirements.txt + +# Add the application source code. +ADD . /app + +# Run a WSGI server to serve the application. gunicorn must be declared as +# a dependency in requirements.txt. +CMD gunicorn -b :$PORT main:app diff --git a/algorithm/yolov5/utils/google_app_engine/additional_requirements.txt b/algorithm/yolov5/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 0000000..d5b7675 --- /dev/null +++ b/algorithm/yolov5/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,5 @@ +# add these requirements in your app on top of the existing ones +pip==21.1 +Flask==1.0.2 +gunicorn==19.10.0 +werkzeug>=2.2.3 # not directly required, pinned by Snyk to avoid a vulnerability diff --git a/algorithm/yolov5/utils/google_app_engine/app.yaml b/algorithm/yolov5/utils/google_app_engine/app.yaml new file mode 100644 index 0000000..5056b7c --- /dev/null +++ b/algorithm/yolov5/utils/google_app_engine/app.yaml @@ -0,0 +1,14 @@ +runtime: custom +env: flex + +service: yolov5app + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 diff --git a/algorithm/yolov5/utils/loggers/__init__.py b/algorithm/yolov5/utils/loggers/__init__.py new file mode 100644 index 0000000..9de1f22 --- /dev/null +++ b/algorithm/yolov5/utils/loggers/__init__.py @@ -0,0 +1,401 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Logging utils +""" + +import os +import warnings +from pathlib import Path + +import pkg_resources as pkg +import torch +from torch.utils.tensorboard import SummaryWriter + +from utils.general import LOGGER, colorstr, cv2 +from utils.loggers.clearml.clearml_utils import ClearmlLogger +from utils.loggers.wandb.wandb_utils import WandbLogger +from utils.plots import plot_images, plot_labels, plot_results +from utils.torch_utils import de_parallel + +LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet') # *.csv, TensorBoard, Weights & Biases, ClearML +RANK = int(os.getenv('RANK', -1)) + +try: + import wandb + + assert hasattr(wandb, '__version__') # verify package import not local dir + if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}: + try: + wandb_login_success = wandb.login(timeout=30) + except wandb.errors.UsageError: # known non-TTY terminal issue + wandb_login_success = False + if not wandb_login_success: + wandb = None +except (ImportError, AssertionError): + wandb = None + +try: + import clearml + + assert hasattr(clearml, '__version__') # verify package import not local dir +except (ImportError, AssertionError): + clearml = None + +try: + if RANK not in [0, -1]: + comet_ml = None + else: + import comet_ml + + assert hasattr(comet_ml, '__version__') # verify package import not local dir + from utils.loggers.comet import CometLogger + +except (ModuleNotFoundError, ImportError, AssertionError): + comet_ml = None + + +class Loggers(): + # YOLOv5 Loggers class + def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): + self.save_dir = save_dir + self.weights = weights + self.opt = opt + self.hyp = hyp + self.plots = not opt.noplots # plot results + self.logger = logger # for printing results to console + self.include = include + self.keys = [ + 'train/box_loss', + 'train/obj_loss', + 'train/cls_loss', # train loss + 'metrics/precision', + 'metrics/recall', + 'metrics/mAP_0.5', + 'metrics/mAP_0.5:0.95', # metrics + 'val/box_loss', + 'val/obj_loss', + 'val/cls_loss', # val loss + 'x/lr0', + 'x/lr1', + 'x/lr2'] # params + self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95'] + for k in LOGGERS: + setattr(self, k, None) # init empty logger dictionary + self.csv = True # always log to csv + + # Messages + if not clearml: + prefix = colorstr('ClearML: ') + s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML" + self.logger.info(s) + if not comet_ml: + prefix = colorstr('Comet: ') + s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet" + self.logger.info(s) + # TensorBoard + s = self.save_dir + if 'tb' in self.include and not self.opt.evolve: + prefix = colorstr('TensorBoard: ') + self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/") + self.tb = SummaryWriter(str(s)) + + # W&B + if wandb and 'wandb' in self.include: + self.opt.hyp = self.hyp # add hyperparameters + self.wandb = WandbLogger(self.opt) + else: + self.wandb = None + + # ClearML + if clearml and 'clearml' in self.include: + try: + self.clearml = ClearmlLogger(self.opt, self.hyp) + except Exception: + self.clearml = None + prefix = colorstr('ClearML: ') + LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' + f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme') + + else: + self.clearml = None + + # Comet + if comet_ml and 'comet' in self.include: + if isinstance(self.opt.resume, str) and self.opt.resume.startswith('comet://'): + run_id = self.opt.resume.split('/')[-1] + self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id) + + else: + self.comet_logger = CometLogger(self.opt, self.hyp) + + else: + self.comet_logger = None + + @property + def remote_dataset(self): + # Get data_dict if custom dataset artifact link is provided + data_dict = None + if self.clearml: + data_dict = self.clearml.data_dict + if self.wandb: + data_dict = self.wandb.data_dict + if self.comet_logger: + data_dict = self.comet_logger.data_dict + + return data_dict + + def on_train_start(self): + if self.comet_logger: + self.comet_logger.on_train_start() + + def on_pretrain_routine_start(self): + if self.comet_logger: + self.comet_logger.on_pretrain_routine_start() + + def on_pretrain_routine_end(self, labels, names): + # Callback runs on pre-train routine end + if self.plots: + plot_labels(labels, names, self.save_dir) + paths = self.save_dir.glob('*labels*.jpg') # training labels + if self.wandb: + self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for x in paths]}) + # if self.clearml: + # pass # ClearML saves these images automatically using hooks + if self.comet_logger: + self.comet_logger.on_pretrain_routine_end(paths) + + def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): + log_dict = dict(zip(self.keys[:3], vals)) + # Callback runs on train batch end + # ni: number integrated batches (since train start) + if self.plots: + if ni < 3: + f = self.save_dir / f'train_batch{ni}.jpg' # filename + plot_images(imgs, targets, paths, f) + if ni == 0 and self.tb and not self.opt.sync_bn: + log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)) + if ni == 10 and (self.wandb or self.clearml): + files = sorted(self.save_dir.glob('train*.jpg')) + if self.wandb: + self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) + if self.clearml: + self.clearml.log_debug_samples(files, title='Mosaics') + + if self.comet_logger: + self.comet_logger.on_train_batch_end(log_dict, step=ni) + + def on_train_epoch_end(self, epoch): + # Callback runs on train epoch end + if self.wandb: + self.wandb.current_epoch = epoch + 1 + + if self.comet_logger: + self.comet_logger.on_train_epoch_end(epoch) + + def on_val_start(self): + if self.comet_logger: + self.comet_logger.on_val_start() + + def on_val_image_end(self, pred, predn, path, names, im): + # Callback runs on val image end + if self.wandb: + self.wandb.val_one_image(pred, predn, path, names, im) + if self.clearml: + self.clearml.log_image_with_boxes(path, pred, names, im) + + def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): + if self.comet_logger: + self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out) + + def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + # Callback runs on val end + if self.wandb or self.clearml: + files = sorted(self.save_dir.glob('val*.jpg')) + if self.wandb: + self.wandb.log({'Validation': [wandb.Image(str(f), caption=f.name) for f in files]}) + if self.clearml: + self.clearml.log_debug_samples(files, title='Validation') + + if self.comet_logger: + self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) + + def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): + # Callback runs at the end of each fit (train+val) epoch + x = dict(zip(self.keys, vals)) + if self.csv: + file = self.save_dir / 'results.csv' + n = len(x) + 1 # number of cols + s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header + with open(file, 'a') as f: + f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') + + if self.tb: + for k, v in x.items(): + self.tb.add_scalar(k, v, epoch) + elif self.clearml: # log to ClearML if TensorBoard not used + for k, v in x.items(): + title, series = k.split('/') + self.clearml.task.get_logger().report_scalar(title, series, v, epoch) + + if self.wandb: + if best_fitness == fi: + best_results = [epoch] + vals[3:7] + for i, name in enumerate(self.best_keys): + self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary + self.wandb.log(x) + self.wandb.end_epoch() + + if self.clearml: + self.clearml.current_epoch_logged_images = set() # reset epoch image limit + self.clearml.current_epoch += 1 + + if self.comet_logger: + self.comet_logger.on_fit_epoch_end(x, epoch=epoch) + + def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + # Callback runs on model save event + if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1: + if self.wandb: + self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) + if self.clearml: + self.clearml.task.update_output_model(model_path=str(last), + model_name='Latest Model', + auto_delete_file=False) + + if self.comet_logger: + self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) + + def on_train_end(self, last, best, epoch, results): + # Callback runs on training end, i.e. saving best model + if self.plots: + plot_results(file=self.save_dir / 'results.csv') # save results.png + files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] + files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter + self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}") + + if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles + for f in files: + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') + + if self.wandb: + self.wandb.log(dict(zip(self.keys[3:10], results))) + self.wandb.log({'Results': [wandb.Image(str(f), caption=f.name) for f in files]}) + # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model + if not self.opt.evolve: + wandb.log_artifact(str(best if best.exists() else last), + type='model', + name=f'run_{self.wandb.wandb_run.id}_model', + aliases=['latest', 'best', 'stripped']) + self.wandb.finish_run() + + if self.clearml and not self.opt.evolve: + self.clearml.task.update_output_model(model_path=str(best if best.exists() else last), + name='Best Model', + auto_delete_file=False) + + if self.comet_logger: + final_results = dict(zip(self.keys[3:10], results)) + self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results) + + def on_params_update(self, params: dict): + # Update hyperparams or configs of the experiment + if self.wandb: + self.wandb.wandb_run.config.update(params, allow_val_change=True) + if self.comet_logger: + self.comet_logger.on_params_update(params) + + +class GenericLogger: + """ + YOLOv5 General purpose logger for non-task specific logging + Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...) + Arguments + opt: Run arguments + console_logger: Console logger + include: loggers to include + """ + + def __init__(self, opt, console_logger, include=('tb', 'wandb')): + # init default loggers + self.save_dir = Path(opt.save_dir) + self.include = include + self.console_logger = console_logger + self.csv = self.save_dir / 'results.csv' # CSV logger + if 'tb' in self.include: + prefix = colorstr('TensorBoard: ') + self.console_logger.info( + f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/") + self.tb = SummaryWriter(str(self.save_dir)) + + if wandb and 'wandb' in self.include: + self.wandb = wandb.init(project=web_project_name(str(opt.project)), + name=None if opt.name == 'exp' else opt.name, + config=opt) + else: + self.wandb = None + + def log_metrics(self, metrics, epoch): + # Log metrics dictionary to all loggers + if self.csv: + keys, vals = list(metrics.keys()), list(metrics.values()) + n = len(metrics) + 1 # number of cols + s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header + with open(self.csv, 'a') as f: + f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') + + if self.tb: + for k, v in metrics.items(): + self.tb.add_scalar(k, v, epoch) + + if self.wandb: + self.wandb.log(metrics, step=epoch) + + def log_images(self, files, name='Images', epoch=0): + # Log images to all loggers + files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path + files = [f for f in files if f.exists()] # filter by exists + + if self.tb: + for f in files: + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') + + if self.wandb: + self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) + + def log_graph(self, model, imgsz=(640, 640)): + # Log model graph to all loggers + if self.tb: + log_tensorboard_graph(self.tb, model, imgsz) + + def log_model(self, model_path, epoch=0, metadata={}): + # Log model to all loggers + if self.wandb: + art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata) + art.add_file(str(model_path)) + wandb.log_artifact(art) + + def update_params(self, params): + # Update the parameters logged + if self.wandb: + wandb.run.config.update(params, allow_val_change=True) + + +def log_tensorboard_graph(tb, model, imgsz=(640, 640)): + # Log model graph to TensorBoard + try: + p = next(model.parameters()) # for device, type + imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand + im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress jit trace warning + tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) + except Exception as e: + LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}') + + +def web_project_name(project): + # Convert local project name to web project name + if not project.startswith('runs/train'): + return project + suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else '' + return f'YOLOv5{suffix}' diff --git a/algorithm/yolov5/utils/loggers/clearml/README.md b/algorithm/yolov5/utils/loggers/clearml/README.md new file mode 100644 index 0000000..ca41c04 --- /dev/null +++ b/algorithm/yolov5/utils/loggers/clearml/README.md @@ -0,0 +1,237 @@ +# ClearML Integration + +Clear|MLClear|ML + +## About ClearML + +[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️. + +🔨 Track every YOLOv5 training run in the experiment manager + +🔧 Version and easily access your custom training data with the integrated ClearML Data Versioning Tool + +🔦 Remotely train and monitor your YOLOv5 training runs using ClearML Agent + +🔬 Get the very best mAP using ClearML Hyperparameter Optimization + +🔭 Turn your newly trained YOLOv5 model into an API with just a few commands using ClearML Serving + +
+And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline! +
+
+ +![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif) + +
+
+ +## 🦾 Setting Things Up + +To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one: + +Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go! + +1. Install the `clearml` python package: + + ```bash + pip install clearml + ``` + +1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions: + + ```bash + clearml-init + ``` + +That's it! You're done 😎 + +
+ +## 🚀 Training YOLOv5 With ClearML + +To enable ClearML experiment tracking, simply install the ClearML pip package. + +```bash +pip install clearml>=1.2.0 +``` + +This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager. + +If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. +PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name! + +```bash +python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache +``` + +or with custom project and task name: + +```bash +python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache +``` + +This will capture: + +- Source code + uncommitted changes +- Installed packages +- (Hyper)parameters +- Model files (use `--save-period n` to save a checkpoint every n epochs) +- Console output +- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...) +- General info such as machine details, runtime, creation date etc. +- All produced plots such as label correlogram and confusion matrix +- Images with bounding boxes per epoch +- Mosaic per epoch +- Validation images per epoch +- ... + +That's a lot right? 🤯 +Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them! + +There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works! + +
+ +## 🔗 Dataset Version Management + +Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment! + +![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif) + +### Prepare Your Dataset + +The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure: + +``` +.. +|_ yolov5 +|_ datasets + |_ coco128 + |_ images + |_ labels + |_ LICENSE + |_ README.txt +``` + +But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure. + +Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls. + +Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`. + +``` +.. +|_ yolov5 +|_ datasets + |_ coco128 + |_ images + |_ labels + |_ coco128.yaml # <---- HERE! + |_ LICENSE + |_ README.txt +``` + +### Upload Your Dataset + +To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command: + +```bash +cd coco128 +clearml-data sync --project YOLOv5 --name coco128 --folder . +``` + +The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other: + +```bash +# Optionally add --parent if you want to base +# this version on another dataset version, so no duplicate files are uploaded! +clearml-data create --name coco128 --project YOLOv5 +clearml-data add --files . +clearml-data close +``` + +### Run Training Using A ClearML Dataset + +Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models! + +```bash +python train.py --img 640 --batch 16 --epochs 3 --data clearml:// --weights yolov5s.pt --cache +``` + +
+ +## 👀 Hyperparameter Optimization + +Now that we have our experiments and data versioned, it's time to take a look at what we can build on top! + +Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does! + +To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters. + +You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead. + +```bash +# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch +pip install optuna +python utils/loggers/clearml/hpo.py +``` + +![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png) + +## 🤯 Remote Execution (advanced) + +Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. +This is where the ClearML Agent comes into play. Check out what the agent can do here: + +- [YouTube video](https://youtu.be/MX3BrXnaULs) +- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent) + +In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager. + +You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running: + +```bash +clearml-agent daemon --queue [--docker] +``` + +### Cloning, Editing And Enqueuing + +With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too! + +🪄 Clone the experiment by right-clicking it + +🎯 Edit the hyperparameters to what you wish them to be + +⏳ Enqueue the task to any of the queues by right-clicking it + +![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif) + +### Executing A Task Remotely + +Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on! + +To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated: + +```python +# ... +# Loggers +data_dict = None +if RANK in {-1, 0}: + loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance + if loggers.clearml: + loggers.clearml.task.execute_remotely(queue="my_queue") # <------ ADD THIS LINE + # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML + data_dict = loggers.clearml.data_dict +# ... +``` + +When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead! + +### Autoscaling workers + +ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying! + +Check out the autoscalers getting started video below. + +[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E) diff --git a/algorithm/yolov5/utils/loggers/clearml/__init__.py b/algorithm/yolov5/utils/loggers/clearml/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5/utils/loggers/clearml/clearml_utils.py b/algorithm/yolov5/utils/loggers/clearml/clearml_utils.py new file mode 100644 index 0000000..2764abe --- /dev/null +++ b/algorithm/yolov5/utils/loggers/clearml/clearml_utils.py @@ -0,0 +1,164 @@ +"""Main Logger class for ClearML experiment tracking.""" +import glob +import re +from pathlib import Path + +import numpy as np +import yaml + +from utils.plots import Annotator, colors + +try: + import clearml + from clearml import Dataset, Task + + assert hasattr(clearml, '__version__') # verify package import not local dir +except (ImportError, AssertionError): + clearml = None + + +def construct_dataset(clearml_info_string): + """Load in a clearml dataset and fill the internal data_dict with its contents. + """ + dataset_id = clearml_info_string.replace('clearml://', '') + dataset = Dataset.get(dataset_id=dataset_id) + dataset_root_path = Path(dataset.get_local_copy()) + + # We'll search for the yaml file definition in the dataset + yaml_filenames = list(glob.glob(str(dataset_root_path / '*.yaml')) + glob.glob(str(dataset_root_path / '*.yml'))) + if len(yaml_filenames) > 1: + raise ValueError('More than one yaml file was found in the dataset root, cannot determine which one contains ' + 'the dataset definition this way.') + elif len(yaml_filenames) == 0: + raise ValueError('No yaml definition found in dataset root path, check that there is a correct yaml file ' + 'inside the dataset root path.') + with open(yaml_filenames[0]) as f: + dataset_definition = yaml.safe_load(f) + + assert set(dataset_definition.keys()).issuperset( + {'train', 'test', 'val', 'nc', 'names'} + ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" + + data_dict = dict() + data_dict['train'] = str( + (dataset_root_path / dataset_definition['train']).resolve()) if dataset_definition['train'] else None + data_dict['test'] = str( + (dataset_root_path / dataset_definition['test']).resolve()) if dataset_definition['test'] else None + data_dict['val'] = str( + (dataset_root_path / dataset_definition['val']).resolve()) if dataset_definition['val'] else None + data_dict['nc'] = dataset_definition['nc'] + data_dict['names'] = dataset_definition['names'] + + return data_dict + + +class ClearmlLogger: + """Log training runs, datasets, models, and predictions to ClearML. + + This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, + this information includes hyperparameters, system configuration and metrics, model metrics, code information and + basic data metrics and analyses. + + By providing additional command line arguments to train.py, datasets, + models and predictions can also be logged. + """ + + def __init__(self, opt, hyp): + """ + - Initialize ClearML Task, this object will capture the experiment + - Upload dataset version to ClearML Data if opt.upload_dataset is True + + arguments: + opt (namespace) -- Commandline arguments for this run + hyp (dict) -- Hyperparameters for this run + + """ + self.current_epoch = 0 + # Keep tracked of amount of logged images to enforce a limit + self.current_epoch_logged_images = set() + # Maximum number of images to log to clearML per epoch + self.max_imgs_to_log_per_epoch = 16 + # Get the interval of epochs when bounding box images should be logged + self.bbox_interval = opt.bbox_interval + self.clearml = clearml + self.task = None + self.data_dict = None + if self.clearml: + self.task = Task.init( + project_name=opt.project if opt.project != 'runs/train' else 'YOLOv5', + task_name=opt.name if opt.name != 'exp' else 'Training', + tags=['YOLOv5'], + output_uri=True, + reuse_last_task_id=opt.exist_ok, + auto_connect_frameworks={'pytorch': False} + # We disconnect pytorch auto-detection, because we added manual model save points in the code + ) + # ClearML's hooks will already grab all general parameters + # Only the hyperparameters coming from the yaml config file + # will have to be added manually! + self.task.connect(hyp, name='Hyperparameters') + self.task.connect(opt, name='Args') + + # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent + self.task.set_base_docker('ultralytics/yolov5:latest', + docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', + docker_setup_bash_script='pip install clearml') + + # Get ClearML Dataset Version if requested + if opt.data.startswith('clearml://'): + # data_dict should have the following keys: + # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) + self.data_dict = construct_dataset(opt.data) + # Set data to data_dict because wandb will crash without this information and opt is the best way + # to give it to them + opt.data = self.data_dict + + def log_debug_samples(self, files, title='Debug Samples'): + """ + Log files (images) as debug samples in the ClearML task. + + arguments: + files (List(PosixPath)) a list of file paths in PosixPath format + title (str) A title that groups together images with the same values + """ + for f in files: + if f.exists(): + it = re.search(r'_batch(\d+)', f.name) + iteration = int(it.groups()[0]) if it else 0 + self.task.get_logger().report_image(title=title, + series=f.name.replace(it.group(), ''), + local_path=str(f), + iteration=iteration) + + def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): + """ + Draw the bounding boxes on a single image and report the result as a ClearML debug sample. + + arguments: + image_path (PosixPath) the path the original image file + boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] + class_names (dict): dict containing mapping of class int to class name + image (Tensor): A torch tensor containing the actual image data + """ + if len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0: + # Log every bbox_interval times and deduplicate for any intermittend extra eval runs + if self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images: + im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2)) + annotator = Annotator(im=im, pil=True) + for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])): + color = colors(i) + + class_name = class_names[int(class_nr)] + confidence_percentage = round(float(conf) * 100, 2) + label = f'{class_name}: {confidence_percentage}%' + + if conf > conf_threshold: + annotator.rectangle(box.cpu().numpy(), outline=color) + annotator.box_label(box.cpu().numpy(), label=label, color=color) + + annotated_image = annotator.result() + self.task.get_logger().report_image(title='Bounding Boxes', + series=image_path.name, + iteration=self.current_epoch, + image=annotated_image) + self.current_epoch_logged_images.add(image_path) diff --git a/algorithm/yolov5/utils/loggers/clearml/hpo.py b/algorithm/yolov5/utils/loggers/clearml/hpo.py new file mode 100644 index 0000000..ee518b0 --- /dev/null +++ b/algorithm/yolov5/utils/loggers/clearml/hpo.py @@ -0,0 +1,84 @@ +from clearml import Task +# Connecting ClearML with the current process, +# from here on everything is logged automatically +from clearml.automation import HyperParameterOptimizer, UniformParameterRange +from clearml.automation.optuna import OptimizerOptuna + +task = Task.init(project_name='Hyper-Parameter Optimization', + task_name='YOLOv5', + task_type=Task.TaskTypes.optimizer, + reuse_last_task_id=False) + +# Example use case: +optimizer = HyperParameterOptimizer( + # This is the experiment we want to optimize + base_task_id='', + # here we define the hyper-parameters to optimize + # Notice: The parameter name should exactly match what you see in the UI: / + # For Example, here we see in the base experiment a section Named: "General" + # under it a parameter named "batch_size", this becomes "General/batch_size" + # If you have `argparse` for example, then arguments will appear under the "Args" section, + # and you should instead pass "Args/batch_size" + hyper_parameters=[ + UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1), + UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0), + UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98), + UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001), + UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0), + UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95), + UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2), + UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2), + UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0), + UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0), + UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0), + UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0), + UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7), + UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0), + UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0), + UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1), + UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9), + UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9), + UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0), + UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9), + UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9), + UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0), + UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001), + UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0), + UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0), + UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0), + UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0), + UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)], + # this is the objective metric we want to maximize/minimize + objective_metric_title='metrics', + objective_metric_series='mAP_0.5', + # now we decide if we want to maximize it or minimize it (accuracy we maximize) + objective_metric_sign='max', + # let us limit the number of concurrent experiments, + # this in turn will make sure we do dont bombard the scheduler with experiments. + # if we have an auto-scaler connected, this, by proxy, will limit the number of machine + max_number_of_concurrent_tasks=1, + # this is the optimizer class (actually doing the optimization) + # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band) + optimizer_class=OptimizerOptuna, + # If specified only the top K performing Tasks will be kept, the others will be automatically archived + save_top_k_tasks_only=5, # 5, + compute_time_limit=None, + total_max_jobs=20, + min_iteration_per_job=None, + max_iteration_per_job=None, +) + +# report every 10 seconds, this is way too often, but we are testing here +optimizer.set_report_period(10 / 60) +# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent +# an_optimizer.start_locally(job_complete_callback=job_complete_callback) +# set the time limit for the optimization process (2 hours) +optimizer.set_time_limit(in_minutes=120.0) +# Start the optimization process in the local environment +optimizer.start_locally() +# wait until process is done (notice we are controlling the optimization process in the background) +optimizer.wait() +# make sure background optimization stopped +optimizer.stop() + +print('We are done, good bye') diff --git a/algorithm/yolov5/utils/loggers/comet/README.md b/algorithm/yolov5/utils/loggers/comet/README.md new file mode 100644 index 0000000..47e6a45 --- /dev/null +++ b/algorithm/yolov5/utils/loggers/comet/README.md @@ -0,0 +1,258 @@ + + +# YOLOv5 with Comet + +This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2) + +# About Comet + +Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models. + +Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! +Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! + +# Getting Started + +## Install Comet + +```shell +pip install comet_ml +``` + +## Configure Comet Credentials + +There are two ways to configure Comet with YOLOv5. + +You can either set your credentials through environment variables + +**Environment Variables** + +```shell +export COMET_API_KEY= +export COMET_PROJECT_NAME= # This will default to 'yolov5' +``` + +Or create a `.comet.config` file in your working directory and set your credentials there. + +**Comet Configuration File** + +``` +[comet] +api_key= +project_name= # This will default to 'yolov5' +``` + +## Run the Training Script + +```shell +# Train YOLOv5s on COCO128 for 5 epochs +python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt +``` + +That's it! Comet will automatically log your hyperparameters, command line arguments, training and validation metrics. You can visualize and analyze your runs in the Comet UI + +yolo-ui + +# Try out an Example! + +Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +Or better yet, try it out yourself in this Colab Notebook + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing) + +# Log automatically + +By default, Comet will log the following items + +## Metrics + +- Box Loss, Object Loss, Classification Loss for the training and validation data +- mAP_0.5, mAP_0.5:0.95 metrics for the validation data. +- Precision and Recall for the validation data + +## Parameters + +- Model Hyperparameters +- All parameters passed through the command line options + +## Visualizations + +- Confusion Matrix of the model predictions on the validation data +- Plots for the PR and F1 curves across all classes +- Correlogram of the Class Labels + +# Configure Comet Logging + +Comet can be configured to log additional data either through command line flags passed to the training script +or through environment variables. + +```shell +export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online +export COMET_MODEL_NAME= #Set the name for the saved model. Defaults to yolov5 +export COMET_LOG_CONFUSION_MATRIX=false # Set to disable logging a Comet Confusion Matrix. Defaults to true +export COMET_MAX_IMAGE_UPLOADS= # Controls how many total image predictions to log to Comet. Defaults to 100. +export COMET_LOG_PER_CLASS_METRICS=true # Set to log evaluation metrics for each detected class at the end of training. Defaults to false +export COMET_DEFAULT_CHECKPOINT_FILENAME= # Set this if you would like to resume training from a different checkpoint. Defaults to 'last.pt' +export COMET_LOG_BATCH_LEVEL_METRICS=true # Set this if you would like to log training metrics at the batch level. Defaults to false. +export COMET_LOG_PREDICTIONS=true # Set this to false to disable logging model predictions +``` + +## Logging Checkpoints with Comet + +Logging Models to Comet is disabled by default. To enable it, pass the `save-period` argument to the training script. This will save the +logged checkpoints to Comet based on the interval value provided by `save-period` + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--save-period 1 +``` + +## Logging Model Predictions + +By default, model predictions (images, ground truth labels and bounding boxes) will be logged to Comet. + +You can control the frequency of logged predictions and the associated images by passing the `bbox_interval` command line argument. Predictions can be visualized using Comet's Object Detection Custom Panel. This frequency corresponds to every Nth batch of data per epoch. In the example below, we are logging every 2nd batch of data for each epoch. + +**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly. + +Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--bbox_interval 2 +``` + +### Controlling the number of Prediction Images logged to Comet + +When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable. + +```shell +env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--bbox_interval 1 +``` + +### Logging Class Level Metrics + +Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision, recall, f1 for each class. + +```shell +env COMET_LOG_PER_CLASS_METRICS=true python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt +``` + +## Uploading a Dataset to Comet Artifacts + +If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag. + +The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--upload_dataset +``` + +You can find the uploaded dataset in the Artifacts tab in your Comet Workspace +artifact-1 + +You can preview the data directly in the Comet UI. +artifact-2 + +Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file +artifact-3 + +### Using a saved Artifact + +If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL. + +``` +# contents of artifact.yaml file +path: "comet:///:" +``` + +Then pass this file to your training script in the following way + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data artifact.yaml \ +--weights yolov5s.pt +``` + +Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. +artifact-4 + +## Resuming a Training Run + +If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path. + +The Run Path has the following format `comet:////`. + +This will restore the run to its state before the interruption, which includes restoring the model from a checkpoint, restoring all hyperparameters and training arguments and downloading Comet dataset Artifacts if they were used in the original run. The resumed run will continue logging to the existing Experiment in the Comet UI + +```shell +python train.py \ +--resume "comet://" +``` + +## Hyperparameter Search with the Comet Optimizer + +YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualize hyperparameter sweeps in the Comet UI. + +### Configuring an Optimizer Sweep + +To configure the Comet Optimizer, you will have to create a JSON file with the information about the sweep. An example file has been provided in `utils/loggers/comet/optimizer_config.json` + +```shell +python utils/loggers/comet/hpo.py \ + --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" +``` + +The `hpo.py` script accepts the same arguments as `train.py`. If you wish to pass additional arguments to your sweep simply add them after +the script. + +```shell +python utils/loggers/comet/hpo.py \ + --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" \ + --save-period 1 \ + --bbox_interval 1 +``` + +### Running a Sweep in Parallel + +```shell +comet optimizer -j utils/loggers/comet/hpo.py \ + utils/loggers/comet/optimizer_config.json" +``` + +### Visualizing Results + +Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +hyperparameter-yolo diff --git a/algorithm/yolov5/utils/loggers/comet/__init__.py b/algorithm/yolov5/utils/loggers/comet/__init__.py new file mode 100644 index 0000000..d459984 --- /dev/null +++ b/algorithm/yolov5/utils/loggers/comet/__init__.py @@ -0,0 +1,508 @@ +import glob +import json +import logging +import os +import sys +from pathlib import Path + +logger = logging.getLogger(__name__) + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +try: + import comet_ml + + # Project Configuration + config = comet_ml.config.get_config() + COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') +except (ModuleNotFoundError, ImportError): + comet_ml = None + COMET_PROJECT_NAME = None + +import PIL +import torch +import torchvision.transforms as T +import yaml + +from utils.dataloaders import img2label_paths +from utils.general import check_dataset, scale_boxes, xywh2xyxy +from utils.metrics import box_iou + +COMET_PREFIX = 'comet://' + +COMET_MODE = os.getenv('COMET_MODE', 'online') + +# Model Saving Settings +COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') + +# Dataset Artifact Settings +COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true' + +# Evaluation Settings +COMET_LOG_CONFUSION_MATRIX = os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true' +COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true' +COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100)) + +# Confusion Matrix Settings +CONF_THRES = float(os.getenv('CONF_THRES', 0.001)) +IOU_THRES = float(os.getenv('IOU_THRES', 0.6)) + +# Batch Logging Settings +COMET_LOG_BATCH_METRICS = os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true' +COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1) +COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1) +COMET_LOG_PER_CLASS_METRICS = os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true' + +RANK = int(os.getenv('RANK', -1)) + +to_pil = T.ToPILImage() + + +class CometLogger: + """Log metrics, parameters, source code, models and much more + with Comet + """ + + def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwargs) -> None: + self.job_type = job_type + self.opt = opt + self.hyp = hyp + + # Comet Flags + self.comet_mode = COMET_MODE + + self.save_model = opt.save_period > -1 + self.model_name = COMET_MODEL_NAME + + # Batch Logging Settings + self.log_batch_metrics = COMET_LOG_BATCH_METRICS + self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL + + # Dataset Artifact Settings + self.upload_dataset = self.opt.upload_dataset if self.opt.upload_dataset else COMET_UPLOAD_DATASET + self.resume = self.opt.resume + + # Default parameters to pass to Experiment objects + self.default_experiment_kwargs = { + 'log_code': False, + 'log_env_gpu': True, + 'log_env_cpu': True, + 'project_name': COMET_PROJECT_NAME,} + self.default_experiment_kwargs.update(experiment_kwargs) + self.experiment = self._get_experiment(self.comet_mode, run_id) + + self.data_dict = self.check_dataset(self.opt.data) + self.class_names = self.data_dict['names'] + self.num_classes = self.data_dict['nc'] + + self.logged_images_count = 0 + self.max_images = COMET_MAX_IMAGE_UPLOADS + + if run_id is None: + self.experiment.log_other('Created from', 'YOLOv5') + if not isinstance(self.experiment, comet_ml.OfflineExperiment): + workspace, project_name, experiment_id = self.experiment.url.split('/')[-3:] + self.experiment.log_other( + 'Run Path', + f'{workspace}/{project_name}/{experiment_id}', + ) + self.log_parameters(vars(opt)) + self.log_parameters(self.opt.hyp) + self.log_asset_data( + self.opt.hyp, + name='hyperparameters.json', + metadata={'type': 'hyp-config-file'}, + ) + self.log_asset( + f'{self.opt.save_dir}/opt.yaml', + metadata={'type': 'opt-config-file'}, + ) + + self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX + + if hasattr(self.opt, 'conf_thres'): + self.conf_thres = self.opt.conf_thres + else: + self.conf_thres = CONF_THRES + if hasattr(self.opt, 'iou_thres'): + self.iou_thres = self.opt.iou_thres + else: + self.iou_thres = IOU_THRES + + self.log_parameters({'val_iou_threshold': self.iou_thres, 'val_conf_threshold': self.conf_thres}) + + self.comet_log_predictions = COMET_LOG_PREDICTIONS + if self.opt.bbox_interval == -1: + self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10 + else: + self.comet_log_prediction_interval = self.opt.bbox_interval + + if self.comet_log_predictions: + self.metadata_dict = {} + self.logged_image_names = [] + + self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS + + self.experiment.log_others({ + 'comet_mode': COMET_MODE, + 'comet_max_image_uploads': COMET_MAX_IMAGE_UPLOADS, + 'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS, + 'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS, + 'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX, + 'comet_model_name': COMET_MODEL_NAME,}) + + # Check if running the Experiment with the Comet Optimizer + if hasattr(self.opt, 'comet_optimizer_id'): + self.experiment.log_other('optimizer_id', self.opt.comet_optimizer_id) + self.experiment.log_other('optimizer_objective', self.opt.comet_optimizer_objective) + self.experiment.log_other('optimizer_metric', self.opt.comet_optimizer_metric) + self.experiment.log_other('optimizer_parameters', json.dumps(self.hyp)) + + def _get_experiment(self, mode, experiment_id=None): + if mode == 'offline': + if experiment_id is not None: + return comet_ml.ExistingOfflineExperiment( + previous_experiment=experiment_id, + **self.default_experiment_kwargs, + ) + + return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,) + + else: + try: + if experiment_id is not None: + return comet_ml.ExistingExperiment( + previous_experiment=experiment_id, + **self.default_experiment_kwargs, + ) + + return comet_ml.Experiment(**self.default_experiment_kwargs) + + except ValueError: + logger.warning('COMET WARNING: ' + 'Comet credentials have not been set. ' + 'Comet will default to offline logging. ' + 'Please set your credentials to enable online logging.') + return self._get_experiment('offline', experiment_id) + + return + + def log_metrics(self, log_dict, **kwargs): + self.experiment.log_metrics(log_dict, **kwargs) + + def log_parameters(self, log_dict, **kwargs): + self.experiment.log_parameters(log_dict, **kwargs) + + def log_asset(self, asset_path, **kwargs): + self.experiment.log_asset(asset_path, **kwargs) + + def log_asset_data(self, asset, **kwargs): + self.experiment.log_asset_data(asset, **kwargs) + + def log_image(self, img, **kwargs): + self.experiment.log_image(img, **kwargs) + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + if not self.save_model: + return + + model_metadata = { + 'fitness_score': fitness_score[-1], + 'epochs_trained': epoch + 1, + 'save_period': opt.save_period, + 'total_epochs': opt.epochs,} + + model_files = glob.glob(f'{path}/*.pt') + for model_path in model_files: + name = Path(model_path).name + + self.experiment.log_model( + self.model_name, + file_or_folder=model_path, + file_name=name, + metadata=model_metadata, + overwrite=True, + ) + + def check_dataset(self, data_file): + with open(data_file) as f: + data_config = yaml.safe_load(f) + + if data_config['path'].startswith(COMET_PREFIX): + path = data_config['path'].replace(COMET_PREFIX, '') + data_dict = self.download_dataset_artifact(path) + + return data_dict + + self.log_asset(self.opt.data, metadata={'type': 'data-config-file'}) + + return check_dataset(data_file) + + def log_predictions(self, image, labelsn, path, shape, predn): + if self.logged_images_count >= self.max_images: + return + detections = predn[predn[:, 4] > self.conf_thres] + iou = box_iou(labelsn[:, 1:], detections[:, :4]) + mask, _ = torch.where(iou > self.iou_thres) + if len(mask) == 0: + return + + filtered_detections = detections[mask] + filtered_labels = labelsn[mask] + + image_id = path.split('/')[-1].split('.')[0] + image_name = f'{image_id}_curr_epoch_{self.experiment.curr_epoch}' + if image_name not in self.logged_image_names: + native_scale_image = PIL.Image.open(path) + self.log_image(native_scale_image, name=image_name) + self.logged_image_names.append(image_name) + + metadata = [] + for cls, *xyxy in filtered_labels.tolist(): + metadata.append({ + 'label': f'{self.class_names[int(cls)]}-gt', + 'score': 100, + 'box': { + 'x': xyxy[0], + 'y': xyxy[1], + 'x2': xyxy[2], + 'y2': xyxy[3]},}) + for *xyxy, conf, cls in filtered_detections.tolist(): + metadata.append({ + 'label': f'{self.class_names[int(cls)]}', + 'score': conf * 100, + 'box': { + 'x': xyxy[0], + 'y': xyxy[1], + 'x2': xyxy[2], + 'y2': xyxy[3]},}) + + self.metadata_dict[image_name] = metadata + self.logged_images_count += 1 + + return + + def preprocess_prediction(self, image, labels, shape, pred): + nl, _ = labels.shape[0], pred.shape[0] + + # Predictions + if self.opt.single_cls: + pred[:, 5] = 0 + + predn = pred.clone() + scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) + + labelsn = None + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred + + return predn, labelsn + + def add_assets_to_artifact(self, artifact, path, asset_path, split): + img_paths = sorted(glob.glob(f'{asset_path}/*')) + label_paths = img2label_paths(img_paths) + + for image_file, label_file in zip(img_paths, label_paths): + image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file]) + + try: + artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split}) + artifact.add(label_file, logical_path=label_logical_path, metadata={'split': split}) + except ValueError as e: + logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.') + logger.error(f'COMET ERROR: {e}') + continue + + return artifact + + def upload_dataset_artifact(self): + dataset_name = self.data_dict.get('dataset_name', 'yolov5-dataset') + path = str((ROOT / Path(self.data_dict['path'])).resolve()) + + metadata = self.data_dict.copy() + for key in ['train', 'val', 'test']: + split_path = metadata.get(key) + if split_path is not None: + metadata[key] = split_path.replace(path, '') + + artifact = comet_ml.Artifact(name=dataset_name, artifact_type='dataset', metadata=metadata) + for key in metadata.keys(): + if key in ['train', 'val', 'test']: + if isinstance(self.upload_dataset, str) and (key != self.upload_dataset): + continue + + asset_path = self.data_dict.get(key) + if asset_path is not None: + artifact = self.add_assets_to_artifact(artifact, path, asset_path, key) + + self.experiment.log_artifact(artifact) + + return + + def download_dataset_artifact(self, artifact_path): + logged_artifact = self.experiment.get_artifact(artifact_path) + artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name) + logged_artifact.download(artifact_save_dir) + + metadata = logged_artifact.metadata + data_dict = metadata.copy() + data_dict['path'] = artifact_save_dir + + metadata_names = metadata.get('names') + if type(metadata_names) == dict: + data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()} + elif type(metadata_names) == list: + data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} + else: + raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary" + + data_dict = self.update_data_paths(data_dict) + return data_dict + + def update_data_paths(self, data_dict): + path = data_dict.get('path', '') + + for split in ['train', 'val', 'test']: + if data_dict.get(split): + split_path = data_dict.get(split) + data_dict[split] = (f'{path}/{split_path}' if isinstance(split, str) else [ + f'{path}/{x}' for x in split_path]) + + return data_dict + + def on_pretrain_routine_end(self, paths): + if self.opt.resume: + return + + for path in paths: + self.log_asset(str(path)) + + if self.upload_dataset: + if not self.resume: + self.upload_dataset_artifact() + + return + + def on_train_start(self): + self.log_parameters(self.hyp) + + def on_train_epoch_start(self): + return + + def on_train_epoch_end(self, epoch): + self.experiment.curr_epoch = epoch + + return + + def on_train_batch_start(self): + return + + def on_train_batch_end(self, log_dict, step): + self.experiment.curr_step = step + if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0): + self.log_metrics(log_dict, step=step) + + return + + def on_train_end(self, files, save_dir, last, best, epoch, results): + if self.comet_log_predictions: + curr_epoch = self.experiment.curr_epoch + self.experiment.log_asset_data(self.metadata_dict, 'image-metadata.json', epoch=curr_epoch) + + for f in files: + self.log_asset(f, metadata={'epoch': epoch}) + self.log_asset(f'{save_dir}/results.csv', metadata={'epoch': epoch}) + + if not self.opt.evolve: + model_path = str(best if best.exists() else last) + name = Path(model_path).name + if self.save_model: + self.experiment.log_model( + self.model_name, + file_or_folder=model_path, + file_name=name, + overwrite=True, + ) + + # Check if running Experiment with Comet Optimizer + if hasattr(self.opt, 'comet_optimizer_id'): + metric = results.get(self.opt.comet_optimizer_metric) + self.experiment.log_other('optimizer_metric_value', metric) + + self.finish_run() + + def on_val_start(self): + return + + def on_val_batch_start(self): + return + + def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): + if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)): + return + + for si, pred in enumerate(outputs): + if len(pred) == 0: + continue + + image = images[si] + labels = targets[targets[:, 0] == si, 1:] + shape = shapes[si] + path = paths[si] + predn, labelsn = self.preprocess_prediction(image, labels, shape, pred) + if labelsn is not None: + self.log_predictions(image, labelsn, path, shape, predn) + + return + + def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + if self.comet_log_per_class_metrics: + if self.num_classes > 1: + for i, c in enumerate(ap_class): + class_name = self.class_names[c] + self.experiment.log_metrics( + { + 'mAP@.5': ap50[i], + 'mAP@.5:.95': ap[i], + 'precision': p[i], + 'recall': r[i], + 'f1': f1[i], + 'true_positives': tp[i], + 'false_positives': fp[i], + 'support': nt[c]}, + prefix=class_name) + + if self.comet_log_confusion_matrix: + epoch = self.experiment.curr_epoch + class_names = list(self.class_names.values()) + class_names.append('background') + num_classes = len(class_names) + + self.experiment.log_confusion_matrix( + matrix=confusion_matrix.matrix, + max_categories=num_classes, + labels=class_names, + epoch=epoch, + column_label='Actual Category', + row_label='Predicted Category', + file_name=f'confusion-matrix-epoch-{epoch}.json', + ) + + def on_fit_epoch_end(self, result, epoch): + self.log_metrics(result, epoch=epoch) + + def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: + self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) + + def on_params_update(self, params): + self.log_parameters(params) + + def finish_run(self): + self.experiment.end() diff --git a/algorithm/yolov5/utils/loggers/comet/comet_utils.py b/algorithm/yolov5/utils/loggers/comet/comet_utils.py new file mode 100644 index 0000000..2760076 --- /dev/null +++ b/algorithm/yolov5/utils/loggers/comet/comet_utils.py @@ -0,0 +1,150 @@ +import logging +import os +from urllib.parse import urlparse + +try: + import comet_ml +except (ModuleNotFoundError, ImportError): + comet_ml = None + +import yaml + +logger = logging.getLogger(__name__) + +COMET_PREFIX = 'comet://' +COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5') +COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv('COMET_DEFAULT_CHECKPOINT_FILENAME', 'last.pt') + + +def download_model_checkpoint(opt, experiment): + model_dir = f'{opt.project}/{experiment.name}' + os.makedirs(model_dir, exist_ok=True) + + model_name = COMET_MODEL_NAME + model_asset_list = experiment.get_model_asset_list(model_name) + + if len(model_asset_list) == 0: + logger.error(f'COMET ERROR: No checkpoints found for model name : {model_name}') + return + + model_asset_list = sorted( + model_asset_list, + key=lambda x: x['step'], + reverse=True, + ) + logged_checkpoint_map = {asset['fileName']: asset['assetId'] for asset in model_asset_list} + + resource_url = urlparse(opt.weights) + checkpoint_filename = resource_url.query + + if checkpoint_filename: + asset_id = logged_checkpoint_map.get(checkpoint_filename) + else: + asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME) + checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME + + if asset_id is None: + logger.error(f'COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment') + return + + try: + logger.info(f'COMET INFO: Downloading checkpoint {checkpoint_filename}') + asset_filename = checkpoint_filename + + model_binary = experiment.get_asset(asset_id, return_type='binary', stream=False) + model_download_path = f'{model_dir}/{asset_filename}' + with open(model_download_path, 'wb') as f: + f.write(model_binary) + + opt.weights = model_download_path + + except Exception as e: + logger.warning('COMET WARNING: Unable to download checkpoint from Comet') + logger.exception(e) + + +def set_opt_parameters(opt, experiment): + """Update the opts Namespace with parameters + from Comet's ExistingExperiment when resuming a run + + Args: + opt (argparse.Namespace): Namespace of command line options + experiment (comet_ml.APIExperiment): Comet API Experiment object + """ + asset_list = experiment.get_asset_list() + resume_string = opt.resume + + for asset in asset_list: + if asset['fileName'] == 'opt.yaml': + asset_id = asset['assetId'] + asset_binary = experiment.get_asset(asset_id, return_type='binary', stream=False) + opt_dict = yaml.safe_load(asset_binary) + for key, value in opt_dict.items(): + setattr(opt, key, value) + opt.resume = resume_string + + # Save hyperparameters to YAML file + # Necessary to pass checks in training script + save_dir = f'{opt.project}/{experiment.name}' + os.makedirs(save_dir, exist_ok=True) + + hyp_yaml_path = f'{save_dir}/hyp.yaml' + with open(hyp_yaml_path, 'w') as f: + yaml.dump(opt.hyp, f) + opt.hyp = hyp_yaml_path + + +def check_comet_weights(opt): + """Downloads model weights from Comet and updates the + weights path to point to saved weights location + + Args: + opt (argparse.Namespace): Command Line arguments passed + to YOLOv5 training script + + Returns: + None/bool: Return True if weights are successfully downloaded + else return None + """ + if comet_ml is None: + return + + if isinstance(opt.weights, str): + if opt.weights.startswith(COMET_PREFIX): + api = comet_ml.API() + resource = urlparse(opt.weights) + experiment_path = f'{resource.netloc}{resource.path}' + experiment = api.get(experiment_path) + download_model_checkpoint(opt, experiment) + return True + + return None + + +def check_comet_resume(opt): + """Restores run parameters to its original state based on the model checkpoint + and logged Experiment parameters. + + Args: + opt (argparse.Namespace): Command Line arguments passed + to YOLOv5 training script + + Returns: + None/bool: Return True if the run is restored successfully + else return None + """ + if comet_ml is None: + return + + if isinstance(opt.resume, str): + if opt.resume.startswith(COMET_PREFIX): + api = comet_ml.API() + resource = urlparse(opt.resume) + experiment_path = f'{resource.netloc}{resource.path}' + experiment = api.get(experiment_path) + set_opt_parameters(opt, experiment) + download_model_checkpoint(opt, experiment) + + return True + + return None diff --git a/algorithm/yolov5/utils/loggers/comet/hpo.py b/algorithm/yolov5/utils/loggers/comet/hpo.py new file mode 100644 index 0000000..fc49115 --- /dev/null +++ b/algorithm/yolov5/utils/loggers/comet/hpo.py @@ -0,0 +1,118 @@ +import argparse +import json +import logging +import os +import sys +from pathlib import Path + +import comet_ml + +logger = logging.getLogger(__name__) + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +from train import train +from utils.callbacks import Callbacks +from utils.general import increment_path +from utils.torch_utils import select_device + +# Project Configuration +config = comet_ml.config.get_config() +COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5') + + +def get_args(known=False): + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=300, help='total training epochs') + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--noval', action='store_true', help='only validate final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') + parser.add_argument('--noplots', action='store_true', help='save no plot files') + parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--quad', action='store_true', help='quad dataloader') + parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler') + parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') + parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') + parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') + parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') + parser.add_argument('--seed', type=int, default=0, help='Global training seed') + parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify') + + # Weights & Biases arguments + parser.add_argument('--entity', default=None, help='W&B: Entity') + parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option') + parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval') + parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use') + + # Comet Arguments + parser.add_argument('--comet_optimizer_config', type=str, help='Comet: Path to a Comet Optimizer Config File.') + parser.add_argument('--comet_optimizer_id', type=str, help='Comet: ID of the Comet Optimizer sweep.') + parser.add_argument('--comet_optimizer_objective', type=str, help="Comet: Set to 'minimize' or 'maximize'.") + parser.add_argument('--comet_optimizer_metric', type=str, help='Comet: Metric to Optimize.') + parser.add_argument('--comet_optimizer_workers', + type=int, + default=1, + help='Comet: Number of Parallel Workers to use with the Comet Optimizer.') + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def run(parameters, opt): + hyp_dict = {k: v for k, v in parameters.items() if k not in ['epochs', 'batch_size']} + + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) + opt.batch_size = parameters.get('batch_size') + opt.epochs = parameters.get('epochs') + + device = select_device(opt.device, batch_size=opt.batch_size) + train(hyp_dict, opt, device, callbacks=Callbacks()) + + +if __name__ == '__main__': + opt = get_args(known=True) + + opt.weights = str(opt.weights) + opt.cfg = str(opt.cfg) + opt.data = str(opt.data) + opt.project = str(opt.project) + + optimizer_id = os.getenv('COMET_OPTIMIZER_ID') + if optimizer_id is None: + with open(opt.comet_optimizer_config) as f: + optimizer_config = json.load(f) + optimizer = comet_ml.Optimizer(optimizer_config) + else: + optimizer = comet_ml.Optimizer(optimizer_id) + + opt.comet_optimizer_id = optimizer.id + status = optimizer.status() + + opt.comet_optimizer_objective = status['spec']['objective'] + opt.comet_optimizer_metric = status['spec']['metric'] + + logger.info('COMET INFO: Starting Hyperparameter Sweep') + for parameter in optimizer.get_parameters(): + run(parameter['parameters'], opt) diff --git a/algorithm/yolov5/utils/loggers/wandb/__init__.py b/algorithm/yolov5/utils/loggers/wandb/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5/utils/loggers/wandb/wandb_utils.py b/algorithm/yolov5/utils/loggers/wandb/wandb_utils.py new file mode 100644 index 0000000..c8ab381 --- /dev/null +++ b/algorithm/yolov5/utils/loggers/wandb/wandb_utils.py @@ -0,0 +1,193 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license + +# WARNING ⚠️ wandb is deprecated and will be removed in future release. +# See supported integrations at https://github.com/ultralytics/yolov5#integrations + +import logging +import os +import sys +from contextlib import contextmanager +from pathlib import Path + +from utils.general import LOGGER, colorstr + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +RANK = int(os.getenv('RANK', -1)) +DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \ + f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.' + +try: + import wandb + + assert hasattr(wandb, '__version__') # verify package import not local dir + LOGGER.warning(DEPRECATION_WARNING) +except (ImportError, AssertionError): + wandb = None + + +class WandbLogger(): + """Log training runs, datasets, models, and predictions to Weights & Biases. + + This logger sends information to W&B at wandb.ai. By default, this information + includes hyperparameters, system configuration and metrics, model metrics, + and basic data metrics and analyses. + + By providing additional command line arguments to train.py, datasets, + models and predictions can also be logged. + + For more on how this logger is used, see the Weights & Biases documentation: + https://docs.wandb.com/guides/integrations/yolov5 + """ + + def __init__(self, opt, run_id=None, job_type='Training'): + """ + - Initialize WandbLogger instance + - Upload dataset if opt.upload_dataset is True + - Setup training processes if job_type is 'Training' + + arguments: + opt (namespace) -- Commandline arguments for this run + run_id (str) -- Run ID of W&B run to be resumed + job_type (str) -- To set the job_type for this run + + """ + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run = wandb, wandb.run if wandb else None + self.val_artifact, self.train_artifact = None, None + self.train_artifact_path, self.val_artifact_path = None, None + self.result_artifact = None + self.val_table, self.result_table = None, None + self.max_imgs_to_log = 16 + self.data_dict = None + if self.wandb: + self.wandb_run = wandb.init(config=opt, + resume='allow', + project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, + entity=opt.entity, + name=opt.name if opt.name != 'exp' else None, + job_type=job_type, + id=run_id, + allow_val_change=True) if not wandb.run else wandb.run + + if self.wandb_run: + if self.job_type == 'Training': + if isinstance(opt.data, dict): + # This means another dataset manager has already processed the dataset info (e.g. ClearML) + # and they will have stored the already processed dict in opt.data + self.data_dict = opt.data + self.setup_training(opt) + + def setup_training(self, opt): + """ + Setup the necessary processes for training YOLO models: + - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX + - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded + - Setup log_dict, initialize bbox_interval + + arguments: + opt (namespace) -- commandline arguments for this run + + """ + self.log_dict, self.current_epoch = {}, 0 + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + model_dir, _ = self.download_model_artifact(opt) + if model_dir: + self.weights = Path(model_dir) / 'last.pt' + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str( + self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \ + config.hyp, config.imgsz + + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + if opt.evolve or opt.noplots: + self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + """ + Log the model checkpoint as W&B artifact + + arguments: + path (Path) -- Path of directory containing the checkpoints + opt (namespace) -- Command line arguments for this run + epoch (int) -- Current epoch number + fitness_score (float) -- fitness score for current epoch + best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. + """ + model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', + type='model', + metadata={ + 'original_url': str(path), + 'epochs_trained': epoch + 1, + 'save period': opt.save_period, + 'project': opt.project, + 'total_epochs': opt.epochs, + 'fitness_score': fitness_score}) + model_artifact.add_file(str(path / 'last.pt'), name='last.pt') + wandb.log_artifact(model_artifact, + aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) + LOGGER.info(f'Saving model artifact on epoch {epoch + 1}') + + def val_one_image(self, pred, predn, path, names, im): + pass + + def log(self, log_dict): + """ + save the metrics to the logging dictionary + + arguments: + log_dict (Dict) -- metrics/media to be logged in current step + """ + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self): + """ + commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. + + arguments: + best_result (boolean): Boolean representing if the result of this evaluation is best or not + """ + if self.wandb_run: + with all_logging_disabled(): + try: + wandb.log(self.log_dict) + except BaseException as e: + LOGGER.info( + f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}' + ) + self.wandb_run.finish() + self.wandb_run = None + self.log_dict = {} + + def finish_run(self): + """ + Log metrics if any and finish the current W&B run + """ + if self.wandb_run: + if self.log_dict: + with all_logging_disabled(): + wandb.log(self.log_dict) + wandb.run.finish() + LOGGER.warning(DEPRECATION_WARNING) + + +@contextmanager +def all_logging_disabled(highest_level=logging.CRITICAL): + """ source - https://gist.github.com/simon-weber/7853144 + A context manager that will prevent any logging messages triggered during the body from being processed. + :param highest_level: the maximum logging level in use. + This would only need to be changed if a custom level greater than CRITICAL is defined. + """ + previous_level = logging.root.manager.disable + logging.disable(highest_level) + try: + yield + finally: + logging.disable(previous_level) diff --git a/algorithm/yolov5/utils/loss.py b/algorithm/yolov5/utils/loss.py new file mode 100644 index 0000000..9b9c3d9 --- /dev/null +++ b/algorithm/yolov5/utils/loss.py @@ -0,0 +1,234 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Loss functions +""" + +import torch +import torch.nn as nn + +from utils.metrics import bbox_iou +from utils.torch_utils import de_parallel + + +def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 + # return positive, negative label smoothing BCE targets + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=0.05): + super().__init__() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class FocalLoss(nn.Module): + # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +class ComputeLoss: + sort_obj_iou = False + + # Compute losses + def __init__(self, model, autobalance=False): + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + m = de_parallel(model).model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 + self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + self.na = m.na # number of anchors + self.nc = m.nc # number of classes + self.nl = m.nl # number of layers + self.anchors = m.anchors + self.device = device + + def __call__(self, p, targets): # predictions, targets + lcls = torch.zeros(1, device=self.device) # class loss + lbox = torch.zeros(1, device=self.device) # box loss + lobj = torch.zeros(1, device=self.device) # object loss + tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj + + n = b.shape[0] # number of targets + if n: + # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0 + pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions + + # Regression + pxy = pxy.sigmoid() * 2 - 0.5 + pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + iou = iou.detach().clamp(0).type(tobj.dtype) + if self.sort_obj_iou: + j = iou.argsort() + b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] + if self.gr < 1: + iou = (1.0 - self.gr) + self.gr * iou + tobj[b, a, gj, gi] = iou # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(pcls, self.cn, device=self.device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(pcls, t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + + return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() + + def build_targets(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=self.device) # normalized to gridspace gain + ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device).float() * g # offsets + + for i in range(self.nl): + anchors, shape = self.anchors[i], p[i].shape + gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain # shape(3,n,7) + if nt: + # Matches + r = t[..., 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1 < g) & (gxy > 1)).T + l, m = ((gxi % 1 < g) & (gxi > 1)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors + a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class + gij = (gxy - offsets).long() + gi, gj = gij.T # grid indices + + # Append + indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch diff --git a/algorithm/yolov5/utils/metrics.py b/algorithm/yolov5/utils/metrics.py new file mode 100644 index 0000000..709f51a --- /dev/null +++ b/algorithm/yolov5/utils/metrics.py @@ -0,0 +1,360 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Model validation metrics +""" + +import math +import warnings +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from algorithm.yolov5.utils import TryExcept, threaded + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def smooth(y, f=0.05): + # Box filter of fraction f + nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) + p = np.ones(nf // 2) # ones padding + yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded + return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed + + +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''): + """ Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes, nt = np.unique(target_cls, return_counts=True) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = nt[ci] # number of labels + n_p = i.sum() # number of predictions + if n_p == 0 or n_l == 0: + continue + + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + eps) # recall curve + r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) + if plot and j == 0: + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + eps) + names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data + names = dict(enumerate(names)) # to dict + if plot: + plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names) + plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1') + plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision') + plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall') + + i = smooth(f1.mean(0), 0.1).argmax() # max F1 index + p, r, f1 = p[:, i], r[:, i], f1[:, i] + tp = (r * nt).round() # true positives + fp = (tp / (p + eps) - tp).round() # false positives + return tp, fp, p, r, f1, ap, unique_classes.astype(int) + + +def compute_ap(recall, precision): + """ Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + # Returns + Average precision, precision curve, recall curve + """ + + # Append sentinel values to beginning and end + mrec = np.concatenate(([0.0], recall, [1.0])) + mpre = np.concatenate(([1.0], precision, [0.0])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = 'interp' # methods: 'continuous', 'interp' + if method == 'interp': + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix + def __init__(self, nc, conf=0.25, iou_thres=0.45): + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + if detections is None: + gt_classes = labels.int() + for gc in gt_classes: + self.matrix[self.nc, gc] += 1 # background FN + return + + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(int) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[detection_classes[m1[j]], gc] += 1 # correct + else: + self.matrix[self.nc, gc] += 1 # true background + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[dc, self.nc] += 1 # predicted background + + def tp_fp(self): + tp = self.matrix.diagonal() # true positives + fp = self.matrix.sum(1) - tp # false positives + # fn = self.matrix.sum(0) - tp # false negatives (missed detections) + return tp[:-1], fp[:-1] # remove background class + + @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure') + def plot(self, normalize=True, save_dir='', names=()): + import seaborn as sn + + array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) + nc, nn = self.nc, len(names) # number of classes, names + sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size + labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels + ticklabels = (names + ['background']) if labels else 'auto' + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered + sn.heatmap(array, + ax=ax, + annot=nc < 30, + annot_kws={ + 'size': 8}, + cmap='Blues', + fmt='.2f', + square=True, + vmin=0.0, + xticklabels=ticklabels, + yticklabels=ticklabels).set_facecolor((1, 1, 1)) + ax.set_xlabel('True') + ax.set_ylabel('Predicted') + ax.set_title('Confusion Matrix') + fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) + plt.close(fig) + + def print(self): + for i in range(self.nc + 1): + print(' '.join(map(str, self.matrix[i]))) + + +def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4) + + # Get the coordinates of bounding boxes + if xywh: # transform from xywh to xyxy + (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) + w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 + b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ + b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ + else: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) + b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) + w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps) + w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps) + + # Intersection area + inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * \ + (b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp(0) + + # Union Area + union = w1 * h1 + w2 * h2 - inter + eps + + # IoU + iou = inter / union + if CIoU or DIoU or GIoU: + cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width + ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 + if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + return iou - rho2 / c2 # DIoU + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf + return iou # IoU + + +def box_iou(box1, box2, eps=1e-7): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) + + +def bbox_ioa(box1, box2, eps=1e-7): + """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 + box1: np.array of shape(4) + box2: np.array of shape(nx4) + returns: np.array of shape(n) + """ + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1 + b2_x1, b2_y1, b2_x2, b2_y2 = box2.T + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ + (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps + + # Intersection over box2 area + return inter_area / box2_area + + +def wh_iou(wh1, wh2, eps=1e-7): + # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + + +@threaded +def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()): + # Precision-recall curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) + ax.set_xlabel('Recall') + ax.set_ylabel('Precision') + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') + ax.set_title('Precision-Recall Curve') + fig.savefig(save_dir, dpi=250) + plt.close(fig) + + +@threaded +def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'): + # Metric-confidence curve + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) + + y = smooth(py.mean(0), 0.05) + ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left') + ax.set_title(f'{ylabel}-Confidence Curve') + fig.savefig(save_dir, dpi=250) + plt.close(fig) diff --git a/algorithm/yolov5/utils/nms_rotated/__init__.py b/algorithm/yolov5/utils/nms_rotated/__init__.py new file mode 100644 index 0000000..9768d17 --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/__init__.py @@ -0,0 +1,3 @@ +from .nms_rotated_wrapper import obb_nms, poly_nms + +__all__ = ['obb_nms', 'poly_nms'] diff --git a/algorithm/yolov5/utils/nms_rotated/nms_rotated_wrapper.py b/algorithm/yolov5/utils/nms_rotated/nms_rotated_wrapper.py new file mode 100644 index 0000000..afba7bd --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/nms_rotated_wrapper.py @@ -0,0 +1,78 @@ +import numpy as np +import torch + +from . import nms_rotated_ext + +def obb_nms(dets, scores, iou_thr, device_id=None): + """ + RIoU NMS - iou_thr. + Args: + dets (tensor/array): (num, [cx cy w h θ]) θ∈[-pi/2, pi/2) + scores (tensor/array): (num) + iou_thr (float): (1) + Returns: + dets (tensor): (n_nms, [cx cy w h θ]) + inds (tensor): (n_nms), nms index of dets + """ + if isinstance(dets, torch.Tensor): + is_numpy = False + dets_th = dets + elif isinstance(dets, np.ndarray): + is_numpy = True + device = 'cpu' if device_id is None else f'cuda:{device_id}' + dets_th = torch.from_numpy(dets).to(device) + else: + raise TypeError('dets must be eithr a Tensor or numpy array, ' + f'but got {type(dets)}') + + if dets_th.numel() == 0: # len(dets) + inds = dets_th.new_zeros(0, dtype=torch.int64) + else: + # same bug will happen when bboxes is too small + too_small = dets_th[:, [2, 3]].min(1)[0] < 0.001 # [n] + if too_small.all(): # all the bboxes is too small + inds = dets_th.new_zeros(0, dtype=torch.int64) + else: + ori_inds = torch.arange(dets_th.size(0)) # 0 ~ n-1 + ori_inds = ori_inds[~too_small] + dets_th = dets_th[~too_small] # (n_filter, 5) + scores = scores[~too_small] + + inds = nms_rotated_ext.nms_rotated(dets_th, scores, iou_thr) + inds = ori_inds[inds] + + if is_numpy: + inds = inds.cpu().numpy() + return dets[inds, :], inds + + +def poly_nms(dets, iou_thr, device_id=None): + if isinstance(dets, torch.Tensor): + is_numpy = False + dets_th = dets + elif isinstance(dets, np.ndarray): + is_numpy = True + device = 'cpu' if device_id is None else f'cuda:{device_id}' + dets_th = torch.from_numpy(dets).to(device) + else: + raise TypeError('dets must be eithr a Tensor or numpy array, ' + f'but got {type(dets)}') + + if dets_th.device == torch.device('cpu'): + raise NotImplementedError + inds = nms_rotated_ext.nms_poly(dets_th.float(), iou_thr) + + if is_numpy: + inds = inds.cpu().numpy() + return dets[inds, :], inds + +if __name__ == '__main__': + rboxes_opencv = torch.tensor(([136.6, 111.6, 200, 100, -60], + [136.6, 111.6, 100, 200, -30], + [100, 100, 141.4, 141.4, -45], + [100, 100, 141.4, 141.4, -45])) + rboxes_longedge = torch.tensor(([136.6, 111.6, 200, 100, -60], + [136.6, 111.6, 200, 100, 120], + [100, 100, 141.4, 141.4, 45], + [100, 100, 141.4, 141.4, 135])) + \ No newline at end of file diff --git a/algorithm/yolov5/utils/nms_rotated/setup.py b/algorithm/yolov5/utils/nms_rotated/setup.py new file mode 100644 index 0000000..a3ee967 --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/setup.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python +import os +import subprocess +import time +from setuptools import find_packages, setup + +import torch +from torch.utils.cpp_extension import (BuildExtension, CppExtension, + CUDAExtension) +def make_cuda_ext(name, module, sources, sources_cuda=[]): + + define_macros = [] + extra_compile_args = {'cxx': []} + + if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1': + define_macros += [('WITH_CUDA', None)] + extension = CUDAExtension + extra_compile_args['nvcc'] = [ + '-D__CUDA_NO_HALF_OPERATORS__', + '-D__CUDA_NO_HALF_CONVERSIONS__', + '-D__CUDA_NO_HALF2_OPERATORS__', + ] + sources += sources_cuda + else: + print(f'Compiling {name} without CUDA') + extension = CppExtension + # raise EnvironmentError('CUDA is required to compile MMDetection!') + + return extension( + name=f'{module}.{name}', + sources=[os.path.join(*module.split('.'), p) for p in sources], + define_macros=define_macros, + extra_compile_args=extra_compile_args) + +# python setup.py develop +if __name__ == '__main__': + #write_version_py() + setup( + name='nms_rotated', + ext_modules=[ + make_cuda_ext( + name='nms_rotated_ext', + module='', + sources=[ + 'src/nms_rotated_cpu.cpp', + 'src/nms_rotated_ext.cpp' + ], + sources_cuda=[ + 'src/nms_rotated_cuda.cu', + 'src/poly_nms_cuda.cu', + ]), + ], + cmdclass={'build_ext': BuildExtension}, + zip_safe=False) \ No newline at end of file diff --git a/algorithm/yolov5/utils/nms_rotated/src/box_iou_rotated_utils.h b/algorithm/yolov5/utils/nms_rotated/src/box_iou_rotated_utils.h new file mode 100644 index 0000000..c017e17 --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/src/box_iou_rotated_utils.h @@ -0,0 +1,360 @@ +// Mortified from +// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/box_iou_rotated +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#pragma once + +#include +#include + +#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1 +// Designates functions callable from the host (CPU) and the device (GPU) +#define HOST_DEVICE __host__ __device__ +#define HOST_DEVICE_INLINE HOST_DEVICE __forceinline__ +#else +#include +#define HOST_DEVICE +#define HOST_DEVICE_INLINE HOST_DEVICE inline +#endif + + +template +struct RotatedBox { + T x_ctr, y_ctr, w, h, a; +}; + +template +struct Point { + T x, y; + HOST_DEVICE_INLINE Point(const T& px = 0, const T& py = 0) : x(px), y(py) {} + HOST_DEVICE_INLINE Point operator+(const Point& p) const { + return Point(x + p.x, y + p.y); + } + HOST_DEVICE_INLINE Point& operator+=(const Point& p) { + x += p.x; + y += p.y; + return *this; + } + HOST_DEVICE_INLINE Point operator-(const Point& p) const { + return Point(x - p.x, y - p.y); + } + HOST_DEVICE_INLINE Point operator*(const T coeff) const { + return Point(x * coeff, y * coeff); + } +}; + +template +HOST_DEVICE_INLINE T dot_2d(const Point& A, const Point& B) { + return A.x * B.x + A.y * B.y; +} + +// R: result type. can be different from input type +template +HOST_DEVICE_INLINE R cross_2d(const Point& A, const Point& B) { + return static_cast(A.x) * static_cast(B.y) - + static_cast(B.x) * static_cast(A.y); +} + +template +HOST_DEVICE_INLINE void get_rotated_vertices( + const RotatedBox& box, + Point (&pts)[4]) { + // M_PI / 180. == 0.01745329251 + //double theta = box.a * 0.01745329251; ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + double theta = box.a; + T cosTheta2 = (T)cos(theta) * 0.5f; + T sinTheta2 = (T)sin(theta) * 0.5f; + + // y: top --> down; x: left --> right + pts[0].x = box.x_ctr + sinTheta2 * box.h + cosTheta2 * box.w; + pts[0].y = box.y_ctr + cosTheta2 * box.h - sinTheta2 * box.w; + pts[1].x = box.x_ctr - sinTheta2 * box.h + cosTheta2 * box.w; + pts[1].y = box.y_ctr - cosTheta2 * box.h - sinTheta2 * box.w; + pts[2].x = 2 * box.x_ctr - pts[0].x; + pts[2].y = 2 * box.y_ctr - pts[0].y; + pts[3].x = 2 * box.x_ctr - pts[1].x; + pts[3].y = 2 * box.y_ctr - pts[1].y; +} + +template +HOST_DEVICE_INLINE int get_intersection_points( + const Point (&pts1)[4], + const Point (&pts2)[4], + Point (&intersections)[24]) { + // Line vector + // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1] + Point vec1[4], vec2[4]; + for (int i = 0; i < 4; i++) { + vec1[i] = pts1[(i + 1) % 4] - pts1[i]; + vec2[i] = pts2[(i + 1) % 4] - pts2[i]; + } + + // Line test - test all line combos for intersection + int num = 0; // number of intersections + for (int i = 0; i < 4; i++) { + for (int j = 0; j < 4; j++) { + // Solve for 2x2 Ax=b + T det = cross_2d(vec2[j], vec1[i]); + + // This takes care of parallel lines + if (fabs(det) <= 1e-14) { + continue; + } + + auto vec12 = pts2[j] - pts1[i]; + + T t1 = cross_2d(vec2[j], vec12) / det; + T t2 = cross_2d(vec1[i], vec12) / det; + + if (t1 >= 0.0f && t1 <= 1.0f && t2 >= 0.0f && t2 <= 1.0f) { + intersections[num++] = pts1[i] + vec1[i] * t1; + } + } + } + + // Check for vertices of rect1 inside rect2 + { + const auto& AB = vec2[0]; + const auto& DA = vec2[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + // assume ABCD is the rectangle, and P is the point to be judged + // P is inside ABCD iff. P's projection on AB lies within AB + // and P's projection on AD lies within AD + + auto AP = pts1[i] - pts2[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) && + (APdotAD <= ADdotAD)) { + intersections[num++] = pts1[i]; + } + } + } + + // Reverse the check - check for vertices of rect2 inside rect1 + { + const auto& AB = vec1[0]; + const auto& DA = vec1[3]; + auto ABdotAB = dot_2d(AB, AB); + auto ADdotAD = dot_2d(DA, DA); + for (int i = 0; i < 4; i++) { + auto AP = pts2[i] - pts1[0]; + + auto APdotAB = dot_2d(AP, AB); + auto APdotAD = -dot_2d(AP, DA); + + if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) && + (APdotAD <= ADdotAD)) { + intersections[num++] = pts2[i]; + } + } + } + + return num; +} + +template +HOST_DEVICE_INLINE int convex_hull_graham( + const Point (&p)[24], + const int& num_in, + Point (&q)[24], + bool shift_to_zero = false) { + assert(num_in >= 2); + + // Step 1: + // Find point with minimum y + // if more than 1 points have the same minimum y, + // pick the one with the minimum x. + int t = 0; + for (int i = 1; i < num_in; i++) { + if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) { + t = i; + } + } + auto& start = p[t]; // starting point + + // Step 2: + // Subtract starting point from every points (for sorting in the next step) + for (int i = 0; i < num_in; i++) { + q[i] = p[i] - start; + } + + // Swap the starting point to position 0 + auto tmp = q[0]; + q[0] = q[t]; + q[t] = tmp; + + // Step 3: + // Sort point 1 ~ num_in according to their relative cross-product values + // (essentially sorting according to angles) + // If the angles are the same, sort according to their distance to origin + T dist[24]; +#if defined(__CUDACC__) || __HCC__ == 1 || __HIP__ == 1 + // compute distance to origin before sort, and sort them together with the + // points + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } + + // CUDA version + // In the future, we can potentially use thrust + // for sorting here to improve speed (though not guaranteed) + for (int i = 1; i < num_in - 1; i++) { + for (int j = i + 1; j < num_in; j++) { + T crossProduct = cross_2d(q[i], q[j]); + if ((crossProduct < -1e-6) || + (fabs(crossProduct) < 1e-6 && dist[i] > dist[j])) { + auto q_tmp = q[i]; + q[i] = q[j]; + q[j] = q_tmp; + auto dist_tmp = dist[i]; + dist[i] = dist[j]; + dist[j] = dist_tmp; + } + } + } +#else + // CPU version + std::sort( + q + 1, q + num_in, [](const Point& A, const Point& B) -> bool { + T temp = cross_2d(A, B); + if (fabs(temp) < 1e-6) { + return dot_2d(A, A) < dot_2d(B, B); + } else { + return temp > 0; + } + }); + // compute distance to origin after sort, since the points are now different. + for (int i = 0; i < num_in; i++) { + dist[i] = dot_2d(q[i], q[i]); + } +#endif + + // Step 4: + // Make sure there are at least 2 points (that don't overlap with each other) + // in the stack + int k; // index of the non-overlapped second point + for (k = 1; k < num_in; k++) { + if (dist[k] > 1e-8) { + break; + } + } + if (k == num_in) { + // We reach the end, which means the convex hull is just one point + q[0] = p[t]; + return 1; + } + q[1] = q[k]; + int m = 2; // 2 points in the stack + // Step 5: + // Finally we can start the scanning process. + // When a non-convex relationship between the 3 points is found + // (either concave shape or duplicated points), + // we pop the previous point from the stack + // until the 3-point relationship is convex again, or + // until the stack only contains two points + for (int i = k + 1; i < num_in; i++) { + while (m > 1) { + auto q1 = q[i] - q[m - 2], q2 = q[m - 1] - q[m - 2]; + // cross_2d() uses FMA and therefore computes round(round(q1.x*q2.y) - + // q2.x*q1.y) So it may not return 0 even when q1==q2. Therefore we + // compare round(q1.x*q2.y) and round(q2.x*q1.y) directly. (round means + // round to nearest floating point). + if (q1.x * q2.y >= q2.x * q1.y) + m--; + else + break; + } + // Using double also helps, but float can solve the issue for now. + // while (m > 1 && cross_2d(q[i] - q[m - 2], q[m - 1] - q[m - 2]) + // >= 0) { + // m--; + // } + q[m++] = q[i]; + } + + // Step 6 (Optional): + // In general sense we need the original coordinates, so we + // need to shift the points back (reverting Step 2) + // But if we're only interested in getting the area/perimeter of the shape + // We can simply return. + if (!shift_to_zero) { + for (int i = 0; i < m; i++) { + q[i] += start; + } + } + + return m; +} + +template +HOST_DEVICE_INLINE T polygon_area(const Point (&q)[24], const int& m) { + if (m <= 2) { + return 0; + } + + T area = 0; + for (int i = 1; i < m - 1; i++) { + area += fabs(cross_2d(q[i] - q[0], q[i + 1] - q[0])); + } + + return area / 2.0; +} + +template +HOST_DEVICE_INLINE T rotated_boxes_intersection( + const RotatedBox& box1, + const RotatedBox& box2) { + // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned + // from rotated_rect_intersection_pts + Point intersectPts[24], orderedPts[24]; + + Point pts1[4]; + Point pts2[4]; + get_rotated_vertices(box1, pts1); + get_rotated_vertices(box2, pts2); + + int num = get_intersection_points(pts1, pts2, intersectPts); + + if (num <= 2) { + return 0.0; + } + + // Convex Hull to order the intersection points in clockwise order and find + // the contour area. + int num_convex = convex_hull_graham(intersectPts, num, orderedPts, true); + return polygon_area(orderedPts, num_convex); +} + + +template +HOST_DEVICE_INLINE T +single_box_iou_rotated(T const* const box1_raw, T const* const box2_raw) { + // shift center to the middle point to achieve higher precision in result + RotatedBox box1, box2; + auto center_shift_x = (box1_raw[0] + box2_raw[0]) / 2.0; + auto center_shift_y = (box1_raw[1] + box2_raw[1]) / 2.0; + box1.x_ctr = box1_raw[0] - center_shift_x; + box1.y_ctr = box1_raw[1] - center_shift_y; + box1.w = box1_raw[2]; + box1.h = box1_raw[3]; + box1.a = box1_raw[4]; + box2.x_ctr = box2_raw[0] - center_shift_x; + box2.y_ctr = box2_raw[1] - center_shift_y; + box2.w = box2_raw[2]; + box2.h = box2_raw[3]; + box2.a = box2_raw[4]; + + T area1 = box1.w * box1.h; + T area2 = box2.w * box2.h; + if (area1 < 1e-14 || area2 < 1e-14) { + return 0.f; + } + + T intersection = rotated_boxes_intersection(box1, box2); + T iou = intersection / (area1 + area2 - intersection); + return iou; +} diff --git a/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_cpu.cpp b/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_cpu.cpp new file mode 100644 index 0000000..185e9a4 --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_cpu.cpp @@ -0,0 +1,74 @@ +// Modified from +// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/nms_rotated +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include "box_iou_rotated_utils.h" + + +template +at::Tensor nms_rotated_cpu_kernel( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + // nms_rotated_cpu_kernel is modified from torchvision's nms_cpu_kernel, + // however, the code in this function is much shorter because + // we delegate the IoU computation for rotated boxes to + // the single_box_iou_rotated function in box_iou_rotated_utils.h + AT_ASSERTM(dets.device().is_cpu(), "dets must be a CPU tensor"); + AT_ASSERTM(scores.device().is_cpu(), "scores must be a CPU tensor"); + AT_ASSERTM( + dets.scalar_type() == scores.scalar_type(), + "dets should have the same type as scores"); + + if (dets.numel() == 0) { + return at::empty({0}, dets.options().dtype(at::kLong)); + } + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + + auto ndets = dets.size(0); + at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte)); + at::Tensor keep_t = at::zeros({ndets}, dets.options().dtype(at::kLong)); + + auto suppressed = suppressed_t.data_ptr(); + auto keep = keep_t.data_ptr(); + auto order = order_t.data_ptr(); + + int64_t num_to_keep = 0; + + for (int64_t _i = 0; _i < ndets; _i++) { + auto i = order[_i]; + if (suppressed[i] == 1) { + continue; + } + + keep[num_to_keep++] = i; + + for (int64_t _j = _i + 1; _j < ndets; _j++) { + auto j = order[_j]; + if (suppressed[j] == 1) { + continue; + } + + auto ovr = single_box_iou_rotated( + dets[i].data_ptr(), dets[j].data_ptr()); + if (ovr >= iou_threshold) { + suppressed[j] = 1; + } + } + } + return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep); +} + +at::Tensor nms_rotated_cpu( + // input must be contiguous + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + auto result = at::empty({0}, dets.options()); + + AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "nms_rotated", [&] { + result = nms_rotated_cpu_kernel(dets, scores, iou_threshold); + }); + return result; +} diff --git a/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_cuda.cu b/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_cuda.cu new file mode 100644 index 0000000..84d5acf --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_cuda.cu @@ -0,0 +1,134 @@ +// Modified from +// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/nms_rotated +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include +#include +#include +#include "box_iou_rotated_utils.h" + +int const threadsPerBlock = sizeof(unsigned long long) * 8; + +template +__global__ void nms_rotated_cuda_kernel( + const int n_boxes, + const float iou_threshold, + const T* dev_boxes, + unsigned long long* dev_mask) { + // nms_rotated_cuda_kernel is modified from torchvision's nms_cuda_kernel + + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + // if (row_start > col_start) return; + + const int row_size = + min(n_boxes - row_start * threadsPerBlock, threadsPerBlock); + const int col_size = + min(n_boxes - col_start * threadsPerBlock, threadsPerBlock); + + // Compared to nms_cuda_kernel, where each box is represented with 4 values + // (x1, y1, x2, y2), each rotated box is represented with 5 values + // (x_center, y_center, width, height, angle_degrees) here. + __shared__ T block_boxes[threadsPerBlock * 5]; + if (threadIdx.x < col_size) { + block_boxes[threadIdx.x * 5 + 0] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0]; + block_boxes[threadIdx.x * 5 + 1] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1]; + block_boxes[threadIdx.x * 5 + 2] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2]; + block_boxes[threadIdx.x * 5 + 3] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3]; + block_boxes[threadIdx.x * 5 + 4] = + dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const T* cur_box = dev_boxes + cur_box_idx * 5; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < col_size; i++) { + // Instead of devIoU used by original horizontal nms, here + // we use the single_box_iou_rotated function from box_iou_rotated_utils.h + if (single_box_iou_rotated(cur_box, block_boxes + i * 5) > + iou_threshold) { + t |= 1ULL << i; + } + } + const int col_blocks = at::cuda::ATenCeilDiv(n_boxes, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + + +at::Tensor nms_rotated_cuda( + // input must be contiguous + const at::Tensor& dets, + const at::Tensor& scores, + float iou_threshold) { + // using scalar_t = float; + AT_ASSERTM(dets.is_cuda(), "dets must be a CUDA tensor"); + AT_ASSERTM(scores.is_cuda(), "scores must be a CUDA tensor"); + at::cuda::CUDAGuard device_guard(dets.device()); + + auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); + auto dets_sorted = dets.index_select(0, order_t); + + auto dets_num = dets.size(0); + + const int col_blocks = + at::cuda::ATenCeilDiv(static_cast(dets_num), threadsPerBlock); + + at::Tensor mask = + at::empty({dets_num * col_blocks}, dets.options().dtype(at::kLong)); + + dim3 blocks(col_blocks, col_blocks); + dim3 threads(threadsPerBlock); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + AT_DISPATCH_FLOATING_TYPES( + dets_sorted.scalar_type(), "nms_rotated_kernel_cuda", [&] { + nms_rotated_cuda_kernel<<>>( + dets_num, + iou_threshold, + dets_sorted.data_ptr(), + (unsigned long long*)mask.data_ptr()); + }); + + at::Tensor mask_cpu = mask.to(at::kCPU); + unsigned long long* mask_host = + (unsigned long long*)mask_cpu.data_ptr(); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = + at::empty({dets_num}, dets.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data_ptr(); + + int num_to_keep = 0; + for (int i = 0; i < dets_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long* p = mask_host + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + AT_CUDA_CHECK(cudaGetLastError()); + return order_t.index( + {keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep) + .to(order_t.device(), keep.scalar_type())}); +} diff --git a/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_ext.cpp b/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_ext.cpp new file mode 100644 index 0000000..287338f --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/src/nms_rotated_ext.cpp @@ -0,0 +1,60 @@ +// Modified from +// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/nms_rotated +// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +#include +#include + + +#ifdef WITH_CUDA +at::Tensor nms_rotated_cuda( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold); + +at::Tensor poly_nms_cuda( + const at::Tensor boxes, + float nms_overlap_thresh); +#endif + +at::Tensor nms_rotated_cpu( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold); + + +inline at::Tensor nms_rotated( + const at::Tensor& dets, + const at::Tensor& scores, + const float iou_threshold) { + assert(dets.device().is_cuda() == scores.device().is_cuda()); + if (dets.device().is_cuda()) { +#ifdef WITH_CUDA + return nms_rotated_cuda( + dets.contiguous(), scores.contiguous(), iou_threshold); +#else + AT_ERROR("Not compiled with GPU support"); +#endif + } + return nms_rotated_cpu(dets.contiguous(), scores.contiguous(), iou_threshold); +} + + +inline at::Tensor nms_poly( + const at::Tensor& dets, + const float iou_threshold) { + if (dets.device().is_cuda()) { +#ifdef WITH_CUDA + if (dets.numel() == 0) + return at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)); + return poly_nms_cuda(dets, iou_threshold); +#else + AT_ERROR("POLY_NMS is not compiled with GPU support"); +#endif + } + AT_ERROR("POLY_NMS is not implemented on CPU"); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("nms_rotated", &nms_rotated, "nms for rotated bboxes"); + m.def("nms_poly", &nms_poly, "nms for poly bboxes"); +} diff --git a/algorithm/yolov5/utils/nms_rotated/src/poly_nms_cpu.cpp b/algorithm/yolov5/utils/nms_rotated/src/poly_nms_cpu.cpp new file mode 100644 index 0000000..75af948 --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/src/poly_nms_cpu.cpp @@ -0,0 +1,5 @@ +#include + +template +at::Tensor poly_nms_cpu_kernel(const at::Tensor& dets, const float threshold) { + diff --git a/algorithm/yolov5/utils/nms_rotated/src/poly_nms_cuda.cu b/algorithm/yolov5/utils/nms_rotated/src/poly_nms_cuda.cu new file mode 100644 index 0000000..efa3286 --- /dev/null +++ b/algorithm/yolov5/utils/nms_rotated/src/poly_nms_cuda.cu @@ -0,0 +1,262 @@ +#include +#include + +#include +#include + +#include +#include + +#define CUDA_CHECK(condition) \ + /* Code block avoids redefinition of cudaError_t error */ \ + do { \ + cudaError_t error = condition; \ + if (error != cudaSuccess) { \ + std::cout << cudaGetErrorString(error) << std::endl; \ + } \ + } while (0) + +#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) +int const threadsPerBlock = sizeof(unsigned long long) * 8; + + +#define maxn 10 +const double eps=1E-8; + +__device__ inline int sig(float d){ + return(d>eps)-(d<-eps); +} + +__device__ inline int point_eq(const float2 a, const float2 b) { + return sig(a.x - b.x) == 0 && sig(a.y - b.y)==0; +} + +__device__ inline void point_swap(float2 *a, float2 *b) { + float2 temp = *a; + *a = *b; + *b = temp; +} + +__device__ inline void point_reverse(float2 *first, float2* last) +{ + while ((first!=last)&&(first!=--last)) { + point_swap (first,last); + ++first; + } +} + +__device__ inline float cross(float2 o,float2 a,float2 b){ //叉积 + return(a.x-o.x)*(b.y-o.y)-(b.x-o.x)*(a.y-o.y); +} +__device__ inline float area(float2* ps,int n){ + ps[n]=ps[0]; + float res=0; + for(int i=0;i0) pp[m++]=p[i]; + if(sig(cross(a,b,p[i]))!=sig(cross(a,b,p[i+1]))) + lineCross(a,b,p[i],p[i+1],pp[m++]); + } + n=0; + for(int i=0;i1&&p[n-1]==p[0])n--; + while(n>1&&point_eq(p[n-1], p[0]))n--; +} + +//---------------华丽的分隔线-----------------// +//返回三角形oab和三角形ocd的有向交面积,o是原点// +__device__ inline float intersectArea(float2 a,float2 b,float2 c,float2 d){ + float2 o = make_float2(0,0); + int s1=sig(cross(o,a,b)); + int s2=sig(cross(o,c,d)); + if(s1==0||s2==0)return 0.0;//退化，面积为0 + // if(s1==-1) swap(a,b); + // if(s2==-1) swap(c,d); + if (s1 == -1) point_swap(&a, &b); + if (s2 == -1) point_swap(&c, &d); + float2 p[10]={o,a,b}; + int n=3; + float2 pp[maxn]; + polygon_cut(p,n,o,c,pp); + polygon_cut(p,n,c,d,pp); + polygon_cut(p,n,d,o,pp); + float res=fabs(area(p,n)); + if(s1*s2==-1) res=-res;return res; +} +//求两多边形的交面积 +__device__ inline float intersectArea(float2*ps1,int n1,float2*ps2,int n2){ + if(area(ps1,n1)<0) point_reverse(ps1,ps1+n1); + if(area(ps2,n2)<0) point_reverse(ps2,ps2+n2); + ps1[n1]=ps1[0]; + ps2[n2]=ps2[0]; + float res=0; + for(int i=0;i nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = THCCeilDiv(n_polys, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +// boxes is a N x 9 tensor +at::Tensor poly_nms_cuda(const at::Tensor boxes, float nms_overlap_thresh) { + + at::DeviceGuard guard(boxes.device()); + + using scalar_t = float; + AT_ASSERTM(boxes.device().is_cuda(), "boxes must be a CUDA tensor"); + auto scores = boxes.select(1, 8); + auto order_t = std::get<1>(scores.sort(0, /*descending=*/true)); + auto boxes_sorted = boxes.index_select(0, order_t); + + int boxes_num = boxes.size(0); + + const int col_blocks = THCCeilDiv(boxes_num, threadsPerBlock); + + scalar_t* boxes_dev = boxes_sorted.data_ptr(); + + THCState *state = at::globalContext().lazyInitCUDA(); + + unsigned long long* mask_dev = NULL; + + mask_dev = (unsigned long long*) THCudaMalloc(state, boxes_num * col_blocks * sizeof(unsigned long long)); + + dim3 blocks(THCCeilDiv(boxes_num, threadsPerBlock), + THCCeilDiv(boxes_num, threadsPerBlock)); + dim3 threads(threadsPerBlock); + poly_nms_kernel<<>>(boxes_num, + nms_overlap_thresh, + boxes_dev, + mask_dev); + + std::vector mask_host(boxes_num * col_blocks); + THCudaCheck(cudaMemcpyAsync( + &mask_host[0], + mask_dev, + sizeof(unsigned long long) * boxes_num * col_blocks, + cudaMemcpyDeviceToHost, + at::cuda::getCurrentCUDAStream() + )); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + + at::Tensor keep = at::empty({boxes_num}, boxes.options().dtype(at::kLong).device(at::kCPU)); + int64_t* keep_out = keep.data_ptr(); + + int num_to_keep = 0; + for (int i = 0; i < boxes_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long *p = &mask_host[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + + THCudaFree(state, mask_dev); + + return order_t.index({ + keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep).to( + order_t.device(), keep.scalar_type())}); +} + diff --git a/algorithm/yolov5/utils/plots.py b/algorithm/yolov5/utils/plots.py new file mode 100644 index 0000000..18ef2dc --- /dev/null +++ b/algorithm/yolov5/utils/plots.py @@ -0,0 +1,560 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Plotting utils +""" + +import contextlib +import math +import os +from copy import copy +from pathlib import Path +from urllib.error import URLError + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sn +import torch +from PIL import Image, ImageDraw, ImageFont + +from algorithm.yolov5.utils import TryExcept, threaded +from algorithm.yolov5.utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path, + is_ascii, xywh2xyxy, xyxy2xywh) +from algorithm.yolov5.utils.metrics import fitness +from algorithm.yolov5.utils.segment.general import scale_image + +# Settings +RANK = int(os.getenv('RANK', -1)) +matplotlib.rc('font', **{'size': 11}) +matplotlib.use('Agg') # for writing to files only + + +class Colors: + # Ultralytics color palette https://ultralytics.com/ + def __init__(self): + # hex = matplotlib.colors.TABLEAU_COLORS.values() + hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', + '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') + self.palette = [self.hex2rgb(f'#{c}') for c in hexs] + self.n = len(self.palette) + + def __call__(self, i, bgr=False): + c = self.palette[int(i) % self.n] + return (c[2], c[1], c[0]) if bgr else c + + @staticmethod + def hex2rgb(h): # rgb order (PIL) + return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + + +colors = Colors() # create instance for 'from utils.plots import colors' + + +def check_pil_font(font=FONT, size=10): + # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary + font = Path(font) + font = font if font.exists() else (CONFIG_DIR / font.name) + try: + return ImageFont.truetype(str(font) if font.exists() else font.name, size) + except Exception: # download if missing + try: + check_font(font) + return ImageFont.truetype(str(font), size) + except TypeError: + check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374 + except URLError: # not online + return ImageFont.load_default() + + +class Annotator: + # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations + def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): + assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' + non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic + self.pil = pil or non_ascii + if self.pil: # use PIL + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font, + size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)) + else: # use cv2 + self.im = im + self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width + + def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)): + # Add one xyxy box to image with label + if self.pil or not is_ascii(label): + self.draw.rectangle(box, width=self.lw, outline=color) # box + if label: + w, h = self.font.getsize(label) # text width, height (WARNING: deprecated) in 9.2.0 + # _, _, w, h = self.font.getbbox(label) # text width, height (New) + outside = box[1] - h >= 0 # label fits outside box + self.draw.rectangle( + (box[0], box[1] - h if outside else box[1], box[0] + w + 1, + box[1] + 1 if outside else box[1] + h + 1), + fill=color, + ) + # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0 + self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font) + else: # cv2 + p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3])) + cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA) + if label: + tf = max(self.lw - 1, 1) # font thickness + w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height + outside = p1[1] - h >= 3 + p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3 + cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled + cv2.putText(self.im, + label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), + 0, + self.lw / 3, + txt_color, + thickness=tf, + lineType=cv2.LINE_AA) + + def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False): + """Plot masks at once. + Args: + masks (tensor): predicted masks on cuda, shape: [n, h, w] + colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n] + im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1] + alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque + """ + if self.pil: + # convert to numpy first + self.im = np.asarray(self.im).copy() + if len(masks) == 0: + self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255 + colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0 + colors = colors[:, None, None] # shape(n,1,1,3) + masks = masks.unsqueeze(3) # shape(n,h,w,1) + masks_color = masks * (colors * alpha) # shape(n,h,w,3) + + inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1) + mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3) + + im_gpu = im_gpu.flip(dims=[0]) # flip channel + im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3) + im_gpu = im_gpu * inv_alph_masks[-1] + mcs + im_mask = (im_gpu * 255).byte().cpu().numpy() + self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape) + if self.pil: + # convert im back to PIL and update draw + self.fromarray(self.im) + + def rectangle(self, xy, fill=None, outline=None, width=1): + # Add rectangle to image (PIL-only) + self.draw.rectangle(xy, fill, outline, width) + + def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'): + # Add text to image (PIL-only) + if anchor == 'bottom': # start y from font bottom + w, h = self.font.getsize(text) # text width, height + xy[1] += 1 - h + self.draw.text(xy, text, fill=txt_color, font=self.font) + + def fromarray(self, im): + # Update self.im from a numpy array + self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) + self.draw = ImageDraw.Draw(self.im) + + def result(self): + # Return annotated image as array + return np.asarray(self.im) + + +def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')): + """ + x: Features to be visualized + module_type: Module type + stage: Module stage within model + n: Maximum number of feature maps to plot + save_dir: Directory to save results + """ + if 'Detect' not in module_type: + batch, channels, height, width = x.shape # batch, channels, height, width + if height > 1 and width > 1: + f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename + + blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels + n = min(n, channels) # number of plots + fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols + ax = ax.ravel() + plt.subplots_adjust(wspace=0.05, hspace=0.05) + for i in range(n): + ax[i].imshow(blocks[i].squeeze()) # cmap='gray' + ax[i].axis('off') + + LOGGER.info(f'Saving {f}... ({n}/{channels})') + plt.savefig(f, dpi=300, bbox_inches='tight') + plt.close() + np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save + + +def hist2d(x, y, n=100): + # 2d histogram used in labels.png and evolve.png + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + from scipy.signal import butter, filtfilt + + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype='low', analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def output_to_target(output, max_det=300): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting + targets = [] + for i, o in enumerate(output): + box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1) + j = torch.full((conf.shape[0], 1), i) + targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1)) + return torch.cat(targets, 0).numpy() + + +@threaded +def plot_images(images, targets, paths=None, fname='images.jpg', names=None): + # Plot image grid with labels + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + max_size = 1920 # max image size + max_subplots = 16 # max image subplots, i.e. 4x4 + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs ** 0.5) # number of subplots (square) + if np.max(images[0]) <= 1: + images *= 255 # de-normalise (optional) + + # Build Image + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, im in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + im = im.transpose(1, 2, 0) + mosaic[y:y + h, x:x + w, :] = im + + # Resize (optional) + scale = max_size / ns / max(h, w) + if scale < 1: + h = math.ceil(scale * h) + w = math.ceil(scale * w) + mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) + + # Annotate + fs = int((h + w) * ns * 0.01) # font size + annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) + for i in range(i + 1): + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders + if paths: + annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames + if len(targets) > 0: + ti = targets[targets[:, 0] == i] # image targets + boxes = xywh2xyxy(ti[:, 2:6]).T + classes = ti[:, 1].astype('int') + labels = ti.shape[1] == 6 # labels if no conf column + conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale < 1: # absolute coords need scale if image scales + boxes *= scale + boxes[[0, 2]] += x + boxes[[1, 3]] += y + for j, box in enumerate(boxes.T.tolist()): + cls = classes[j] + color = colors(cls) + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' + annotator.box_label(box, label, color=color) + annotator.im.save(fname) # save + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): + # Plot LR simulating training for full epochs + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]['lr']) + plt.plot(y, '.-', label='LR') + plt.xlabel('epoch') + plt.ylabel('LR') + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / 'LR.png', dpi=200) + plt.close() + + +def plot_val_txt(): # from utils.plots import *; plot_val() + # Plot val.txt histograms + x = np.loadtxt('val.txt', dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect('equal') + plt.savefig('hist2d.png', dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig('hist1d.png', dpi=200) + + +def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() + # Plot targets.txt histograms + x = np.loadtxt('targets.txt', dtype=np.float32).T + s = ['x targets', 'y targets', 'width targets', 'height targets'] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}') + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig('targets.jpg', dpi=200) + + +def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study() + # Plot file=study.txt generated by val.py (or plot all study*.txt in dir) + save_dir = Path(file).parent if file else Path(dir) + plot2 = False # plot additional results + if plot2: + ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: + for f in sorted(save_dir.glob('study*.txt')): + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + if plot2: + s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)'] + for i in range(7): + ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot(y[5, 1:j], + y[3, 1:j] * 1E2, + '.-', + linewidth=2, + markersize=8, + label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) + + ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + 'k.-', + linewidth=2, + markersize=8, + alpha=.25, + label='EfficientDet') + + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(25, 55) + ax2.set_xlabel('GPU Speed (ms/img)') + ax2.set_ylabel('COCO AP val') + ax2.legend(loc='lower right') + f = save_dir / 'study.png' + print(f'Saving {f}...') + plt.savefig(f, dpi=300) + + +@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 +def plot_labels(labels, names=(), save_dir=Path('')): + # plot dataset labels + LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") + c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) + + # seaborn correlogram + sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use('svg') # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + with contextlib.suppress(Exception): # color histogram bars by class + [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195 + ax[0].set_ylabel('instances') + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10) + else: + ax[0].set_xlabel('classes') + sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) + sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + for cls, *box in labels[:1000]: + ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot + ax[1].imshow(img) + ax[1].axis('off') + + for a in [0, 1, 2, 3]: + for s in ['top', 'right', 'left', 'bottom']: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / 'labels.jpg', dpi=200) + matplotlib.use('Agg') + plt.close() + + +def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')): + # Show classification image grid with labels (optional) and predictions (optional) + from utils.augmentations import denormalize + + names = names or [f'class{i}' for i in range(1000)] + blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im), + dim=0) # select batch index 0, block by channels + n = min(len(blocks), nmax) # number of plots + m = min(8, round(n ** 0.5)) # 8 x 8 default + fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols + ax = ax.ravel() if m > 1 else [ax] + # plt.subplots_adjust(wspace=0.05, hspace=0.05) + for i in range(n): + ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0)) + ax[i].axis('off') + if labels is not None: + s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '') + ax[i].set_title(s, fontsize=8, verticalalignment='top') + plt.savefig(f, dpi=300, bbox_inches='tight') + plt.close() + if verbose: + LOGGER.info(f'Saving {f}') + if labels is not None: + LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax])) + if pred is not None: + LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax])) + return f + + +def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve() + # Plot evolve.csv hyp evolution results + evolve_csv = Path(evolve_csv) + data = pd.read_csv(evolve_csv) + keys = [x.strip() for x in data.columns] + x = data.values + f = fitness(x) + j = np.argmax(f) # max fitness index + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc('font', **{'size': 8}) + print(f'Best results from row {j} of {evolve_csv}:') + for i, k in enumerate(keys[7:]): + v = x[:, 7 + i] + mu = v[j] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none') + plt.plot(mu, f.max(), 'k+', markersize=15) + plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print(f'{k:>15}: {mu:.3g}') + f = evolve_csv.with_suffix('.png') # filename + plt.savefig(f, dpi=200) + plt.close() + print(f'Saved {f}') + + +def plot_results(file='path/to/results.csv', dir=''): + # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') + save_dir = Path(file).parent if file else Path(dir) + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + files = list(save_dir.glob('results*.csv')) + assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' + for f in files: + try: + data = pd.read_csv(f) + s = [x.strip() for x in data.columns] + x = data.values[:, 0] + for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]): + y = data.values[:, j].astype('float') + # y[y == 0] = np.nan # don't show zero values + ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8) + ax[i].set_title(s[j], fontsize=12) + # if j in [8, 9, 10]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + LOGGER.info(f'Warning: Plotting error for {f}: {e}') + ax[1].legend() + fig.savefig(save_dir / 'results.png', dpi=200) + plt.close() + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=''): + # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] + files = list(Path(save_dir).glob('frames*.txt')) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = (results[0] - results[0].min()) # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace('frames_', '') + a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) + a.set_title(s[i]) + a.set_xlabel('time (s)') + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ['top', 'right']: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print(f'Warning: Plotting error for {f}; {e}') + ax[1].legend() + plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) + + +def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True): + # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop + xyxy = torch.tensor(xyxy).view(-1, 4) + b = xyxy2xywh(xyxy) # boxes + if square: + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square + b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad + xyxy = xywh2xyxy(b).long() + clip_boxes(xyxy, im.shape) + crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)] + if save: + file.parent.mkdir(parents=True, exist_ok=True) # make directory + f = str(increment_path(file).with_suffix('.jpg')) + # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue + Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB + return crop diff --git a/algorithm/yolov5/utils/segment/__init__.py b/algorithm/yolov5/utils/segment/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/algorithm/yolov5/utils/segment/augmentations.py b/algorithm/yolov5/utils/segment/augmentations.py new file mode 100644 index 0000000..169adde --- /dev/null +++ b/algorithm/yolov5/utils/segment/augmentations.py @@ -0,0 +1,104 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Image augmentation functions +""" + +import math +import random + +import cv2 +import numpy as np + +from ..augmentations import box_candidates +from ..general import resample_segments, segment2box + + +def mixup(im, labels, segments, im2, labels2, segments2): + # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf + r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 + im = (im * r + im2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + segments = np.concatenate((segments, segments2), 0) + return im, labels, segments + + +def random_perspective(im, + targets=(), + segments=(), + degrees=10, + translate=.1, + scale=.1, + shear=10, + perspective=0.0, + border=(0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = im.shape[0] + border[0] * 2 # shape(h,w,c) + width = im.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -im.shape[1] / 2 # x translation (pixels) + C[1, 2] = -im.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels) + T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(im[:, :, ::-1]) # base + # ax[1].imshow(im2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + new_segments = [] + if n: + new = np.zeros((n, 4)) + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + new_segments.append(xy) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01) + targets = targets[i] + targets[:, 1:5] = new[i] + new_segments = np.array(new_segments)[i] + + return im, targets, new_segments diff --git a/algorithm/yolov5/utils/segment/dataloaders.py b/algorithm/yolov5/utils/segment/dataloaders.py new file mode 100644 index 0000000..097a5d5 --- /dev/null +++ b/algorithm/yolov5/utils/segment/dataloaders.py @@ -0,0 +1,332 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Dataloaders +""" + +import os +import random + +import cv2 +import numpy as np +import torch +from torch.utils.data import DataLoader, distributed + +from ..augmentations import augment_hsv, copy_paste, letterbox +from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, seed_worker +from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn +from ..torch_utils import torch_distributed_zero_first +from .augmentations import mixup, random_perspective + +RANK = int(os.getenv('RANK', -1)) + + +def create_dataloader(path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix='', + shuffle=False, + mask_downsample_ratio=1, + overlap_mask=False, + seed=0): + if rect and shuffle: + LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False') + shuffle = False + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = LoadImagesAndLabelsAndMasks( + path, + imgsz, + batch_size, + augment=augment, # augmentation + hyp=hyp, # hyperparameters + rect=rect, # rectangular batches + cache_images=cache, + single_cls=single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix, + downsample_ratio=mask_downsample_ratio, + overlap=overlap_mask) + + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() # number of CUDA devices + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + seed + RANK) + return loader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn, + worker_init_fn=seed_worker, + generator=generator, + ), dataset + + +class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing + + def __init__( + self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0, + min_items=0, + prefix='', + downsample_ratio=1, + overlap=False, + ): + super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls, + stride, pad, min_items, prefix) + self.downsample_ratio = downsample_ratio + self.overlap = overlap + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + masks = [] + if mosaic: + # Load mosaic + img, labels, segments = self.load_mosaic(index) + shapes = None + + # MixUp augmentation + if random.random() < hyp['mixup']: + img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1))) + + else: + # Load image + img, (h0, w0), (h, w) = self.load_image(index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy + segments = self.segments[index].copy() + if len(segments): + for i_s in range(len(segments)): + segments[i_s] = xyn2xy( + segments[i_s], + ratio[0] * w, + ratio[1] * h, + padw=pad[0], + padh=pad[1], + ) + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + img, labels, segments = random_perspective(img, + labels, + segments=segments, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + nl = len(labels) # number of labels + if nl: + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) + if self.overlap: + masks, sorted_idx = polygons2masks_overlap(img.shape[:2], + segments, + downsample_ratio=self.downsample_ratio) + masks = masks[None] # (640, 640) -> (1, 640, 640) + labels = labels[sorted_idx] + else: + masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio) + + masks = (torch.from_numpy(masks) if len(masks) else torch.zeros(1 if self.overlap else nl, img.shape[0] // + self.downsample_ratio, img.shape[1] // + self.downsample_ratio)) + # TODO: albumentations support + if self.augment: + # Albumentations + # there are some augmentation that won't change boxes and masks, + # so just be it for now. + img, labels = self.albumentations(img, labels) + nl = len(labels) # update after albumentations + + # HSV color-space + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Flip up-down + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nl: + labels[:, 2] = 1 - labels[:, 2] + masks = torch.flip(masks, dims=[1]) + + # Flip left-right + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nl: + labels[:, 1] = 1 - labels[:, 1] + masks = torch.flip(masks, dims=[2]) + + # Cutouts # labels = cutout(img, labels, p=0.5) + + labels_out = torch.zeros((nl, 6)) + if nl: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + img = np.ascontiguousarray(img) + + return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks) + + def load_mosaic(self, index): + # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic + labels4, segments4 = [], [] + s = self.img_size + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y + + # 3 additional image indices + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + labels, segments = self.labels[index].copy(), self.segments[index].copy() + + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) + img4, labels4, segments4 = random_perspective(img4, + labels4, + segments4, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + return img4, labels4, segments4 + + @staticmethod + def collate_fn(batch): + img, label, path, shapes, masks = zip(*batch) # transposed + batched_masks = torch.cat(masks, 0) + for i, l in enumerate(label): + l[:, 0] = i # add target image index for build_targets() + return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks + + +def polygon2mask(img_size, polygons, color=1, downsample_ratio=1): + """ + Args: + img_size (tuple): The image size. + polygons (np.ndarray): [N, M], N is the number of polygons, + M is the number of points(Be divided by 2). + """ + mask = np.zeros(img_size, dtype=np.uint8) + polygons = np.asarray(polygons) + polygons = polygons.astype(np.int32) + shape = polygons.shape + polygons = polygons.reshape(shape[0], -1, 2) + cv2.fillPoly(mask, polygons, color=color) + nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio) + # NOTE: fillPoly firstly then resize is trying the keep the same way + # of loss calculation when mask-ratio=1. + mask = cv2.resize(mask, (nw, nh)) + return mask + + +def polygons2masks(img_size, polygons, color, downsample_ratio=1): + """ + Args: + img_size (tuple): The image size. + polygons (list[np.ndarray]): each polygon is [N, M], + N is the number of polygons, + M is the number of points(Be divided by 2). + """ + masks = [] + for si in range(len(polygons)): + mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio) + masks.append(mask) + return np.array(masks) + + +def polygons2masks_overlap(img_size, segments, downsample_ratio=1): + """Return a (640, 640) overlap mask.""" + masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio), + dtype=np.int32 if len(segments) > 255 else np.uint8) + areas = [] + ms = [] + for si in range(len(segments)): + mask = polygon2mask( + img_size, + [segments[si].reshape(-1)], + downsample_ratio=downsample_ratio, + color=1, + ) + ms.append(mask) + areas.append(mask.sum()) + areas = np.asarray(areas) + index = np.argsort(-areas) + ms = np.array(ms)[index] + for i in range(len(segments)): + mask = ms[i] * (i + 1) + masks = masks + mask + masks = np.clip(masks, a_min=0, a_max=i + 1) + return masks, index diff --git a/algorithm/yolov5/utils/segment/general.py b/algorithm/yolov5/utils/segment/general.py new file mode 100644 index 0000000..9da8945 --- /dev/null +++ b/algorithm/yolov5/utils/segment/general.py @@ -0,0 +1,160 @@ +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + + +def crop_mask(masks, boxes): + """ + "Crop" predicted masks by zeroing out everything not in the predicted bbox. + Vectorized by Chong (thanks Chong). + + Args: + - masks should be a size [h, w, n] tensor of masks + - boxes should be a size [n, 4] tensor of bbox coords in relative point form + """ + + n, h, w = masks.shape + x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n) + r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1) + c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1) + + return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) + + +def process_mask_upsample(protos, masks_in, bboxes, shape): + """ + Crop after upsample. + protos: [mask_dim, mask_h, mask_w] + masks_in: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape: input_image_size, (h, w) + + return: h, w, n + """ + + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) + masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.5) + + +def process_mask(protos, masks_in, bboxes, shape, upsample=False): + """ + Crop before upsample. + proto_out: [mask_dim, mask_h, mask_w] + out_masks: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape:input_image_size, (h, w) + + return: h, w, n + """ + + c, mh, mw = protos.shape # CHW + ih, iw = shape + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW + + downsampled_bboxes = bboxes.clone() + downsampled_bboxes[:, 0] *= mw / iw + downsampled_bboxes[:, 2] *= mw / iw + downsampled_bboxes[:, 3] *= mh / ih + downsampled_bboxes[:, 1] *= mh / ih + + masks = crop_mask(masks, downsampled_bboxes) # CHW + if upsample: + masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + return masks.gt_(0.5) + + +def process_mask_native(protos, masks_in, bboxes, shape): + """ + Crop after upsample. + protos: [mask_dim, mask_h, mask_w] + masks_in: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape: input_image_size, (h, w) + + return: h, w, n + """ + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) + gain = min(mh / shape[0], mw / shape[1]) # gain = old / new + pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(mh - pad[1]), int(mw - pad[0]) + masks = masks[:, top:bottom, left:right] + + masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.5) + + +def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): + """ + img1_shape: model input shape, [h, w] + img0_shape: origin pic shape, [h, w, 3] + masks: [h, w, num] + """ + # Rescale coordinates (xyxy) from im1_shape to im0_shape + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + # masks = masks.permute(2, 0, 1).contiguous() + # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] + # masks = masks.permute(1, 2, 0).contiguous() + masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) + + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + + +def mask_iou(mask1, mask2, eps=1e-7): + """ + mask1: [N, n] m1 means number of predicted objects + mask2: [M, n] m2 means number of gt objects + Note: n means image_w x image_h + + return: masks iou, [N, M] + """ + intersection = torch.matmul(mask1, mask2.t()).clamp(0) + union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def masks_iou(mask1, mask2, eps=1e-7): + """ + mask1: [N, n] m1 means number of predicted objects + mask2: [N, n] m2 means number of gt objects + Note: n means image_w x image_h + + return: masks iou, (N, ) + """ + intersection = (mask1 * mask2).sum(1).clamp(0) # (N, ) + union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def masks2segments(masks, strategy='largest'): + # Convert masks(n,160,160) into segments(n,xy) + segments = [] + for x in masks.int().cpu().numpy().astype('uint8'): + c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + if c: + if strategy == 'concat': # concatenate all segments + c = np.concatenate([x.reshape(-1, 2) for x in c]) + elif strategy == 'largest': # select largest segment + c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) + else: + c = np.zeros((0, 2)) # no segments found + segments.append(c.astype('float32')) + return segments diff --git a/algorithm/yolov5/utils/segment/loss.py b/algorithm/yolov5/utils/segment/loss.py new file mode 100644 index 0000000..2a8a4c6 --- /dev/null +++ b/algorithm/yolov5/utils/segment/loss.py @@ -0,0 +1,186 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..general import xywh2xyxy +from ..loss import FocalLoss, smooth_BCE +from ..metrics import bbox_iou +from ..torch_utils import de_parallel +from .general import crop_mask + + +class ComputeLoss: + # Compute losses + def __init__(self, model, autobalance=False, overlap=False): + self.sort_obj_iou = False + self.overlap = overlap + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + self.device = device + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + m = de_parallel(model).model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 + self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + self.na = m.na # number of anchors + self.nc = m.nc # number of classes + self.nl = m.nl # number of layers + self.nm = m.nm # number of masks + self.anchors = m.anchors + self.device = device + + def __call__(self, preds, targets, masks): # predictions, targets, model + p, proto = preds + bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width + lcls = torch.zeros(1, device=self.device) + lbox = torch.zeros(1, device=self.device) + lobj = torch.zeros(1, device=self.device) + lseg = torch.zeros(1, device=self.device) + tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj + + n = b.shape[0] # number of targets + if n: + pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions + + # Box regression + pxy = pxy.sigmoid() * 2 - 0.5 + pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + iou = iou.detach().clamp(0).type(tobj.dtype) + if self.sort_obj_iou: + j = iou.argsort() + b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] + if self.gr < 1: + iou = (1.0 - self.gr) + self.gr * iou + tobj[b, a, gj, gi] = iou # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(pcls, self.cn, device=self.device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(pcls, t) # BCE + + # Mask regression + if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample + masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0] + marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized + mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) + for bi in b.unique(): + j = b == bi # matching index + if self.overlap: + mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) + else: + mask_gti = masks[tidxs[i]][j] + lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + lseg *= self.hyp['box'] / bs + + loss = lbox + lobj + lcls + lseg + return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() + + def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): + # Mask loss for one image + pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) + loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none') + return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() + + def build_targets(self, p, targets): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], [] + gain = torch.ones(8, device=self.device) # normalized to gridspace gain + ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + if self.overlap: + batch = p[0].shape[0] + ti = [] + for i in range(batch): + num = (targets[:, 0] == i).sum() # find number of targets of each image + ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) + ti = torch.cat(ti, 1) # (na, nt) + else: + ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) + targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device).float() * g # offsets + + for i in range(self.nl): + anchors, shape = self.anchors[i], p[i].shape + gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain # shape(3,n,7) + if nt: + # Matches + r = t[..., 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1 < g) & (gxy > 1)).T + l, m = ((gxi % 1 < g) & (gxi > 1)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors + (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class + gij = (gxy - offsets).long() + gi, gj = gij.T # grid indices + + # Append + indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + tidxs.append(tidx) + xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized + + return tcls, tbox, indices, anch, tidxs, xywhn diff --git a/algorithm/yolov5/utils/segment/metrics.py b/algorithm/yolov5/utils/segment/metrics.py new file mode 100644 index 0000000..c9f137e --- /dev/null +++ b/algorithm/yolov5/utils/segment/metrics.py @@ -0,0 +1,210 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Model validation metrics +""" + +import numpy as np + +from ..metrics import ap_per_class + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9] + return (x[:, :8] * w).sum(1) + + +def ap_per_class_box_and_mask( + tp_m, + tp_b, + conf, + pred_cls, + target_cls, + plot=False, + save_dir='.', + names=(), +): + """ + Args: + tp_b: tp of boxes. + tp_m: tp of masks. + other arguments see `func: ap_per_class`. + """ + results_boxes = ap_per_class(tp_b, + conf, + pred_cls, + target_cls, + plot=plot, + save_dir=save_dir, + names=names, + prefix='Box')[2:] + results_masks = ap_per_class(tp_m, + conf, + pred_cls, + target_cls, + plot=plot, + save_dir=save_dir, + names=names, + prefix='Mask')[2:] + + results = { + 'boxes': { + 'p': results_boxes[0], + 'r': results_boxes[1], + 'ap': results_boxes[3], + 'f1': results_boxes[2], + 'ap_class': results_boxes[4]}, + 'masks': { + 'p': results_masks[0], + 'r': results_masks[1], + 'ap': results_masks[3], + 'f1': results_masks[2], + 'ap_class': results_masks[4]}} + return results + + +class Metric: + + def __init__(self) -> None: + self.p = [] # (nc, ) + self.r = [] # (nc, ) + self.f1 = [] # (nc, ) + self.all_ap = [] # (nc, 10) + self.ap_class_index = [] # (nc, ) + + @property + def ap50(self): + """AP@0.5 of all classes. + Return: + (nc, ) or []. + """ + return self.all_ap[:, 0] if len(self.all_ap) else [] + + @property + def ap(self): + """AP@0.5:0.95 + Return: + (nc, ) or []. + """ + return self.all_ap.mean(1) if len(self.all_ap) else [] + + @property + def mp(self): + """mean precision of all classes. + Return: + float. + """ + return self.p.mean() if len(self.p) else 0.0 + + @property + def mr(self): + """mean recall of all classes. + Return: + float. + """ + return self.r.mean() if len(self.r) else 0.0 + + @property + def map50(self): + """Mean AP@0.5 of all classes. + Return: + float. + """ + return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 + + @property + def map(self): + """Mean AP@0.5:0.95 of all classes. + Return: + float. + """ + return self.all_ap.mean() if len(self.all_ap) else 0.0 + + def mean_results(self): + """Mean of results, return mp, mr, map50, map""" + return (self.mp, self.mr, self.map50, self.map) + + def class_result(self, i): + """class-aware result, return p[i], r[i], ap50[i], ap[i]""" + return (self.p[i], self.r[i], self.ap50[i], self.ap[i]) + + def get_maps(self, nc): + maps = np.zeros(nc) + self.map + for i, c in enumerate(self.ap_class_index): + maps[c] = self.ap[i] + return maps + + def update(self, results): + """ + Args: + results: tuple(p, r, ap, f1, ap_class) + """ + p, r, all_ap, f1, ap_class_index = results + self.p = p + self.r = r + self.all_ap = all_ap + self.f1 = f1 + self.ap_class_index = ap_class_index + + +class Metrics: + """Metric for boxes and masks.""" + + def __init__(self) -> None: + self.metric_box = Metric() + self.metric_mask = Metric() + + def update(self, results): + """ + Args: + results: Dict{'boxes': Dict{}, 'masks': Dict{}} + """ + self.metric_box.update(list(results['boxes'].values())) + self.metric_mask.update(list(results['masks'].values())) + + def mean_results(self): + return self.metric_box.mean_results() + self.metric_mask.mean_results() + + def class_result(self, i): + return self.metric_box.class_result(i) + self.metric_mask.class_result(i) + + def get_maps(self, nc): + return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc) + + @property + def ap_class_index(self): + # boxes and masks have the same ap_class_index + return self.metric_box.ap_class_index + + +KEYS = [ + 'train/box_loss', + 'train/seg_loss', # train loss + 'train/obj_loss', + 'train/cls_loss', + 'metrics/precision(B)', + 'metrics/recall(B)', + 'metrics/mAP_0.5(B)', + 'metrics/mAP_0.5:0.95(B)', # metrics + 'metrics/precision(M)', + 'metrics/recall(M)', + 'metrics/mAP_0.5(M)', + 'metrics/mAP_0.5:0.95(M)', # metrics + 'val/box_loss', + 'val/seg_loss', # val loss + 'val/obj_loss', + 'val/cls_loss', + 'x/lr0', + 'x/lr1', + 'x/lr2',] + +BEST_KEYS = [ + 'best/epoch', + 'best/precision(B)', + 'best/recall(B)', + 'best/mAP_0.5(B)', + 'best/mAP_0.5:0.95(B)', + 'best/precision(M)', + 'best/recall(M)', + 'best/mAP_0.5(M)', + 'best/mAP_0.5:0.95(M)',] diff --git a/algorithm/yolov5/utils/segment/plots.py b/algorithm/yolov5/utils/segment/plots.py new file mode 100644 index 0000000..1b22ec8 --- /dev/null +++ b/algorithm/yolov5/utils/segment/plots.py @@ -0,0 +1,143 @@ +import contextlib +import math +from pathlib import Path + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch + +from .. import threaded +from ..general import xywh2xyxy +from ..plots import Annotator, colors + + +@threaded +def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None): + # Plot image grid with labels + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + if isinstance(masks, torch.Tensor): + masks = masks.cpu().numpy().astype(int) + + max_size = 1920 # max image size + max_subplots = 16 # max image subplots, i.e. 4x4 + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs ** 0.5) # number of subplots (square) + if np.max(images[0]) <= 1: + images *= 255 # de-normalise (optional) + + # Build Image + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, im in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + im = im.transpose(1, 2, 0) + mosaic[y:y + h, x:x + w, :] = im + + # Resize (optional) + scale = max_size / ns / max(h, w) + if scale < 1: + h = math.ceil(scale * h) + w = math.ceil(scale * w) + mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) + + # Annotate + fs = int((h + w) * ns * 0.01) # font size + annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) + for i in range(i + 1): + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders + if paths: + annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames + if len(targets) > 0: + idx = targets[:, 0] == i + ti = targets[idx] # image targets + + boxes = xywh2xyxy(ti[:, 2:6]).T + classes = ti[:, 1].astype('int') + labels = ti.shape[1] == 6 # labels if no conf column + conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale < 1: # absolute coords need scale if image scales + boxes *= scale + boxes[[0, 2]] += x + boxes[[1, 3]] += y + for j, box in enumerate(boxes.T.tolist()): + cls = classes[j] + color = colors(cls) + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' + annotator.box_label(box, label, color=color) + + # Plot masks + if len(masks): + if masks.max() > 1.0: # mean that masks are overlap + image_masks = masks[[i]] # (1, 640, 640) + nl = len(ti) + index = np.arange(nl).reshape(nl, 1, 1) + 1 + image_masks = np.repeat(image_masks, nl, axis=0) + image_masks = np.where(image_masks == index, 1.0, 0.0) + else: + image_masks = masks[idx] + + im = np.asarray(annotator.im).copy() + for j, box in enumerate(boxes.T.tolist()): + if labels or conf[j] > 0.25: # 0.25 conf thresh + color = colors(classes[j]) + mh, mw = image_masks[j].shape + if mh != h or mw != w: + mask = image_masks[j].astype(np.uint8) + mask = cv2.resize(mask, (w, h)) + mask = mask.astype(bool) + else: + mask = image_masks[j].astype(bool) + with contextlib.suppress(Exception): + im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6 + annotator.fromarray(im) + annotator.im.save(fname) # save + + +def plot_results_with_masks(file='path/to/results.csv', dir='', best=True): + # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') + save_dir = Path(file).parent if file else Path(dir) + fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) + ax = ax.ravel() + files = list(save_dir.glob('results*.csv')) + assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' + for f in files: + try: + data = pd.read_csv(f) + index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + + 0.1 * data.values[:, 11]) + s = [x.strip() for x in data.columns] + x = data.values[:, 0] + for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]): + y = data.values[:, j] + # y[y == 0] = np.nan # don't show zero values + ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=2) + if best: + # best + ax[i].scatter(index, y[index], color='r', label=f'best:{index}', marker='*', linewidth=3) + ax[i].set_title(s[j] + f'\n{round(y[index], 5)}') + else: + # last + ax[i].scatter(x[-1], y[-1], color='r', label='last', marker='*', linewidth=3) + ax[i].set_title(s[j] + f'\n{round(y[-1], 5)}') + # if j in [8, 9, 10]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print(f'Warning: Plotting error for {f}: {e}') + ax[1].legend() + fig.savefig(save_dir / 'results.png', dpi=200) + plt.close() diff --git a/algorithm/yolov5/utils/torch_utils.py b/algorithm/yolov5/utils/torch_utils.py new file mode 100644 index 0000000..0fe54f8 --- /dev/null +++ b/algorithm/yolov5/utils/torch_utils.py @@ -0,0 +1,432 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +PyTorch utils +""" + +import math +import os +import platform +import subprocess +import time +import warnings +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.parallel import DistributedDataParallel as DDP + +from algorithm.yolov5.utils.general import LOGGER, check_version, colorstr, file_date, git_describe + +LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv('RANK', -1)) +WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) + +try: + import thop # for FLOPs computation +except ImportError: + thop = None + +# Suppress PyTorch warnings +warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling') +warnings.filterwarnings('ignore', category=UserWarning) + + +def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')): + # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator + def decorate(fn): + return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) + + return decorate + + +def smartCrossEntropyLoss(label_smoothing=0.0): + # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0 + if check_version(torch.__version__, '1.10.0'): + return nn.CrossEntropyLoss(label_smoothing=label_smoothing) + if label_smoothing > 0: + LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0') + return nn.CrossEntropyLoss() + + +def smart_DDP(model): + # Model DDP creation with checks + assert not check_version(torch.__version__, '1.12.0', pinned=True), \ + 'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \ + 'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395' + if check_version(torch.__version__, '1.11.0'): + return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) + else: + return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) + + +def reshape_classifier_output(model, n=1000): + # Update a TorchVision classification model to class count 'n' if required + from models.common import Classify + name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module + if isinstance(m, Classify): # YOLOv5 Classify() head + if m.linear.out_features != n: + m.linear = nn.Linear(m.linear.in_features, n) + elif isinstance(m, nn.Linear): # ResNet, EfficientNet + if m.out_features != n: + setattr(model, name, nn.Linear(m.in_features, n)) + elif isinstance(m, nn.Sequential): + types = [type(x) for x in m] + if nn.Linear in types: + i = types.index(nn.Linear) # nn.Linear index + if m[i].out_features != n: + m[i] = nn.Linear(m[i].in_features, n) + elif nn.Conv2d in types: + i = types.index(nn.Conv2d) # nn.Conv2d index + if m[i].out_channels != n: + m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + # Decorator to make all processes in distributed training wait for each local_master to do something + if local_rank not in [-1, 0]: + dist.barrier(device_ids=[local_rank]) + yield + if local_rank == 0: + dist.barrier(device_ids=[0]) + + +def device_count(): + # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows + assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows' + try: + cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' # Windows + return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) + except Exception: + return 0 + + +def select_device(device='', batch_size=0, newline=True): + # device = None or 'cpu' or 0 or '0' or '0,1,2,3' + s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} ' + device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0' + cpu = device == 'cpu' + mps = device == 'mps' # Apple Metal Performance Shaders (MPS) + if cpu or mps: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available() + assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \ + f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" + + if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available + devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7 + n = len(devices) # device count + if n > 1 and batch_size > 0: # check batch_size is divisible by device_count + assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' + space = ' ' * (len(s) + 1) + for i, d in enumerate(devices): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB + arg = 'cuda:0' + elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available + s += 'MPS\n' + arg = 'mps' + else: # revert to CPU + s += 'CPU\n' + arg = 'cpu' + + if not newline: + s = s.rstrip() + LOGGER.info(s) + return torch.device(arg) + + +def time_sync(): + # PyTorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(input, ops, n=10, device=None): + """ YOLOv5 speed/memory/FLOPs profiler + Usage: + input = torch.randn(16, 3, 640, 640) + m1 = lambda x: x * torch.sigmoid(x) + m2 = nn.SiLU() + profile(input, [m1, m2], n=100) # profile over 100 iterations + """ + results = [] + if not isinstance(device, torch.device): + device = select_device(device) + print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" + f"{'input':>24s}{'output':>24s}") + + for x in input if isinstance(input, list) else [input]: + x = x.to(device) + x.requires_grad = True + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, 'to') else m # device + m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m + tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs + except Exception: + flops = 0 + + try: + for _ in range(n): + t[0] = time_sync() + y = m(x) + t[1] = time_sync() + try: + _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() + t[2] = time_sync() + except Exception: # no backward method + # print(e) # for debug + t[2] = float('nan') + tf += (t[1] - t[0]) * 1000 / n # ms per op forward + tb += (t[2] - t[1]) * 1000 / n # ms per op backward + mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB) + s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes + p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters + print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}') + results.append([p, flops, mem, tf, tb, s_in, s_out]) + except Exception as e: + print(e) + results.append(None) + torch.cuda.empty_cache() + return results + + +def is_parallel(model): + # Returns True if model is of type DP or DDP + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def de_parallel(model): + # De-parallelize a model: returns single-GPU model if model is of type DP or DDP + return model.module if is_parallel(model) else model + + +def initialize_weights(model): + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + # Finds layer indices matching module class 'mclass' + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + # Return global model sparsity + a, b = 0, 0 + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + # Prune model to requested global sparsity + import torch.nn.utils.prune as prune + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name='weight', amount=amount) # prune + prune.remove(m, 'weight') # make permanent + LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity') + + +def fuse_conv_and_bn(conv, bn): + # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + fusedconv = nn.Conv2d(conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + dilation=conv.dilation, + groups=conv.groups, + bias=True).requires_grad_(False).to(conv.weight.device) + + # Prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) + + # Prepare spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, imgsz=640): + # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace('module_list.', '') + print('%5g %40s %9s %12g %20s %10.3g %10.3g' % + (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + + try: # FLOPs + p = next(model.parameters()) + stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride + im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format + flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs + imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float + fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs + except Exception: + fs = '' + + name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model' + LOGGER.info(f'{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}') + + +def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) + # Scales img(bs,3,y,x) by ratio constrained to gs-multiple + if ratio == 1.0: + return img + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize + if not same_shape: # pad/crop img + h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) + return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith('_') or k in exclude: + continue + else: + setattr(a, k, v) + + +def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5): + # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay + g = [], [], [] # optimizer parameter groups + bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d() + for v in model.modules(): + for p_name, p in v.named_parameters(recurse=0): + if p_name == 'bias': # bias (no decay) + g[2].append(p) + elif p_name == 'weight' and isinstance(v, bn): # weight (no decay) + g[1].append(p) + else: + g[0].append(p) # weight (with decay) + + if name == 'Adam': + optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum + elif name == 'AdamW': + optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) + elif name == 'RMSProp': + optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) + elif name == 'SGD': + optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) + else: + raise NotImplementedError(f'Optimizer {name} not implemented.') + + optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay + optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights) + LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " + f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias') + return optimizer + + +def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs): + # YOLOv5 torch.hub.load() wrapper with smart error/issue handling + if check_version(torch.__version__, '1.9.1'): + kwargs['skip_validation'] = True # validation causes GitHub API rate limit errors + if check_version(torch.__version__, '1.12.0'): + kwargs['trust_repo'] = True # argument required starting in torch 0.12 + try: + return torch.hub.load(repo, model, **kwargs) + except Exception: + return torch.hub.load(repo, model, force_reload=True, **kwargs) + + +def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True): + # Resume training from a partially trained checkpoint + best_fitness = 0.0 + start_epoch = ckpt['epoch'] + 1 + if ckpt['optimizer'] is not None: + optimizer.load_state_dict(ckpt['optimizer']) # optimizer + best_fitness = ckpt['best_fitness'] + if ema and ckpt.get('ema'): + ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA + ema.updates = ckpt['updates'] + if resume: + assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \ + f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" + LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs') + if epochs < start_epoch: + LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") + epochs += ckpt['epoch'] # finetune additional epochs + return best_fitness, start_epoch, epochs + + +class EarlyStopping: + # YOLOv5 simple early stopper + def __init__(self, patience=30): + self.best_fitness = 0.0 # i.e. mAP + self.best_epoch = 0 + self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop + self.possible_stop = False # possible stop may occur next epoch + + def __call__(self, epoch, fitness): + if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training + self.best_epoch = epoch + self.best_fitness = fitness + delta = epoch - self.best_epoch # epochs without improvement + self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch + stop = delta >= self.patience # stop training if patience exceeded + if stop: + LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. ' + f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n' + f'To update EarlyStopping(patience={self.patience}) pass a new patience value, ' + f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.') + return stop + + +class ModelEMA: + """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models + Keeps a moving average of everything in the model state_dict (parameters and buffers) + For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + """ + + def __init__(self, model, decay=0.9999, tau=2000, updates=0): + # Create EMA + self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + self.updates += 1 + d = self.decay(self.updates) + + msd = de_parallel(model).state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: # true for FP16 and FP32 + v *= d + v += (1 - d) * msd[k].detach() + # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' + + def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) diff --git a/algorithm/yolov5/utils/triton.py b/algorithm/yolov5/utils/triton.py new file mode 100644 index 0000000..2592802 --- /dev/null +++ b/algorithm/yolov5/utils/triton.py @@ -0,0 +1,85 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" Utils to interact with the Triton Inference Server +""" + +import typing +from urllib.parse import urlparse + +import torch + + +class TritonRemoteModel: + """ A wrapper over a model served by the Triton Inference Server. It can + be configured to communicate over GRPC or HTTP. It accepts Torch Tensors + as input and returns them as outputs. + """ + + def __init__(self, url: str): + """ + Keyword arguments: + url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000 + """ + + parsed_url = urlparse(url) + if parsed_url.scheme == 'grpc': + from tritonclient.grpc import InferenceServerClient, InferInput + + self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client + model_repository = self.client.get_model_repository_index() + self.model_name = model_repository.models[0].name + self.metadata = self.client.get_model_metadata(self.model_name, as_json=True) + + def create_input_placeholders() -> typing.List[InferInput]: + return [ + InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']] + + else: + from tritonclient.http import InferenceServerClient, InferInput + + self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client + model_repository = self.client.get_model_repository_index() + self.model_name = model_repository[0]['name'] + self.metadata = self.client.get_model_metadata(self.model_name) + + def create_input_placeholders() -> typing.List[InferInput]: + return [ + InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']] + + self._create_input_placeholders_fn = create_input_placeholders + + @property + def runtime(self): + """Returns the model runtime""" + return self.metadata.get('backend', self.metadata.get('platform')) + + def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]: + """ Invokes the model. Parameters can be provided via args or kwargs. + args, if provided, are assumed to match the order of inputs of the model. + kwargs are matched with the model input names. + """ + inputs = self._create_inputs(*args, **kwargs) + response = self.client.infer(model_name=self.model_name, inputs=inputs) + result = [] + for output in self.metadata['outputs']: + tensor = torch.as_tensor(response.as_numpy(output['name'])) + result.append(tensor) + return result[0] if len(result) == 1 else result + + def _create_inputs(self, *args, **kwargs): + args_len, kwargs_len = len(args), len(kwargs) + if not args_len and not kwargs_len: + raise RuntimeError('No inputs provided.') + if args_len and kwargs_len: + raise RuntimeError('Cannot specify args and kwargs at the same time') + + placeholders = self._create_input_placeholders_fn() + if args_len: + if args_len != len(placeholders): + raise RuntimeError(f'Expected {len(placeholders)} inputs, got {args_len}.') + for input, value in zip(placeholders, args): + input.set_data_from_numpy(value.cpu().numpy()) + else: + for input in placeholders: + value = kwargs[input.name] + input.set_data_from_numpy(value.cpu().numpy()) + return placeholders diff --git a/algorithm/yolov5/val.py b/algorithm/yolov5/val.py new file mode 100644 index 0000000..d4073b4 --- /dev/null +++ b/algorithm/yolov5/val.py @@ -0,0 +1,409 @@ +# YOLOv5 🚀 by Ultralytics, GPL-3.0 license +""" +Validate a trained YOLOv5 detection model on a detection dataset + +Usage: + $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640 + +Usage - formats: + $ python val.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path + +import numpy as np +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.callbacks import Callbacks +from utils.dataloaders import create_dataloader +from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements, + check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, + print_args, scale_boxes, xywh2xyxy, xyxy2xywh) +from utils.metrics import ConfusionMatrix, ap_per_class, box_iou +from utils.plots import output_to_target, plot_images, plot_val_study +from utils.torch_utils import select_device, smart_inference_mode + + +def save_one_txt(predn, save_conf, shape, file): + # Save one txt result + gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh + for *xyxy, conf, cls in predn.tolist(): + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(file, 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + +def save_one_json(predn, jdict, path, class_map): + # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} + image_id = int(path.stem) if path.stem.isnumeric() else path.stem + box = xyxy2xywh(predn[:, :4]) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + for p, b in zip(predn.tolist(), box.tolist()): + jdict.append({ + 'image_id': image_id, + 'category_id': class_map[int(p[5])], + 'bbox': [round(x, 3) for x in b], + 'score': round(p[4], 5)}) + + +def process_batch(detections, labels, iouv): + """ + Return correct prediction matrix + Arguments: + detections (array[N, 6]), x1, y1, x2, y2, conf, class + labels (array[M, 5]), class, x1, y1, x2, y2 + Returns: + correct (array[N, 10]), for 10 IoU levels + """ + correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) + iou = box_iou(labels[:, 1:], detections[:, :4]) + correct_class = labels[:, 0:1] == detections[:, 5] + for i in range(len(iouv)): + x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + # matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True + return torch.tensor(correct, dtype=torch.bool, device=iouv.device) + + +@smart_inference_mode() +def run( + data, + weights=None, # model.pt path(s) + batch_size=32, # batch size + imgsz=640, # inference size (pixels) + conf_thres=0.001, # confidence threshold + iou_thres=0.6, # NMS IoU threshold + max_det=300, # maximum detections per image + task='val', # train, val, test, speed or study + device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + single_cls=False, # treat as single-class dataset + augment=False, # augmented inference + verbose=False, # verbose output + save_txt=False, # save results to *.txt + save_hybrid=False, # save label+prediction hybrid results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_json=False, # save a COCO-JSON results file + project=ROOT / 'runs/val', # save to project/name + name='exp', # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + save_dir=Path(''), + plots=True, + callbacks=Callbacks(), + compute_loss=None, +): + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != 'cpu' # half precision only supported on CUDA + model.half() if half else model.float() + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') + + # Data + data = check_dataset(data) # check + + # Configure + model.eval() + cuda = device.type != 'cpu' + is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset + nc = 1 if single_cls else int(data['nc']) # number of classes + iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Dataloader + if not training: + if pt and not single_cls: # check --weights are trained on --data + ncm = model.model.nc + assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ + f'classes). Pass correct combination of --weights and --data that are trained together.' + model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup + pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks + task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images + dataloader = create_dataloader(data[task], + imgsz, + batch_size, + stride, + single_cls, + pad=pad, + rect=rect, + workers=workers, + prefix=colorstr(f'{task}: '))[0] + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = model.names if hasattr(model, 'names') else model.module.names # get class names + if isinstance(names, (list, tuple)): # old format + names = dict(enumerate(names)) + class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) + s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95') + tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 + dt = Profile(), Profile(), Profile() # profiling times + loss = torch.zeros(3, device=device) + jdict, stats, ap, ap_class = [], [], [], [] + callbacks.run('on_val_start') + pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar + for batch_i, (im, targets, paths, shapes) in enumerate(pbar): + callbacks.run('on_val_batch_start') + with dt[0]: + if cuda: + im = im.to(device, non_blocking=True) + targets = targets.to(device) + im = im.half() if half else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + nb, _, height, width = im.shape # batch size, channels, height, width + + # Inference + with dt[1]: + preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None) + + # Loss + if compute_loss: + loss += compute_loss(train_out, targets)[1] # box, obj, cls + + # NMS + targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + with dt[2]: + preds = non_max_suppression(preds, + conf_thres, + iou_thres, + labels=lb, + multi_label=True, + agnostic=single_cls, + max_det=max_det) + + # Metrics + for si, pred in enumerate(preds): + labels = targets[targets[:, 0] == si, 1:] + nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions + path, shape = Path(paths[si]), shapes[si][0] + correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + seen += 1 + + if npr == 0: + if nl: + stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0])) + if plots: + confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) + continue + + # Predictions + if single_cls: + pred[:, 5] = 0 + predn = pred.clone() + scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred + + # Evaluate + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + correct = process_batch(predn, labelsn, iouv) + if plots: + confusion_matrix.process_batch(predn, labelsn) + stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls) + + # Save/log + if save_txt: + save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') + if save_json: + save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary + callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) + + # Plot images + if plots and batch_i < 3: + plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels + plot_images(im, output_to_target(preds), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred + + callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds) + + # Compute metrics + stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) + ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 + mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() + nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class + + # Print results + pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format + LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) + if nt.sum() == 0: + LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels') + + # Print results per class + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): + for i, c in enumerate(ap_class): + LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) + + # Print speeds + t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image + if not training: + shape = (batch_size, 3, imgsz, imgsz) + LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) + + # Save JSON + if save_json and len(jdict): + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights + anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations + pred_json = str(save_dir / f'{w}_predictions.json') # predictions + LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...') + with open(pred_json, 'w') as f: + json.dump(jdict, f) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + check_requirements('pycocotools>=2.0.6') + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + anno = COCO(anno_json) # init annotations api + pred = anno.loadRes(pred_json) # init predictions api + eval = COCOeval(anno, pred, 'bbox') + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) + except Exception as e: + LOGGER.info(f'pycocotools unable to run: {e}') + + # Return results + model.float() # for training + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + maps = np.zeros(nc) + map + for i, c in enumerate(ap_class): + maps[c] = ap[i] + return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t + + +def parse_opt(): + parser = argparse.ArgumentParser() + parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') + parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)') + parser.add_argument('--batch-size', type=int, default=32, help='batch size') + parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold') + parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image') + parser.add_argument('--task', default='val', help='train, val, test, speed or study') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') + parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--verbose', action='store_true', help='report mAP by class') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file') + parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') + parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + opt.save_json |= opt.data.endswith('coco.yaml') + opt.save_txt |= opt.save_hybrid + print_args(vars(opt)) + return opt + + +def main(opt): + check_requirements(exclude=('tensorboard', 'thop')) + + if opt.task in ('train', 'val', 'test'): # run normally + if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 + LOGGER.info(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results') + if opt.save_hybrid: + LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone') + run(**vars(opt)) + + else: + weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] + opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results + if opt.task == 'speed': # speed benchmarks + # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... + opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False + for opt.weights in weights: + run(**vars(opt), plots=False) + + elif opt.task == 'study': # speed vs mAP benchmarks + # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... + for opt.weights in weights: + f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to + x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis + for opt.imgsz in x: # img-size + LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...') + r, _, t = run(**vars(opt), plots=False) + y.append(r + t) # results and times + np.savetxt(f, y, fmt='%10.4g') # save + subprocess.run(['zip', '-r', 'study.zip', 'study_*.txt']) + plot_val_study(x=x) # plot + else: + raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') + + +if __name__ == '__main__': + opt = parse_opt() + main(opt) diff --git a/app copy.py b/app copy.py new file mode 100644 index 0000000..d2f354a --- /dev/null +++ b/app copy.py @@ -0,0 +1,326 @@ +from flask import Flask, render_template, request, jsonify, Response, session,g +from werkzeug.utils import secure_filename +from algorithm.people_detection import VideoPeopleDetection +from algorithm.fire_detection import FireDetection +from algorithm.smog_detection import SmogDetection +from algorithm.helmet_detection import HelmetDetection +from algorithm.mask_detection import MaskDetection +from algorithm.electromobile_detection import ElectromobileDetection +from algorithm.glove_detection import GloveDetection +from algorithm.reflective_detection import ReflectiveDetection +from algorithm.phone_detection import PhoneDetection +from algorithm.pose_detection import PoseDetection +from algorithm.image_segmentation import ImageSegmentation +from algorithm.drowsy_detection import DrowsyDetection +from algorithm.lane_detection import LaneDetection +from algorithm.easyocr import OCR +from algorithm.detect_emotion.emotion_detection import Emotion_Detection +from algorithm.face_recognition.face_recognition import Face_Recognizer +from algorithm.Car_recognition.car_detection import CarDetection +from algorithm.pcb_detection import PCBDetection +from algorithm.Remote_sense.remote_sense import Remote_Sense + + + +from algorithm.yolo_segment import YOLO_Segment + + +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '1' +import multiprocessing +from MyThreadFunc import MyThreadFunc +import json +import pymysql +import threading +import torch +from flask_cors import CORS + +app = Flask(__name__) +app.secret_key = 'super_secret_key' # Needed to use sessions +CORS(app, resources={r"/*": {"origins": "http://127.0.0.1:5173"}}) + + +# Configure the upload folder and allowed extensions +UPLOAD_FOLDER = 'uploads' +ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', 'mp4'} +app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER +global cameraLocation +cameraLocation = 'rtsp://192.168.30.18:8557/h264' + + +#常用模型率先开启 +model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/best_person_29.05.2023-yolov5s-1.pt', force_reload=True) +fire_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/fire2.pt', force_reload=True) +face_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/face.pt', force_reload=True) + +somg_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/smog.pt', force_reload=True) + +drowsy_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/drowsy.pt', force_reload=True) + + + +global algorithm_name +global algNameNew +algNameNew = '行人检测' +if not os.path.exists(UPLOAD_FOLDER): + os.makedirs(UPLOAD_FOLDER) + +def allowed_file(filename): + return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS + +def readFileTojson(input): + with open(input, "r", encoding="utf-8") as f: + parse_data = json.load(f) + return parse_data + +@app.route("/") +def home(): + + return render_template("index.html") +# 获取数据库摄像头信息,传给前端 +@app.route('/getSqlCamera', methods=['GET','POST']) +def getSqlCamera(): + conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8') + cursor = conn.cursor() + cursor.execute("select * from camera") + res = cursor.fetchall() + cursor.close() + conn.close() + #print(res) + return jsonify(res) +# 获取选择的算法 +@app.route('/api/algname', methods=['POST','GET']) +def get_alg_name(): + # 从 JSON 数据中提取 algName + alg_name = request.json.get('algName') + global algNameNew + algNameNew = alg_name + + # 检查是否成功提取 algName + if alg_name is None: + return jsonify({"error": "algName not provided in JSON"}), 400 + + # 在这里处理 algName，例如保存到数据库、执行算法等 + # ... + # 返回一个成功的响应 + return jsonify(alg_name), 200 + +@app.route('/getSelectAlg', methods=['GET','POST']) +def getSelectAlg(): + res = algNameNew + #print(res) + return jsonify(res) +# 获取前端用户选择的摄像头信息 +@app.route('/getFrontCamera', methods=['GET','POST']) +def getFrontCamera(): + + cameraId= request.form['camID'] + + # print(cameraId) + conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8') + cursor = conn.cursor() + cursor.execute("select * from camera where cameraID =" +cameraId) + chsCamera = cursor.fetchall() + # 摄像头IP + global cameraLocation + cameraLocation = chsCamera[0][1] + print(cameraLocation) + + cursor.close() + conn.close() + #print(chsCamera[0][1]) + return jsonify(message='successfully'), 200 + + +# 获取数据库算法信息,传给前端 +@app.route('/getSqlAlg', methods=['GET','POST']) +def getSqlAlg(): + conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8') + cursor = conn.cursor() + cursor.execute("select * from algorithm") + res = cursor.fetchall() + cursor.close() + conn.close() + #print(res) + return jsonify(res) + +# 获取前端选择的算法信息 +@app.route('/getSelectAlgorithm', methods=['GET','POST']) +def getSelectAlgorithm(): + global algorithm_name + algorithm_name = request.form['selectAlgorithm'] + print(algorithm_name+"-----") + return jsonify(message='successfully'), 200 + + +last_uploaded_filename = None +@app.route('/upload', methods=['POST']) +def upload_file(): + if 'file' not in request.files: + return jsonify(message='No file part'), 400 + file = request.files['file'] + if file.filename == '': + return jsonify(message='No selected file'), 400 + if file and allowed_file(file.filename): + filename = secure_filename(file.filename) + file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) + file.save(file_path) + session['uploaded_file_path'] = file_path # Save the file path to the session + return jsonify(message=f'File {filename} uploaded successfully'), 200 + else: + return jsonify(message='File type not allowed'), 400 + +def gen(camera): + + while True: + frame, resText = camera.get_frame() + + # 将 JSON 字符串发送给前端 + yield (b'--frame\r\n' + b'Content-Type: image/jpeg\r\n\r\n' + frame + + b'\r\n\r\n' + b'--resText\r\n' + b'Content-Type: application/json\r\n\r\n' + + json.dumps({'resText': resText}).encode('utf-8') + + b'\r\n\r\n') + +@app.route("/use_webcam") +def use_webcam(): + + global cameraLocation + source = cameraLocation + print("使用RTSP", source) + if algorithm_name == '行人检测': + camera = VideoPeopleDetection(model=model) + elif algorithm_name == '火焰检测': + camera = FireDetection(model=fire_model) + elif algorithm_name == '人脸识别': + camera = Face_Recognizer(model=face_model) + + elif algorithm_name == '表情识别': + camera = Emotion_Detection(model=face_model) + + elif algorithm_name == 'OCR': + camera = OCR() + + elif algorithm_name == '车辆检测': + camera = CarDetection() + + elif algorithm_name == '实例分割': + camera = YOLO_Segment() + + elif algorithm_name == '疲劳检测': + camera = DrowsyDetection(model=drowsy_model) + + + elif algorithm_name == '车道线检测': + camera = LaneDetection() + + elif algorithm_name == 'PCB缺陷检测': + camera = PCBDetection() + + elif algorithm_name == '遥感目标检测': + camera = Remote_Sense() + + elif algorithm_name == '未佩戴头盔检测': + camera = HelmetDetection() + + elif algorithm_name == '口罩检测': + camera = MaskDetection() + + elif algorithm_name == '电动车检测': + camera = ElectromobileDetection() + + elif algorithm_name == '反光衣检测': + camera = ReflectiveDetection() + + elif algorithm_name == '使用手机检测': + camera = PhoneDetection() + + elif algorithm_name == '姿态检测': + camera = PoseDetection() + + camera.use_webcam(source) + + return Response(gen(camera), + mimetype="multipart/x-mixed-replace; boundary=frame") +# @app.route("/getDetectResult") +# def getDetectResult(): +# camera = VideoPeopleDetection() +# numPeople, accuracy=camera.getDetectResult() +# return jsonify(numPeople,accuracy) + + +@app.route("/video_feed") +def video_feed(): + # Check if a file has been uploaded and saved in the session + if 'uploaded_file_path' in session: + # Initialize VideoPeopleDetection with the uploaded file path + if algorithm_name == '行人检测': + camera = VideoPeopleDetection(video_path = session['uploaded_file_path'], model=model) + + elif algorithm_name == '火焰检测': + camera = FireDetection(video_path = session['uploaded_file_path'], model=fire_model) + + elif algorithm_name == '烟雾检测': + camera = SmogDetection(video_path = session['uploaded_file_path'], model=fire_model) + + elif algorithm_name == '人脸识别': + camera = Face_Recognizer(video_path = session['uploaded_file_path'], model=face_model) + + elif algorithm_name == '表情识别': + camera = Emotion_Detection(video_path = session['uploaded_file_path'], model=face_model) + + elif algorithm_name == 'OCR': + camera = OCR(video_path = session['uploaded_file_path']) + + elif algorithm_name == '车辆检测': + camera = CarDetection(video_path = session['uploaded_file_path']) + + elif algorithm_name == '实例分割': + camera = YOLO_Segment(video_path = session['uploaded_file_path']) + + elif algorithm_name == '疲劳检测': + camera = DrowsyDetection(video_path = session['uploaded_file_path'], model=drowsy_model) + + elif algorithm_name == '车道线检测': + camera = LaneDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == 'PCB缺陷检测': + camera = PCBDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == '遥感目标检测': + camera = Remote_Sense(video_path=session['uploaded_file_path']) + + elif algorithm_name == '未佩戴头盔检测': + camera = HelmetDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == '口罩检测': + camera = MaskDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == '电动车检测': + camera = ElectromobileDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == '手套检测': + camera = GloveDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == '反光衣检测': + camera = ReflectiveDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == '使用手机检测': + camera = PhoneDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == '姿态检测': + camera = PoseDetection(video_path=session['uploaded_file_path']) + + elif algorithm_name == '肝脏图像分割': + camera = ImageSegmentation(video_path = session['uploaded_file_path']) + + return Response(gen(camera), + mimetype="multipart/x-mixed-replace; boundary=frame") + else: + # If no file has been uploaded, return a default message or empty feed + return "No video uploaded yet", 200 + +if __name__ == "__main__": + + app.run(host='10.51.10.122',debug=True, port=5001) diff --git a/app.py b/app.py new file mode 100644 index 0000000..a82efcf --- /dev/null +++ b/app.py @@ -0,0 +1,350 @@ +from flask import Flask, render_template, request, jsonify, Response, session,g +from werkzeug.utils import secure_filename +from algorithm.people_detection import VideoPeopleDetection +from algorithm.fire_detection import FireDetection +from algorithm.smog_detection import SmogDetection +from algorithm.helmet_detection import HelmetDetection +from algorithm.mask_detection import MaskDetection +from algorithm.electromobile_detection import ElectromobileDetection +from algorithm.glove_detection import GloveDetection +from algorithm.reflective_detection import ReflectiveDetection +from algorithm.phone_detection import PhoneDetection +from algorithm.pose_detection import PoseDetection +from algorithm.image_segmentation import ImageSegmentation +from algorithm.drowsy_detection import DrowsyDetection +from algorithm.lane_detection import LaneDetection +from algorithm.trafiic_lights import TrafficLightsDetection +from algorithm.easyocr import OCR +from algorithm.detect_emotion.emotion_detection import Emotion_Detection +from algorithm.face_recognition.face_recognition import Face_Recognizer +from algorithm.Car_recognition.car_detection import CarDetection +from algorithm.pcb_detection import PCBDetection +from algorithm.Remote_sense.remote_sense import Remote_Sense +from algorithm.safe_detection import SafeDetection +from algorithm.traffic_logo_detection import TrafficLogoDetection + + +from algorithm.yolo_segment import YOLO_Segment + + +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '1' +import multiprocessing +from MyThreadFunc import MyThreadFunc +import json +import pymysql +import threading +import torch +from flask_cors import CORS + +app = Flask(__name__) +app.secret_key = 'super_secret_key' # Needed to use sessions +CORS(app, resources={r"/*": {"origins": "http://127.0.0.1:5173"}}) + + +# Configure the upload folder and allowed extensions +UPLOAD_FOLDER = 'uploads' +ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', 'mp4'} +app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER +global cameraLocation +cameraLocation = 'rtsp://192.168.30.18:8557/h264' + + +#常用模型率先开启 +model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/best_person_29.05.2023-yolov5s-1.pt', force_reload=True) +fire_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/fire2.pt', force_reload=True) +face_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/face.pt', force_reload=True) + +somg_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/smog.pt', force_reload=True) + +drowsy_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/drowsy.pt', force_reload=True) +# traffic_lights_model = torch.hub.load((os.getcwd()) + "/algorithm/yolov5", 'custom', source='local', path='./weight/traffic/traffic.pt', force_reload=True) + +global algTextNew +algTextNew = '对生产环境的行人进行检测' +global algorithm_name +global algNameNew +algNameNew = '行人检测' +if not os.path.exists(UPLOAD_FOLDER): + os.makedirs(UPLOAD_FOLDER) + +def allowed_file(filename): + return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS + +def readFileTojson(input): + with open(input, "r", encoding="utf-8") as f: + parse_data = json.load(f) + return parse_data + +@app.route("/") +def home(): + + return render_template("index.html") +# 获取数据库摄像头信息,传给前端 +@app.route('/getSqlCamera', methods=['GET','POST']) +def getSqlCamera(): + conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8') + cursor = conn.cursor() + cursor.execute("select * from camera") + res = cursor.fetchall() + cursor.close() + conn.close() + #print(res) + return jsonify(res) +# 获取选择的算法 +@app.route('/api/algname', methods=['POST','GET']) +def get_alg_name(): + # 从 JSON 数据中提取 algName + alg_name = request.json.get('algName') + alg_descrip = request.json.get('algText') + global algNameNew + algNameNew = alg_name + global algTextNew + algTextNew = alg_descrip + # 检查是否成功提取 algName + if alg_name is None: + return jsonify({"error": "algName not provided in JSON"}), 400 + if alg_descrip is None: + return jsonify({"error": "alg_descrip not provided in JSON"}), 400 + + # 在这里处理 algName，例如保存到数据库、执行算法等 + # ... + # 返回一个成功的响应 + return jsonify(alg_name,alg_descrip ), 200 + +@app.route('/getSelectAlg', methods=['GET','POST']) +def getSelectAlg(): + res = [algNameNew, algTextNew] + #print(res) + return jsonify(res) +# 获取前端用户选择的摄像头信息 +@app.route('/getFrontCamera', methods=['GET','POST']) +def getFrontCamera(): + + cameraId= request.form['camID'] + + # print(cameraId) + conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8') + cursor = conn.cursor() + cursor.execute("select * from camera where cameraID =" +cameraId) + chsCamera = cursor.fetchall() + # 摄像头IP + global cameraLocation + cameraLocation = chsCamera[0][1] + print(cameraLocation) + + cursor.close() + conn.close() + #print(chsCamera[0][1]) + return jsonify(message='successfully'), 200 + + +# 获取数据库算法信息,传给前端 +@app.route('/getSqlAlg', methods=['GET','POST']) +def getSqlAlg(): + conn = pymysql.connect(host='10.51.10.122', user='root', password='fate_dev', port=3306, database="camera_rtsp", charset='utf8') + cursor = conn.cursor() + cursor.execute("select * from algorithm") + res = cursor.fetchall() + cursor.close() + conn.close() + #print(res) + return jsonify(res) + +# 获取前端选择的算法信息 +@app.route('/getSelectAlgorithm', methods=['GET','POST']) +def getSelectAlgorithm(): + global algorithm_name + algorithm_name = request.form['selectAlgorithm'] + print(algorithm_name+"-----") + return jsonify(message='successfully'), 200 + + +last_uploaded_filename = None +@app.route('/upload', methods=['POST']) +def upload_file(): + if 'file' not in request.files: + return jsonify(message='No file part'), 400 + file = request.files['file'] + if file.filename == '': + return jsonify(message='No selected file'), 400 + if file and allowed_file(file.filename): + filename = secure_filename(file.filename) + file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) + file.save(file_path) + session['uploaded_file_path'] = file_path # Save the file path to the session + return jsonify(message=f'File {filename} uploaded successfully'), 200 + else: + return jsonify(message='File type not allowed'), 400 + +def gen(camera): + + while True: + frame, resText = camera.get_frame() + + # 将 JSON 字符串发送给前端 + yield (b'--frame\r\n' + b'Content-Type: image/jpeg\r\n\r\n' + frame + + b'\r\n\r\n' + b'--resText\r\n' + b'Content-Type: application/json\r\n\r\n' + + json.dumps({'resText': resText}).encode('utf-8') + + b'\r\n\r\n') + +@app.route("/use_webcam") +def use_webcam(): + + global cameraLocation + source = cameraLocation + print("使用RTSP", source) + if algNameNew == '行人检测': + camera = VideoPeopleDetection(model=model) + elif algNameNew == '火焰识别': + camera = FireDetection(model=fire_model) + elif algNameNew == '人脸识别': + camera = Face_Recognizer(model=face_model) + + elif algNameNew == '表情识别': + camera = Emotion_Detection(model=face_model) + + elif algNameNew == 'OCR': + camera = OCR() + + elif algNameNew == '车辆检测': + camera = CarDetection() + + elif algNameNew == '实例分割': + camera = YOLO_Segment() + + elif algNameNew == '疲劳检测': + camera = DrowsyDetection(model=drowsy_model) + elif algNameNew == '车道线检测': + camera = LaneDetection() + elif algNameNew == '红绿灯检测': + camera = TrafficLightsDetection() + + elif algNameNew == 'PCB缺陷检测': + camera = PCBDetection() + + elif algNameNew == '遥感目标检测': + camera = Remote_Sense() + + elif algNameNew == '电动车头盔识别': + camera = HelmetDetection() + + elif algNameNew == '戴口罩识别': + camera = MaskDetection() + + elif algNameNew == '电动车检测': + camera = ElectromobileDetection() + # + elif algNameNew == '反光衣识别': + camera = ReflectiveDetection() + + elif algNameNew == '玩手机识别': + camera = PhoneDetection() + + elif algNameNew == '姿态检测': + camera = PoseDetection() + + + elif algNameNew == '安全检测': + camera = SafeDetection() + + elif algNameNew == '交通标志检测': + camera = TrafficLogoDetection() + + camera.use_webcam(source) + + return Response(gen(camera), + mimetype="multipart/x-mixed-replace; boundary=frame") +# @app.route("/getDetectResult") +# def getDetectResult(): +# camera = VideoPeopleDetection() +# numPeople, accuracy=camera.getDetectResult() +# return jsonify(numPeople,accuracy) + + +@app.route("/video_feed") +def video_feed(): + # Check if a file has been uploaded and saved in the session + if 'uploaded_file_path' in session: + # Initialize VideoPeopleDetection with the uploaded file path + if algNameNew == '行人检测': + camera = VideoPeopleDetection(video_path = session['uploaded_file_path'], model=model) + + elif algNameNew == '火焰识别': + camera = FireDetection(video_path = session['uploaded_file_path'], model=fire_model) + + elif algNameNew == '烟雾识别': + camera = SmogDetection(video_path = session['uploaded_file_path'], model=fire_model) + + elif algNameNew == '人脸识别': + camera = Face_Recognizer(video_path = session['uploaded_file_path'], model=face_model) + + elif algNameNew == '表情识别': + camera = Emotion_Detection(video_path = session['uploaded_file_path'], model=face_model) + + elif algNameNew == 'OCR': + camera = OCR(video_path = session['uploaded_file_path']) + + elif algNameNew == '车辆检测': + camera = CarDetection(video_path = session['uploaded_file_path']) + + elif algNameNew == '实例分割': + camera = YOLO_Segment(video_path = session['uploaded_file_path']) + + elif algNameNew == '疲劳检测': + camera = DrowsyDetection(video_path = session['uploaded_file_path'], model=drowsy_model) + + elif algNameNew == '车道线检测': + camera = LaneDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '红绿灯检测': + camera = TrafficLightsDetection(video_path=session['uploaded_file_path']) + elif algNameNew == 'PCB缺陷检测': + camera = PCBDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '遥感目标检测': + camera = Remote_Sense(video_path=session['uploaded_file_path']) + + elif algNameNew == '电动车头盔识别': + camera = HelmetDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '戴口罩识别': + camera = MaskDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '电动车检测': + camera = ElectromobileDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '手套检测': + camera = GloveDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '反光衣识别': + camera = ReflectiveDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '玩手机识别': + camera = PhoneDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '姿态检测': + camera = PoseDetection(video_path=session['uploaded_file_path']) + + elif algNameNew == '肝脏图像分割': + camera = ImageSegmentation(video_path = session['uploaded_file_path']) + + + elif algNameNew == '安全检测': + camera = SafeDetection(video_path = session['uploaded_file_path']) + + + elif algNameNew == '交通标志检测': + camera = TrafficLogoDetection(video_path = session['uploaded_file_path']) + + return Response(gen(camera), + mimetype="multipart/x-mixed-replace; boundary=frame") + else: + # If no file has been uploaded, return a default message or empty feed + return "No video uploaded yet", 200 + +if __name__ == "__main__": + + app.run(host='10.51.10.122',debug=True, port=5001) diff --git a/backup.py b/backup.py new file mode 100644 index 0000000..d14ed83 --- /dev/null +++ b/backup.py @@ -0,0 +1,61 @@ +from flask import Flask, render_template, request, jsonify, Response, session +from werkzeug.utils import secure_filename +from camera_people_detection import VideoPeopleDetection +import os + +app = Flask(__name__) +app.secret_key = 'super_secret_key' # Needed to use sessions + + +# Configure the upload folder and allowed extensions +UPLOAD_FOLDER = 'uploads' +ALLOWED_EXTENSIONS = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', '.mp4'} +app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER + +if not os.path.exists(UPLOAD_FOLDER): + os.makedirs(UPLOAD_FOLDER) + +def allowed_file(filename): + return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS + +@app.route("/") +def home(): + return render_template("index.html") + +last_uploaded_filename = None +@app.route('/upload', methods=['POST']) +def upload_file(): + if 'file' not in request.files: + return jsonify(message='No file part'), 400 + file = request.files['file'] + if file.filename == '': + return jsonify(message='No selected file'), 400 + if file and allowed_file(file.filename): + filename = secure_filename(file.filename) + file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) + file.save(file_path) + session['uploaded_file_path'] = file_path # Save the file path to the session + return jsonify(message=f'File {filename} uploaded successfully'), 200 + else: + return jsonify(message='File type not allowed'), 400 + +def gen(camera): + while True: + frame = camera.get_frame() + yield (b'--frame\r\n' + b'Content-Type: image/jpeg\r\n\r\n' + frame + + b'\r\n\r\n') + +@app.route("/video_feed") +def video_feed(): + # Check if a file has been uploaded and saved in the session + if 'uploaded_file_path' in session: + # Initialize VideoPeopleDetection with the uploaded file path + return Response(gen(VideoPeopleDetection(session['uploaded_file_path'])), + mimetype="multipart/x-mixed-replace; boundary=frame") + else: + # If no file has been uploaded, return a default message or empty feed + return "No video uploaded yet", 200 + +if __name__ == "__main__": + app.run(debug=True, port=5001) diff --git a/config.json b/config.json new file mode 100644 index 0000000..48fde17 --- /dev/null +++ b/config.json @@ -0,0 +1,18 @@ +{ + "inputUrl": "rtsp://192.168.1.138:8557/h264", + "cameraId": "yxy-office", + "alg":{ + "mask": + {"var": 50.0, + "conf": 0.5, + "iou": 0.5, + "devId": 0, + "pointList": "(509,203)(1313,201)(1317,955)(505,961)", + "ydTime": 5, + "lgTime": 100, + "jgTime": 60, + "czTime": 2, + "personNumber": 2 + } + } +} diff --git a/hgface.py b/hgface.py new file mode 100644 index 0000000..344add1 --- /dev/null +++ b/hgface.py @@ -0,0 +1,18 @@ +from PIL import Image +import requests +from transformers import SamModel, SamProcessor +import os +os.environ['TRANSFORMERS_OFFLINE']="1" +os.environ['CUDA_VISIBLE_DEVICES'] = '1' + +model = SamModel.from_pretrained("weight/segment/SamModel",local_files_only=True).to("cuda") +processor = SamProcessor.from_pretrained("weight/segment/SamModel",local_files_only=True) + +img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" +raw_image = Image.open("/home/ykn/algorithm_system/flask_web/11.jpg").convert("RGB") +input_points = [[[450, 600]]] # 2D localization of a window + +inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to("cuda") +outputs = model(**inputs) +masks = processor.image_processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()) +scores = outputs.iou_scores diff --git a/index copy.html b/index copy.html new file mode 100644 index 0000000..9c0131f --- /dev/null +++ b/index copy.html @@ -0,0 +1,570 @@ + + + + + + + + 公共安全技术研究中心算法集 + + + +
+
+

算法配置

+
+ + + + + + 确定 +
+ +
+ + + + +
+ +
+ + + +
+ +
+ +
+

输入源选择

+
+ + + + + 上传 +
+
+ + + + 打开相机RTSP + + + + +
+ +
+
+ +
+
+ + +
+ +
+ +
+
+ 检测到的人数为： + +
+ + +
+ + +
+ + + + + + + + + diff --git a/index1226.html b/index1226.html new file mode 100644 index 0000000..0785523 --- /dev/null +++ b/index1226.html @@ -0,0 +1,518 @@ + + + + + + + + 公共安全技术研究中心算法集 + + + + + +
+

算法配置

+
+ + + +
+ +
+ + + + +
+ +
+ + + +
+ +
+ +
+

输入源选择

+
+ + + + +
+
+ + +
+ +
+ + + +
+ +
+
+ + +
+ +
+ +
+
+ 检到结果： + +
+ + +
+ + + + + + diff --git a/index240103.html b/index240103.html new file mode 100644 index 0000000..1b9e933 --- /dev/null +++ b/index240103.html @@ -0,0 +1,581 @@ + + + + + + + + 公共安全技术研究中心算法集 + + + +
+
+

算法配置

+
+ + + + + + 确定 +
+ +
+ + + + +
+ +
+ + + +
+ +
+ +
+

输入源选择

+
+ + + + + 上传 +
+
+ + + + 打开相机RTSP + + + + +
+ +
+
+ +
+
+ + +
+ + +
+ +
+ + +
+ 检测结果： + +
+ + + +
+ + +
+ + + + + + + + + diff --git a/myenv/bin/Activate.ps1 b/myenv/bin/Activate.ps1 new file mode 100644 index 0000000..eeea358 --- /dev/null +++ b/myenv/bin/Activate.ps1 @@ -0,0 +1,247 @@ +<# +.Synopsis +Activate a Python virtual environment for the current PowerShell session. + +.Description +Pushes the python executable for a virtual environment to the front of the +$Env:PATH environment variable and sets the prompt to signify that you are +in a Python virtual environment. Makes use of the command line switches as +well as the `pyvenv.cfg` file values present in the virtual environment. + +.Parameter VenvDir +Path to the directory that contains the virtual environment to activate. The +default value for this is the parent of the directory that the Activate.ps1 +script is located within. + +.Parameter Prompt +The prompt prefix to display when this virtual environment is activated. By +default, this prompt is the name of the virtual environment folder (VenvDir) +surrounded by parentheses and followed by a single space (ie. '(.venv) '). + +.Example +Activate.ps1 +Activates the Python virtual environment that contains the Activate.ps1 script. + +.Example +Activate.ps1 -Verbose +Activates the Python virtual environment that contains the Activate.ps1 script, +and shows extra information about the activation as it executes. + +.Example +Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv +Activates the Python virtual environment located in the specified location. + +.Example +Activate.ps1 -Prompt "MyPython" +Activates the Python virtual environment that contains the Activate.ps1 script, +and prefixes the current prompt with the specified string (surrounded in +parentheses) while the virtual environment is active. + +.Notes +On Windows, it may be required to enable this Activate.ps1 script by setting the +execution policy for the user. You can do this by issuing the following PowerShell +command: + +PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser + +For more information on Execution Policies: +https://go.microsoft.com/fwlink/?LinkID=135170 + +#> +Param( + [Parameter(Mandatory = $false)] + [String] + $VenvDir, + [Parameter(Mandatory = $false)] + [String] + $Prompt +) + +<# Function declarations --------------------------------------------------- #> + +<# +.Synopsis +Remove all shell session elements added by the Activate script, including the +addition of the virtual environment's Python executable from the beginning of +the PATH variable. + +.Parameter NonDestructive +If present, do not remove this function from the global namespace for the +session. + +#> +function global:deactivate ([switch]$NonDestructive) { + # Revert to original values + + # The prior prompt: + if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) { + Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt + Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT + } + + # The prior PYTHONHOME: + if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) { + Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME + Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME + } + + # The prior PATH: + if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) { + Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH + Remove-Item -Path Env:_OLD_VIRTUAL_PATH + } + + # Just remove the VIRTUAL_ENV altogether: + if (Test-Path -Path Env:VIRTUAL_ENV) { + Remove-Item -Path env:VIRTUAL_ENV + } + + # Just remove VIRTUAL_ENV_PROMPT altogether. + if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) { + Remove-Item -Path env:VIRTUAL_ENV_PROMPT + } + + # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether: + if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) { + Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force + } + + # Leave deactivate function in the global namespace if requested: + if (-not $NonDestructive) { + Remove-Item -Path function:deactivate + } +} + +<# +.Description +Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the +given folder, and returns them in a map. + +For each line in the pyvenv.cfg file, if that line can be parsed into exactly +two strings separated by `=` (with any amount of whitespace surrounding the =) +then it is considered a `key = value` line. The left hand string is the key, +the right hand is the value. + +If the value starts with a `'` or a `"` then the first and last character is +stripped from the value before being captured. + +.Parameter ConfigDir +Path to the directory that contains the `pyvenv.cfg` file. +#> +function Get-PyVenvConfig( + [String] + $ConfigDir +) { + Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg" + + # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue). + $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue + + # An empty map will be returned if no config file is found. + $pyvenvConfig = @{ } + + if ($pyvenvConfigPath) { + + Write-Verbose "File exists, parse `key = value` lines" + $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath + + $pyvenvConfigContent | ForEach-Object { + $keyval = $PSItem -split "\s*=\s*", 2 + if ($keyval[0] -and $keyval[1]) { + $val = $keyval[1] + + # Remove extraneous quotations around a string value. + if ("'""".Contains($val.Substring(0, 1))) { + $val = $val.Substring(1, $val.Length - 2) + } + + $pyvenvConfig[$keyval[0]] = $val + Write-Verbose "Adding Key: '$($keyval[0])'='$val'" + } + } + } + return $pyvenvConfig +} + + +<# Begin Activate script --------------------------------------------------- #> + +# Determine the containing directory of this script +$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition +$VenvExecDir = Get-Item -Path $VenvExecPath + +Write-Verbose "Activation script is located in path: '$VenvExecPath'" +Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)" +Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)" + +# Set values required in priority: CmdLine, ConfigFile, Default +# First, get the location of the virtual environment, it might not be +# VenvExecDir if specified on the command line. +if ($VenvDir) { + Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values" +} +else { + Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir." + $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/") + Write-Verbose "VenvDir=$VenvDir" +} + +# Next, read the `pyvenv.cfg` file to determine any required value such +# as `prompt`. +$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir + +# Next, set the prompt from the command line, or the config file, or +# just use the name of the virtual environment folder. +if ($Prompt) { + Write-Verbose "Prompt specified as argument, using '$Prompt'" +} +else { + Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value" + if ($pyvenvCfg -and $pyvenvCfg['prompt']) { + Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'" + $Prompt = $pyvenvCfg['prompt']; + } + else { + Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)" + Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'" + $Prompt = Split-Path -Path $venvDir -Leaf + } +} + +Write-Verbose "Prompt = '$Prompt'" +Write-Verbose "VenvDir='$VenvDir'" + +# Deactivate any currently active virtual environment, but leave the +# deactivate function in place. +deactivate -nondestructive + +# Now set the environment variable VIRTUAL_ENV, used by many tools to determine +# that there is an activated venv. +$env:VIRTUAL_ENV = $VenvDir + +if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) { + + Write-Verbose "Setting prompt to '$Prompt'" + + # Set the prompt to include the env name + # Make sure _OLD_VIRTUAL_PROMPT is global + function global:_OLD_VIRTUAL_PROMPT { "" } + Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT + New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt + + function global:prompt { + Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) " + _OLD_VIRTUAL_PROMPT + } + $env:VIRTUAL_ENV_PROMPT = $Prompt +} + +# Clear PYTHONHOME +if (Test-Path -Path Env:PYTHONHOME) { + Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME + Remove-Item -Path Env:PYTHONHOME +} + +# Add the venv to the PATH +Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH +$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" diff --git a/myenv/bin/activate b/myenv/bin/activate new file mode 100644 index 0000000..3cf6ae0 --- /dev/null +++ b/myenv/bin/activate @@ -0,0 +1,69 @@ +# This file must be used with "source bin/activate" *from bash* +# you cannot run it directly + +deactivate () { + # reset old environment variables + if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then + PATH="${_OLD_VIRTUAL_PATH:-}" + export PATH + unset _OLD_VIRTUAL_PATH + fi + if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then + PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}" + export PYTHONHOME + unset _OLD_VIRTUAL_PYTHONHOME + fi + + # This should detect bash and zsh, which have a hash command that must + # be called to get it to forget past commands. Without forgetting + # past commands the $PATH changes we made may not be respected + if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then + hash -r 2> /dev/null + fi + + if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then + PS1="${_OLD_VIRTUAL_PS1:-}" + export PS1 + unset _OLD_VIRTUAL_PS1 + fi + + unset VIRTUAL_ENV + unset VIRTUAL_ENV_PROMPT + if [ ! "${1:-}" = "nondestructive" ] ; then + # Self destruct! + unset -f deactivate + fi +} + +# unset irrelevant variables +deactivate nondestructive + +VIRTUAL_ENV="/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv" +export VIRTUAL_ENV + +_OLD_VIRTUAL_PATH="$PATH" +PATH="$VIRTUAL_ENV/bin:$PATH" +export PATH + +# unset PYTHONHOME if set +# this will fail if PYTHONHOME is set to the empty string (which is bad anyway) +# could use `if (set -u; : $PYTHONHOME) ;` in bash +if [ -n "${PYTHONHOME:-}" ] ; then + _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}" + unset PYTHONHOME +fi + +if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then + _OLD_VIRTUAL_PS1="${PS1:-}" + PS1="(myenv) ${PS1:-}" + export PS1 + VIRTUAL_ENV_PROMPT="(myenv) " + export VIRTUAL_ENV_PROMPT +fi + +# This should detect bash and zsh, which have a hash command that must +# be called to get it to forget past commands. Without forgetting +# past commands the $PATH changes we made may not be respected +if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then + hash -r 2> /dev/null +fi diff --git a/myenv/bin/activate.csh b/myenv/bin/activate.csh new file mode 100644 index 0000000..131a17c --- /dev/null +++ b/myenv/bin/activate.csh @@ -0,0 +1,26 @@ +# This file must be used with "source bin/activate.csh" *from csh*. +# You cannot run it directly. +# Created by Davide Di Blasi . +# Ported to Python 3.3 venv by Andrew Svetlov + +alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate' + +# Unset irrelevant variables. +deactivate nondestructive + +setenv VIRTUAL_ENV "/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv" + +set _OLD_VIRTUAL_PATH="$PATH" +setenv PATH "$VIRTUAL_ENV/bin:$PATH" + + +set _OLD_VIRTUAL_PROMPT="$prompt" + +if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then + set prompt = "(myenv) $prompt" + setenv VIRTUAL_ENV_PROMPT "(myenv) " +endif + +alias pydoc python -m pydoc + +rehash diff --git a/myenv/bin/activate.fish b/myenv/bin/activate.fish new file mode 100644 index 0000000..e27b9d2 --- /dev/null +++ b/myenv/bin/activate.fish @@ -0,0 +1,69 @@ +# This file must be used with "source /bin/activate.fish" *from fish* +# (https://fishshell.com/); you cannot run it directly. + +function deactivate -d "Exit virtual environment and return to normal shell environment" + # reset old environment variables + if test -n "$_OLD_VIRTUAL_PATH" + set -gx PATH $_OLD_VIRTUAL_PATH + set -e _OLD_VIRTUAL_PATH + end + if test -n "$_OLD_VIRTUAL_PYTHONHOME" + set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME + set -e _OLD_VIRTUAL_PYTHONHOME + end + + if test -n "$_OLD_FISH_PROMPT_OVERRIDE" + set -e _OLD_FISH_PROMPT_OVERRIDE + # prevents error when using nested fish instances (Issue #93858) + if functions -q _old_fish_prompt + functions -e fish_prompt + functions -c _old_fish_prompt fish_prompt + functions -e _old_fish_prompt + end + end + + set -e VIRTUAL_ENV + set -e VIRTUAL_ENV_PROMPT + if test "$argv[1]" != "nondestructive" + # Self-destruct! + functions -e deactivate + end +end + +# Unset irrelevant variables. +deactivate nondestructive + +set -gx VIRTUAL_ENV "/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv" + +set -gx _OLD_VIRTUAL_PATH $PATH +set -gx PATH "$VIRTUAL_ENV/bin" $PATH + +# Unset PYTHONHOME if set. +if set -q PYTHONHOME + set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME + set -e PYTHONHOME +end + +if test -z "$VIRTUAL_ENV_DISABLE_PROMPT" + # fish uses a function instead of an env var to generate the prompt. + + # Save the current fish_prompt function as the function _old_fish_prompt. + functions -c fish_prompt _old_fish_prompt + + # With the original prompt function renamed, we can override with our own. + function fish_prompt + # Save the return status of the last command. + set -l old_status $status + + # Output the venv prompt; color taken from the blue of the Python logo. + printf "%s%s%s" (set_color 4B8BBE) "(myenv) " (set_color normal) + + # Restore the return status of the previous command. + echo "exit $old_status" | . + # Output the original/"old" prompt. + _old_fish_prompt + end + + set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV" + set -gx VIRTUAL_ENV_PROMPT "(myenv) " +end diff --git a/myenv/bin/convert-caffe2-to-onnx b/myenv/bin/convert-caffe2-to-onnx new file mode 100644 index 0000000..ea07428 --- /dev/null +++ b/myenv/bin/convert-caffe2-to-onnx @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from caffe2.python.onnx.bin.conversion import caffe2_to_onnx +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(caffe2_to_onnx()) diff --git a/myenv/bin/convert-onnx-to-caffe2 b/myenv/bin/convert-onnx-to-caffe2 new file mode 100644 index 0000000..770e1ce --- /dev/null +++ b/myenv/bin/convert-onnx-to-caffe2 @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from caffe2.python.onnx.bin.conversion import onnx_to_caffe2 +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(onnx_to_caffe2()) diff --git a/myenv/bin/cpuinfo b/myenv/bin/cpuinfo new file mode 100644 index 0000000..6c0b2ac --- /dev/null +++ b/myenv/bin/cpuinfo @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from cpuinfo import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/f2py b/myenv/bin/f2py new file mode 100644 index 0000000..30084a2 --- /dev/null +++ b/myenv/bin/f2py @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from numpy.f2py.f2py2e import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/flask b/myenv/bin/flask new file mode 100644 index 0000000..b512b28 --- /dev/null +++ b/myenv/bin/flask @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from flask.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/fonttools b/myenv/bin/fonttools new file mode 100644 index 0000000..18aa134 --- /dev/null +++ b/myenv/bin/fonttools @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/ipython b/myenv/bin/ipython new file mode 100644 index 0000000..7b1db35 --- /dev/null +++ b/myenv/bin/ipython @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from IPython import start_ipython +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(start_ipython()) diff --git a/myenv/bin/ipython3 b/myenv/bin/ipython3 new file mode 100644 index 0000000..7b1db35 --- /dev/null +++ b/myenv/bin/ipython3 @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from IPython import start_ipython +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(start_ipython()) diff --git a/myenv/bin/isympy b/myenv/bin/isympy new file mode 100644 index 0000000..15b2a6c --- /dev/null +++ b/myenv/bin/isympy @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from isympy import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/normalizer b/myenv/bin/normalizer new file mode 100644 index 0000000..e7f557c --- /dev/null +++ b/myenv/bin/normalizer @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from charset_normalizer.cli import cli_detect +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli_detect()) diff --git a/myenv/bin/pip b/myenv/bin/pip new file mode 100644 index 0000000..2011823 --- /dev/null +++ b/myenv/bin/pip @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/pip3 b/myenv/bin/pip3 new file mode 100644 index 0000000..2011823 --- /dev/null +++ b/myenv/bin/pip3 @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/pip3.10 b/myenv/bin/pip3.10 new file mode 100644 index 0000000..2011823 --- /dev/null +++ b/myenv/bin/pip3.10 @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/pyftmerge b/myenv/bin/pyftmerge new file mode 100644 index 0000000..dffa642 --- /dev/null +++ b/myenv/bin/pyftmerge @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.merge import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/pyftsubset b/myenv/bin/pyftsubset new file mode 100644 index 0000000..d96ad2c --- /dev/null +++ b/myenv/bin/pyftsubset @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.subset import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/pygmentize b/myenv/bin/pygmentize new file mode 100644 index 0000000..9b2cc83 --- /dev/null +++ b/myenv/bin/pygmentize @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from pygments.cmdline import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/python b/myenv/bin/python new file mode 100644 index 0000000..4237c10 --- /dev/null +++ b/myenv/bin/python @@ -0,0 +1 @@ +/Users/souvikmallick/miniconda3/bin/python \ No newline at end of file diff --git a/myenv/bin/python3 b/myenv/bin/python3 new file mode 100644 index 0000000..d8654aa --- /dev/null +++ b/myenv/bin/python3 @@ -0,0 +1 @@ +python \ No newline at end of file diff --git a/myenv/bin/python3.10 b/myenv/bin/python3.10 new file mode 100644 index 0000000..d8654aa --- /dev/null +++ b/myenv/bin/python3.10 @@ -0,0 +1 @@ +python \ No newline at end of file diff --git a/myenv/bin/torchrun b/myenv/bin/torchrun new file mode 100644 index 0000000..1826e23 --- /dev/null +++ b/myenv/bin/torchrun @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from torch.distributed.run import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/tqdm b/myenv/bin/tqdm new file mode 100644 index 0000000..a0e58eb --- /dev/null +++ b/myenv/bin/tqdm @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from tqdm.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/ttx b/myenv/bin/ttx new file mode 100644 index 0000000..95599b2 --- /dev/null +++ b/myenv/bin/ttx @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.ttx import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/myenv/bin/ultralytics b/myenv/bin/ultralytics new file mode 100644 index 0000000..5961090 --- /dev/null +++ b/myenv/bin/ultralytics @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from ultralytics.cfg import entrypoint +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(entrypoint()) diff --git a/myenv/bin/yolo b/myenv/bin/yolo new file mode 100644 index 0000000..5961090 --- /dev/null +++ b/myenv/bin/yolo @@ -0,0 +1,8 @@ +#!/Users/souvikmallick/Desktop/cv_project/LiveProcessedVideoStream/myenv/bin/python +# -*- coding: utf-8 -*- +import re +import sys +from ultralytics.cfg import entrypoint +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(entrypoint()) diff --git a/myenv/pyvenv.cfg b/myenv/pyvenv.cfg new file mode 100644 index 0000000..24dae8e --- /dev/null +++ b/myenv/pyvenv.cfg @@ -0,0 +1,3 @@ +home = /Users/souvikmallick/miniconda3/bin +include-system-site-packages = false +version = 3.10.9 diff --git a/myenv/share/man/man1/ipython.1 b/myenv/share/man/man1/ipython.1 new file mode 100644 index 0000000..0f4a191 --- /dev/null +++ b/myenv/share/man/man1/ipython.1 @@ -0,0 +1,60 @@ +.\" Hey, EMACS: -*- nroff -*- +.\" First parameter, NAME, should be all caps +.\" Second parameter, SECTION, should be 1-8, maybe w/ subsection +.\" other parameters are allowed: see man(7), man(1) +.TH IPYTHON 1 "July 15, 2011" +.\" Please adjust this date whenever revising the manpage. +.\" +.\" Some roff macros, for reference: +.\" .nh disable hyphenation +.\" .hy enable hyphenation +.\" .ad l left justify +.\" .ad b justify to both left and right margins +.\" .nf disable filling +.\" .fi enable filling +.\" .br insert line break +.\" .sp insert n+1 empty lines +.\" for manpage-specific macros, see man(7) and groff_man(7) +.\" .SH section heading +.\" .SS secondary section heading +.\" +.\" +.\" To preview this page as plain text: nroff -man ipython.1 +.\" +.SH NAME +ipython \- Tools for Interactive Computing in Python. +.SH SYNOPSIS +.B ipython +.RI [ options ] " files" ... + +.B ipython subcommand +.RI [ options ] ... + +.SH DESCRIPTION +An interactive Python shell with automatic history (input and output), dynamic +object introspection, easier configuration, command completion, access to the +system shell, integration with numerical and scientific computing tools, +web notebook, Qt console, and more. + +For more information on how to use IPython, see 'ipython \-\-help', +or 'ipython \-\-help\-all' for all available command\(hyline options. + +.SH "ENVIRONMENT VARIABLES" +.sp +.PP +\fIIPYTHONDIR\fR +.RS 4 +This is the location where IPython stores all its configuration files. The default +is $HOME/.ipython if IPYTHONDIR is not defined. + +You can see the computed value of IPYTHONDIR with `ipython locate`. + +.SH FILES + +IPython uses various configuration files stored in profiles within IPYTHONDIR. +To generate the default configuration files and start configuring IPython, +do 'ipython profile create', and edit '*_config.py' files located in +IPYTHONDIR/profile_default. + +.SH AUTHORS +IPython is written by the IPython Development Team . diff --git a/myenv/share/man/man1/isympy.1 b/myenv/share/man/man1/isympy.1 new file mode 100644 index 0000000..0ff9661 --- /dev/null +++ b/myenv/share/man/man1/isympy.1 @@ -0,0 +1,188 @@ +'\" -*- coding: us-ascii -*- +.if \n(.g .ds T< \\FC +.if \n(.g .ds T> \\F[\n[.fam]] +.de URL +\\$2 \(la\\$1\(ra\\$3 +.. +.if \n(.g .mso www.tmac +.TH isympy 1 2007-10-8 "" "" +.SH NAME +isympy \- interactive shell for SymPy +.SH SYNOPSIS +'nh +.fi +.ad l +\fBisympy\fR \kx +.if (\nx>(\n(.l/2)) .nr x (\n(.l/5) +'in \n(.iu+\nxu +[\fB-c\fR | \fB--console\fR] [\fB-p\fR ENCODING | \fB--pretty\fR ENCODING] [\fB-t\fR TYPE | \fB--types\fR TYPE] [\fB-o\fR ORDER | \fB--order\fR ORDER] [\fB-q\fR | \fB--quiet\fR] [\fB-d\fR | \fB--doctest\fR] [\fB-C\fR | \fB--no-cache\fR] [\fB-a\fR | \fB--auto\fR] [\fB-D\fR | \fB--debug\fR] [ +-- | PYTHONOPTIONS] +'in \n(.iu-\nxu +.ad b +'hy +'nh +.fi +.ad l +\fBisympy\fR \kx +.if (\nx>(\n(.l/2)) .nr x (\n(.l/5) +'in \n(.iu+\nxu +[ +{\fB-h\fR | \fB--help\fR} +| +{\fB-v\fR | \fB--version\fR} +] +'in \n(.iu-\nxu +.ad b +'hy +.SH DESCRIPTION +isympy is a Python shell for SymPy. It is just a normal python shell +(ipython shell if you have the ipython package installed) that executes +the following commands so that you don't have to: +.PP +.nf +\*(T< +>>> from __future__ import division +>>> from sympy import * +>>> x, y, z = symbols("x,y,z") +>>> k, m, n = symbols("k,m,n", integer=True) + \*(T> +.fi +.PP +So starting isympy is equivalent to starting python (or ipython) and +executing the above commands by hand. It is intended for easy and quick +experimentation with SymPy. For more complicated programs, it is recommended +to write a script and import things explicitly (using the "from sympy +import sin, log, Symbol, ..." idiom). +.SH OPTIONS +.TP +\*(T<\fB\-c \fR\*(T>\fISHELL\fR, \*(T<\fB\-\-console=\fR\*(T>\fISHELL\fR +Use the specified shell (python or ipython) as +console backend instead of the default one (ipython +if present or python otherwise). + +Example: isympy -c python + +\fISHELL\fR could be either +\&'ipython' or 'python' +.TP +\*(T<\fB\-p \fR\*(T>\fIENCODING\fR, \*(T<\fB\-\-pretty=\fR\*(T>\fIENCODING\fR +Setup pretty printing in SymPy. By default, the most pretty, unicode +printing is enabled (if the terminal supports it). You can use less +pretty ASCII printing instead or no pretty printing at all. + +Example: isympy -p no + +\fIENCODING\fR must be one of 'unicode', +\&'ascii' or 'no'. +.TP +\*(T<\fB\-t \fR\*(T>\fITYPE\fR, \*(T<\fB\-\-types=\fR\*(T>\fITYPE\fR +Setup the ground types for the polys. By default, gmpy ground types +are used if gmpy2 or gmpy is installed, otherwise it falls back to python +ground types, which are a little bit slower. You can manually +choose python ground types even if gmpy is installed (e.g., for testing purposes). + +Note that sympy ground types are not supported, and should be used +only for experimental purposes. + +Note that the gmpy1 ground type is primarily intended for testing; it the +use of gmpy even if gmpy2 is available. + +This is the same as setting the environment variable +SYMPY_GROUND_TYPES to the given ground type (e.g., +SYMPY_GROUND_TYPES='gmpy') + +The ground types can be determined interactively from the variable +sympy.polys.domains.GROUND_TYPES inside the isympy shell itself. + +Example: isympy -t python + +\fITYPE\fR must be one of 'gmpy', +\&'gmpy1' or 'python'. +.TP +\*(T<\fB\-o \fR\*(T>\fIORDER\fR, \*(T<\fB\-\-order=\fR\*(T>\fIORDER\fR +Setup the ordering of terms for printing. The default is lex, which +orders terms lexicographically (e.g., x**2 + x + 1). You can choose +other orderings, such as rev-lex, which will use reverse +lexicographic ordering (e.g., 1 + x + x**2). + +Note that for very large expressions, ORDER='none' may speed up +printing considerably, with the tradeoff that the order of the terms +in the printed expression will have no canonical order + +Example: isympy -o rev-lax + +\fIORDER\fR must be one of 'lex', 'rev-lex', 'grlex', +\&'rev-grlex', 'grevlex', 'rev-grevlex', 'old', or 'none'. +.TP +\*(T<\fB\-q\fR\*(T>, \*(T<\fB\-\-quiet\fR\*(T> +Print only Python's and SymPy's versions to stdout at startup, and nothing else. +.TP +\*(T<\fB\-d\fR\*(T>, \*(T<\fB\-\-doctest\fR\*(T> +Use the same format that should be used for doctests. This is +equivalent to '\fIisympy -c python -p no\fR'. +.TP +\*(T<\fB\-C\fR\*(T>, \*(T<\fB\-\-no\-cache\fR\*(T> +Disable the caching mechanism. Disabling the cache may slow certain +operations down considerably. This is useful for testing the cache, +or for benchmarking, as the cache can result in deceptive benchmark timings. + +This is the same as setting the environment variable SYMPY_USE_CACHE +to 'no'. +.TP +\*(T<\fB\-a\fR\*(T>, \*(T<\fB\-\-auto\fR\*(T> +Automatically create missing symbols. Normally, typing a name of a +Symbol that has not been instantiated first would raise NameError, +but with this option enabled, any undefined name will be +automatically created as a Symbol. This only works in IPython 0.11. + +Note that this is intended only for interactive, calculator style +usage. In a script that uses SymPy, Symbols should be instantiated +at the top, so that it's clear what they are. + +This will not override any names that are already defined, which +includes the single character letters represented by the mnemonic +QCOSINE (see the "Gotchas and Pitfalls" document in the +documentation). You can delete existing names by executing "del +name" in the shell itself. You can see if a name is defined by typing +"'name' in globals()". + +The Symbols that are created using this have default assumptions. +If you want to place assumptions on symbols, you should create them +using symbols() or var(). + +Finally, this only works in the top level namespace. So, for +example, if you define a function in isympy with an undefined +Symbol, it will not work. +.TP +\*(T<\fB\-D\fR\*(T>, \*(T<\fB\-\-debug\fR\*(T> +Enable debugging output. This is the same as setting the +environment variable SYMPY_DEBUG to 'True'. The debug status is set +in the variable SYMPY_DEBUG within isympy. +.TP +-- \fIPYTHONOPTIONS\fR +These options will be passed on to \fIipython (1)\fR shell. +Only supported when ipython is being used (standard python shell not supported). + +Two dashes (--) are required to separate \fIPYTHONOPTIONS\fR +from the other isympy options. + +For example, to run iSymPy without startup banner and colors: + +isympy -q -c ipython -- --colors=NoColor +.TP +\*(T<\fB\-h\fR\*(T>, \*(T<\fB\-\-help\fR\*(T> +Print help output and exit. +.TP +\*(T<\fB\-v\fR\*(T>, \*(T<\fB\-\-version\fR\*(T> +Print isympy version information and exit. +.SH FILES +.TP +\*(T<\fI${HOME}/.sympy\-history\fR\*(T> +Saves the history of commands when using the python +shell as backend. +.SH BUGS +The upstreams BTS can be found at \(lahttps://github.com/sympy/sympy/issues\(ra +Please report all bugs that you find in there, this will help improve +the overall quality of SymPy. +.SH "SEE ALSO" +\fBipython\fR(1), \fBpython\fR(1) diff --git a/myenv/share/man/man1/ttx.1 b/myenv/share/man/man1/ttx.1 new file mode 100644 index 0000000..bba23b5 --- /dev/null +++ b/myenv/share/man/man1/ttx.1 @@ -0,0 +1,225 @@ +.Dd May 18, 2004 +.\" ttx is not specific to any OS, but contrary to what groff_mdoc(7) +.\" seems to imply, entirely omitting the .Os macro causes 'BSD' to +.\" be used, so I give a zero-width space as its argument. +.Os \& +.\" The "FontTools Manual" argument apparently has no effect in +.\" groff 1.18.1. I think it is a bug in the -mdoc groff package. +.Dt TTX 1 "FontTools Manual" +.Sh NAME +.Nm ttx +.Nd tool for manipulating TrueType and OpenType fonts +.Sh SYNOPSIS +.Nm +.Bk +.Op Ar option ... +.Ek +.Bk +.Ar file ... +.Ek +.Sh DESCRIPTION +.Nm +is a tool for manipulating TrueType and OpenType fonts. It can convert +TrueType and OpenType fonts to and from an +.Tn XML Ns -based format called +.Tn TTX . +.Tn TTX +files have a +.Ql .ttx +extension. +.Pp +For each +.Ar file +argument it is given, +.Nm +detects whether it is a +.Ql .ttf , +.Ql .otf +or +.Ql .ttx +file and acts accordingly: if it is a +.Ql .ttf +or +.Ql .otf +file, it generates a +.Ql .ttx +file; if it is a +.Ql .ttx +file, it generates a +.Ql .ttf +or +.Ql .otf +file. +.Pp +By default, every output file is created in the same directory as the +corresponding input file and with the same name except for the +extension, which is substituted appropriately. +.Nm +never overwrites existing files; if necessary, it appends a suffix to +the output file name before the extension, as in +.Pa Arial#1.ttf . +.Ss "General options" +.Bl -tag -width ".Fl t Ar table" +.It Fl h +Display usage information. +.It Fl d Ar dir +Write the output files to directory +.Ar dir +instead of writing every output file to the same directory as the +corresponding input file. +.It Fl o Ar file +Write the output to +.Ar file +instead of writing it to the same directory as the +corresponding input file. +.It Fl v +Be verbose. Write more messages to the standard output describing what +is being done. +.It Fl a +Allow virtual glyphs ID's on compile or decompile. +.El +.Ss "Dump options" +The following options control the process of dumping font files +(TrueType or OpenType) to +.Tn TTX +files. +.Bl -tag -width ".Fl t Ar table" +.It Fl l +List table information. Instead of dumping the font to a +.Tn TTX +file, display minimal information about each table. +.It Fl t Ar table +Dump table +.Ar table . +This option may be given multiple times to dump several tables at +once. When not specified, all tables are dumped. +.It Fl x Ar table +Exclude table +.Ar table +from the list of tables to dump. This option may be given multiple +times to exclude several tables from the dump. The +.Fl t +and +.Fl x +options are mutually exclusive. +.It Fl s +Split tables. Dump each table to a separate +.Tn TTX +file and write (under the name that would have been used for the output +file if the +.Fl s +option had not been given) one small +.Tn TTX +file containing references to the individual table dump files. This +file can be used as input to +.Nm +as long as the referenced files can be found in the same directory. +.It Fl i +.\" XXX: I suppose OpenType programs (exist and) are also affected. +Don't disassemble TrueType instructions. When this option is specified, +all TrueType programs (glyph programs, the font program and the +pre-program) are written to the +.Tn TTX +file as hexadecimal data instead of +assembly. This saves some time and results in smaller +.Tn TTX +files. +.It Fl y Ar n +When decompiling a TrueType Collection (TTC) file, +decompile font number +.Ar n , +starting from 0. +.El +.Ss "Compilation options" +The following options control the process of compiling +.Tn TTX +files into font files (TrueType or OpenType): +.Bl -tag -width ".Fl t Ar table" +.It Fl m Ar fontfile +Merge the input +.Tn TTX +file +.Ar file +with +.Ar fontfile . +No more than one +.Ar file +argument can be specified when this option is used. +.It Fl b +Don't recalculate glyph bounding boxes. Use the values in the +.Tn TTX +file as is. +.El +.Sh "THE TTX FILE FORMAT" +You can find some information about the +.Tn TTX +file format in +.Pa documentation.html . +In particular, you will find in that file the list of tables understood by +.Nm +and the relations between TrueType GlyphIDs and the glyph names used in +.Tn TTX +files. +.Sh EXAMPLES +In the following examples, all files are read from and written to the +current directory. Additionally, the name given for the output file +assumes in every case that it did not exist before +.Nm +was invoked. +.Pp +Dump the TrueType font contained in +.Pa FreeSans.ttf +to +.Pa FreeSans.ttx : +.Pp +.Dl ttx FreeSans.ttf +.Pp +Compile +.Pa MyFont.ttx +into a TrueType or OpenType font file: +.Pp +.Dl ttx MyFont.ttx +.Pp +List the tables in +.Pa FreeSans.ttf +along with some information: +.Pp +.Dl ttx -l FreeSans.ttf +.Pp +Dump the +.Sq cmap +table from +.Pa FreeSans.ttf +to +.Pa FreeSans.ttx : +.Pp +.Dl ttx -t cmap FreeSans.ttf +.Sh NOTES +On MS\-Windows and MacOS, +.Nm +is available as a graphical application to which files can be dropped. +.Sh SEE ALSO +.Pa documentation.html +.Pp +.Xr fontforge 1 , +.Xr ftinfo 1 , +.Xr gfontview 1 , +.Xr xmbdfed 1 , +.Xr Font::TTF 3pm +.Sh AUTHORS +.Nm +was written by +.An -nosplit +.An "Just van Rossum" Aq just@letterror.com . +.Pp +This manual page was written by +.An "Florent Rougon" Aq f.rougon@free.fr +for the Debian GNU/Linux system based on the existing FontTools +documentation. It may be freely used, modified and distributed without +restrictions. +.\" For Emacs: +.\" Local Variables: +.\" fill-column: 72 +.\" sentence-end: "[.?!][]\"')}]*\\($\\| $\\| \\| \\)[ \n]*" +.\" sentence-end-double-space: t +.\" End: \ No newline at end of file diff --git a/nohup.out b/nohup.out new file mode 100644 index 0000000..7504c67 --- /dev/null +++ b/nohup.out @@ -0,0 +1,9683 @@ +10.51.120.224 - - [28/Dec/2023 15:07:31] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:07:35] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:07:36] "POST /upload HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:07:36] "GET /video_feed HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:07:36] "GET /video_feed?1703747255814 HTTP/1.1" 200 - +torch.Size([1, 3, 4, 4, 2]) torch.Size([1, 3, 4, 4, 2]) +torch.Size([1, 3, 2, 2, 2]) torch.Size([1, 3, 2, 2, 2]) +torch.Size([1, 3, 1, 1, 2]) torch.Size([1, 3, 1, 1, 2]) +torch.Size([1, 3, 4, 4, 2]) torch.Size([1, 3, 4, 4, 2]) +torch.Size([1, 3, 2, 2, 2]) torch.Size([1, 3, 2, 2, 2]) +torch.Size([1, 3, 1, 1, 2]) torch.Size([1, 3, 1, 1, 2]) +torch.Size([1, 3, 4, 4, 2]) torch.Size([1, 3, 4, 4, 2]) +torch.Size([1, 3, 2, 2, 2]) torch.Size([1, 3, 2, 2, 2]) +torch.Size([1, 3, 1, 1, 2]) torch.Size([1, 3, 1, 1, 2]) +torch.Size([1, 3, 4, 4, 2]) torch.Size([1, 3, 4, 4, 2]) +torch.Size([1, 3, 2, 2, 2]) torch.Size([1, 3, 2, 2, 2]) +torch.Size([1, 3, 1, 1, 2]) torch.Size([1, 3, 1, 1, 2]) +torch.Size([1, 3, 4, 4, 2]) torch.Size([1, 3, 4, 4, 2]) +torch.Size([1, 3, 2, 2, 2]) torch.Size([1, 3, 2, 2, 2]) +torch.Size([1, 3, 1, 1, 2]) torch.Size([1, 3, 1, 1, 2]) +torch.Size([1, 3, 4, 4, 2]) torch.Size([1, 3, 4, 4, 2]) +torch.Size([1, 3, 2, 2, 2]) torch.Size([1, 3, 2, 2, 2]) +torch.Size([1, 3, 1, 1, 2]) torch.Size([1, 3, 1, 1, 2]) +疲劳检测----- +疲劳检测----- +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +10.51.120.224 - - [28/Dec/2023 15:07:54] "POST /upload HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:07:54] "GET /video_feed?1703747273735 HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:07:54] "GET /video_feed HTTP/1.1" 200 - +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) 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20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) 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40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 22, 40, 2]) torch.Size([1, 3, 22, 40, 2]) +torch.Size([1, 3, 11, 20, 2]) torch.Size([1, 3, 11, 20, 2]) +torch.Size([1, 3, 44, 80, 2]) torch.Size([1, 3, 44, 80, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 147, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/drowsy_detection.py", line 56, in get_frame + results = self.model(img, size=640) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 740, in forward + y = self.model(x, augment=augment) # forward + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 550, in forward + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 216, in forward + return self._forward_once(x, profile, visualize) # single-scale inference, train + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 127, in _forward_once + x = m(x) # run + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 78, in forward + wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh +RuntimeError: The size of tensor a (80) must match the size of tensor b (48) at non-singleton dimension 3 +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) 10.51.120.224 - - [28/Dec/2023 15:08:09] "POST /upload HTTP/1.1" 200 - +torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) 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12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +10.51.120.224 - - [28/Dec/2023 15:08:09] "GET /video_feed HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:08:09] "GET /video_feed?1703747288630 HTTP/1.1" 200 - +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) 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40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 147, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/drowsy_detection.py", line 56, in get_frame + results = self.model(img, size=640) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 740, in forward + y = self.model(x, augment=augment) # forward + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 550, in forward + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 216, in forward + return self._forward_once(x, profile, visualize) # single-scale inference, train + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 127, in _forward_once + x = m(x) # run + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 76, in forward + xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy +RuntimeError: The size of tensor a (80) must match the size of tensor b (48) at non-singleton dimension 3 +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 147, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/drowsy_detection.py", line 56, in get_frame + results = self.model(img, size=640) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 740, in forward + y = self.model(x, augment=augment) # forward + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 550, in forward + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 216, in forward + return self._forward_once(x, profile, visualize) # single-scale inference, train + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 127, in _forward_once + x = m(x) # run + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 76, in forward + xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy +RuntimeError: The size of tensor a (80) must match the size of tensor b (48) at non-singleton dimension 3 +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 48, 80, 2]) torch.Size([1, 3, 48, 80, 2]) +torch.Size([1, 3, 24, 40, 2]) torch.Size([1, 3, 24, 40, 2]) +torch.Size([1, 3, 12, 20, 2]) torch.Size([1, 3, 12, 20, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +torch.Size([1, 3, 80, 48, 2]) torch.Size([1, 3, 80, 48, 2]) +torch.Size([1, 3, 40, 24, 2]) torch.Size([1, 3, 40, 24, 2]) +torch.Size([1, 3, 20, 12, 2]) torch.Size([1, 3, 20, 12, 2]) +terminate called without an active exception +Traceback (most recent call last): + File "/home/ykn/algorithm_system/flask_web/app.py", line 8, in + from algorithm.detect_emotion.emotion_detection import Emotion_Detection + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/emotion_detection.py", line 3, in + from algorithm.detect_emotion.rmn import RMN + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/rmn/__init__.py", line 9, in + from algorithm.detect_emotion.rmn.models import densenet121, resmasking_dropout1 + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/rmn/models/__init__.py", line 25, in + from pytorchcv.model_provider import get_model as ptcv_get_model + File "/home/ykn/anaconda3/lib/python3.9/site-packages/pytorchcv/model_provider.py", line 120, in + from .models.icnet import * + File "/home/ykn/anaconda3/lib/python3.9/site-packages/pytorchcv/models/icnet.py", line 13, in + from .resnetd import resnetd50b +KeyboardInterrupt +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Serving Flask app 'app' + * Debug mode: on +WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://10.51.10.122:5001 +Press CTRL+C to quit + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET / HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET /static/element.css HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET /static/vue.js HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET /static/element.js HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET /video_feed HTTP/1.1" 500 - +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1478, in __call__ + return self.wsgi_app(environ, start_response) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1458, in wsgi_app + response = self.handle_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1455, in wsgi_app + response = self.full_dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 869, in full_dispatch_request + rv = self.handle_user_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 867, in full_dispatch_request + rv = self.dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 852, in dispatch_request + return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) + File "/home/ykn/algorithm_system/flask_web/app.py", line 206, in video_feed + if algorithm_name == '行人检测': +NameError: name 'algorithm_name' is not defined +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET /static/fonts/element-icons.woff HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET /video_feed HTTP/1.1" 500 - +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1478, in __call__ + return self.wsgi_app(environ, start_response) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1458, in wsgi_app + response = self.handle_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1455, in wsgi_app + response = self.full_dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 869, in full_dispatch_request + rv = self.handle_user_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 867, in full_dispatch_request + rv = self.dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 852, in dispatch_request + return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) + File "/home/ykn/algorithm_system/flask_web/app.py", line 206, in video_feed + if algorithm_name == '行人检测': +NameError: name 'algorithm_name' is not defined +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET /getSqlCamera HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:39:31] "GET /getSqlAlg HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:39:35] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:39:42] "POST /upload HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:39:43] "GET /video_feed?1703749182124 HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:39:43] "GET /video_feed HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:39:52] "POST /upload HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:39:52] "GET /video_feed HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 147, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/drowsy_detection.py", line 56, in get_frame + results = self.model(img, size=640) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 740, in forward + y = self.model(x, augment=augment) # forward + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 550, in forward + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 216, in forward + return self._forward_once(x, profile, visualize) # single-scale inference, train + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 127, in _forward_once + x = m(x) # run + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 76, in forward + xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy +RuntimeError: The size of tensor a (28) must match the size of tensor b (24) at non-singleton dimension 2 +10.51.120.224 - - [28/Dec/2023 15:39:52] "GET /video_feed?1703749191962 HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 147, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/drowsy_detection.py", line 56, in get_frame + results = self.model(img, size=640) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 740, in forward + y = self.model(x, augment=augment) # forward + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 550, in forward + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 216, in forward + return self._forward_once(x, profile, visualize) # single-scale inference, train + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 127, in _forward_once + x = m(x) # run + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 77, in forward + wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh +RuntimeError: The size of tensor a (48) must match the size of tensor b (56) at non-singleton dimension 2 +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 147, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/drowsy_detection.py", line 56, in get_frame + results = self.model(img, size=640) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context + return func(*args, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 740, in forward + y = self.model(x, augment=augment) # forward + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 550, in forward + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 216, in forward + return self._forward_once(x, profile, visualize) # single-scale inference, train + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 127, in _forward_once + x = m(x) # run + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 77, in forward + wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh +RuntimeError: The size of tensor a (48) must match the size of tensor b (56) at non-singleton dimension 2 +10.51.120.224 - - [28/Dec/2023 15:40:11] "POST /upload HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:40:11] "GET /video_feed?1703749210423 HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 15:40:11] "GET /video_feed HTTP/1.1" 200 - +疲劳检测----- +32 +32 +32 +32 +32 +32 +terminate called without an active exception +terminate called recursively +terminate called recursively +Traceback (most recent call last): + File "/home/ykn/algorithm_system/flask_web/app.py", line 8, in + from algorithm.detect_emotion.emotion_detection import Emotion_Detection + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/emotion_detection.py", line 59, in + x = Emotion_Detection() + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/emotion_detection.py", line 24, in __init__ + self.emotion_model = RMN(face_detector = self.face_detector) + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/rmn/__init__.py", line 144, in __init__ + self.emo_model = get_emo_model() + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/rmn/__init__.py", line 117, in get_emo_model + emo_model = resmasking_dropout1(in_channels=3, num_classes=7) + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/rmn/models/resmasking.py", line 140, in resmasking_dropout1 + model = ResMasking(weight_path) + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/rmn/models/resmasking.py", line 21, in __init__ + super(ResMasking, self).__init__( + File "/home/ykn/algorithm_system/flask_web/algorithm/detect_emotion/rmn/models/resnet.py", line 213, in __init__ + nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/init.py", line 413, in kaiming_normal_ + return tensor.normal_(0, std) +KeyboardInterrupt +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Serving Flask app 'app' + * Debug mode: on +WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://10.51.10.122:5001 +Press CTRL+C to quit + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 16:47:25] "GET / HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:47:25] "GET /static/element.css HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 16:47:25] "GET /static/vue.js HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 16:47:25] "GET /static/element.js HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 16:47:25] "GET /video_feed HTTP/1.1" 500 - +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1478, in __call__ + return self.wsgi_app(environ, start_response) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1458, in wsgi_app + response = self.handle_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1455, in wsgi_app + response = self.full_dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 869, in full_dispatch_request + rv = self.handle_user_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 867, in full_dispatch_request + rv = self.dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 852, in dispatch_request + return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) + File "/home/ykn/algorithm_system/flask_web/app.py", line 209, in video_feed + if algorithm_name == '行人检测': +NameError: name 'algorithm_name' is not defined +10.51.120.224 - - [28/Dec/2023 16:47:26] "GET /static/fonts/element-icons.woff HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 16:47:26] "GET /video_feed HTTP/1.1" 500 - +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1478, in __call__ + return self.wsgi_app(environ, start_response) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1458, in wsgi_app + response = self.handle_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1455, in wsgi_app + response = self.full_dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 869, in full_dispatch_request + rv = self.handle_user_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 867, in full_dispatch_request + rv = self.dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 852, in dispatch_request + return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) + File "/home/ykn/algorithm_system/flask_web/app.py", line 209, in video_feed + if algorithm_name == '行人检测': +NameError: name 'algorithm_name' is not defined +10.51.120.224 - - [28/Dec/2023 16:47:26] "GET /getSqlAlg HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:47:26] "GET /getSqlCamera HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:47:29] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:47:43] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:47:45] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:47:47] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:47:56] "POST /upload HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 71, in get_frame + for i, det in enumerate(pred): # per image +TypeError: 'Detections' object is not iterable +10.51.120.224 - - [28/Dec/2023 16:47:56] "GET /video_feed?1703753275445 HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 71, in get_frame + for i, det in enumerate(pred): # per image +TypeError: 'Detections' object is not iterable +10.51.120.224 - - [28/Dec/2023 16:47:56] "GET /video_feed HTTP/1.1" 200 - + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading +车道线检测----- +车道线检测----- +车道线检测----- +车道线检测----- + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 16:49:12] "GET / HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:49:12] "GET /static/element.css HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 16:49:13] "GET /static/vue.js HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 16:49:13] "GET /static/element.js HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 16:49:13] "GET /video_feed HTTP/1.1" 500 - +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1478, in __call__ + return self.wsgi_app(environ, start_response) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1458, in wsgi_app + response = self.handle_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1455, in wsgi_app + response = self.full_dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 869, in full_dispatch_request + rv = self.handle_user_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 867, in full_dispatch_request + rv = self.dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 852, in dispatch_request + return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) + File "/home/ykn/algorithm_system/flask_web/app.py", line 209, in video_feed + if algorithm_name == '行人检测': +NameError: name 'algorithm_name' is not defined +10.51.120.224 - - [28/Dec/2023 16:49:13] "GET /static/fonts/element-icons.woff HTTP/1.1" 304 - +10.51.120.224 - - [28/Dec/2023 16:49:13] "GET /video_feed HTTP/1.1" 500 - +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1478, in __call__ + return self.wsgi_app(environ, start_response) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1458, in wsgi_app + response = self.handle_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1455, in wsgi_app + response = self.full_dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 869, in full_dispatch_request + rv = self.handle_user_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 867, in full_dispatch_request + rv = self.dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 852, in dispatch_request + return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) + File "/home/ykn/algorithm_system/flask_web/app.py", line 209, in video_feed + if algorithm_name == '行人检测': +NameError: name 'algorithm_name' is not defined +10.51.120.224 - - [28/Dec/2023 16:49:13] "GET /getSqlAlg HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:49:13] "GET /getSqlCamera HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:49:18] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:49:26] "POST /upload HTTP/1.1" 200 - +车道线检测----- +AutoShape( + (model): DetectMultiBackend( + (model): DetectionModel( + (model): Sequential( + (0): Conv( + (conv): Conv2d(3, 32, kernel_size=(6, 6), stride=(2, 2), padding=(2, 2)) + (act): SiLU(inplace=True) + ) + (1): Conv( + (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (2): C3( + (cv1): Conv( + (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (3): Conv( + (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (4): C3( + (cv1): Conv( + (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + (1): Bottleneck( + (cv1): Conv( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (5): Conv( + (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (6): C3( + (cv1): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + (1): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + (2): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (7): Conv( + (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (8): C3( + (cv1): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (9): SPPF( + (cv1): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) + ) + (10): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (11): Upsample(scale_factor=2.0, mode=nearest) + (12): Concat() + (13): C3( + (cv1): Conv( + (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (14): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (15): Upsample(scale_factor=2.0, mode=nearest) + (16): Concat() + (17): C3( + (cv1): Conv( + (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (18): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (19): Concat() + (20): C3( + (cv1): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (21): Conv( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (22): Concat() + (23): C3( + (cv1): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (24): Detect( + (m): ModuleList( + (0): Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1)) + (1): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1)) + (2): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1)) + ) + ) + ) + ) + ) +) +AutoShape( + (model): DetectMultiBackend( + (model): DetectionModel( + (model): Sequential( + (0): Conv( + (conv): Conv2d(3, 32, kernel_size=(6, 6), stride=(2, 2), padding=(2, 2)) + (act): SiLU(inplace=True) + ) + (1): Conv( + (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (2): C3( + (cv1): Conv( + (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (3): Conv( + (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (4): C3( + (cv1): Conv( + (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + (1): Bottleneck( + (cv1): Conv( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (5): Conv( + (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (6): C3( + (cv1): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + (1): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + (2): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (7): Conv( + (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (8): C3( + (cv1): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (9): SPPF( + (cv1): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False) + ) + (10): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (11): Upsample(scale_factor=2.0, mode=nearest) + (12): Concat() + (13): C3( + (cv1): Conv( + (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (14): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (15): Upsample(scale_factor=2.0, mode=nearest) + (16): Concat() + (17): C3( + (cv1): Conv( + (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (18): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (19): Concat() + (20): C3( + (cv1): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (21): Conv( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + (22): Concat() + (23): C3( + (cv1): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv3): Conv( + (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (m): Sequential( + (0): Bottleneck( + (cv1): Conv( + (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) + (act): SiLU(inplace=True) + ) + (cv2): Conv( + (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (act): SiLU(inplace=True) + ) + ) + ) + ) + (24): Detect( + (m): ModuleList( + (0): Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1)) + (1): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1)) + (2): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1)) + ) + ) + ) + ) + ) +)Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 72, in get_frame + for i, det in enumerate(pred): # per image +TypeError: 'Detections' object is not iterable +10.51.120.224 - - [28/Dec/2023 16:49:26] "GET /video_feed HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 72, in get_frame + for i, det in enumerate(pred): # per image +TypeError: 'Detections' object is not iterable +10.51.120.224 - - [28/Dec/2023 16:49:26] "GET /video_feed?1703753365742 HTTP/1.1" 200 - + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 16:51:21] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:51:26] "POST /upload HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 73, in get_frame + for i, det in enumerate(pred): # per image +TypeError: 'Detections' object is not iterable +10.51.120.224 - - [28/Dec/2023 16:51:26] "GET /video_feed?1703753485900 HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 73, in get_frame + for i, det in enumerate(pred): # per image +TypeError: 'Detections' object is not iterable +10.51.120.224 - - [28/Dec/2023 16:51:26] "GET /video_feed HTTP/1.1" 200 - +车道线检测----- +image 1/1: 512x640 (no detections) +Speed: 22.6ms pre-process, 34.1ms inference, 1.4ms NMS per image at shape (1, 3, 512, 640) +image 1/1: 512x640 (no detections) +Speed: 23.1ms pre-process, 34.7ms inference, 1.3ms NMS per image at shape (1, 3, 512, 640) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Serving Flask app 'app' + * Debug mode: on +WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. + * Running on http://10.51.10.122:5001 +Press CTRL+C to quit + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 16:55:16] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:55:22] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:55:24] "POST /upload HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 71, in get_frame + pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000) + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 482, in non_max_suppression + device = prediction.device +AttributeError: 'Detections' object has no attribute 'device' +10.51.120.224 - - [28/Dec/2023 16:55:24] "GET /video_feed?1703753723720 HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 71, in get_frame + pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000) + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 482, in non_max_suppression + device = prediction.device +AttributeError: 'Detections' object has no attribute 'device' +10.51.120.224 - - [28/Dec/2023 16:55:24] "GET /video_feed HTTP/1.1" 200 - + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading +车道线检测----- +车道线检测----- + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 16:57:09] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:57:16] "POST /upload HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 72, in get_frame + pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000) + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 483, in non_max_suppression + device = prediction.device +AttributeError: 'Detections' object has no attribute 'device' +10.51.120.224 - - [28/Dec/2023 16:57:16] "GET /video_feed HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 72, in get_frame + pred = non_max_suppression(pred, 0.25, 0.45, None, False, max_det=1000) + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 483, in non_max_suppression + device = prediction.device +AttributeError: 'Detections' object has no attribute 'device' +10.51.120.224 - - [28/Dec/2023 16:57:16] "GET /video_feed?1703753835503 HTTP/1.1" 200 - + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading +车道线检测----- + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 16:58:45] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 16:58:49] "POST /upload HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 71, in get_frame + pred = self.model(img)[0] +TypeError: 'Detections' object is not subscriptable +10.51.120.224 - - [28/Dec/2023 16:58:50] "GET /video_feed?1703753929075 HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 148, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 71, in get_frame + pred = self.model(img)[0] +TypeError: 'Detections' object is not subscriptable +10.51.120.224 - - [28/Dec/2023 16:58:50] "GET /video_feed HTTP/1.1" 200 - + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading +车道线检测----- + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 + * Detected change in '/home/ykn/algorithm_system/flask_web/app.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/app.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/app.py', reloading + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 17:00:41] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 17:00:43] "POST /upload HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 17:00:43] "GET /video_feed HTTP/1.1" 500 - +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1478, in __call__ + return self.wsgi_app(environ, start_response) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1458, in wsgi_app + response = self.handle_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1455, in wsgi_app + response = self.full_dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 869, in full_dispatch_request + rv = self.handle_user_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 867, in full_dispatch_request + rv = self.dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 852, in dispatch_request + return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) + File "/home/ykn/algorithm_system/flask_web/app.py", line 236, in video_feed + camera = LaneDetection(video_path=session['uploaded_file_path']) + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 24, in __init__ + self.model = torch.load('weight/traffic/lane.pt', map_location=self.device)['model'].float().fuse() +AttributeError: 'LaneDetection' object has no attribute 'device' +10.51.120.224 - - [28/Dec/2023 17:00:43] "GET /video_feed?1703754042291 HTTP/1.1" 500 - +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1478, in __call__ + return self.wsgi_app(environ, start_response) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1458, in wsgi_app + response = self.handle_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 1455, in wsgi_app + response = self.full_dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 869, in full_dispatch_request + rv = self.handle_user_exception(e) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 867, in full_dispatch_request + rv = self.dispatch_request() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/flask/app.py", line 852, in dispatch_request + return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) + File "/home/ykn/algorithm_system/flask_web/app.py", line 236, in video_feed + camera = LaneDetection(video_path=session['uploaded_file_path']) + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 24, in __init__ + self.model = torch.load('weight/traffic/lane.pt', map_location=self.device)['model'].float().fuse() +AttributeError: 'LaneDetection' object has no attribute 'device' + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading +车道线检测----- + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 17:01:16] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 17:01:16] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 17:01:16] "POST /upload HTTP/1.1" 200 - +Fusing layers... +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 147, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 70, in get_frame + pred = self.model(img)[0] + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 216, in forward + return self._forward_once(x, profile, visualize) # single-scale inference, train + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 127, in _forward_once + x = m(x) # run + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 75, in forward_fuse + return self.act(self.conv(x)) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 399, in forward + return self._conv_forward(input, self.weight, self.bias) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 395, in _conv_forward + return F.conv2d(input, weight, bias, self.stride, +TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not numpy.ndarray +10.51.120.224 - - [28/Dec/2023 17:01:17] "GET /video_feed?1703754075673 HTTP/1.1" 200 - +Debugging middleware caught exception in streamed response at a point where response headers were already sent. +Traceback (most recent call last): + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wsgi.py", line 256, in __next__ + return self._next() + File "/home/ykn/anaconda3/lib/python3.9/site-packages/werkzeug/wrappers/response.py", line 32, in _iter_encoded + for item in iterable: + File "/home/ykn/algorithm_system/flask_web/app.py", line 147, in gen + frame, num_people, accuracy = camera.get_frame() + File "/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py", line 70, in get_frame + pred = self.model(img)[0] + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 216, in forward + return self._forward_once(x, profile, visualize) # single-scale inference, train + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/yolo.py", line 127, in _forward_once + x = m(x) # run + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/algorithm_system/flask_web/algorithm/yolov5/models/common.py", line 75, in forward_fuse + return self.act(self.conv(x)) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl + result = self.forward(*input, **kwargs) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 399, in forward + return self._conv_forward(input, self.weight, self.bias) + File "/home/ykn/anaconda3/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 395, in _conv_forward + return F.conv2d(input, weight, bias, self.stride, +TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not numpy.ndarray +10.51.120.224 - - [28/Dec/2023 17:01:17] "GET /video_feed HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 17:01:17] "POST /getSelectAlgorithm HTTP/1.1" 200 - + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading + * Detected change in '/home/ykn/algorithm_system/flask_web/algorithm/lane_detection.py', reloading +车道线检测----- +车道线检测----- +车道线检测----- + * Restarting with watchdog (inotify) +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc3 summary: 213 layers, 7018216 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5s_nc1 summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs +Adding AutoShape... +YOLOv5 🚀 2023-12-25 Python-3.9.7 torch-1.8.0+cu111 CUDA:0 (NVIDIA TITAN RTX, 24220MiB) + +Fusing layers... +YOLOv5n_drowsy summary: 213 layers, 1764577 parameters, 0 gradients, 4.1 GFLOPs +Adding AutoShape... + * Debugger is active! + * Debugger PIN: 108-437-323 +10.51.120.224 - - [28/Dec/2023 17:02:53] "POST /getSelectAlgorithm HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 17:02:54] "POST /upload HTTP/1.1" 200 - +Fusing layers... +Fusing layers... +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs +10.51.120.224 - - [28/Dec/2023 17:02:55] "GET /video_feed?1703754174039 HTTP/1.1" 200 - +10.51.120.224 - - [28/Dec/2023 17:02:55] "GET /video_feed HTTP/1.1" 200 - +车道线检测----- +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 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device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], 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device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +[tensor([[180.14493, 6.80343, 186.55728, 20.87873, 0.35265, 9.00000]], device='cuda:0')] +terminate called without an active exception diff --git a/pyvenv.cfg b/pyvenv.cfg new file mode 100644 index 0000000..b5a0502 --- /dev/null +++ b/pyvenv.cfg @@ -0,0 +1,8 @@ +home = C:\Users\ka\AppData\Local\Programs\Python\Python39 +implementation = CPython +version_info = 3.9.0.final.0 +virtualenv = 20.13.0 +include-system-site-packages = false +base-prefix = C:\Users\ka\AppData\Local\Programs\Python\Python39 +base-exec-prefix = C:\Users\ka\AppData\Local\Programs\Python\Python39 +base-executable = C:\Users\ka\AppData\Local\Programs\Python\Python39\python.exe diff --git a/read_data.py b/read_data.py new file mode 100644 index 0000000..f2ecefa --- /dev/null +++ b/read_data.py @@ -0,0 +1,200 @@ +# Dataset utils and dataloaders + +import glob +import logging +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from threading import Thread +import re +import cv2 +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image, ExifTags +from torch.utils.data import Dataset +from tqdm import tqdm + +# Parameters +help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes +vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes +logger = logging.getLogger(__name__) + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == 'Orientation': + break + + + + +class LoadImages: # for inference + def __init__(self, path, stride=32, img_size=640): + p = str(Path(path)) # os-agnostic + p = os.path.abspath(p) # absolute path + if '*' in p: + files = sorted(glob.glob(p, recursive=True)) # glob + elif os.path.isdir(p): + files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir + elif os.path.isfile(p): + files = [p] # files + else: + raise Exception(f'ERROR: {p} does not exist') + + images = [x for x in files if x.split('.')[-1].lower() in img_formats] + videos = [x for x in files if x.split('.')[-1].lower() in vid_formats] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = 'image' + self.stride = stride + if any(videos): + self.new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, f'No images or videos found in {p}. ' \ + f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}' + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = 'video' + ret_val, img0 = self.cap.read() # ret：表示读取是否成功的布尔值； + if not ret_val: + self.cap.set(cv2.CAP_PROP_POS_FRAMES, 0) + _, img0 = self.cap.read() + # print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='\n') + + else: + # Read image + self.count += 1 + img0 = cv2.imread(path) # BGR + assert img0 is not None, 'Image Not Found ' + path + # print(f'image {self.count}/{self.nf} {path}: ', end='\n') + + + img0 = letterbox(img0, new_shape=self.img_size, stride =self.stride)[0] + # img0 = cv2.cvtColor(img0, cv2.COLOR_BGR2RGB) + return img0 + + def new_video(self, path): + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + def __len__(self): + return self.nf # number of files + + +class LoadStreams: # multiple IP or RTSP cameras + def __init__(self, sources='streams.txt', img_size=640): + self.mode = 'stream' + self.img_size = img_size + + if os.path.isfile(sources): + with open(sources, 'r') as f: + sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] + else: + sources = [sources] + + n = len(sources) + self.imgs = [None] * n + self.sources = [clean_str(x) for x in sources] # clean source names for later + for i, s in enumerate(sources): + # Start the thread to read frames from the video stream + print(f'{i + 1}/{n}: {s}... ', end='') + cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s) + assert cap.isOpened(), f'Failed to open {s}' + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) % 100 + _, self.imgs[i] = cap.read() # guarantee first frame + thread = Thread(target=self.update, args=([i, cap]), daemon=True) + print(f' success ({w}x{h} at {fps:.2f} FPS).') + thread.start() + print('') # newline + + # check for common shapes + s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + if not self.rect: + print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') + + def update(self, index, cap): + # Read next stream frame in a daemon thread + n = 0 + while cap.isOpened(): + n += 1 + # _, self.imgs[index] = cap.read() + cap.grab() + if n == 4: # read every 4th frame + _, self.imgs[index] = cap.retrieve() + n = 0 + time.sleep(0.01) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + img0 = self.imgs.copy() + + return img0 + + def __len__(self): + return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def clean_str(s): + # Cleans a string by replacing special characters with underscore _ + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3e96694 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,62 @@ +asttokens==2.4.1 +blinker==1.7.0 +certifi==2023.11.17 +charset-normalizer==3.3.2 +click==8.1.7 +contourpy==1.2.0 +cycler==0.12.1 +decorator==5.1.1 +exceptiongroup==1.2.0 +executing==2.0.1 +filelock==3.13.1 +Flask==3.0.0 +fonttools==4.45.1 +fsspec==2023.10.0 +gitdb==4.0.11 +GitPython==3.1.40 +idna==3.5 +ipython==8.18.0 +itsdangerous==2.1.2 +jedi==0.19.1 +Jinja2==3.1.2 +kiwisolver==1.4.5 +MarkupSafe==2.1.3 +matplotlib==3.8.2 +matplotlib-inline==0.1.6 +mpmath==1.3.0 +networkx==3.2.1 +numpy==1.26.2 +opencv-python==4.8.1.78 +packaging==23.2 +pandas==2.1.3 +parso==0.8.3 +pexpect==4.8.0 +Pillow==10.1.0 +prompt-toolkit==3.0.41 +psutil==5.9.6 +ptyprocess==0.7.0 +pure-eval==0.2.2 +py-cpuinfo==9.0.0 +Pygments==2.17.2 +pyparsing==3.1.1 +python-dateutil==2.8.2 +pytz==2023.3.post1 +PyYAML==6.0.1 +requests==2.31.0 +scipy==1.11.4 +seaborn==0.13.0 +six==1.16.0 +smmap==5.0.1 +stack-data==0.6.3 +sympy==1.12 +thop==0.1.1.post2209072238 +torch==2.1.1 +torchvision==0.16.1 +tqdm==4.66.1 +traitlets==5.13.0 +typing_extensions==4.8.0 +tzdata==2023.3 +ultralytics==8.0.217 +urllib3==2.1.0 +wcwidth==0.2.12 +Werkzeug==3.0.1 diff --git a/share/doc/networkx-3.0/LICENSE.txt b/share/doc/networkx-3.0/LICENSE.txt new file mode 100644 index 0000000..42b6f17 --- /dev/null +++ b/share/doc/networkx-3.0/LICENSE.txt @@ -0,0 +1,37 @@ +NetworkX is distributed with the 3-clause BSD license. + +:: + + Copyright (C) 2004-2023, NetworkX Developers + Aric Hagberg + Dan Schult + Pieter Swart + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + + * Neither the name of the NetworkX Developers nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/share/doc/networkx-3.0/examples/3d_drawing/README.txt b/share/doc/networkx-3.0/examples/3d_drawing/README.txt new file mode 100644 index 0000000..1a76682 --- /dev/null +++ b/share/doc/networkx-3.0/examples/3d_drawing/README.txt @@ -0,0 +1,2 @@ +3D Drawing +---------- diff --git a/share/doc/networkx-3.0/examples/3d_drawing/mayavi2_spring.py b/share/doc/networkx-3.0/examples/3d_drawing/mayavi2_spring.py new file mode 100644 index 0000000..bec0969 --- /dev/null +++ b/share/doc/networkx-3.0/examples/3d_drawing/mayavi2_spring.py @@ -0,0 +1,43 @@ +""" +======= +Mayavi2 +======= + +""" + +import networkx as nx +import numpy as np +from mayavi import mlab + +# some graphs to try +# H=nx.krackhardt_kite_graph() +# H=nx.Graph();H.add_edge('a','b');H.add_edge('a','c');H.add_edge('a','d') +# H=nx.grid_2d_graph(4,5) +H = nx.cycle_graph(20) + +# reorder nodes from 0,len(G)-1 +G = nx.convert_node_labels_to_integers(H) +# 3d spring layout +pos = nx.spring_layout(G, dim=3, seed=1001) +# numpy array of x,y,z positions in sorted node order +xyz = np.array([pos[v] for v in sorted(G)]) +# scalar colors +scalars = np.array(list(G.nodes())) + 5 + +mlab.figure() + +pts = mlab.points3d( + xyz[:, 0], + xyz[:, 1], + xyz[:, 2], + scalars, + scale_factor=0.1, + scale_mode="none", + colormap="Blues", + resolution=20, +) + +pts.mlab_source.dataset.lines = np.array(list(G.edges())) +tube = mlab.pipeline.tube(pts, tube_radius=0.01) +mlab.pipeline.surface(tube, color=(0.8, 0.8, 0.8)) +mlab.orientation_axes() diff --git a/share/doc/networkx-3.0/examples/3d_drawing/plot_basic.py b/share/doc/networkx-3.0/examples/3d_drawing/plot_basic.py new file mode 100644 index 0000000..75a9c79 --- /dev/null +++ b/share/doc/networkx-3.0/examples/3d_drawing/plot_basic.py @@ -0,0 +1,51 @@ +""" +================ +Basic matplotlib +================ + +A basic example of 3D Graph visualization using `mpl_toolkits.mplot_3d`. + +""" + +import networkx as nx +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D + +# The graph to visualize +G = nx.cycle_graph(20) + +# 3d spring layout +pos = nx.spring_layout(G, dim=3, seed=779) +# Extract node and edge positions from the layout +node_xyz = np.array([pos[v] for v in sorted(G)]) +edge_xyz = np.array([(pos[u], pos[v]) for u, v in G.edges()]) + +# Create the 3D figure +fig = plt.figure() +ax = fig.add_subplot(111, projection="3d") + +# Plot the nodes - alpha is scaled by "depth" automatically +ax.scatter(*node_xyz.T, s=100, ec="w") + +# Plot the edges +for vizedge in edge_xyz: + ax.plot(*vizedge.T, color="tab:gray") + + +def _format_axes(ax): + """Visualization options for the 3D axes.""" + # Turn gridlines off + ax.grid(False) + # Suppress tick labels + for dim in (ax.xaxis, ax.yaxis, ax.zaxis): + dim.set_ticks([]) + # Set axes labels + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_zlabel("z") + + +_format_axes(ax) +fig.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/README.txt b/share/doc/networkx-3.0/examples/README.txt new file mode 100644 index 0000000..d0049bd --- /dev/null +++ b/share/doc/networkx-3.0/examples/README.txt @@ -0,0 +1,8 @@ +.. _examples_gallery: + +Gallery +======= + +General-purpose and introductory examples for NetworkX. +The `tutorial <../tutorial.html>`_ introduces conventions and basic graph +manipulations. diff --git a/share/doc/networkx-3.0/examples/algorithms/README.txt 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30 +208 50 +210 38 +210 207 +211 37 +213 35 +213 38 +214 13 +214 14 +214 171 +214 213 +215 75 +217 39 +218 68 +218 222 +221 198 +222 198 +222 218 +223 39 +225 3 +226 22 +229 65 +230 68 +231 43 +232 95 +232 203 +233 99 +234 68 +234 230 +237 244 +238 145 +242 3 +242 113 +244 237 +249 96 +250 156 +252 65 +254 65 +258 113 +268 4 +270 183 +272 6 +275 96 +280 183 +280 206 +282 37 +285 75 +290 285 +293 290 \ No newline at end of file diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_beam_search.py b/share/doc/networkx-3.0/examples/algorithms/plot_beam_search.py new file mode 100644 index 0000000..c465bbb --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_beam_search.py @@ -0,0 +1,112 @@ +""" +=========== +Beam Search +=========== + +Beam search with dynamic beam width. + +The progressive widening beam search repeatedly executes a beam search +with increasing beam width until the target node is found. +""" +import math + +import matplotlib.pyplot as plt +import networkx as nx + + +def progressive_widening_search(G, source, value, condition, initial_width=1): + """Progressive widening beam search to find a node. + + The progressive widening beam search involves a repeated beam + search, starting with a small beam width then extending to + progressively larger beam widths if the target node is not + found. This implementation simply returns the first node found that + matches the termination condition. + + `G` is a NetworkX graph. + + `source` is a node in the graph. The search for the node of interest + begins here and extends only to those nodes in the (weakly) + connected component of this node. + + `value` is a function that returns a real number indicating how good + a potential neighbor node is when deciding which neighbor nodes to + enqueue in the breadth-first search. Only the best nodes within the + current beam width will be enqueued at each step. + + `condition` is the termination condition for the search. This is a + function that takes a node as input and return a Boolean indicating + whether the node is the target. If no node matches the termination + condition, this function raises :exc:`NodeNotFound`. + + `initial_width` is the starting beam width for the beam search (the + default is one). If no node matching the `condition` is found with + this beam width, the beam search is restarted from the `source` node + with a beam width that is twice as large (so the beam width + increases exponentially). The search terminates after the beam width + exceeds the number of nodes in the graph. + + """ + # Check for the special case in which the source node satisfies the + # termination condition. + if condition(source): + return source + # The largest possible value of `i` in this range yields a width at + # least the number of nodes in the graph, so the final invocation of + # `bfs_beam_edges` is equivalent to a plain old breadth-first + # search. Therefore, all nodes will eventually be visited. + log_m = math.ceil(math.log2(len(G))) + for i in range(log_m): + width = initial_width * pow(2, i) + # Since we are always starting from the same source node, this + # search may visit the same nodes many times (depending on the + # implementation of the `value` function). + for u, v in nx.bfs_beam_edges(G, source, value, width): + if condition(v): + return v + # At this point, since all nodes have been visited, we know that + # none of the nodes satisfied the termination condition. + raise nx.NodeNotFound("no node satisfied the termination condition") + + +############################################################################### +# Search for a node with high centrality. +# --------------------------------------- +# +# We generate a random graph, compute the centrality of each node, then perform +# the progressive widening search in order to find a node of high centrality. + +# Set a seed for random number generation so the example is reproducible +seed = 89 + +G = nx.gnp_random_graph(100, 0.5, seed=seed) +centrality = nx.eigenvector_centrality(G) +avg_centrality = sum(centrality.values()) / len(G) + + +def has_high_centrality(v): + return centrality[v] >= avg_centrality + + +source = 0 +value = centrality.get +condition = has_high_centrality + +found_node = progressive_widening_search(G, source, value, condition) +c = centrality[found_node] +print(f"found node {found_node} with centrality {c}") + + +# Draw graph +pos = nx.spring_layout(G, seed=seed) +options = { + "node_color": "blue", + "node_size": 20, + "edge_color": "grey", + "linewidths": 0, + "width": 0.1, +} +nx.draw(G, pos, **options) +# Draw node with high centrality as large and red +nx.draw_networkx_nodes(G, pos, nodelist=[found_node], node_size=100, node_color="r") +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_betweenness_centrality.py b/share/doc/networkx-3.0/examples/algorithms/plot_betweenness_centrality.py new file mode 100644 index 0000000..52994d5 --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_betweenness_centrality.py @@ -0,0 +1,83 @@ +""" +===================== +Betweenness Centrality +===================== + +Betweenness centrality measures of positive gene functional associations +using WormNet v.3-GS. + +Data from: https://www.inetbio.org/wormnet/downloadnetwork.php +""" + +from random import sample +import networkx as nx +import matplotlib.pyplot as plt + +# Gold standard data of positive gene functional associations +# from https://www.inetbio.org/wormnet/downloadnetwork.php +G = nx.read_edgelist("WormNet.v3.benchmark.txt") + +# remove randomly selected nodes (to make example fast) +num_to_remove = int(len(G) / 1.5) +nodes = sample(list(G.nodes), num_to_remove) +G.remove_nodes_from(nodes) + +# remove low-degree nodes +low_degree = [n for n, d in G.degree() if d < 10] +G.remove_nodes_from(low_degree) + +# largest connected component +components = nx.connected_components(G) +largest_component = max(components, key=len) +H = G.subgraph(largest_component) + +# compute centrality +centrality = nx.betweenness_centrality(H, k=10, endpoints=True) + +# compute community structure +lpc = nx.community.label_propagation_communities(H) +community_index = {n: i for i, com in enumerate(lpc) for n in com} + +#### draw graph #### +fig, ax = plt.subplots(figsize=(20, 15)) +pos = nx.spring_layout(H, k=0.15, seed=4572321) +node_color = [community_index[n] for n in H] +node_size = [v * 20000 for v in centrality.values()] +nx.draw_networkx( + H, + pos=pos, + with_labels=False, + node_color=node_color, + node_size=node_size, + edge_color="gainsboro", + alpha=0.4, +) + +# Title/legend +font = {"color": "k", "fontweight": "bold", "fontsize": 20} +ax.set_title("Gene functional association network (C. elegans)", font) +# Change font color for legend +font["color"] = "r" + +ax.text( + 0.80, + 0.10, + "node color = community structure", + horizontalalignment="center", + transform=ax.transAxes, + fontdict=font, +) +ax.text( + 0.80, + 0.06, + "node size = betweenness centrality", + horizontalalignment="center", + transform=ax.transAxes, + fontdict=font, +) + +# Resize figure for label readability +ax.margins(0.1, 0.05) +fig.tight_layout() +plt.axis("off") +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_blockmodel.py b/share/doc/networkx-3.0/examples/algorithms/plot_blockmodel.py new file mode 100644 index 0000000..b41f0c3 --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_blockmodel.py @@ -0,0 +1,79 @@ +""" +========== +Blockmodel +========== + +Example of creating a block model using the quotient_graph function in NX. Data +used is the Hartford, CT drug users network:: + + @article{weeks2002social, + title={Social networks of drug users in high-risk sites: Finding the connections}, + url = {https://doi.org/10.1023/A:1015457400897}, + doi = {10.1023/A:1015457400897}, + author={Weeks, Margaret R and Clair, Scott and Borgatti, Stephen P and Radda, Kim and Schensul, Jean J}, + journal={{AIDS and Behavior}}, + volume={6}, + number={2}, + pages={193--206}, + year={2002}, + publisher={Springer} + } + +""" + +from collections import defaultdict + +import matplotlib.pyplot as plt +import networkx as nx +import numpy as np +from scipy.cluster import hierarchy +from scipy.spatial import distance + + +def create_hc(G): + """Creates hierarchical cluster of graph G from distance matrix""" + path_length = nx.all_pairs_shortest_path_length(G) + distances = np.zeros((len(G), len(G))) + for u, p in path_length: + for v, d in p.items(): + distances[u][v] = d + # Create hierarchical cluster + Y = distance.squareform(distances) + Z = hierarchy.complete(Y) # Creates HC using farthest point linkage + # This partition selection is arbitrary, for illustrive purposes + membership = list(hierarchy.fcluster(Z, t=1.15)) + # Create collection of lists for blockmodel + partition = defaultdict(list) + for n, p in zip(list(range(len(G))), membership): + partition[p].append(n) + return list(partition.values()) + + +G = nx.read_edgelist("hartford_drug.edgelist") + +# Extract largest connected component into graph H +H = G.subgraph(next(nx.connected_components(G))) +# Makes life easier to have consecutively labeled integer nodes +H = nx.convert_node_labels_to_integers(H) +# Create partitions with hierarchical clustering +partitions = create_hc(H) +# Build blockmodel graph +BM = nx.quotient_graph(H, partitions, relabel=True) + +# Draw original graph +pos = nx.spring_layout(H, iterations=100, seed=83) # Seed for reproducibility +plt.subplot(211) +nx.draw(H, pos, with_labels=False, node_size=10) + +# Draw block model with weighted edges and nodes sized by number of internal nodes +node_size = [BM.nodes[x]["nnodes"] * 10 for x in BM.nodes()] +edge_width = [(2 * d["weight"]) for (u, v, d) in BM.edges(data=True)] +# Set positions to mean of positions of internal nodes from original graph +posBM = {} +for n in BM: + xy = np.array([pos[u] for u in BM.nodes[n]["graph"]]) + posBM[n] = xy.mean(axis=0) +plt.subplot(212) +nx.draw(BM, posBM, node_size=node_size, width=edge_width, with_labels=False) +plt.axis("off") +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_circuits.py b/share/doc/networkx-3.0/examples/algorithms/plot_circuits.py new file mode 100644 index 0000000..5481aed --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_circuits.py @@ -0,0 +1,103 @@ +""" +======== +Circuits +======== + +Convert a Boolean circuit to an equivalent Boolean formula. + +A Boolean circuit can be exponentially more expressive than an +equivalent formula in the worst case, since the circuit can reuse +subcircuits multiple times, whereas a formula cannot reuse subformulas +more than once. Thus creating a Boolean formula from a Boolean circuit +in this way may be infeasible if the circuit is large. + +""" +import matplotlib.pyplot as plt +import networkx as nx + + +def circuit_to_formula(circuit): + # Convert the circuit to an equivalent formula. + formula = nx.dag_to_branching(circuit) + # Transfer the operator or variable labels for each node from the + # circuit to the formula. + for v in formula: + source = formula.nodes[v]["source"] + formula.nodes[v]["label"] = circuit.nodes[source]["label"] + return formula + + +def formula_to_string(formula): + def _to_string(formula, root): + # If there are no children, this is a variable node. + label = formula.nodes[root]["label"] + if not formula[root]: + return label + # Otherwise, this is an operator. + children = formula[root] + # If one child, the label must be a NOT operator. + if len(children) == 1: + child = nx.utils.arbitrary_element(children) + return f"{label}({_to_string(formula, child)})" + # NB "left" and "right" here are a little misleading: there is + # no order on the children of a node. That's okay because the + # Boolean AND and OR operators are symmetric. It just means that + # the order of the operands cannot be predicted and hence the + # function does not necessarily behave the same way on every + # invocation. + left, right = formula[root] + left_subformula = _to_string(formula, left) + right_subformula = _to_string(formula, right) + return f"({left_subformula} {label} {right_subformula})" + + root = next(v for v, d in formula.in_degree() if d == 0) + return _to_string(formula, root) + + +############################################################################### +# Create an example Boolean circuit. +# ---------------------------------- +# +# This circuit has a ∧ at the output and two ∨s at the next layer. +# The third layer has a variable x that appears in the left ∨, a +# variable y that appears in both the left and right ∨s, and a +# negation for the variable z that appears as the sole node in the +# fourth layer. +circuit = nx.DiGraph() +# Layer 0 +circuit.add_node(0, label="∧", layer=0) +# Layer 1 +circuit.add_node(1, label="∨", layer=1) +circuit.add_node(2, label="∨", layer=1) +circuit.add_edge(0, 1) +circuit.add_edge(0, 2) +# Layer 2 +circuit.add_node(3, label="x", layer=2) +circuit.add_node(4, label="y", layer=2) +circuit.add_node(5, label="¬", layer=2) +circuit.add_edge(1, 3) +circuit.add_edge(1, 4) +circuit.add_edge(2, 4) +circuit.add_edge(2, 5) +# Layer 3 +circuit.add_node(6, label="z", layer=3) +circuit.add_edge(5, 6) +# Convert the circuit to an equivalent formula. +formula = circuit_to_formula(circuit) +print(formula_to_string(formula)) + + +labels = nx.get_node_attributes(circuit, "label") +options = { + "node_size": 600, + "alpha": 0.5, + "node_color": "blue", + "labels": labels, + "font_size": 22, +} +plt.figure(figsize=(8, 8)) +pos = nx.multipartite_layout(circuit, subset_key="layer") +nx.draw_networkx(circuit, pos, **options) +plt.title(formula_to_string(formula)) +plt.axis("equal") +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_davis_club.py b/share/doc/networkx-3.0/examples/algorithms/plot_davis_club.py new file mode 100644 index 0000000..517d523 --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_davis_club.py @@ -0,0 +1,43 @@ +""" +========== +Davis Club +========== + +Davis Southern Club Women + +Shows how to make unipartite projections of the graph and compute the +properties of those graphs. + +These data were collected by Davis et al. in the 1930s. +They represent observed attendance at 14 social events by 18 Southern women. +The graph is bipartite (clubs, women). +""" +import matplotlib.pyplot as plt +import networkx as nx +import networkx.algorithms.bipartite as bipartite + +G = nx.davis_southern_women_graph() +women = G.graph["top"] +clubs = G.graph["bottom"] + +print("Biadjacency matrix") +print(bipartite.biadjacency_matrix(G, women, clubs)) + +# project bipartite graph onto women nodes +W = bipartite.projected_graph(G, women) +print() +print("#Friends, Member") +for w in women: + print(f"{W.degree(w)} {w}") + +# project bipartite graph onto women nodes keeping number of co-occurence +# the degree computed is weighted and counts the total number of shared contacts +W = bipartite.weighted_projected_graph(G, women) +print() +print("#Friend meetings, Member") +for w in women: + print(f"{W.degree(w, weight='weight')} {w}") + +pos = nx.spring_layout(G, seed=648) # Seed layout for reproducible node positions +nx.draw(G, pos) +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_dedensification.py b/share/doc/networkx-3.0/examples/algorithms/plot_dedensification.py new file mode 100644 index 0000000..9104d54 --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_dedensification.py @@ -0,0 +1,92 @@ +""" +=============== +Dedensification +=============== + +Examples of dedensification of a graph. Dedensification retains the structural +pattern of the original graph and will only add compressor nodes when doing so +would result in fewer edges in the compressed graph. +""" +import matplotlib.pyplot as plt +import networkx as nx + +plt.suptitle("Dedensification") + +original_graph = nx.DiGraph() +white_nodes = ["1", "2", "3", "4", "5", "6"] +red_nodes = ["A", "B", "C"] +node_sizes = [250 for node in white_nodes + red_nodes] +node_colors = ["white" for n in white_nodes] + ["red" for n in red_nodes] + +original_graph.add_nodes_from(white_nodes + red_nodes) +original_graph.add_edges_from( + [ + ("1", "C"), + ("1", "B"), + ("2", "C"), + ("2", "B"), + ("2", "A"), + ("3", "B"), + ("3", "A"), + ("3", "6"), + ("4", "C"), + ("4", "B"), + ("4", "A"), + ("5", "B"), + ("5", "A"), + ("6", "5"), + ("A", "6"), + ] +) +base_options = dict(with_labels=True, edgecolors="black") +pos = { + "3": (0, 1), + "2": (0, 2), + "1": (0, 3), + "6": (1, 0), + "A": (1, 1), + "B": (1, 2), + "C": (1, 3), + "4": (2, 3), + "5": (2, 1), +} +ax1 = plt.subplot(1, 2, 1) +plt.title("Original (%s edges)" % original_graph.number_of_edges()) +nx.draw_networkx(original_graph, pos=pos, node_color=node_colors, **base_options) + +nonexp_graph, compression_nodes = nx.summarization.dedensify( + original_graph, threshold=2, copy=False +) +nonexp_node_colors = list(node_colors) +nonexp_node_sizes = list(node_sizes) +for node in compression_nodes: + nonexp_node_colors.append("yellow") + nonexp_node_sizes.append(600) +plt.subplot(1, 2, 2) + +plt.title("Dedensified (%s edges)" % nonexp_graph.number_of_edges()) +nonexp_pos = { + "5": (0, 0), + "B": (0, 2), + "1": (0, 3), + "6": (1, 0.75), + "3": (1.5, 1.5), + "A": (2, 0), + "C": (2, 3), + "4": (3, 1.5), + "2": (3, 2.5), +} +c_nodes = list(compression_nodes) +c_nodes.sort() +for spot, node in enumerate(c_nodes): + nonexp_pos[node] = (2, spot + 2) +nx.draw_networkx( + nonexp_graph, + pos=nonexp_pos, + node_color=nonexp_node_colors, + node_size=nonexp_node_sizes, + **base_options +) + +plt.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_iterated_dynamical_systems.py b/share/doc/networkx-3.0/examples/algorithms/plot_iterated_dynamical_systems.py new file mode 100644 index 0000000..a19baeb --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_iterated_dynamical_systems.py @@ -0,0 +1,210 @@ +""" +========================== +Iterated Dynamical Systems +========================== + +Digraphs from Integer-valued Iterated Functions + +Sums of cubes on 3N +------------------- + +The number 153 has a curious property. + +Let 3N={3,6,9,12,...} be the set of positive multiples of 3. Define an +iterative process f:3N->3N as follows: for a given n, take each digit +of n (in base 10), cube it and then sum the cubes to obtain f(n). + +When this process is repeated, the resulting series n, f(n), f(f(n)),... +terminate in 153 after a finite number of iterations (the process ends +because 153 = 1**3 + 5**3 + 3**3). + +In the language of discrete dynamical systems, 153 is the global +attractor for the iterated map f restricted to the set 3N. + +For example: take the number 108 + +f(108) = 1**3 + 0**3 + 8**3 = 513 + +and + +f(513) = 5**3 + 1**3 + 3**3 = 153 + +So, starting at 108 we reach 153 in two iterations, +represented as: + +108->513->153 + +Computing all orbits of 3N up to 10**5 reveals that the attractor +153 is reached in a maximum of 14 iterations. In this code we +show that 13 cycles is the maximum required for all integers (in 3N) +less than 10,000. + +The smallest number that requires 13 iterations to reach 153, is 177, i.e., + +177->687->1071->345->216->225->141->66->432->99->1458->702->351->153 + +The resulting large digraphs are useful for testing network software. + +The general problem +------------------- + +Given numbers n, a power p and base b, define F(n; p, b) as the sum of +the digits of n (in base b) raised to the power p. The above example +corresponds to f(n)=F(n; 3,10), and below F(n; p, b) is implemented as +the function powersum(n,p,b). The iterative dynamical system defined by +the mapping n:->f(n) above (over 3N) converges to a single fixed point; +153. Applying the map to all positive integers N, leads to a discrete +dynamical process with 5 fixed points: 1, 153, 370, 371, 407. Modulo 3 +those numbers are 1, 0, 1, 2, 2. The function f above has the added +property that it maps a multiple of 3 to another multiple of 3; i.e. it +is invariant on the subset 3N. + + +The squaring of digits (in base 10) result in cycles and the +single fixed point 1. I.e., from a certain point on, the process +starts repeating itself. + +keywords: "Recurring Digital Invariant", "Narcissistic Number", +"Happy Number" + +The 3n+1 problem +---------------- + +There is a rich history of mathematical recreations +associated with discrete dynamical systems. The most famous +is the Collatz 3n+1 problem. See the function +collatz_problem_digraph below. The Collatz conjecture +--- that every orbit returns to the fixed point 1 in finite time +--- is still unproven. Even the great Paul Erdos said "Mathematics +is not yet ready for such problems", and offered $500 +for its solution. + +keywords: "3n+1", "3x+1", "Collatz problem", "Thwaite's conjecture" +""" + +import networkx as nx + +nmax = 10000 +p = 3 + + +def digitsrep(n, b=10): + """Return list of digits comprising n represented in base b. + n must be a nonnegative integer""" + + if n <= 0: + return [0] + + dlist = [] + while n > 0: + # Prepend next least-significant digit + dlist = [n % b] + dlist + # Floor-division + n = n // b + return dlist + + +def powersum(n, p, b=10): + """Return sum of digits of n (in base b) raised to the power p.""" + dlist = digitsrep(n, b) + sum = 0 + for k in dlist: + sum += k**p + return sum + + +def attractor153_graph(n, p, multiple=3, b=10): + """Return digraph of iterations of powersum(n,3,10).""" + G = nx.DiGraph() + for k in range(1, n + 1): + if k % multiple == 0 and k not in G: + k1 = k + knext = powersum(k1, p, b) + while k1 != knext: + G.add_edge(k1, knext) + k1 = knext + knext = powersum(k1, p, b) + return G + + +def squaring_cycle_graph_old(n, b=10): + """Return digraph of iterations of powersum(n,2,10).""" + G = nx.DiGraph() + for k in range(1, n + 1): + k1 = k + G.add_node(k1) # case k1==knext, at least add node + knext = powersum(k1, 2, b) + G.add_edge(k1, knext) + while k1 != knext: # stop if fixed point + k1 = knext + knext = powersum(k1, 2, b) + G.add_edge(k1, knext) + if G.out_degree(knext) >= 1: + # knext has already been iterated in and out + break + return G + + +def sum_of_digits_graph(nmax, b=10): + def f(n): + return powersum(n, 1, b) + + return discrete_dynamics_digraph(nmax, f) + + +def squaring_cycle_digraph(nmax, b=10): + def f(n): + return powersum(n, 2, b) + + return discrete_dynamics_digraph(nmax, f) + + +def cubing_153_digraph(nmax): + def f(n): + return powersum(n, 3, 10) + + return discrete_dynamics_digraph(nmax, f) + + +def discrete_dynamics_digraph(nmax, f, itermax=50000): + G = nx.DiGraph() + for k in range(1, nmax + 1): + kold = k + G.add_node(kold) + knew = f(kold) + G.add_edge(kold, knew) + while kold != knew and kold << itermax: + # iterate until fixed point reached or itermax is exceeded + kold = knew + knew = f(kold) + G.add_edge(kold, knew) + if G.out_degree(knew) >= 1: + # knew has already been iterated in and out + break + return G + + +def collatz_problem_digraph(nmax): + def f(n): + if n % 2 == 0: + return n // 2 + else: + return 3 * n + 1 + + return discrete_dynamics_digraph(nmax, f) + + +def fixed_points(G): + """Return a list of fixed points for the discrete dynamical + system represented by the digraph G. + """ + return [n for n in G if G.out_degree(n) == 0] + + +nmax = 10000 +print(f"Building cubing_153_digraph({nmax})") +G = cubing_153_digraph(nmax) +print("Resulting digraph has", len(G), "nodes and", G.size(), " edges") +print("Shortest path from 177 to 153 is:") +print(nx.shortest_path(G, 177, 153)) +print(f"fixed points are {fixed_points(G)}") diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_krackhardt_centrality.py b/share/doc/networkx-3.0/examples/algorithms/plot_krackhardt_centrality.py new file mode 100644 index 0000000..9d3e2a2 --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_krackhardt_centrality.py @@ -0,0 +1,31 @@ +""" +===================== +Krackhardt Centrality +===================== + +Centrality measures of Krackhardt social network. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.krackhardt_kite_graph() + +print("Betweenness") +b = nx.betweenness_centrality(G) +for v in G.nodes(): + print(f"{v:2} {b[v]:.3f}") + +print("Degree centrality") +d = nx.degree_centrality(G) +for v in G.nodes(): + print(f"{v:2} {d[v]:.3f}") + +print("Closeness centrality") +c = nx.closeness_centrality(G) +for v in G.nodes(): + print(f"{v:2} {c[v]:.3f}") + +pos = nx.spring_layout(G, seed=367) # Seed layout for reproducibility +nx.draw(G, pos) +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_parallel_betweenness.py b/share/doc/networkx-3.0/examples/algorithms/plot_parallel_betweenness.py new file mode 100644 index 0000000..e6d238d --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_parallel_betweenness.py @@ -0,0 +1,82 @@ +""" +==================== +Parallel Betweenness +==================== + +Example of parallel implementation of betweenness centrality using the +multiprocessing module from Python Standard Library. + +The function betweenness centrality accepts a bunch of nodes and computes +the contribution of those nodes to the betweenness centrality of the whole +network. Here we divide the network in chunks of nodes and we compute their +contribution to the betweenness centrality of the whole network. + +Note: The example output below shows that the non-parallel implementation is +faster. This is a limitation of our CI/CD pipeline running on a single core. + +Depending on your setup, you will likely observe a speedup. +""" +from multiprocessing import Pool +import time +import itertools + +import matplotlib.pyplot as plt +import networkx as nx + + +def chunks(l, n): + """Divide a list of nodes `l` in `n` chunks""" + l_c = iter(l) + while 1: + x = tuple(itertools.islice(l_c, n)) + if not x: + return + yield x + + +def betweenness_centrality_parallel(G, processes=None): + """Parallel betweenness centrality function""" + p = Pool(processes=processes) + node_divisor = len(p._pool) * 4 + node_chunks = list(chunks(G.nodes(), G.order() // node_divisor)) + num_chunks = len(node_chunks) + bt_sc = p.starmap( + nx.betweenness_centrality_subset, + zip( + [G] * num_chunks, + node_chunks, + [list(G)] * num_chunks, + [True] * num_chunks, + [None] * num_chunks, + ), + ) + + # Reduce the partial solutions + bt_c = bt_sc[0] + for bt in bt_sc[1:]: + for n in bt: + bt_c[n] += bt[n] + return bt_c + + +G_ba = nx.barabasi_albert_graph(1000, 3) +G_er = nx.gnp_random_graph(1000, 0.01) +G_ws = nx.connected_watts_strogatz_graph(1000, 4, 0.1) +for G in [G_ba, G_er, G_ws]: + print("") + print("Computing betweenness centrality for:") + print(G) + print("\tParallel version") + start = time.time() + bt = betweenness_centrality_parallel(G) + print(f"\t\tTime: {(time.time() - start):.4F} seconds") + print(f"\t\tBetweenness centrality for node 0: {bt[0]:.5f}") + print("\tNon-Parallel version") + start = time.time() + bt = nx.betweenness_centrality(G) + print(f"\t\tTime: {(time.time() - start):.4F} seconds") + print(f"\t\tBetweenness centrality for node 0: {bt[0]:.5f}") +print("") + +nx.draw(G_ba, node_size=100) +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_rcm.py b/share/doc/networkx-3.0/examples/algorithms/plot_rcm.py new file mode 100644 index 0000000..fc08739 --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_rcm.py @@ -0,0 +1,40 @@ +""" +====================== +Reverse Cuthill--McKee +====================== + +Cuthill-McKee ordering of matrices + +The reverse Cuthill--McKee algorithm gives a sparse matrix ordering that +reduces the matrix bandwidth. +""" + +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import networkx as nx + + +# build low-bandwidth matrix +G = nx.grid_2d_graph(3, 3) +rcm = list(nx.utils.reverse_cuthill_mckee_ordering(G)) +print("ordering", rcm) + +print("unordered Laplacian matrix") +A = nx.laplacian_matrix(G) +x, y = np.nonzero(A) +# print(f"lower bandwidth: {(y - x).max()}") +# print(f"upper bandwidth: {(x - y).max()}") +print(f"bandwidth: {(y - x).max() + (x - y).max() + 1}") +print(A) + +B = nx.laplacian_matrix(G, nodelist=rcm) +print("low-bandwidth Laplacian matrix") +x, y = np.nonzero(B) +# print(f"lower bandwidth: {(y - x).max()}") +# print(f"upper bandwidth: {(x - y).max()}") +print(f"bandwidth: {(y - x).max() + (x - y).max() + 1}") +print(B) + +sns.heatmap(B.todense(), cbar=False, square=True, linewidths=0.5, annot=True) +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_snap.py b/share/doc/networkx-3.0/examples/algorithms/plot_snap.py new file mode 100644 index 0000000..85ea71d --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_snap.py @@ -0,0 +1,108 @@ +""" +================== +SNAP Graph Summary +================== +An example of summarizing a graph based on node attributes and edge attributes +using the Summarization by Grouping Nodes on Attributes and Pairwise +edges (SNAP) algorithm (not to be confused with the Stanford Network +Analysis Project). The algorithm groups nodes by their unique +combinations of node attribute values and edge types with other groups +of nodes to produce a summary graph. The summary graph can then be used to +infer how nodes with different attributes values relate to other nodes in the +graph. +""" +import networkx as nx +import matplotlib.pyplot as plt + + +nodes = { + "A": dict(color="Red"), + "B": dict(color="Red"), + "C": dict(color="Red"), + "D": dict(color="Red"), + "E": dict(color="Blue"), + "F": dict(color="Blue"), + "G": dict(color="Blue"), + "H": dict(color="Blue"), + "I": dict(color="Yellow"), + "J": dict(color="Yellow"), + "K": dict(color="Yellow"), + "L": dict(color="Yellow"), +} +edges = [ + ("A", "B", "Strong"), + ("A", "C", "Weak"), + ("A", "E", "Strong"), + ("A", "I", "Weak"), + ("B", "D", "Weak"), + ("B", "J", "Weak"), + ("B", "F", "Strong"), + ("C", "G", "Weak"), + ("D", "H", "Weak"), + ("I", "J", "Strong"), + ("J", "K", "Strong"), + ("I", "L", "Strong"), +] +original_graph = nx.Graph() +original_graph.add_nodes_from(n for n in nodes.items()) +original_graph.add_edges_from((u, v, {"type": label}) for u, v, label in edges) + + +plt.suptitle("SNAP Summarization") + +base_options = dict(with_labels=True, edgecolors="black", node_size=500) + +ax1 = plt.subplot(1, 2, 1) +plt.title( + "Original (%s nodes, %s edges)" + % (original_graph.number_of_nodes(), original_graph.number_of_edges()) +) +pos = nx.spring_layout(original_graph, seed=7482934) +node_colors = [d["color"] for _, d in original_graph.nodes(data=True)] + +edge_type_visual_weight_lookup = {"Weak": 1.0, "Strong": 3.0} +edge_weights = [ + edge_type_visual_weight_lookup[d["type"]] + for _, _, d in original_graph.edges(data=True) +] + +nx.draw_networkx( + original_graph, pos=pos, node_color=node_colors, width=edge_weights, **base_options +) + +node_attributes = ("color",) +edge_attributes = ("type",) +summary_graph = nx.snap_aggregation( + original_graph, node_attributes, edge_attributes, prefix="S-" +) + +plt.subplot(1, 2, 2) + +plt.title( + "SNAP Aggregation (%s nodes, %s edges)" + % (summary_graph.number_of_nodes(), summary_graph.number_of_edges()) +) +summary_pos = nx.spring_layout(summary_graph, seed=8375428) +node_colors = [] +for node in summary_graph: + color = summary_graph.nodes[node]["color"] + node_colors.append(color) + +edge_weights = [] +for edge in summary_graph.edges(): + edge_types = summary_graph.get_edge_data(*edge)["types"] + edge_weight = 0.0 + for edge_type in edge_types: + edge_weight += edge_type_visual_weight_lookup[edge_type["type"]] + edge_weights.append(edge_weight) + +nx.draw_networkx( + summary_graph, + pos=summary_pos, + node_color=node_colors, + width=edge_weights, + **base_options +) + +plt.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/algorithms/plot_subgraphs.py b/share/doc/networkx-3.0/examples/algorithms/plot_subgraphs.py new file mode 100644 index 0000000..0812702 --- /dev/null +++ b/share/doc/networkx-3.0/examples/algorithms/plot_subgraphs.py @@ -0,0 +1,170 @@ +""" +========= +Subgraphs +========= +Example of partitioning a directed graph with nodes labeled as +supported and unsupported nodes into a list of subgraphs +that contain only entirely supported or entirely unsupported nodes. +Adopted from +https://github.com/lobpcg/python_examples/blob/master/networkx_example.py +""" + +import networkx as nx +import matplotlib.pyplot as plt + + +def graph_partitioning(G, plotting=True): + """Partition a directed graph into a list of subgraphs that contain + only entirely supported or entirely unsupported nodes. + """ + # Categorize nodes by their node_type attribute + supported_nodes = {n for n, d in G.nodes(data="node_type") if d == "supported"} + unsupported_nodes = {n for n, d in G.nodes(data="node_type") if d == "unsupported"} + + # Make a copy of the graph. + H = G.copy() + # Remove all edges connecting supported and unsupported nodes. + H.remove_edges_from( + (n, nbr, d) + for n, nbrs in G.adj.items() + if n in supported_nodes + for nbr, d in nbrs.items() + if nbr in unsupported_nodes + ) + H.remove_edges_from( + (n, nbr, d) + for n, nbrs in G.adj.items() + if n in unsupported_nodes + for nbr, d in nbrs.items() + if nbr in supported_nodes + ) + + # Collect all removed edges for reconstruction. + G_minus_H = nx.DiGraph() + G_minus_H.add_edges_from(set(G.edges) - set(H.edges)) + + if plotting: + # Plot the stripped graph with the edges removed. + _node_colors = [c for _, c in H.nodes(data="node_color")] + _pos = nx.spring_layout(H) + plt.figure(figsize=(8, 8)) + nx.draw_networkx_edges(H, _pos, alpha=0.3, edge_color="k") + nx.draw_networkx_nodes(H, _pos, node_color=_node_colors) + nx.draw_networkx_labels(H, _pos, font_size=14) + plt.axis("off") + plt.title("The stripped graph with the edges removed.") + plt.show() + # Plot the the edges removed. + _pos = nx.spring_layout(G_minus_H) + plt.figure(figsize=(8, 8)) + ncl = [G.nodes[n]["node_color"] for n in G_minus_H.nodes] + nx.draw_networkx_edges(G_minus_H, _pos, alpha=0.3, edge_color="k") + nx.draw_networkx_nodes(G_minus_H, _pos, node_color=ncl) + nx.draw_networkx_labels(G_minus_H, _pos, font_size=14) + plt.axis("off") + plt.title("The removed edges.") + plt.show() + + # Find the connected components in the stripped undirected graph. + # And use the sets, specifying the components, to partition + # the original directed graph into a list of directed subgraphs + # that contain only entirely supported or entirely unsupported nodes. + subgraphs = [ + H.subgraph(c).copy() for c in nx.connected_components(H.to_undirected()) + ] + + return subgraphs, G_minus_H + + +############################################################################### +# Create an example directed graph. +# --------------------------------- +# +# This directed graph has one input node labeled `in` and plotted in blue color +# and one output node labeled `out` and plotted in magenta color. +# The other six nodes are classified as four `supported` plotted in green color +# and two `unsupported` plotted in red color. The goal is computing a list +# of subgraphs that contain only entirely `supported` or `unsupported` nodes. +G_ex = nx.DiGraph() +G_ex.add_nodes_from(["In"], node_type="input", node_color="b") +G_ex.add_nodes_from(["A", "C", "E", "F"], node_type="supported", node_color="g") +G_ex.add_nodes_from(["B", "D"], node_type="unsupported", node_color="r") +G_ex.add_nodes_from(["Out"], node_type="output", node_color="m") +G_ex.add_edges_from( + [ + ("In", "A"), + ("A", "B"), + ("B", "C"), + ("B", "D"), + ("D", "E"), + ("C", "F"), + ("E", "F"), + ("F", "Out"), + ] +) + +############################################################################### +# Plot the original graph. +# ------------------------ +# +node_color_list = [nc for _, nc in G_ex.nodes(data="node_color")] +pos = nx.spectral_layout(G_ex) +plt.figure(figsize=(8, 8)) +nx.draw_networkx_edges(G_ex, pos, alpha=0.3, edge_color="k") +nx.draw_networkx_nodes(G_ex, pos, alpha=0.8, node_color=node_color_list) +nx.draw_networkx_labels(G_ex, pos, font_size=14) +plt.axis("off") +plt.title("The original graph.") +plt.show() + +############################################################################### +# Calculate the subgraphs with plotting all results of intemediate steps. +# ----------------------------------------------------------------------- +# +subgraphs_of_G_ex, removed_edges = graph_partitioning(G_ex, plotting=True) + +############################################################################### +# Plot the results: every subgraph in the list. +# --------------------------------------------- +# +for subgraph in subgraphs_of_G_ex: + _pos = nx.spring_layout(subgraph) + plt.figure(figsize=(8, 8)) + nx.draw_networkx_edges(subgraph, _pos, alpha=0.3, edge_color="k") + node_color_list_c = [nc for _, nc in subgraph.nodes(data="node_color")] + nx.draw_networkx_nodes(subgraph, _pos, node_color=node_color_list_c) + nx.draw_networkx_labels(subgraph, _pos, font_size=14) + plt.axis("off") + plt.title("One of the subgraphs.") + plt.show() + +############################################################################### +# Put the graph back from the list of subgraphs +# --------------------------------------------- +# +G_ex_r = nx.DiGraph() +# Composing all subgraphs. +for subgraph in subgraphs_of_G_ex: + G_ex_r = nx.compose(G_ex_r, subgraph) +# Adding the previously stored edges. +G_ex_r.add_edges_from(removed_edges.edges()) + +############################################################################### +# Check that the original graph and the reconstructed graphs are isomorphic. +# -------------------------------------------------------------------------- +# +assert nx.is_isomorphic(G_ex, G_ex_r) + +############################################################################### +# Plot the reconstructed graph. +# ----------------------------- +# +node_color_list = [nc for _, nc in G_ex_r.nodes(data="node_color")] +pos = nx.spectral_layout(G_ex_r) +plt.figure(figsize=(8, 8)) +nx.draw_networkx_edges(G_ex_r, pos, alpha=0.3, edge_color="k") +nx.draw_networkx_nodes(G_ex_r, pos, alpha=0.8, node_color=node_color_list) +nx.draw_networkx_labels(G_ex_r, pos, font_size=14) +plt.axis("off") +plt.title("The reconstructed graph.") +plt.show() diff --git a/share/doc/networkx-3.0/examples/basic/README.txt b/share/doc/networkx-3.0/examples/basic/README.txt new file mode 100644 index 0000000..c1cc18b --- /dev/null +++ b/share/doc/networkx-3.0/examples/basic/README.txt @@ -0,0 +1,2 @@ +Basic +----- diff --git a/share/doc/networkx-3.0/examples/basic/plot_properties.py b/share/doc/networkx-3.0/examples/basic/plot_properties.py new file mode 100644 index 0000000..7eb25f3 --- /dev/null +++ b/share/doc/networkx-3.0/examples/basic/plot_properties.py @@ -0,0 +1,49 @@ +""" +========== +Properties +========== + +Compute some network properties for the lollipop graph. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.lollipop_graph(4, 6) + +pathlengths = [] + +print("source vertex {target:length, }") +for v in G.nodes(): + spl = dict(nx.single_source_shortest_path_length(G, v)) + print(f"{v} {spl} ") + for p in spl: + pathlengths.append(spl[p]) + +print() +print(f"average shortest path length {sum(pathlengths) / len(pathlengths)}") + +# histogram of path lengths +dist = {} +for p in pathlengths: + if p in dist: + dist[p] += 1 + else: + dist[p] = 1 + +print() +print("length #paths") +verts = dist.keys() +for d in sorted(verts): + print(f"{d} {dist[d]}") + +print(f"radius: {nx.radius(G)}") +print(f"diameter: {nx.diameter(G)}") +print(f"eccentricity: {nx.eccentricity(G)}") +print(f"center: {nx.center(G)}") +print(f"periphery: {nx.periphery(G)}") +print(f"density: {nx.density(G)}") + +pos = nx.spring_layout(G, seed=3068) # Seed layout for reproducibility +nx.draw(G, pos=pos, with_labels=True) +plt.show() diff --git a/share/doc/networkx-3.0/examples/basic/plot_read_write.py b/share/doc/networkx-3.0/examples/basic/plot_read_write.py new file mode 100644 index 0000000..c9fa89e --- /dev/null +++ b/share/doc/networkx-3.0/examples/basic/plot_read_write.py @@ -0,0 +1,24 @@ +""" +====================== +Read and write graphs. +====================== + +Read and write graphs. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.grid_2d_graph(5, 5) # 5x5 grid + +# print the adjacency list +for line in nx.generate_adjlist(G): + print(line) +# write edgelist to grid.edgelist +nx.write_edgelist(G, path="grid.edgelist", delimiter=":") +# read edgelist from grid.edgelist +H = nx.read_edgelist(path="grid.edgelist", delimiter=":") + +pos = nx.spring_layout(H, seed=200) +nx.draw(H, pos) +plt.show() diff --git a/share/doc/networkx-3.0/examples/basic/plot_simple_graph.py b/share/doc/networkx-3.0/examples/basic/plot_simple_graph.py new file mode 100644 index 0000000..fcd7caf --- /dev/null +++ b/share/doc/networkx-3.0/examples/basic/plot_simple_graph.py @@ -0,0 +1,60 @@ +""" +============ +Simple graph +============ + +Draw simple graph with manual layout. +""" + +import networkx as nx +import matplotlib.pyplot as plt + +G = nx.Graph() +G.add_edge(1, 2) +G.add_edge(1, 3) +G.add_edge(1, 5) +G.add_edge(2, 3) +G.add_edge(3, 4) +G.add_edge(4, 5) + +# explicitly set positions +pos = {1: (0, 0), 2: (-1, 0.3), 3: (2, 0.17), 4: (4, 0.255), 5: (5, 0.03)} + +options = { + "font_size": 36, + "node_size": 3000, + "node_color": "white", + "edgecolors": "black", + "linewidths": 5, + "width": 5, +} +nx.draw_networkx(G, pos, **options) + +# Set margins for the axes so that nodes aren't clipped +ax = plt.gca() +ax.margins(0.20) +plt.axis("off") +plt.show() + +# %% +# A directed graph + +G = nx.DiGraph([(0, 3), (1, 3), (2, 4), (3, 5), (3, 6), (4, 6), (5, 6)]) + +# group nodes by column +left_nodes = [0, 1, 2] +middle_nodes = [3, 4] +right_nodes = [5, 6] + +# set the position according to column (x-coord) +pos = {n: (0, i) for i, n in enumerate(left_nodes)} +pos.update({n: (1, i + 0.5) for i, n in enumerate(middle_nodes)}) +pos.update({n: (2, i + 0.5) for i, n in enumerate(right_nodes)}) + +nx.draw_networkx(G, pos, **options) + +# Set margins for the axes so that nodes aren't clipped +ax = plt.gca() +ax.margins(0.20) +plt.axis("off") +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/README.txt b/share/doc/networkx-3.0/examples/drawing/README.txt new file mode 100644 index 0000000..c25993d --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/README.txt @@ -0,0 +1,2 @@ +Drawing +------- diff --git a/share/doc/networkx-3.0/examples/drawing/chess_masters_WCC.pgn.bz2 b/share/doc/networkx-3.0/examples/drawing/chess_masters_WCC.pgn.bz2 new file mode 100644 index 0000000..3761ce5 Binary files /dev/null and b/share/doc/networkx-3.0/examples/drawing/chess_masters_WCC.pgn.bz2 differ diff --git a/share/doc/networkx-3.0/examples/drawing/knuth_miles.txt.gz b/share/doc/networkx-3.0/examples/drawing/knuth_miles.txt.gz new file mode 100644 index 0000000..62b7f95 Binary files /dev/null and b/share/doc/networkx-3.0/examples/drawing/knuth_miles.txt.gz differ diff --git a/share/doc/networkx-3.0/examples/drawing/plot_center_node.py b/share/doc/networkx-3.0/examples/drawing/plot_center_node.py new file mode 100644 index 0000000..d7eb470 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_center_node.py @@ -0,0 +1,20 @@ +""" +==================== +Custom Node Position +==================== + +Draw a graph with node(s) located at user-defined positions. + +When a position is set by the user, the other nodes can still be neatly organised in a layout. +""" + +import networkx as nx +import numpy as np + +G = nx.path_graph(20) # An example graph +center_node = 5 # Or any other node to be in the center +edge_nodes = set(G) - {center_node} +# Ensures the nodes around the circle are evenly distributed +pos = nx.circular_layout(G.subgraph(edge_nodes)) +pos[center_node] = np.array([0, 0]) # manually specify node position +nx.draw(G, pos, with_labels=True) diff --git a/share/doc/networkx-3.0/examples/drawing/plot_chess_masters.py b/share/doc/networkx-3.0/examples/drawing/plot_chess_masters.py new file mode 100644 index 0000000..7e7bfed --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_chess_masters.py @@ -0,0 +1,152 @@ +""" +============= +Chess Masters +============= + +An example of the MultiDiGraph class. + +The function `chess_pgn_graph` reads a collection of chess matches stored in +the specified PGN file (PGN ="Portable Game Notation"). Here the (compressed) +default file:: + + chess_masters_WCC.pgn.bz2 + +contains all 685 World Chess Championship matches from 1886--1985. +(data from http://chessproblem.my-free-games.com/chess/games/Download-PGN.php) + +The `chess_pgn_graph()` function returns a `MultiDiGraph` with multiple edges. +Each node is the last name of a chess master. Each edge is directed from white +to black and contains selected game info. + +The key statement in `chess_pgn_graph` below is:: + + G.add_edge(white, black, game_info) + +where `game_info` is a `dict` describing each game. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +# tag names specifying what game info should be +# stored in the dict on each digraph edge +game_details = ["Event", "Date", "Result", "ECO", "Site"] + + +def chess_pgn_graph(pgn_file="chess_masters_WCC.pgn.bz2"): + """Read chess games in pgn format in pgn_file. + + Filenames ending in .bz2 will be uncompressed. + + Return the MultiDiGraph of players connected by a chess game. + Edges contain game data in a dict. + + """ + import bz2 + + G = nx.MultiDiGraph() + game = {} + with bz2.BZ2File(pgn_file) as datafile: + lines = [line.decode().rstrip("\r\n") for line in datafile] + for line in lines: + if line.startswith("["): + tag, value = line[1:-1].split(" ", 1) + game[str(tag)] = value.strip('"') + else: + # empty line after tag set indicates + # we finished reading game info + if game: + white = game.pop("White") + black = game.pop("Black") + G.add_edge(white, black, **game) + game = {} + return G + + +G = chess_pgn_graph() + +print( + f"Loaded {G.number_of_edges()} chess games between {G.number_of_nodes()} players\n" +) + +# identify connected components of the undirected version +H = G.to_undirected() +Gcc = [H.subgraph(c) for c in nx.connected_components(H)] +if len(Gcc) > 1: + print(f"Note the disconnected component consisting of:\n{Gcc[1].nodes()}") + +# find all games with B97 opening (as described in ECO) +openings = {game_info["ECO"] for (white, black, game_info) in G.edges(data=True)} +print(f"\nFrom a total of {len(openings)} different openings,") +print("the following games used the Sicilian opening") +print('with the Najdorff 7...Qb6 "Poisoned Pawn" variation.\n') + +for (white, black, game_info) in G.edges(data=True): + if game_info["ECO"] == "B97": + summary = f"{white} vs {black}\n" + for k, v in game_info.items(): + summary += f" {k}: {v}\n" + summary += "\n" + print(summary) + +# make new undirected graph H without multi-edges +H = nx.Graph(G) + +# edge width is proportional number of games played +edgewidth = [len(G.get_edge_data(u, v)) for u, v in H.edges()] + +# node size is proportional to number of games won +wins = dict.fromkeys(G.nodes(), 0.0) +for (u, v, d) in G.edges(data=True): + r = d["Result"].split("-") + if r[0] == "1": + wins[u] += 1.0 + elif r[0] == "1/2": + wins[u] += 0.5 + wins[v] += 0.5 + else: + wins[v] += 1.0 +nodesize = [wins[v] * 50 for v in H] + +# Generate layout for visualization +pos = nx.kamada_kawai_layout(H) +# Manual tweaking to limit node label overlap in the visualization +pos["Reshevsky, Samuel H"] += (0.05, -0.10) +pos["Botvinnik, Mikhail M"] += (0.03, -0.06) +pos["Smyslov, Vassily V"] += (0.05, -0.03) + +fig, ax = plt.subplots(figsize=(12, 12)) +# Visualize graph components +nx.draw_networkx_edges(H, pos, alpha=0.3, width=edgewidth, edge_color="m") +nx.draw_networkx_nodes(H, pos, node_size=nodesize, node_color="#210070", alpha=0.9) +label_options = {"ec": "k", "fc": "white", "alpha": 0.7} +nx.draw_networkx_labels(H, pos, font_size=14, bbox=label_options) + +# Title/legend +font = {"fontname": "Helvetica", "color": "k", "fontweight": "bold", "fontsize": 14} +ax.set_title("World Chess Championship Games: 1886 - 1985", font) +# Change font color for legend +font["color"] = "r" + +ax.text( + 0.80, + 0.10, + "edge width = # games played", + horizontalalignment="center", + transform=ax.transAxes, + fontdict=font, +) +ax.text( + 0.80, + 0.06, + "node size = # games won", + horizontalalignment="center", + transform=ax.transAxes, + fontdict=font, +) + +# Resize figure for label readability +ax.margins(0.1, 0.05) +fig.tight_layout() +plt.axis("off") +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_custom_node_icons.py b/share/doc/networkx-3.0/examples/drawing/plot_custom_node_icons.py new file mode 100644 index 0000000..c13458f --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_custom_node_icons.py @@ -0,0 +1,75 @@ +""" +================= +Custom node icons +================= + +Example of using custom icons to represent nodes with matplotlib. + +Images for node icons courtesy of www.materialui.co +""" + +import matplotlib.pyplot as plt +import networkx as nx +import PIL + +# Image URLs for graph nodes +icons = { + "router": "icons/router_black_144x144.png", + "switch": "icons/switch_black_144x144.png", + "PC": "icons/computer_black_144x144.png", +} + +# Load images +images = {k: PIL.Image.open(fname) for k, fname in icons.items()} + +# Generate the computer network graph +G = nx.Graph() + +G.add_node("router", image=images["router"]) +for i in range(1, 4): + G.add_node(f"switch_{i}", image=images["switch"]) + for j in range(1, 4): + G.add_node("PC_" + str(i) + "_" + str(j), image=images["PC"]) + +G.add_edge("router", "switch_1") +G.add_edge("router", "switch_2") +G.add_edge("router", "switch_3") +for u in range(1, 4): + for v in range(1, 4): + G.add_edge("switch_" + str(u), "PC_" + str(u) + "_" + str(v)) + +# Get a reproducible layout and create figure +pos = nx.spring_layout(G, seed=1734289230) +fig, ax = plt.subplots() + +# Note: the min_source/target_margin kwargs only work with FancyArrowPatch objects. +# Force the use of FancyArrowPatch for edge drawing by setting `arrows=True`, +# but suppress arrowheads with `arrowstyle="-"` +nx.draw_networkx_edges( + G, + pos=pos, + ax=ax, + arrows=True, + arrowstyle="-", + min_source_margin=15, + min_target_margin=15, +) + +# Transform from data coordinates (scaled between xlim and ylim) to display coordinates +tr_figure = ax.transData.transform +# Transform from display to figure coordinates +tr_axes = fig.transFigure.inverted().transform + +# Select the size of the image (relative to the X axis) +icon_size = (ax.get_xlim()[1] - ax.get_xlim()[0]) * 0.025 +icon_center = icon_size / 2.0 + +# Add the respective image to each node +for n in G.nodes: + xf, yf = tr_figure(pos[n]) + xa, ya = tr_axes((xf, yf)) + # get overlapped axes and plot icon + a = plt.axes([xa - icon_center, ya - icon_center, icon_size, icon_size]) + a.imshow(G.nodes[n]["image"]) + a.axis("off") +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_degree.py b/share/doc/networkx-3.0/examples/drawing/plot_degree.py new file mode 100644 index 0000000..9d7abe4 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_degree.py @@ -0,0 +1,50 @@ +""" +=============== +Degree Analysis +=============== + +This example shows several ways to visualize the distribution of the degree of +nodes with two common techniques: a *degree-rank plot* and a +*degree histogram*. + +In this example, a random Graph is generated with 100 nodes. The degree of +each node is determined, and a figure is generated showing three things: +1. The subgraph of connected components +2. The degree-rank plot for the Graph, and +3. The degree histogram +""" +import networkx as nx +import numpy as np +import matplotlib.pyplot as plt + +G = nx.gnp_random_graph(100, 0.02, seed=10374196) + +degree_sequence = sorted((d for n, d in G.degree()), reverse=True) +dmax = max(degree_sequence) + +fig = plt.figure("Degree of a random graph", figsize=(8, 8)) +# Create a gridspec for adding subplots of different sizes +axgrid = fig.add_gridspec(5, 4) + +ax0 = fig.add_subplot(axgrid[0:3, :]) +Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0]) +pos = nx.spring_layout(Gcc, seed=10396953) +nx.draw_networkx_nodes(Gcc, pos, ax=ax0, node_size=20) +nx.draw_networkx_edges(Gcc, pos, ax=ax0, alpha=0.4) +ax0.set_title("Connected components of G") +ax0.set_axis_off() + +ax1 = fig.add_subplot(axgrid[3:, :2]) +ax1.plot(degree_sequence, "b-", marker="o") +ax1.set_title("Degree Rank Plot") +ax1.set_ylabel("Degree") +ax1.set_xlabel("Rank") + +ax2 = fig.add_subplot(axgrid[3:, 2:]) +ax2.bar(*np.unique(degree_sequence, return_counts=True)) +ax2.set_title("Degree histogram") +ax2.set_xlabel("Degree") +ax2.set_ylabel("# of Nodes") + +fig.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_directed.py b/share/doc/networkx-3.0/examples/drawing/plot_directed.py new file mode 100644 index 0000000..701c45a --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_directed.py @@ -0,0 +1,46 @@ +""" +============== +Directed Graph +============== + +Draw a graph with directed edges using a colormap and different node sizes. + +Edges have different colors and alphas (opacity). Drawn using matplotlib. +""" + +import matplotlib as mpl +import matplotlib.pyplot as plt +import networkx as nx + +seed = 13648 # Seed random number generators for reproducibility +G = nx.random_k_out_graph(10, 3, 0.5, seed=seed) +pos = nx.spring_layout(G, seed=seed) + +node_sizes = [3 + 10 * i for i in range(len(G))] +M = G.number_of_edges() +edge_colors = range(2, M + 2) +edge_alphas = [(5 + i) / (M + 4) for i in range(M)] +cmap = plt.cm.plasma + +nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color="indigo") +edges = nx.draw_networkx_edges( + G, + pos, + node_size=node_sizes, + arrowstyle="->", + arrowsize=10, + edge_color=edge_colors, + edge_cmap=cmap, + width=2, +) +# set alpha value for each edge +for i in range(M): + edges[i].set_alpha(edge_alphas[i]) + +pc = mpl.collections.PatchCollection(edges, cmap=cmap) +pc.set_array(edge_colors) + +ax = plt.gca() +ax.set_axis_off() +plt.colorbar(pc, ax=ax) +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_edge_colormap.py b/share/doc/networkx-3.0/examples/drawing/plot_edge_colormap.py new file mode 100644 index 0000000..e0d569b --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_edge_colormap.py @@ -0,0 +1,23 @@ +""" +============= +Edge Colormap +============= + +Draw a graph with matplotlib, color edges. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.star_graph(20) +pos = nx.spring_layout(G, seed=63) # Seed layout for reproducibility +colors = range(20) +options = { + "node_color": "#A0CBE2", + "edge_color": colors, + "width": 4, + "edge_cmap": plt.cm.Blues, + "with_labels": False, +} +nx.draw(G, pos, **options) +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_ego_graph.py b/share/doc/networkx-3.0/examples/drawing/plot_ego_graph.py new file mode 100644 index 0000000..fde5740 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_ego_graph.py @@ -0,0 +1,35 @@ +""" +========= +Ego Graph +========= + +Example using the NetworkX ego_graph() function to return the main egonet of +the largest hub in a Barabási-Albert network. +""" + +from operator import itemgetter + +import matplotlib.pyplot as plt +import networkx as nx + +# Create a BA model graph - use seed for reproducibility +n = 1000 +m = 2 +seed = 20532 +G = nx.barabasi_albert_graph(n, m, seed=seed) + +# find node with largest degree +node_and_degree = G.degree() +(largest_hub, degree) = sorted(node_and_degree, key=itemgetter(1))[-1] + +# Create ego graph of main hub +hub_ego = nx.ego_graph(G, largest_hub) + +# Draw graph +pos = nx.spring_layout(hub_ego, seed=seed) # Seed layout for reproducibility +nx.draw(hub_ego, pos, node_color="b", node_size=50, with_labels=False) + +# Draw ego as large and red +options = {"node_size": 300, "node_color": "r"} +nx.draw_networkx_nodes(hub_ego, pos, nodelist=[largest_hub], **options) +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_eigenvalues.py b/share/doc/networkx-3.0/examples/drawing/plot_eigenvalues.py new file mode 100644 index 0000000..67322cf --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_eigenvalues.py @@ -0,0 +1,22 @@ +""" +=========== +Eigenvalues +=========== + +Create an G{n,m} random graph and compute the eigenvalues. +""" +import matplotlib.pyplot as plt +import networkx as nx +import numpy.linalg + +n = 1000 # 1000 nodes +m = 5000 # 5000 edges +G = nx.gnm_random_graph(n, m, seed=5040) # Seed for reproducibility + +L = nx.normalized_laplacian_matrix(G) +e = numpy.linalg.eigvals(L.toarray()) +print("Largest eigenvalue:", max(e)) +print("Smallest eigenvalue:", min(e)) +plt.hist(e, bins=100) # histogram with 100 bins +plt.xlim(0, 2) # eigenvalues between 0 and 2 +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_four_grids.py b/share/doc/networkx-3.0/examples/drawing/plot_four_grids.py new file mode 100644 index 0000000..17af4e5 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_four_grids.py @@ -0,0 +1,52 @@ +""" +========== +Four Grids +========== + +Draw a 4x4 graph with matplotlib. + +This example illustrates the use of keyword arguments to `networkx.draw` to +customize the visualization of a simple Graph comprising a 4x4 grid. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.grid_2d_graph(4, 4) # 4x4 grid + +pos = nx.spring_layout(G, iterations=100, seed=39775) + +# Create a 2x2 subplot +fig, all_axes = plt.subplots(2, 2) +ax = all_axes.flat + +nx.draw(G, pos, ax=ax[0], font_size=8) +nx.draw(G, pos, ax=ax[1], node_size=0, with_labels=False) +nx.draw( + G, + pos, + ax=ax[2], + node_color="tab:green", + edgecolors="tab:gray", # Node surface color + edge_color="tab:gray", # Color of graph edges + node_size=250, + with_labels=False, + width=6, +) +H = G.to_directed() +nx.draw( + H, + pos, + ax=ax[3], + node_color="tab:orange", + node_size=20, + with_labels=False, + arrowsize=10, + width=2, +) + +# Set margins for the axes so that nodes aren't clipped +for a in ax: + a.margins(0.10) +fig.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_house_with_colors.py b/share/doc/networkx-3.0/examples/drawing/plot_house_with_colors.py new file mode 100644 index 0000000..c3a2809 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_house_with_colors.py @@ -0,0 +1,26 @@ +""" +================= +House With Colors +================= + +Draw a graph with matplotlib. +""" +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.house_graph() +# explicitly set positions +pos = {0: (0, 0), 1: (1, 0), 2: (0, 1), 3: (1, 1), 4: (0.5, 2.0)} + +# Plot nodes with different properties for the "wall" and "roof" nodes +nx.draw_networkx_nodes( + G, pos, node_size=3000, nodelist=[0, 1, 2, 3], node_color="tab:blue" +) +nx.draw_networkx_nodes(G, pos, node_size=2000, nodelist=[4], node_color="tab:orange") +nx.draw_networkx_edges(G, pos, alpha=0.5, width=6) +# Customize axes +ax = plt.gca() +ax.margins(0.11) +plt.tight_layout() +plt.axis("off") +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_knuth_miles.py b/share/doc/networkx-3.0/examples/drawing/plot_knuth_miles.py new file mode 100644 index 0000000..e0ebea8 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_knuth_miles.py @@ -0,0 +1,142 @@ +""" +=========== +Knuth Miles +=========== + +`miles_graph()` returns an undirected graph over 128 US cities. The +cities each have location and population data. The edges are labeled with the +distance between the two cities. + +This example is described in Section 1.1 of + + Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial + Computing", ACM Press, New York, 1993. + http://www-cs-faculty.stanford.edu/~knuth/sgb.html + +The data file can be found at: + +- https://github.com/networkx/networkx/blob/main/examples/drawing/knuth_miles.txt.gz +""" + +import gzip +import re + +# Ignore any warnings related to downloading shpfiles with cartopy +import warnings + +warnings.simplefilter("ignore") + +import numpy as np +import matplotlib.pyplot as plt +import networkx as nx + + +def miles_graph(): + """Return the cites example graph in miles_dat.txt + from the Stanford GraphBase. + """ + # open file miles_dat.txt.gz (or miles_dat.txt) + + fh = gzip.open("knuth_miles.txt.gz", "r") + + G = nx.Graph() + G.position = {} + G.population = {} + + cities = [] + for line in fh.readlines(): + line = line.decode() + if line.startswith("*"): # skip comments + continue + + numfind = re.compile(r"^\d+") + + if numfind.match(line): # this line is distances + dist = line.split() + for d in dist: + G.add_edge(city, cities[i], weight=int(d)) + i = i + 1 + else: # this line is a city, position, population + i = 1 + (city, coordpop) = line.split("[") + cities.insert(0, city) + (coord, pop) = coordpop.split("]") + (y, x) = coord.split(",") + + G.add_node(city) + # assign position - Convert string to lat/long + G.position[city] = (-float(x) / 100, float(y) / 100) + G.population[city] = float(pop) / 1000 + return G + + +G = miles_graph() + +print("Loaded miles_dat.txt containing 128 cities.") +print(G) + +# make new graph of cites, edge if less then 300 miles between them +H = nx.Graph() +for v in G: + H.add_node(v) +for (u, v, d) in G.edges(data=True): + if d["weight"] < 300: + H.add_edge(u, v) + +# draw with matplotlib/pylab +fig = plt.figure(figsize=(8, 6)) + +# nodes colored by degree sized by population +node_color = [float(H.degree(v)) for v in H] + +# Use cartopy to provide a backdrop for the visualization +try: + import cartopy.crs as ccrs + import cartopy.io.shapereader as shpreader + + ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False) + ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic()) + # Add map of countries & US states as a backdrop + for shapename in ("admin_1_states_provinces_lakes_shp", "admin_0_countries"): + shp = shpreader.natural_earth( + resolution="110m", category="cultural", name=shapename + ) + ax.add_geometries( + shpreader.Reader(shp).geometries(), + ccrs.PlateCarree(), + facecolor="none", + edgecolor="k", + ) + # NOTE: When using cartopy, use matplotlib directly rather than nx.draw + # to take advantage of the cartopy transforms + ax.scatter( + *np.array([v for v in G.position.values()]).T, + s=[G.population[v] for v in H], + c=node_color, + transform=ccrs.PlateCarree(), + zorder=100 # Ensure nodes lie on top of edges/state lines + ) + # Plot edges between the cities + for edge in H.edges(): + edge_coords = np.array([G.position[v] for v in edge]) + ax.plot( + edge_coords[:, 0], + edge_coords[:, 1], + transform=ccrs.PlateCarree(), + linewidth=0.75, + color="k", + ) + +except ImportError: + # If cartopy is unavailable, the backdrop for the plot will be blank; + # though you should still be able to discern the general shape of the US + # from graph nodes and edges! + nx.draw( + H, + G.position, + node_size=[G.population[v] for v in H], + node_color=node_color, + with_labels=False, + ) + +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_labels_and_colors.py b/share/doc/networkx-3.0/examples/drawing/plot_labels_and_colors.py new file mode 100644 index 0000000..b7eddfd --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_labels_and_colors.py @@ -0,0 +1,54 @@ +""" +================= +Labels And Colors +================= + +Use `nodelist` and `edgelist` to apply custom coloring and labels to various +components of a graph. +""" +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.cubical_graph() +pos = nx.spring_layout(G, seed=3113794652) # positions for all nodes + +# nodes +options = {"edgecolors": "tab:gray", "node_size": 800, "alpha": 0.9} +nx.draw_networkx_nodes(G, pos, nodelist=[0, 1, 2, 3], node_color="tab:red", **options) +nx.draw_networkx_nodes(G, pos, nodelist=[4, 5, 6, 7], node_color="tab:blue", **options) + +# edges +nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5) +nx.draw_networkx_edges( + G, + pos, + edgelist=[(0, 1), (1, 2), (2, 3), (3, 0)], + width=8, + alpha=0.5, + edge_color="tab:red", +) +nx.draw_networkx_edges( + G, + pos, + edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)], + width=8, + alpha=0.5, + edge_color="tab:blue", +) + + +# some math labels +labels = {} +labels[0] = r"$a$" +labels[1] = r"$b$" +labels[2] = r"$c$" +labels[3] = r"$d$" +labels[4] = r"$\alpha$" +labels[5] = r"$\beta$" +labels[6] = r"$\gamma$" +labels[7] = r"$\delta$" +nx.draw_networkx_labels(G, pos, labels, font_size=22, font_color="whitesmoke") + +plt.tight_layout() +plt.axis("off") +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_multipartite_graph.py b/share/doc/networkx-3.0/examples/drawing/plot_multipartite_graph.py new file mode 100644 index 0000000..15c4d82 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_multipartite_graph.py @@ -0,0 +1,41 @@ +""" +=================== +Multipartite Layout +=================== +""" + +import itertools +import matplotlib.pyplot as plt +import networkx as nx + +subset_sizes = [5, 5, 4, 3, 2, 4, 4, 3] +subset_color = [ + "gold", + "violet", + "violet", + "violet", + "violet", + "limegreen", + "limegreen", + "darkorange", +] + + +def multilayered_graph(*subset_sizes): + extents = nx.utils.pairwise(itertools.accumulate((0,) + subset_sizes)) + layers = [range(start, end) for start, end in extents] + G = nx.Graph() + for (i, layer) in enumerate(layers): + G.add_nodes_from(layer, layer=i) + for layer1, layer2 in nx.utils.pairwise(layers): + G.add_edges_from(itertools.product(layer1, layer2)) + return G + + +G = multilayered_graph(*subset_sizes) +color = [subset_color[data["layer"]] for v, data in G.nodes(data=True)] +pos = nx.multipartite_layout(G, subset_key="layer") +plt.figure(figsize=(8, 8)) +nx.draw(G, pos, node_color=color, with_labels=False) +plt.axis("equal") +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_node_colormap.py b/share/doc/networkx-3.0/examples/drawing/plot_node_colormap.py new file mode 100644 index 0000000..ea31a46 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_node_colormap.py @@ -0,0 +1,15 @@ +""" +============= +Node Colormap +============= + +Draw a graph with matplotlib, color by degree. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.cycle_graph(24) +pos = nx.circular_layout(G) +nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues) +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_rainbow_coloring.py b/share/doc/networkx-3.0/examples/drawing/plot_rainbow_coloring.py new file mode 100644 index 0000000..1d9e607 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_rainbow_coloring.py @@ -0,0 +1,68 @@ +""" +================ +Rainbow Coloring +================ + +Generate a complete graph with 13 nodes in a circular layout with the +edges colored by node distance. The node distance is given by the minimum +number of nodes traversed along an arc between any two nodes on the circle. + +Such graphs are the subject of Ringel's conjecture, which states: any complete +graph with ``2n + 1`` nodes can be tiled by any tree with ``n + 1`` nodes +(i.e. copies of the tree can be placed over the complete graph such that each +edge in the complete graph is covered exactly once). The edge coloring is +helpful in determining how to place the tree copies. + +References +---------- +https://www.quantamagazine.org/mathematicians-prove-ringels-graph-theory-conjecture-20200219/ +""" +import matplotlib.pyplot as plt +import networkx as nx + +# A rainbow color mapping using matplotlib's tableau colors +node_dist_to_color = { + 1: "tab:red", + 2: "tab:orange", + 3: "tab:olive", + 4: "tab:green", + 5: "tab:blue", + 6: "tab:purple", +} + +# Create a complete graph with an odd number of nodes +nnodes = 13 +G = nx.complete_graph(nnodes) + +# A graph with (2n + 1) nodes requires n colors for the edges +n = (nnodes - 1) // 2 +ndist_iter = list(range(1, n + 1)) + +# Take advantage of circular symmetry in determining node distances +ndist_iter += ndist_iter[::-1] + + +def cycle(nlist, n): + return nlist[-n:] + nlist[:-n] + + +# Rotate nodes around the circle and assign colors for each edge based on +# node distance +nodes = list(G.nodes()) +for i, nd in enumerate(ndist_iter): + for u, v in zip(nodes, cycle(nodes, i + 1)): + G[u][v]["color"] = node_dist_to_color[nd] + +pos = nx.circular_layout(G) +# Create a figure with 1:1 aspect ratio to preserve the circle. +fig, ax = plt.subplots(figsize=(8, 8)) +node_opts = {"node_size": 500, "node_color": "w", "edgecolors": "k", "linewidths": 2.0} +nx.draw_networkx_nodes(G, pos, **node_opts) +nx.draw_networkx_labels(G, pos, font_size=14) +# Extract color from edge data +edge_colors = [edgedata["color"] for _, _, edgedata in G.edges(data=True)] +nx.draw_networkx_edges(G, pos, width=2.0, edge_color=edge_colors) + +ax.set_axis_off() +fig.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_random_geometric_graph.py b/share/doc/networkx-3.0/examples/drawing/plot_random_geometric_graph.py new file mode 100644 index 0000000..6b7c95d --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_random_geometric_graph.py @@ -0,0 +1,44 @@ +""" +====================== +Random Geometric Graph +====================== + +Example +""" + +import matplotlib.pyplot as plt +import networkx as nx + +# Use seed when creating the graph for reproducibility +G = nx.random_geometric_graph(200, 0.125, seed=896803) +# position is stored as node attribute data for random_geometric_graph +pos = nx.get_node_attributes(G, "pos") + +# find node near center (0.5,0.5) +dmin = 1 +ncenter = 0 +for n in pos: + x, y = pos[n] + d = (x - 0.5) ** 2 + (y - 0.5) ** 2 + if d < dmin: + ncenter = n + dmin = d + +# color by path length from node near center +p = dict(nx.single_source_shortest_path_length(G, ncenter)) + +plt.figure(figsize=(8, 8)) +nx.draw_networkx_edges(G, pos, alpha=0.4) +nx.draw_networkx_nodes( + G, + pos, + nodelist=list(p.keys()), + node_size=80, + node_color=list(p.values()), + cmap=plt.cm.Reds_r, +) + +plt.xlim(-0.05, 1.05) +plt.ylim(-0.05, 1.05) +plt.axis("off") +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_sampson.py b/share/doc/networkx-3.0/examples/drawing/plot_sampson.py new file mode 100644 index 0000000..d6eff64 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_sampson.py @@ -0,0 +1,47 @@ +""" +======= +Sampson +======= + +Sampson's monastery data. + +Shows how to read data from a zip file and plot multiple frames. + +The data file can be found at: + +- https://github.com/networkx/networkx/blob/main/examples/drawing/sampson_data.zip +""" + +import zipfile +from io import BytesIO as StringIO + +import matplotlib.pyplot as plt +import networkx as nx + +with zipfile.ZipFile("sampson_data.zip") as zf: + e1 = StringIO(zf.read("samplike1.txt")) + e2 = StringIO(zf.read("samplike2.txt")) + e3 = StringIO(zf.read("samplike3.txt")) + +G1 = nx.read_edgelist(e1, delimiter="\t") +G2 = nx.read_edgelist(e2, delimiter="\t") +G3 = nx.read_edgelist(e3, delimiter="\t") +pos = nx.spring_layout(G3, iterations=100, seed=173) +plt.clf() + +plt.subplot(221) +plt.title("samplike1") +nx.draw(G1, pos, node_size=50, with_labels=False) +plt.subplot(222) +plt.title("samplike2") +nx.draw(G2, pos, node_size=50, with_labels=False) +plt.subplot(223) +plt.title("samplike3") +nx.draw(G3, pos, node_size=50, with_labels=False) +plt.subplot(224) +plt.title("samplike1,2,3") +nx.draw(G3, pos, edgelist=list(G3.edges()), node_size=50, with_labels=False) +nx.draw_networkx_edges(G1, pos, alpha=0.25) +nx.draw_networkx_edges(G2, pos, alpha=0.25) +plt.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_selfloops.py b/share/doc/networkx-3.0/examples/drawing/plot_selfloops.py new file mode 100644 index 0000000..11bcf50 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_selfloops.py @@ -0,0 +1,29 @@ +""" +========== +Self-loops +========== + +A self-loop is an edge that originates from and terminates the same node. +This example shows how to draw self-loops with `nx_pylab`. + +""" +import networkx as nx +import matplotlib.pyplot as plt + +# Create a graph and add a self-loop to node 0 +G = nx.complete_graph(3, create_using=nx.DiGraph) +G.add_edge(0, 0) +pos = nx.circular_layout(G) + +# As of version 2.6, self-loops are drawn by default with the same styling as +# other edges +nx.draw(G, pos, with_labels=True) + +# Add self-loops to the remaining nodes +edgelist = [(1, 1), (2, 2)] +G.add_edges_from(edgelist) + +# Draw the newly added self-loops with different formatting +nx.draw_networkx_edges(G, pos, edgelist=edgelist, arrowstyle="<|-", style="dashed") + +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_simple_path.py b/share/doc/networkx-3.0/examples/drawing/plot_simple_path.py new file mode 100644 index 0000000..ec52185 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_simple_path.py @@ -0,0 +1,14 @@ +""" +=========== +Simple Path +=========== + +Draw a graph with matplotlib. +""" +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.path_graph(8) +pos = nx.spring_layout(G, seed=47) # Seed layout for reproducibility +nx.draw(G, pos=pos) +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_spectral_grid.py b/share/doc/networkx-3.0/examples/drawing/plot_spectral_grid.py new file mode 100644 index 0000000..80ef6f1 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_spectral_grid.py @@ -0,0 +1,58 @@ +""" +================== +Spectral Embedding +================== + +The spectral layout positions the nodes of the graph based on the +eigenvectors of the graph Laplacian $L = D - A$, where $A$ is the +adjacency matrix and $D$ is the degree matrix of the graph. +By default, the spectral layout will embed the graph in two +dimensions (you can embed your graph in other dimensions using the +``dim`` argument to either :func:`~drawing.nx_pylab.draw_spectral` or +:func:`~drawing.layout.spectral_layout`). + +When the edges of the graph represent similarity between the incident +nodes, the spectral embedding will place highly similar nodes closer +to one another than nodes which are less similar. + +This is particularly striking when you spectrally embed a grid +graph. In the full grid graph, the nodes in the center of the +graph are pulled apart more than nodes on the periphery. +As you remove internal nodes, this effect increases. +""" + +import matplotlib.pyplot as plt +import networkx as nx + + +options = {"node_color": "C0", "node_size": 100} + +G = nx.grid_2d_graph(6, 6) +plt.subplot(332) +nx.draw_spectral(G, **options) + +G.remove_edge((2, 2), (2, 3)) +plt.subplot(334) +nx.draw_spectral(G, **options) + +G.remove_edge((3, 2), (3, 3)) +plt.subplot(335) +nx.draw_spectral(G, **options) + +G.remove_edge((2, 2), (3, 2)) +plt.subplot(336) +nx.draw_spectral(G, **options) + +G.remove_edge((2, 3), (3, 3)) +plt.subplot(337) +nx.draw_spectral(G, **options) + +G.remove_edge((1, 2), (1, 3)) +plt.subplot(338) +nx.draw_spectral(G, **options) + +G.remove_edge((4, 2), (4, 3)) +plt.subplot(339) +nx.draw_spectral(G, **options) + +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_tsp.py b/share/doc/networkx-3.0/examples/drawing/plot_tsp.py new file mode 100644 index 0000000..78c731b --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_tsp.py @@ -0,0 +1,52 @@ +""" +========================== +Traveling Salesman Problem +========================== + +This is an example of a drawing solution of the traveling salesman problem + +The function is used to produce the solution is christofides, +where given a set of nodes, it calculates the route of the nodes +that the traveler has to follow in order to minimize the total cost. +""" + +import matplotlib.pyplot as plt +import networkx as nx +import networkx.algorithms.approximation as nx_app +import math + +G = nx.random_geometric_graph(20, radius=0.4, seed=3) +pos = nx.get_node_attributes(G, "pos") + +# Depot should be at (0,0) +pos[0] = (0.5, 0.5) + +H = G.copy() + + +# Calculating the distances between the nodes as edge's weight. +for i in range(len(pos)): + for j in range(i + 1, len(pos)): + dist = math.hypot(pos[i][0] - pos[j][0], pos[i][1] - pos[j][1]) + dist = dist + G.add_edge(i, j, weight=dist) + +cycle = nx_app.christofides(G, weight="weight") +edge_list = list(nx.utils.pairwise(cycle)) + +# Draw closest edges on each node only +nx.draw_networkx_edges(H, pos, edge_color="blue", width=0.5) + +# Draw the route +nx.draw_networkx( + G, + pos, + with_labels=True, + edgelist=edge_list, + edge_color="red", + node_size=200, + width=3, +) + +print("The route of the traveller is:", cycle) +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_unix_email.py b/share/doc/networkx-3.0/examples/drawing/plot_unix_email.py new file mode 100644 index 0000000..25fce7a --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_unix_email.py @@ -0,0 +1,62 @@ +""" +========== +Unix Email +========== + +Create a directed graph, allowing multiple edges and self loops, from a unix +mailbox. The nodes are email addresses with links that point from the sender +to the receivers. The edge data is a Python email.Message object which +contains all of the email message data. + +This example shows the power of `DiGraph` to hold edge data of arbitrary Python +objects (in this case a list of email messages). + + +The sample unix email mailbox called "unix_email.mbox" may be found here: + +- https://github.com/networkx/networkx/blob/main/examples/drawing/unix_email.mbox +""" + +from email.utils import getaddresses, parseaddr +import mailbox + +import matplotlib.pyplot as plt +import networkx as nx + +# unix mailbox recipe +# see https://docs.python.org/3/library/mailbox.html + + +def mbox_graph(): + mbox = mailbox.mbox("unix_email.mbox") # parse unix mailbox + + G = nx.MultiDiGraph() # create empty graph + + # parse each messages and build graph + for msg in mbox: # msg is python email.Message.Message object + (source_name, source_addr) = parseaddr(msg["From"]) # sender + # get all recipients + # see https://docs.python.org/3/library/email.html + tos = msg.get_all("to", []) + ccs = msg.get_all("cc", []) + resent_tos = msg.get_all("resent-to", []) + resent_ccs = msg.get_all("resent-cc", []) + all_recipients = getaddresses(tos + ccs + resent_tos + resent_ccs) + # now add the edges for this mail message + for (target_name, target_addr) in all_recipients: + G.add_edge(source_addr, target_addr, message=msg) + + return G + + +G = mbox_graph() + +# print edges with message subject +for (u, v, d) in G.edges(data=True): + print(f"From: {u} To: {v} Subject: {d['message']['Subject']}") + +pos = nx.spring_layout(G, iterations=10, seed=227) +nx.draw(G, pos, node_size=0, alpha=0.4, edge_color="r", font_size=16, with_labels=True) +ax = plt.gca() +ax.margins(0.08) +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/plot_weighted_graph.py b/share/doc/networkx-3.0/examples/drawing/plot_weighted_graph.py new file mode 100644 index 0000000..ba7721c --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/plot_weighted_graph.py @@ -0,0 +1,44 @@ +""" +============== +Weighted Graph +============== + +An example using Graph as a weighted network. +""" +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.Graph() + +G.add_edge("a", "b", weight=0.6) +G.add_edge("a", "c", weight=0.2) +G.add_edge("c", "d", weight=0.1) +G.add_edge("c", "e", weight=0.7) +G.add_edge("c", "f", weight=0.9) +G.add_edge("a", "d", weight=0.3) + +elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] > 0.5] +esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= 0.5] + +pos = nx.spring_layout(G, seed=7) # positions for all nodes - seed for reproducibility + +# nodes +nx.draw_networkx_nodes(G, pos, node_size=700) + +# edges +nx.draw_networkx_edges(G, pos, edgelist=elarge, width=6) +nx.draw_networkx_edges( + G, pos, edgelist=esmall, width=6, alpha=0.5, edge_color="b", style="dashed" +) + +# node labels +nx.draw_networkx_labels(G, pos, font_size=20, font_family="sans-serif") +# edge weight labels +edge_labels = nx.get_edge_attributes(G, "weight") +nx.draw_networkx_edge_labels(G, pos, edge_labels) + +ax = plt.gca() +ax.margins(0.08) +plt.axis("off") +plt.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/drawing/unix_email.mbox b/share/doc/networkx-3.0/examples/drawing/unix_email.mbox new file mode 100644 index 0000000..a3a7cf8 --- /dev/null +++ b/share/doc/networkx-3.0/examples/drawing/unix_email.mbox @@ -0,0 +1,84 @@ +From alice@edu Thu Jun 16 16:12:12 2005 +From: Alice +Subject: NetworkX +Date: Thu, 16 Jun 2005 16:12:13 -0700 +To: Bob +Status: RO +Content-Length: 86 +Lines: 5 + +Bob, check out the new networkx release - you and +Carol might really like it. + +Alice + + +From bob@gov Thu Jun 16 18:13:12 2005 +Return-Path: +Subject: Re: NetworkX +From: Bob +To: Alice +Content-Type: text/plain +Date: Thu, 16 Jun 2005 18:13:12 -0700 +Status: RO +Content-Length: 26 +Lines: 4 + +Thanks for the tip. + +Bob + + +From ted@com Thu Jul 28 09:53:31 2005 +Return-Path: +Subject: Graph package in Python? +From: Ted +To: Bob +Content-Type: text/plain +Date: Thu, 28 Jul 2005 09:47:03 -0700 +Status: RO +Content-Length: 90 +Lines: 3 + +Hey Ted - I'm looking for a Python package for +graphs and networks. Do you know of any? + + +From bob@gov Thu Jul 28 09:59:31 2005 +Return-Path: +Subject: Re: Graph package in Python? +From: Bob +To: Ted +Content-Type: text/plain +Date: Thu, 28 Jul 2005 09:59:03 -0700 +Status: RO +Content-Length: 180 +Lines: 9 + + +Check out the NetworkX package - Alice sent me the tip! + +Bob + +>> bob@gov scrawled: +>> Hey Ted - I'm looking for a Python package for +>> graphs and networks. Do you know of any? + + +From ted@com Thu Jul 28 15:53:31 2005 +Return-Path: +Subject: get together for lunch to discuss Networks? +From: Ted +To: Bob , Carol , Alice +Content-Type: text/plain +Date: Thu, 28 Jul 2005 15:47:03 -0700 +Status: RO +Content-Length: 139 +Lines: 5 + +Hey everyrone! Want to meet at that restaurant on the +island in Konigsburg tonight? Bring your laptops +and we can install NetworkX. + +Ted + diff --git a/share/doc/networkx-3.0/examples/graph/README.txt b/share/doc/networkx-3.0/examples/graph/README.txt new file mode 100644 index 0000000..9b0e3b2 --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/README.txt @@ -0,0 +1,2 @@ +Graph +----- diff --git a/share/doc/networkx-3.0/examples/graph/plot_dag_layout.py b/share/doc/networkx-3.0/examples/graph/plot_dag_layout.py new file mode 100644 index 0000000..0b12013 --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_dag_layout.py @@ -0,0 +1,42 @@ +""" +======================== +DAG - Topological Layout +======================== + +This example combines the `topological_generations` generator with +`multipartite_layout` to show how to visualize a DAG in topologically-sorted +order. +""" + +import networkx as nx +import matplotlib.pyplot as plt + + +G = nx.DiGraph( + [ + ("f", "a"), + ("a", "b"), + ("a", "e"), + ("b", "c"), + ("b", "d"), + ("d", "e"), + ("f", "c"), + ("f", "g"), + ("h", "f"), + ] +) + +for layer, nodes in enumerate(nx.topological_generations(G)): + # `multipartite_layout` expects the layer as a node attribute, so add the + # numeric layer value as a node attribute + for node in nodes: + G.nodes[node]["layer"] = layer + +# Compute the multipartite_layout using the "layer" node attribute +pos = nx.multipartite_layout(G, subset_key="layer") + +fig, ax = plt.subplots() +nx.draw_networkx(G, pos=pos, ax=ax) +ax.set_title("DAG layout in topological order") +fig.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/plot_degree_sequence.py b/share/doc/networkx-3.0/examples/graph/plot_degree_sequence.py new file mode 100644 index 0000000..87abb64 --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_degree_sequence.py @@ -0,0 +1,36 @@ +""" +=============== +Degree Sequence +=============== + +Random graph from given degree sequence. +""" +import matplotlib.pyplot as plt +import networkx as nx + +# Specify seed for reproducibility +seed = 668273 + +z = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1] +print(nx.is_graphical(z)) + +print("Configuration model") +G = nx.configuration_model( + z, seed=seed +) # configuration model, seed for reproducibility +degree_sequence = [d for n, d in G.degree()] # degree sequence +print(f"Degree sequence {degree_sequence}") +print("Degree histogram") +hist = {} +for d in degree_sequence: + if d in hist: + hist[d] += 1 + else: + hist[d] = 1 +print("degree #nodes") +for d in hist: + print(f"{d:4} {hist[d]:6}") + +pos = nx.spring_layout(G, seed=seed) # Seed layout for reproducibility +nx.draw(G, pos=pos) +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/plot_erdos_renyi.py b/share/doc/networkx-3.0/examples/graph/plot_erdos_renyi.py new file mode 100644 index 0000000..e132676 --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_erdos_renyi.py @@ -0,0 +1,36 @@ +""" +=========== +Erdos Renyi +=========== + +Create an G{n,m} random graph with n nodes and m edges +and report some properties. + +This graph is sometimes called the Erdős-Rényi graph +but is different from G{n,p} or binomial_graph which is also +sometimes called the Erdős-Rényi graph. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +n = 10 # 10 nodes +m = 20 # 20 edges +seed = 20160 # seed random number generators for reproducibility + +# Use seed for reproducibility +G = nx.gnm_random_graph(n, m, seed=seed) + +# some properties +print("node degree clustering") +for v in nx.nodes(G): + print(f"{v} {nx.degree(G, v)} {nx.clustering(G, v)}") + +print() +print("the adjacency list") +for line in nx.generate_adjlist(G): + print(line) + +pos = nx.spring_layout(G, seed=seed) # Seed for reproducible layout +nx.draw(G, pos=pos) +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/plot_expected_degree_sequence.py b/share/doc/networkx-3.0/examples/graph/plot_expected_degree_sequence.py new file mode 100644 index 0000000..1fc6799 --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_expected_degree_sequence.py @@ -0,0 +1,20 @@ +""" +======================== +Expected Degree Sequence +======================== + +Random graph from given degree sequence. +""" + +import networkx as nx + +# make a random graph of 500 nodes with expected degrees of 50 +n = 500 # n nodes +p = 0.1 +w = [p * n for i in range(n)] # w = p*n for all nodes +G = nx.expected_degree_graph(w) # configuration model +print("Degree histogram") +print("degree (#nodes) ****") +dh = nx.degree_histogram(G) +for i, d in enumerate(dh): + print(f"{i:2} ({d:2}) {'*'*d}") diff --git a/share/doc/networkx-3.0/examples/graph/plot_football.py b/share/doc/networkx-3.0/examples/graph/plot_football.py new file mode 100644 index 0000000..f6397f0 --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_football.py @@ -0,0 +1,44 @@ +""" +======== +Football +======== + +Load football network in GML format and compute some network statistcs. + +Shows how to download GML graph in a zipped file, unpack it, and load +into a NetworkX graph. + +Requires Internet connection to download the URL +http://www-personal.umich.edu/~mejn/netdata/football.zip +""" + +import urllib.request +import io +import zipfile + +import matplotlib.pyplot as plt +import networkx as nx + +url = "http://www-personal.umich.edu/~mejn/netdata/football.zip" + +sock = urllib.request.urlopen(url) # open URL +s = io.BytesIO(sock.read()) # read into BytesIO "file" +sock.close() + +zf = zipfile.ZipFile(s) # zipfile object +txt = zf.read("football.txt").decode() # read info file +gml = zf.read("football.gml").decode() # read gml data +# throw away bogus first line with # from mejn files +gml = gml.split("\n")[1:] +G = nx.parse_gml(gml) # parse gml data + +print(txt) +# print degree for each team - number of games +for n, d in G.degree(): + print(f"{n:20} {d:2}") + +options = {"node_color": "black", "node_size": 50, "linewidths": 0, "width": 0.1} + +pos = nx.spring_layout(G, seed=1969) # Seed for reproducible layout +nx.draw(G, pos, **options) +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/plot_karate_club.py b/share/doc/networkx-3.0/examples/graph/plot_karate_club.py new file mode 100644 index 0000000..c4fe5bc --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_karate_club.py @@ -0,0 +1,25 @@ +""" +=========== +Karate Club +=========== + +Zachary's Karate Club graph + +Data file from: +http://vlado.fmf.uni-lj.si/pub/networks/data/Ucinet/UciData.htm + +Zachary W. (1977). +An information flow model for conflict and fission in small groups. +Journal of Anthropological Research, 33, 452-473. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.karate_club_graph() +print("Node Degree") +for v in G: + print(f"{v:4} {G.degree(v):6}") + +nx.draw_circular(G, with_labels=True) +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/plot_morse_trie.py b/share/doc/networkx-3.0/examples/graph/plot_morse_trie.py new file mode 100644 index 0000000..88b424c --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_morse_trie.py @@ -0,0 +1,96 @@ +""" +========== +Morse Trie +========== + +A prefix tree (aka a "trie") representing the Morse encoding of the alphabet. +A letter can be encoded by tracing the path from the corresponding node in the +tree to the root node, reversing the order of the symbols encountered along +the path. +""" +import networkx as nx + +# Unicode characters to represent the dots/dashes (or dits/dahs) of Morse code +dot = "•" +dash = "—" + +# Start with the direct mapping of letter -> code +morse_direct_mapping = { + "a": dot + dash, + "b": dash + dot * 3, + "c": dash + dot + dash + dot, + "d": dash + dot * 2, + "e": dot, + "f": dot * 2 + dash + dot, + "g": dash * 2 + dot, + "h": dot * 4, + "i": dot * 2, + "j": dot + dash * 3, + "k": dash + dot + dash, + "l": dot + dash + dot * 2, + "m": dash * 2, + "n": dash + dot, + "o": dash * 3, + "p": dot + dash * 2 + dot, + "q": dash * 2 + dot + dash, + "r": dot + dash + dot, + "s": dot * 3, + "t": dash, + "u": dot * 2 + dash, + "v": dot * 3 + dash, + "w": dot + dash * 2, + "x": dash + dot * 2 + dash, + "y": dash + dot + dash * 2, + "z": dash * 2 + dot * 2, +} + +### Manually construct the prefix tree from this mapping + +# Some preprocessing: sort the original mapping by code length and character +# value +morse_mapping_sorted = dict( + sorted(morse_direct_mapping.items(), key=lambda item: (len(item[1]), item[1])) +) + +# More preprocessing: create the reverse mapping to simplify lookup +reverse_mapping = {v: k for k, v in morse_direct_mapping.items()} +reverse_mapping[""] = "" # Represent the "root" node with an empty string + +# Construct the prefix tree from the sorted mapping +G = nx.DiGraph() +for node, char in morse_mapping_sorted.items(): + pred = char[:-1] + # Store the dot/dash relating the two letters as an edge attribute "char" + G.add_edge(reverse_mapping[pred], node, char=char[-1]) + +# For visualization purposes, layout the nodes in topological order +for i, layer in enumerate(nx.topological_generations(G)): + for n in layer: + G.nodes[n]["layer"] = i +pos = nx.multipartite_layout(G, subset_key="layer", align="horizontal") +# Flip the layout so the root node is on top +for k in pos: + pos[k][-1] *= -1 + +# Visualize the trie +nx.draw(G, pos=pos, with_labels=True) +elabels = {(u, v): l for u, v, l in G.edges(data="char")} +nx.draw_networkx_edge_labels(G, pos, edge_labels=elabels) + +# A letter can be encoded by following the path from the given letter (node) to +# the root node +def morse_encode(letter): + pred = next(G.predecessors(letter)) # Each letter has only 1 predecessor + symbol = G[pred][letter]["char"] + if pred != "": + return morse_encode(pred) + symbol # Traversing the trie in reverse + return symbol + + +# Verify that the trie encoding is correct +import string + +for letter in string.ascii_lowercase: + assert morse_encode(letter) == morse_direct_mapping[letter] + +print(" ".join([morse_encode(ltr) for ltr in "ilovenetworkx"])) diff --git a/share/doc/networkx-3.0/examples/graph/plot_napoleon_russian_campaign.py b/share/doc/networkx-3.0/examples/graph/plot_napoleon_russian_campaign.py new file mode 100644 index 0000000..46ef64d --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_napoleon_russian_campaign.py @@ -0,0 +1,133 @@ +""" +========================= +Napoleon Russian Campaign +========================= + +Minard's data from Napoleon's 1812-1813 Russian Campaign. +https://web.archive.org/web/20080112042656/http://www.math.yorku.ca/SCS/Gallery/minard/minard.txt +""" + +import matplotlib.pyplot as plt +import networkx as nx + + +def minard_graph(): + data1 = """\ +24.0,54.9,340000,A,1 +24.5,55.0,340000,A,1 +25.5,54.5,340000,A,1 +26.0,54.7,320000,A,1 +27.0,54.8,300000,A,1 +28.0,54.9,280000,A,1 +28.5,55.0,240000,A,1 +29.0,55.1,210000,A,1 +30.0,55.2,180000,A,1 +30.3,55.3,175000,A,1 +32.0,54.8,145000,A,1 +33.2,54.9,140000,A,1 +34.4,55.5,127100,A,1 +35.5,55.4,100000,A,1 +36.0,55.5,100000,A,1 +37.6,55.8,100000,A,1 +37.7,55.7,100000,R,1 +37.5,55.7,98000,R,1 +37.0,55.0,97000,R,1 +36.8,55.0,96000,R,1 +35.4,55.3,87000,R,1 +34.3,55.2,55000,R,1 +33.3,54.8,37000,R,1 +32.0,54.6,24000,R,1 +30.4,54.4,20000,R,1 +29.2,54.3,20000,R,1 +28.5,54.2,20000,R,1 +28.3,54.3,20000,R,1 +27.5,54.5,20000,R,1 +26.8,54.3,12000,R,1 +26.4,54.4,14000,R,1 +25.0,54.4,8000,R,1 +24.4,54.4,4000,R,1 +24.2,54.4,4000,R,1 +24.1,54.4,4000,R,1""" + data2 = """\ +24.0,55.1,60000,A,2 +24.5,55.2,60000,A,2 +25.5,54.7,60000,A,2 +26.6,55.7,40000,A,2 +27.4,55.6,33000,A,2 +28.7,55.5,33000,R,2 +29.2,54.2,30000,R,2 +28.5,54.1,30000,R,2 +28.3,54.2,28000,R,2""" + data3 = """\ +24.0,55.2,22000,A,3 +24.5,55.3,22000,A,3 +24.6,55.8,6000,A,3 +24.6,55.8,6000,R,3 +24.2,54.4,6000,R,3 +24.1,54.4,6000,R,3""" + cities = """\ +24.0,55.0,Kowno +25.3,54.7,Wilna +26.4,54.4,Smorgoni +26.8,54.3,Moiodexno +27.7,55.2,Gloubokoe +27.6,53.9,Minsk +28.5,54.3,Studienska +28.7,55.5,Polotzk +29.2,54.4,Bobr +30.2,55.3,Witebsk +30.4,54.5,Orscha +30.4,53.9,Mohilow +32.0,54.8,Smolensk +33.2,54.9,Dorogobouge +34.3,55.2,Wixma +34.4,55.5,Chjat +36.0,55.5,Mojaisk +37.6,55.8,Moscou +36.6,55.3,Tarantino +36.5,55.0,Malo-Jarosewii""" + + c = {} + for line in cities.split("\n"): + x, y, name = line.split(",") + c[name] = (float(x), float(y)) + + g = [] + + for data in [data1, data2, data3]: + G = nx.Graph() + i = 0 + G.pos = {} # location + G.pop = {} # size + last = None + for line in data.split("\n"): + x, y, p, r, n = line.split(",") + G.pos[i] = (float(x), float(y)) + G.pop[i] = int(p) + if last is None: + last = i + else: + G.add_edge(i, last, **{r: int(n)}) + last = i + i = i + 1 + g.append(G) + + return g, c + + +(g, city) = minard_graph() + +plt.figure(1, figsize=(11, 5)) +plt.clf() +colors = ["b", "g", "r"] +for G in g: + c = colors.pop(0) + node_size = [G.pop[n] // 300 for n in G] + nx.draw_networkx_edges(G, G.pos, edge_color=c, width=4, alpha=0.5) + nx.draw_networkx_nodes(G, G.pos, node_size=node_size, node_color=c, alpha=0.5) + nx.draw_networkx_nodes(G, G.pos, node_size=5, node_color="k") + +for c in city: + x, y = city[c] + plt.text(x, y + 0.1, c) +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/plot_roget.py b/share/doc/networkx-3.0/examples/graph/plot_roget.py new file mode 100644 index 0000000..777c017 --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_roget.py @@ -0,0 +1,80 @@ +""" +===== +Roget +===== + +Build a directed graph of 1022 categories and 5075 cross-references as defined +in the 1879 version of Roget's Thesaurus. This example is described in Section +1.2 of + + Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial + Computing", ACM Press, New York, 1993. + http://www-cs-faculty.stanford.edu/~knuth/sgb.html + +Note that one of the 5075 cross references is a self loop yet it is included in +the graph built here because the standard networkx `DiGraph` class allows self +loops. (cf. 400pungency:400 401 403 405). + +The data file can be found at: + +- https://github.com/networkx/networkx/blob/main/examples/graph/roget_dat.txt.gz +""" + +import gzip +import re +import sys + +import matplotlib.pyplot as plt +import networkx as nx + + +def roget_graph(): + """Return the thesaurus graph from the roget.dat example in + the Stanford Graph Base. + """ + # open file roget_dat.txt.gz + fh = gzip.open("roget_dat.txt.gz", "r") + + G = nx.DiGraph() + + for line in fh.readlines(): + line = line.decode() + if line.startswith("*"): # skip comments + continue + if line.startswith(" "): # this is a continuation line, append + line = oldline + line + if line.endswith("\\\n"): # continuation line, buffer, goto next + oldline = line.strip("\\\n") + continue + + (headname, tails) = line.split(":") + + # head + numfind = re.compile(r"^\d+") # re to find the number of this word + head = numfind.findall(headname)[0] # get the number + + G.add_node(head) + + for tail in tails.split(): + if head == tail: + print("skipping self loop", head, tail, file=sys.stderr) + G.add_edge(head, tail) + + return G + + +G = roget_graph() +print("Loaded roget_dat.txt containing 1022 categories.") +print(G) +UG = G.to_undirected() +print(nx.number_connected_components(UG), "connected components") + +options = { + "node_color": "black", + "node_size": 1, + "edge_color": "gray", + "linewidths": 0, + "width": 0.1, +} +nx.draw_circular(UG, **options) +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/plot_triad_types.py b/share/doc/networkx-3.0/examples/graph/plot_triad_types.py new file mode 100644 index 0000000..eacbc6e --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_triad_types.py @@ -0,0 +1,63 @@ +""" +====== +Triads +====== +According to the paper by Snijders, T. (2012). “Transitivity and triads.” +University of Oxford, there are 16 Triad Types possible. This plot shows +the 16 Triad Types that can be identified within directed networks. +Triadic relationships are especially useful when analysing Social Networks. +The first three digits refer to the number of mutual, asymmetric and null +dyads (bidirectional, unidirection and nonedges) and the letter gives +the Orientation as Up (U), Down (D) , Cyclical (C) or Transitive (T). +""" + +import networkx as nx +import matplotlib.pyplot as plt + +fig, axes = plt.subplots(4, 4, figsize=(10, 10)) +triads = { + "003": [], + "012": [(1, 2)], + "102": [(1, 2), (2, 1)], + "021D": [(3, 1), (3, 2)], + "021U": [(1, 3), (2, 3)], + "021C": [(1, 3), (3, 2)], + "111D": [(1, 2), (2, 1), (3, 1)], + "111U": [(1, 2), (2, 1), (1, 3)], + "030T": [(1, 2), (3, 2), (1, 3)], + "030C": [(1, 3), (3, 2), (2, 1)], + "201": [(1, 2), (2, 1), (3, 1), (1, 3)], + "120D": [(1, 2), (2, 1), (3, 1), (3, 2)], + "120U": [(1, 2), (2, 1), (1, 3), (2, 3)], + "120C": [(1, 2), (2, 1), (1, 3), (3, 2)], + "210": [(1, 2), (2, 1), (1, 3), (3, 2), (2, 3)], + "300": [(1, 2), (2, 1), (2, 3), (3, 2), (1, 3), (3, 1)], +} + +for (title, triad), ax in zip(triads.items(), axes.flatten()): + G = nx.DiGraph() + G.add_nodes_from([1, 2, 3]) + G.add_edges_from(triad) + nx.draw_networkx( + G, + ax=ax, + with_labels=False, + node_color=["green"], + node_size=200, + arrowsize=20, + width=2, + pos=nx.planar_layout(G), + ) + ax.set_xlim(val * 1.2 for val in ax.get_xlim()) + ax.set_ylim(val * 1.2 for val in ax.get_ylim()) + ax.text( + 0, + 0, + title, + fontsize=15, + fontweight="extra bold", + horizontalalignment="center", + bbox=dict(boxstyle="square,pad=0.3", fc="none"), + ) +fig.tight_layout() +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/plot_words.py b/share/doc/networkx-3.0/examples/graph/plot_words.py new file mode 100644 index 0000000..e5211f5 --- /dev/null +++ b/share/doc/networkx-3.0/examples/graph/plot_words.py @@ -0,0 +1,88 @@ +""" +================== +Words/Ladder Graph +================== + +Generate an undirected graph over the 5757 5-letter words in the datafile +`words_dat.txt.gz`. Two words are connected by an edge if they differ in one +letter, resulting in 14,135 edges. This example is described in Section 1.1 of + + Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial + Computing", ACM Press, New York, 1993. + http://www-cs-faculty.stanford.edu/~knuth/sgb.html + +The data file can be found at: + +- https://github.com/networkx/networkx/blob/main/examples/graph/words_dat.txt.gz +""" + +import gzip +from string import ascii_lowercase as lowercase + +import matplotlib.pyplot as plt +import networkx as nx + + +def generate_graph(words): + G = nx.Graph(name="words") + lookup = {c: lowercase.index(c) for c in lowercase} + + def edit_distance_one(word): + for i in range(len(word)): + left, c, right = word[0:i], word[i], word[i + 1 :] + j = lookup[c] # lowercase.index(c) + for cc in lowercase[j + 1 :]: + yield left + cc + right + + candgen = ( + (word, cand) + for word in sorted(words) + for cand in edit_distance_one(word) + if cand in words + ) + G.add_nodes_from(words) + for word, cand in candgen: + G.add_edge(word, cand) + return G + + +def words_graph(): + """Return the words example graph from the Stanford GraphBase""" + fh = gzip.open("words_dat.txt.gz", "r") + words = set() + for line in fh.readlines(): + line = line.decode() + if line.startswith("*"): + continue + w = str(line[0:5]) + words.add(w) + return generate_graph(words) + + +G = words_graph() +print("Loaded words_dat.txt containing 5757 five-letter English words.") +print("Two words are connected if they differ in one letter.") +print(G) +print(f"{nx.number_connected_components(G)} connected components") + +for (source, target) in [("chaos", "order"), ("nodes", "graph"), ("pound", "marks")]: + print(f"Shortest path between {source} and {target} is") + try: + shortest_path = nx.shortest_path(G, source, target) + for n in shortest_path: + print(n) + except nx.NetworkXNoPath: + print("None") + + +# draw a subset of the graph +boundary = list(nx.node_boundary(G, shortest_path)) +G.add_nodes_from(shortest_path, color="red") +G.add_nodes_from(boundary, color="blue") +H = G.subgraph(shortest_path + boundary) +colors = nx.get_node_attributes(H, "color") +options = {"node_size": 1500, "alpha": 0.3, "node_color": colors.values()} +pos = nx.kamada_kawai_layout(H) +nx.draw(H, pos, **options) +nx.draw_networkx_labels(H, pos, font_weight="bold") +plt.show() diff --git a/share/doc/networkx-3.0/examples/graph/roget_dat.txt.gz b/share/doc/networkx-3.0/examples/graph/roget_dat.txt.gz new file mode 100644 index 0000000..6552465 Binary files /dev/null and b/share/doc/networkx-3.0/examples/graph/roget_dat.txt.gz differ diff --git a/share/doc/networkx-3.0/examples/graph/words_dat.txt.gz b/share/doc/networkx-3.0/examples/graph/words_dat.txt.gz new file mode 100644 index 0000000..78aff79 Binary files /dev/null and b/share/doc/networkx-3.0/examples/graph/words_dat.txt.gz differ diff --git a/share/doc/networkx-3.0/examples/subclass/README.txt b/share/doc/networkx-3.0/examples/subclass/README.txt new file mode 100644 index 0000000..e3650b1 --- /dev/null +++ b/share/doc/networkx-3.0/examples/subclass/README.txt @@ -0,0 +1,2 @@ +Subclass +-------- diff --git a/share/doc/networkx-3.0/examples/subclass/plot_antigraph.py b/share/doc/networkx-3.0/examples/subclass/plot_antigraph.py new file mode 100644 index 0000000..154a321 --- /dev/null +++ b/share/doc/networkx-3.0/examples/subclass/plot_antigraph.py @@ -0,0 +1,192 @@ +""" +========= +Antigraph +========= + +Complement graph class for small footprint when working on dense graphs. + +This class allows you to add the edges that *do not exist* in the dense +graph. However, when applying algorithms to this complement graph data +structure, it behaves as if it were the dense version. So it can be used +directly in several NetworkX algorithms. + +This subclass has only been tested for k-core, connected_components, +and biconnected_components algorithms but might also work for other +algorithms. + +""" +import matplotlib.pyplot as plt +import networkx as nx +from networkx import Graph + + +class AntiGraph(Graph): + """ + Class for complement graphs. + + The main goal is to be able to work with big and dense graphs with + a low memory footprint. + + In this class you add the edges that *do not exist* in the dense graph, + the report methods of the class return the neighbors, the edges and + the degree as if it was the dense graph. Thus it's possible to use + an instance of this class with some of NetworkX functions. + """ + + all_edge_dict = {"weight": 1} + + def single_edge_dict(self): + return self.all_edge_dict + + edge_attr_dict_factory = single_edge_dict + + def __getitem__(self, n): + """Return a dict of neighbors of node n in the dense graph. + + Parameters + ---------- + n : node + A node in the graph. + + Returns + ------- + adj_dict : dictionary + The adjacency dictionary for nodes connected to n. + + """ + return { + node: self.all_edge_dict for node in set(self.adj) - set(self.adj[n]) - {n} + } + + def neighbors(self, n): + """Return an iterator over all neighbors of node n in the + dense graph. + + """ + try: + return iter(set(self.adj) - set(self.adj[n]) - {n}) + except KeyError as err: + raise nx.NetworkXError(f"The node {n} is not in the graph.") from err + + def degree(self, nbunch=None, weight=None): + """Return an iterator for (node, degree) in the dense graph. + + The node degree is the number of edges adjacent to the node. + + Parameters + ---------- + nbunch : iterable container, optional (default=all nodes) + A container of nodes. The container will be iterated + through once. + + weight : string or None, optional (default=None) + The edge attribute that holds the numerical value used + as a weight. If None, then each edge has weight 1. + The degree is the sum of the edge weights adjacent to the node. + + Returns + ------- + nd_iter : iterator + The iterator returns two-tuples of (node, degree). + + See Also + -------- + degree + + Examples + -------- + >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc + >>> G.degree(0) # node 0 with degree 1 + 1 + >>> list(G.degree([0, 1])) + [(0, 1), (1, 2)] + + """ + if nbunch is None: + nodes_nbrs = ( + ( + n, + { + v: self.all_edge_dict + for v in set(self.adj) - set(self.adj[n]) - {n} + }, + ) + for n in self.nodes() + ) + elif nbunch in self: + nbrs = set(self.nodes()) - set(self.adj[nbunch]) - {nbunch} + return len(nbrs) + else: + nodes_nbrs = ( + ( + n, + { + v: self.all_edge_dict + for v in set(self.nodes()) - set(self.adj[n]) - {n} + }, + ) + for n in self.nbunch_iter(nbunch) + ) + + if weight is None: + return ((n, len(nbrs)) for n, nbrs in nodes_nbrs) + else: + # AntiGraph is a ThinGraph so all edges have weight 1 + return ( + (n, sum((nbrs[nbr].get(weight, 1)) for nbr in nbrs)) + for n, nbrs in nodes_nbrs + ) + + def adjacency(self): + """Return an iterator of (node, adjacency set) tuples for all nodes + in the dense graph. + + This is the fastest way to look at every edge. + For directed graphs, only outgoing adjacencies are included. + + Returns + ------- + adj_iter : iterator + An iterator of (node, adjacency set) for all nodes in + the graph. + """ + nodes = set(self.adj) + for n, nbrs in self.adj.items(): + yield (n, nodes - set(nbrs) - {n}) + + +# Build several pairs of graphs, a regular graph +# and the AntiGraph of it's complement, which behaves +# as if it were the original graph. +Gnp = nx.gnp_random_graph(20, 0.8, seed=42) +Anp = AntiGraph(nx.complement(Gnp)) +Gd = nx.davis_southern_women_graph() +Ad = AntiGraph(nx.complement(Gd)) +Gk = nx.karate_club_graph() +Ak = AntiGraph(nx.complement(Gk)) +pairs = [(Gnp, Anp), (Gd, Ad), (Gk, Ak)] +# test connected components +for G, A in pairs: + gc = [set(c) for c in nx.connected_components(G)] + ac = [set(c) for c in nx.connected_components(A)] + for comp in ac: + assert comp in gc +# test biconnected components +for G, A in pairs: + gc = [set(c) for c in nx.biconnected_components(G)] + ac = [set(c) for c in nx.biconnected_components(A)] + for comp in ac: + assert comp in gc +# test degree +for G, A in pairs: + node = list(G.nodes())[0] + nodes = list(G.nodes())[1:4] + assert G.degree(node) == A.degree(node) + assert sum(d for n, d in G.degree()) == sum(d for n, d in A.degree()) + # AntiGraph is a ThinGraph, so all the weights are 1 + assert sum(d for n, d in A.degree()) == sum(d for n, d in A.degree(weight="weight")) + assert sum(d for n, d in G.degree(nodes)) == sum(d for n, d in A.degree(nodes)) + +pos = nx.spring_layout(G, seed=268) # Seed for reproducible layout +nx.draw(Gnp, pos=pos) +plt.show() diff --git a/share/doc/networkx-3.0/examples/subclass/plot_printgraph.py b/share/doc/networkx-3.0/examples/subclass/plot_printgraph.py new file mode 100644 index 0000000..08efcf1 --- /dev/null +++ b/share/doc/networkx-3.0/examples/subclass/plot_printgraph.py @@ -0,0 +1,88 @@ +""" +=========== +Print Graph +=========== + +Example subclass of the Graph class. +""" + +import matplotlib.pyplot as plt +import networkx as nx +from networkx import Graph + + +class PrintGraph(Graph): + """ + Example subclass of the Graph class. + + Prints activity log to file or standard output. + """ + + def __init__(self, data=None, name="", file=None, **attr): + super().__init__(data=data, name=name, **attr) + if file is None: + import sys + + self.fh = sys.stdout + else: + self.fh = open(file, "w") + + def add_node(self, n, attr_dict=None, **attr): + super().add_node(n, attr_dict=attr_dict, **attr) + self.fh.write(f"Add node: {n}\n") + + def add_nodes_from(self, nodes, **attr): + for n in nodes: + self.add_node(n, **attr) + + def remove_node(self, n): + super().remove_node(n) + self.fh.write(f"Remove node: {n}\n") + + def remove_nodes_from(self, nodes): + for n in nodes: + self.remove_node(n) + + def add_edge(self, u, v, attr_dict=None, **attr): + super().add_edge(u, v, attr_dict=attr_dict, **attr) + self.fh.write(f"Add edge: {u}-{v}\n") + + def add_edges_from(self, ebunch, attr_dict=None, **attr): + for e in ebunch: + u, v = e[0:2] + self.add_edge(u, v, attr_dict=attr_dict, **attr) + + def remove_edge(self, u, v): + super().remove_edge(u, v) + self.fh.write(f"Remove edge: {u}-{v}\n") + + def remove_edges_from(self, ebunch): + for e in ebunch: + u, v = e[0:2] + self.remove_edge(u, v) + + def clear(self): + super().clear() + self.fh.write("Clear graph\n") + + +G = PrintGraph() +G.add_node("foo") +G.add_nodes_from("bar", weight=8) +G.remove_node("b") +G.remove_nodes_from("ar") +print("Nodes in G: ", G.nodes(data=True)) +G.add_edge(0, 1, weight=10) +print("Edges in G: ", G.edges(data=True)) +G.remove_edge(0, 1) +G.add_edges_from(zip(range(0, 3), range(1, 4)), weight=10) +print("Edges in G: ", G.edges(data=True)) +G.remove_edges_from(zip(range(0, 3), range(1, 4))) +print("Edges in G: ", G.edges(data=True)) + +G = PrintGraph() +nx.add_path(G, range(10)) +nx.add_star(G, range(9, 13)) +pos = nx.spring_layout(G, seed=225) # Seed for reproducible layout +nx.draw(G, pos) +plt.show() diff --git a/share/man/man1/ipython.1 b/share/man/man1/ipython.1 new file mode 100644 index 0000000..0f4a191 --- /dev/null +++ b/share/man/man1/ipython.1 @@ -0,0 +1,60 @@ +.\" Hey, EMACS: -*- nroff -*- +.\" First parameter, NAME, should be all caps +.\" Second parameter, SECTION, should be 1-8, maybe w/ subsection +.\" other parameters are allowed: see man(7), man(1) +.TH IPYTHON 1 "July 15, 2011" +.\" Please adjust this date whenever revising the manpage. +.\" +.\" Some roff macros, for reference: +.\" .nh disable hyphenation +.\" .hy enable hyphenation +.\" .ad l left justify +.\" .ad b justify to both left and right margins +.\" .nf disable filling +.\" .fi enable filling +.\" .br insert line break +.\" .sp insert n+1 empty lines +.\" for manpage-specific macros, see man(7) and groff_man(7) +.\" .SH section heading +.\" .SS secondary section heading +.\" +.\" +.\" To preview this page as plain text: nroff -man ipython.1 +.\" +.SH NAME +ipython \- Tools for Interactive Computing in Python. +.SH SYNOPSIS +.B ipython +.RI [ options ] " files" ... + +.B ipython subcommand +.RI [ options ] ... + +.SH DESCRIPTION +An interactive Python shell with automatic history (input and output), dynamic +object introspection, easier configuration, command completion, access to the +system shell, integration with numerical and scientific computing tools, +web notebook, Qt console, and more. + +For more information on how to use IPython, see 'ipython \-\-help', +or 'ipython \-\-help\-all' for all available command\(hyline options. + +.SH "ENVIRONMENT VARIABLES" +.sp +.PP +\fIIPYTHONDIR\fR +.RS 4 +This is the location where IPython stores all its configuration files. The default +is $HOME/.ipython if IPYTHONDIR is not defined. + +You can see the computed value of IPYTHONDIR with `ipython locate`. + +.SH FILES + +IPython uses various configuration files stored in profiles within IPYTHONDIR. +To generate the default configuration files and start configuring IPython, +do 'ipython profile create', and edit '*_config.py' files located in +IPYTHONDIR/profile_default. + +.SH AUTHORS +IPython is written by the IPython Development Team . diff --git a/share/man/man1/isympy.1 b/share/man/man1/isympy.1 new file mode 100644 index 0000000..0ff9661 --- /dev/null +++ b/share/man/man1/isympy.1 @@ -0,0 +1,188 @@ +'\" -*- coding: us-ascii -*- +.if \n(.g .ds T< \\FC +.if \n(.g .ds T> \\F[\n[.fam]] +.de URL +\\$2 \(la\\$1\(ra\\$3 +.. +.if \n(.g .mso www.tmac +.TH isympy 1 2007-10-8 "" "" +.SH NAME +isympy \- interactive shell for SymPy +.SH SYNOPSIS +'nh +.fi +.ad l +\fBisympy\fR \kx +.if (\nx>(\n(.l/2)) .nr x (\n(.l/5) +'in \n(.iu+\nxu +[\fB-c\fR | \fB--console\fR] [\fB-p\fR ENCODING | \fB--pretty\fR ENCODING] [\fB-t\fR TYPE | \fB--types\fR TYPE] [\fB-o\fR ORDER | \fB--order\fR ORDER] [\fB-q\fR | \fB--quiet\fR] [\fB-d\fR | \fB--doctest\fR] [\fB-C\fR | \fB--no-cache\fR] [\fB-a\fR | \fB--auto\fR] [\fB-D\fR | \fB--debug\fR] [ +-- | PYTHONOPTIONS] +'in \n(.iu-\nxu +.ad b +'hy +'nh +.fi +.ad l +\fBisympy\fR \kx +.if (\nx>(\n(.l/2)) .nr x (\n(.l/5) +'in \n(.iu+\nxu +[ +{\fB-h\fR | \fB--help\fR} +| +{\fB-v\fR | \fB--version\fR} +] +'in \n(.iu-\nxu +.ad b +'hy +.SH DESCRIPTION +isympy is a Python shell for SymPy. It is just a normal python shell +(ipython shell if you have the ipython package installed) that executes +the following commands so that you don't have to: +.PP +.nf +\*(T< +>>> from __future__ import division +>>> from sympy import * +>>> x, y, z = symbols("x,y,z") +>>> k, m, n = symbols("k,m,n", integer=True) + \*(T> +.fi +.PP +So starting isympy is equivalent to starting python (or ipython) and +executing the above commands by hand. It is intended for easy and quick +experimentation with SymPy. For more complicated programs, it is recommended +to write a script and import things explicitly (using the "from sympy +import sin, log, Symbol, ..." idiom). +.SH OPTIONS +.TP +\*(T<\fB\-c \fR\*(T>\fISHELL\fR, \*(T<\fB\-\-console=\fR\*(T>\fISHELL\fR +Use the specified shell (python or ipython) as +console backend instead of the default one (ipython +if present or python otherwise). + +Example: isympy -c python + +\fISHELL\fR could be either +\&'ipython' or 'python' +.TP +\*(T<\fB\-p \fR\*(T>\fIENCODING\fR, \*(T<\fB\-\-pretty=\fR\*(T>\fIENCODING\fR +Setup pretty printing in SymPy. By default, the most pretty, unicode +printing is enabled (if the terminal supports it). You can use less +pretty ASCII printing instead or no pretty printing at all. + +Example: isympy -p no + +\fIENCODING\fR must be one of 'unicode', +\&'ascii' or 'no'. +.TP +\*(T<\fB\-t \fR\*(T>\fITYPE\fR, \*(T<\fB\-\-types=\fR\*(T>\fITYPE\fR +Setup the ground types for the polys. By default, gmpy ground types +are used if gmpy2 or gmpy is installed, otherwise it falls back to python +ground types, which are a little bit slower. You can manually +choose python ground types even if gmpy is installed (e.g., for testing purposes). + +Note that sympy ground types are not supported, and should be used +only for experimental purposes. + +Note that the gmpy1 ground type is primarily intended for testing; it the +use of gmpy even if gmpy2 is available. + +This is the same as setting the environment variable +SYMPY_GROUND_TYPES to the given ground type (e.g., +SYMPY_GROUND_TYPES='gmpy') + +The ground types can be determined interactively from the variable +sympy.polys.domains.GROUND_TYPES inside the isympy shell itself. + +Example: isympy -t python + +\fITYPE\fR must be one of 'gmpy', +\&'gmpy1' or 'python'. +.TP +\*(T<\fB\-o \fR\*(T>\fIORDER\fR, \*(T<\fB\-\-order=\fR\*(T>\fIORDER\fR +Setup the ordering of terms for printing. The default is lex, which +orders terms lexicographically (e.g., x**2 + x + 1). You can choose +other orderings, such as rev-lex, which will use reverse +lexicographic ordering (e.g., 1 + x + x**2). + +Note that for very large expressions, ORDER='none' may speed up +printing considerably, with the tradeoff that the order of the terms +in the printed expression will have no canonical order + +Example: isympy -o rev-lax + +\fIORDER\fR must be one of 'lex', 'rev-lex', 'grlex', +\&'rev-grlex', 'grevlex', 'rev-grevlex', 'old', or 'none'. +.TP +\*(T<\fB\-q\fR\*(T>, \*(T<\fB\-\-quiet\fR\*(T> +Print only Python's and SymPy's versions to stdout at startup, and nothing else. +.TP +\*(T<\fB\-d\fR\*(T>, \*(T<\fB\-\-doctest\fR\*(T> +Use the same format that should be used for doctests. This is +equivalent to '\fIisympy -c python -p no\fR'. +.TP +\*(T<\fB\-C\fR\*(T>, \*(T<\fB\-\-no\-cache\fR\*(T> +Disable the caching mechanism. Disabling the cache may slow certain +operations down considerably. This is useful for testing the cache, +or for benchmarking, as the cache can result in deceptive benchmark timings. + +This is the same as setting the environment variable SYMPY_USE_CACHE +to 'no'. +.TP +\*(T<\fB\-a\fR\*(T>, \*(T<\fB\-\-auto\fR\*(T> +Automatically create missing symbols. Normally, typing a name of a +Symbol that has not been instantiated first would raise NameError, +but with this option enabled, any undefined name will be +automatically created as a Symbol. This only works in IPython 0.11. + +Note that this is intended only for interactive, calculator style +usage. In a script that uses SymPy, Symbols should be instantiated +at the top, so that it's clear what they are. + +This will not override any names that are already defined, which +includes the single character letters represented by the mnemonic +QCOSINE (see the "Gotchas and Pitfalls" document in the +documentation). You can delete existing names by executing "del +name" in the shell itself. You can see if a name is defined by typing +"'name' in globals()". + +The Symbols that are created using this have default assumptions. +If you want to place assumptions on symbols, you should create them +using symbols() or var(). + +Finally, this only works in the top level namespace. So, for +example, if you define a function in isympy with an undefined +Symbol, it will not work. +.TP +\*(T<\fB\-D\fR\*(T>, \*(T<\fB\-\-debug\fR\*(T> +Enable debugging output. This is the same as setting the +environment variable SYMPY_DEBUG to 'True'. The debug status is set +in the variable SYMPY_DEBUG within isympy. +.TP +-- \fIPYTHONOPTIONS\fR +These options will be passed on to \fIipython (1)\fR shell. +Only supported when ipython is being used (standard python shell not supported). + +Two dashes (--) are required to separate \fIPYTHONOPTIONS\fR +from the other isympy options. + +For example, to run iSymPy without startup banner and colors: + +isympy -q -c ipython -- --colors=NoColor +.TP +\*(T<\fB\-h\fR\*(T>, \*(T<\fB\-\-help\fR\*(T> +Print help output and exit. +.TP +\*(T<\fB\-v\fR\*(T>, \*(T<\fB\-\-version\fR\*(T> +Print isympy version information and exit. +.SH FILES +.TP +\*(T<\fI${HOME}/.sympy\-history\fR\*(T> +Saves the history of commands when using the python +shell as backend. +.SH BUGS +The upstreams BTS can be found at \(lahttps://github.com/sympy/sympy/issues\(ra +Please report all bugs that you find in there, this will help improve +the overall quality of SymPy. +.SH "SEE ALSO" +\fBipython\fR(1), \fBpython\fR(1) diff --git a/share/man/man1/ttx.1 b/share/man/man1/ttx.1 new file mode 100644 index 0000000..bba23b5 --- /dev/null +++ b/share/man/man1/ttx.1 @@ -0,0 +1,225 @@ +.Dd May 18, 2004 +.\" ttx is not specific to any OS, but contrary to what groff_mdoc(7) +.\" seems to imply, entirely omitting the .Os macro causes 'BSD' to +.\" be used, so I give a zero-width space as its argument. +.Os \& +.\" The "FontTools Manual" argument apparently has no effect in +.\" groff 1.18.1. I think it is a bug in the -mdoc groff package. +.Dt TTX 1 "FontTools Manual" +.Sh NAME +.Nm ttx +.Nd tool for manipulating TrueType and OpenType fonts +.Sh SYNOPSIS +.Nm +.Bk +.Op Ar option ... +.Ek +.Bk +.Ar file ... +.Ek +.Sh DESCRIPTION +.Nm +is a tool for manipulating TrueType and OpenType fonts. It can convert +TrueType and OpenType fonts to and from an +.Tn XML Ns -based format called +.Tn TTX . +.Tn TTX +files have a +.Ql .ttx +extension. +.Pp +For each +.Ar file +argument it is given, +.Nm +detects whether it is a +.Ql .ttf , +.Ql .otf +or +.Ql .ttx +file and acts accordingly: if it is a +.Ql .ttf +or +.Ql .otf +file, it generates a +.Ql .ttx +file; if it is a +.Ql .ttx +file, it generates a +.Ql .ttf +or +.Ql .otf +file. +.Pp +By default, every output file is created in the same directory as the +corresponding input file and with the same name except for the +extension, which is substituted appropriately. +.Nm +never overwrites existing files; if necessary, it appends a suffix to +the output file name before the extension, as in +.Pa Arial#1.ttf . +.Ss "General options" +.Bl -tag -width ".Fl t Ar table" +.It Fl h +Display usage information. +.It Fl d Ar dir +Write the output files to directory +.Ar dir +instead of writing every output file to the same directory as the +corresponding input file. +.It Fl o Ar file +Write the output to +.Ar file +instead of writing it to the same directory as the +corresponding input file. +.It Fl v +Be verbose. Write more messages to the standard output describing what +is being done. +.It Fl a +Allow virtual glyphs ID's on compile or decompile. +.El +.Ss "Dump options" +The following options control the process of dumping font files +(TrueType or OpenType) to +.Tn TTX +files. +.Bl -tag -width ".Fl t Ar table" +.It Fl l +List table information. Instead of dumping the font to a +.Tn TTX +file, display minimal information about each table. +.It Fl t Ar table +Dump table +.Ar table . +This option may be given multiple times to dump several tables at +once. When not specified, all tables are dumped. +.It Fl x Ar table +Exclude table +.Ar table +from the list of tables to dump. This option may be given multiple +times to exclude several tables from the dump. The +.Fl t +and +.Fl x +options are mutually exclusive. +.It Fl s +Split tables. Dump each table to a separate +.Tn TTX +file and write (under the name that would have been used for the output +file if the +.Fl s +option had not been given) one small +.Tn TTX +file containing references to the individual table dump files. This +file can be used as input to +.Nm +as long as the referenced files can be found in the same directory. +.It Fl i +.\" XXX: I suppose OpenType programs (exist and) are also affected. +Don't disassemble TrueType instructions. When this option is specified, +all TrueType programs (glyph programs, the font program and the +pre-program) are written to the +.Tn TTX +file as hexadecimal data instead of +assembly. This saves some time and results in smaller +.Tn TTX +files. +.It Fl y Ar n +When decompiling a TrueType Collection (TTC) file, +decompile font number +.Ar n , +starting from 0. +.El +.Ss "Compilation options" +The following options control the process of compiling +.Tn TTX +files into font files (TrueType or OpenType): +.Bl -tag -width ".Fl t Ar table" +.It Fl m Ar fontfile +Merge the input +.Tn TTX +file +.Ar file +with +.Ar fontfile . +No more than one +.Ar file +argument can be specified when this option is used. +.It Fl b +Don't recalculate glyph bounding boxes. Use the values in the +.Tn TTX +file as is. +.El +.Sh "THE TTX FILE FORMAT" +You can find some information about the +.Tn TTX +file format in +.Pa documentation.html . +In particular, you will find in that file the list of tables understood by +.Nm +and the relations between TrueType GlyphIDs and the glyph names used in +.Tn TTX +files. +.Sh EXAMPLES +In the following examples, all files are read from and written to the +current directory. Additionally, the name given for the output file +assumes in every case that it did not exist before +.Nm +was invoked. +.Pp +Dump the TrueType font contained in +.Pa FreeSans.ttf +to +.Pa FreeSans.ttx : +.Pp +.Dl ttx FreeSans.ttf +.Pp +Compile +.Pa MyFont.ttx +into a TrueType or OpenType font file: +.Pp +.Dl ttx MyFont.ttx +.Pp +List the tables in +.Pa FreeSans.ttf +along with some information: +.Pp +.Dl ttx -l FreeSans.ttf +.Pp +Dump the +.Sq cmap +table from +.Pa FreeSans.ttf +to +.Pa FreeSans.ttx : +.Pp +.Dl ttx -t cmap FreeSans.ttf +.Sh NOTES +On MS\-Windows and MacOS, +.Nm +is available as a graphical application to which files can be dropped. +.Sh SEE ALSO +.Pa documentation.html +.Pp +.Xr fontforge 1 , +.Xr ftinfo 1 , +.Xr gfontview 1 , +.Xr xmbdfed 1 , +.Xr Font::TTF 3pm +.Sh AUTHORS +.Nm +was written by +.An -nosplit +.An "Just van Rossum" Aq just@letterror.com . +.Pp +This manual page was written by +.An "Florent Rougon" Aq f.rougon@free.fr +for the Debian GNU/Linux system based on the existing FontTools +documentation. It may be freely used, modified and distributed without +restrictions. +.\" For Emacs: +.\" Local Variables: +.\" fill-column: 72 +.\" sentence-end: "[.?!][]\"')}]*\\($\\| $\\| \\| \\)[ \n]*" +.\" sentence-end-double-space: t +.\" End: \ No newline at end of file diff --git a/static/css/circulationSales.css b/static/css/circulationSales.css new file mode 100644 index 0000000..ad54e24 --- /dev/null +++ b/static/css/circulationSales.css @@ -0,0 +1,33 @@ +.centerBoxTop{ + width: 100%; + height:49%; + +} +.centerBoxBottom{ + width: 100%; + height: 49%; + margin-top: 2%; +} +.chain-item{ + float: left; + width: 21%; + height: 10vw; + background-color: rgba(13, 226, 232, .2); + /* opacity: .8; */ + margin-left: .5vw; + margin-top: 1vw; + color: #0dd8df; + text-align: center; +} +.chain-item h3{ + font-size: .8vw; + padding:.5vw 0; +} +.chain-item p{ + font-size: .5vw; + padding:.1vw 0; +} +/* #liquidfill-chart{ + background: url(../img/2019072517094175.gif) no-repeat center bottom; + 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.icon-success{fill:#67C23A}.el-result .icon-error{fill:#F56C6C}.el-result .icon-info{fill:#909399}.el-result .icon-warning{fill:#E6A23C} \ No newline at end of file diff --git a/static/css/index.css b/static/css/index.css new file mode 100644 index 0000000..129138c --- /dev/null +++ b/static/css/index.css @@ -0,0 +1,788 @@ +/**/ +/**/ +/**/ +#dropdown_cam { + border: 1px solid #0EFCFF; /* 设置边框颜色为 #0EFCFF */ + height: 36px; /* 设置高度，你可以根据需要调整这个值 */ + width: 150px; +} +#dropdown_cam option { + background-color: transparent; + color: #000000; /* 黑色文本 */ + border: 1px solid #0EFCFF !important; +} +.my_img_cam { + height: 6vh; + width: 4.5vw; +} +.webcam-btn { + background-color: transparent; + border: 1px solid #0EFCFF !important; + + color: #ffffff; /* 蓝色字体 */ + padding: 0.5vw 0.9vw; + text-align: center; + text-decoration: none; + font-size: 2.5vh; + cursor: pointer; + border-radius: 4px; + height: 6vh; + width: 4.5vw; + +} +.webcam-btn1 { + background-color: transparent; + border: 1px solid #0EFCFF !important; + + color: #ffffff; /* 蓝色字体 */ + padding: 0.5vw 0.9vw; + text-align: center; + text-decoration: none; + font-size: 2.5vh; + cursor: pointer; + border-radius: 4px; + height: 6vh; + width: 10.5vw; + +} +.webcam-btn:hover { + /* 悬停时变为蓝色背景 */ + color: #0EFCFF; /* 悬停时变为白色字体 */ +} +.webcam-btn1:hover { + /* 悬停时变为蓝色背景 */ + color: #0EFCFF; /* 悬停时变为白色字体 */ +} +.custom-select .el-input__inner { + background-color: transparent; /* 背景透明 */ + color: white; /* 字体白色 */ + border-color: #0EFCFF; /* 边框颜色 */ + } + + /* 可选：如果你还想改变下拉列表中的选项样式 */ + .custom-select .el-select-dropdown__item { + color: white; /* 下拉列表字体颜色 */ + background-color: transparent; /* 下拉列表背景颜色，可根据需要调整 */ + } + + /* 可选：如果你需要改变下拉列表的边框颜色（通常不需要，因为下拉列表没有边框） */ + .custom-select .el-select-dropdown { + border-color: #0EFCFF; /* 这行可能不需要，取决于你的具体需求 */ + } + +.custom-file-label { + /* border: 2px solid whitesmoke; */ + display: inline-block; + width: 9vw; + height: 4vh; + cursor: pointer; + border: 1px solid #0EFCFF !important; + /* 设置边框宽度、样式和颜色 */ + color: rgb(250, 250, 247); + /* 设置字体颜色为黄色 */ + padding: 0.1vh; + /* 可选：添加一些内边距以使文本与边框有一定的间距 */ + font-size: 2.2vh; +} +.bg-purple { + /* background: #d3dce6; */ + border: 1px dashed #0EFCFF; +} +.bg-purple-light { + font-size: 30px; + /* background: #e5e9f2; */ + border: 1px dashed #0EFCFF; +} +.grid-content { + z-index: 10; + max-width: 100%; + max-height: 100%; + object-fit: contain; + display: flex; + justify-content: center; + align-items: center; + border-radius: 4px; + min-height: 36px; +} +.grid-content1 { + border-radius: 4px; + min-height: 36px; +} + + +.el-card { + /* display: flex; */ + height: 300px; + width: 250px; + + + + border: 1px solid #0EFCFF !important; + + /* + margin-inline-start: 8% */ + .el-card__body { + padding: 0px; + } +} + + +.box-card { + cursor: pointer; +} + +.card-container { + display: flex; + justify-content: left; + height: 400px; + margin-top: 30%; + + +} + + +.main_con { + position: absolute; + width: 97%; + height: 95%; + left: 0; + right: 0; + top: 0; + bottom: 0; + margin: auto; + /* background: white; */ +} + +.main_top { + width: 100%; + height: 37%; +} + +.main_top_left { + float: left; + width: 17.3%; + height: 100%; + /* background: gold; */ +} + +.main_top_left_top { + position: relative; + width: 100%; + height: 100%; + z-index: 1; + /* background: green; */ +} + +.main_top_left_bottom { + margin-top: 4%; +} + +.file-input { + display: none; + + /* Hide the default file input */ +} + +.res-input { + width: 100; + font-size: large; + + +} + +.main_top_left_bottom_num { + width: 100%; +} + +.main_top_left_bottom_num span { + float: left; + display: block; + font-size: .65vw; + /* -webkit-transform-origin-x: 0; + transform: scale(0.9); + -webkit-transform: scale(0.9); */ +} + +.main_top_left_bottom_num_list { + float: left; + width: 8%; + height: 1.4vw; + line-height: 1.4vw; + margin-left: .2vw; + text-indent: .4vw; + font-size: .8vw; + color: white; + margin-top: .2vw; + background: #37D2D4; +} + +.main_top_left_bottom_bar { + float: left; + width: 100%; + height: .6vw; + line-height: 0; + margin-top: .6vw; +} + +.main_top_left_bottom_bar span { + position: relative; + float: left; + display: block; + font-size: .65vw; + top: -.15vw; + -webkit-transform-origin-x: 0; + transform: scale(0.7); + -webkit-transform: scale(0.7); +} + +.main_top_left_bottom_bar .bar_num { + color: #0EFCFF; + margin-left: 3%; + -webkit-transform-origin-x: 0; + transform: scale(0.7); + -webkit-transform: scale(0.7); +} + +.bar_father { + float: left; + position: relative; + width: 75%; + background: rgba(31, 103, 163, 0.2); + height: 100%; + margin-left: 3%; + border-radius: 90px; +} + +.bar_child { + position: absolute; + left: 0; + width: 0%; + height: 100%; + border-radius: 90px; + transition: all 2s; + background-image: linear-gradient(90deg, #3E94CD 0%, #56D4F1 49%, #38E1E1 99%); +} + +.main_top_left_top img { + position: absolute; + width: 100%; + height: 100%; + z-index: -1; +} + +.my_text { + text-align: center; + color: white; + font-size: 1.3vw; + padding-top: .1vw; +} + +.main_top_left_top_title { + text-align: center; + color: #79d8f0; + font-size: 1.75vw; + padding-top: .2vw; +} + +.main_top_left_top_con { + width: 16vw; + height: 40vh; + margin: auto; + margin-top: 3%; + color: white; + /* margin-left: 4%; */ + /* text-indent: .5vw; */ + font-size: 0.9vw; + /* letter-spacing: .15vw; */ +} + +.my_file_upload { + font-size: 1.0vw !important; + margin-left: 1%; + margin-top: 5%; + z-index: 90; +} +.my_file_upload_choose { + font-size: 1.0vw !important; + margin-left: 9%; + margin-top: 20%; + z-index: 90; +} +.my_file_upload_choose1 { + font-size: 1.0vw !important; + margin-left: 1%; + margin-top: 30%; + z-index: 90; +} + +.main_top_left_top_con span { + display: inline-block; + margin-top: .4vw; + text-indent: 0vw; +} + +.main_top_left_top_con_left { + float: left; + height: 48%; + width: 75%; + text-align: center; + margin-top: 1.5%; +} + +.main_top_left_t_c_r_right {} + +.main_top_left_t_c_l_left, +.main_top_left_t_c_l_right, +.main_top_left_t_c_r_left { + float: left; + width: 48%; + height: 50%; + text-align: center; + background: #0EFCFF; + font-size: .7vw; +} + +.main_top_left_t_c_l_right { + background: #f5f4f2; + margin-left: 4%; +} + +/* .main_top_left_t_c_r_right { + background: #F6580E; + } */ +.main_top_left_t_c_r_left { + background: #e5edf7; +} + + + +.main_top_left_top_con_left p { + /* -webkit-transform-origin-x: 0; */ + transform: scale(0.8); + -webkit-transform: scale(0.8); + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; +} + +.main_top_left_top_con_right { + float: right; + height: 48%; + width: 49%; + text-align: center; + margin-top: 1.5%; +} + +.main_top_left_top_con_right .main_top_left_c_l_n { + /* -webkit-transform-origin-x: 0; */ + transform: scale(0.8); + -webkit-transform: scale(0.8); + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; +} + +.main_top_left_top_con_right p { + /* -webkit-transform-origin-x: 0; */ + transform: scale(0.8); + -webkit-transform: scale(0.8); + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; +} + +.main_top_left_top_con_right2 p { + margin-top: -7%; +} + +.main_top_left_top_con_list { + float: left; + height: 48%; + width: 32%; + text-align: center; + margin-left: 2%; +} + +.main_top_left_top_con_list .main_top_left_c_l_n { + /* -webkit-transform-origin-x: 0; */ + transform: scale(0.8); + -webkit-transform: scale(0.8); +} + +.main_top_left_top_con_list p { + /* -webkit-transform-origin-x: 0; */ + transform: scale(0.8); + -webkit-transform: scale(0.8); +} + +.main_top_left_top_con_list:nth-child(1) { + background: #37D2D4; + margin-left: 0; +} + +.main_top_left_top_con_list:nth-child(2) { + background: #19CA88; +} + +.main_top_left_top_con_list:nth-child(3) { + background: #858FF8; +} + +.main_top_middle { + float: left; + width: 63%; + height: 100%; + margin-left: 1.2%; +} + +.main_top_middle_top_title { + position: relative; + width: 100%; + text-align: center; + font-size: 1.7vw; + font-weight: bold; + color: #0EFCFF; + height: 14%; +} + +.main_top_middle_top_title .title_bg { + position: absolute; + left: 7%; + top: -20%; + width: 86%; + height: 140%; +} + +.title_web { + position: absolute; + right: -.8%; + top: 0; + padding: .5% 2%; + font-size: .7vw; + color: #ffffff; + border: 1px solid #0EFCFF; + -webkit-transform-origin-x: 0; + transform: scale(0.9); + -webkit-transform: scale(0.9); +} + +.title_admin { + position: absolute; + left: 0; + top: 0; + padding: .5% 2%; + font-size: .7vw; + color: #ffffff; + border: 1px solid #0EFCFF; + -webkit-transform-origin-x: 0; + transform: scale(0.9); + -webkit-transform: scale(0.9); +} + +.main_top_middle_num_title { + float: left; + color: #f3f2ea; + font-size: 1.5vw; + margin-left: 43%; + line-height: 4.5vw; + width: 18%; + margin-top: .5vw; +} + +.main_top_middle_num { + float: left; + width: 55%; + height: 23%; + margin: .7% auto; + margin-top: 1.5%; +} + +.main_top_middle_num_list { + position: relative; + float: left; + height: 100%; + width: 12%; + margin-left: 2.6%; + font-size: 2vw; + font-weight: bold; + color: #0EFCFF; + line-height: 240%; +} + +.main_top_middle_num_list p { + text-align: center; +} + +.main_top_middle_num_list:nth-child(1) { + margin-left: 0; +} + +.main_top_middle_num_list img { + position: absolute; + width: 100%; + height: 100%; +} + +.main_top_middle_bottom { + float: left; + width: 100%; + height: 49.8%; + margin-top: .5%; +} + +.main_top_middle_bottom_echarts { + position: relative; + float: left; + width: 49%; + height: 50%; +} + +.main_top_middle_bottom_echarts_right { + float: right; +} + +.main_top_middle_bottom_echarts img { + position: absolute; + width: 100%; + height: 100%; +} + +.main_top_echarts_con { + width: 92%; + height: 82%; + margin: 2% auto; + /* background: white; */ +} + +.main_top_echarts_con_title { + font-size: 1.8vw; + color: #ecebe3; +} + +.main_top_echarts_pie { + width: 100%; + height: 90%; +} + +.main_top_right { + float: right; +} + +.main_top_right .main_top_left_top_con_left { + float: left; + height: 48%; + width: 49%; + background: #37D2D4; + +} + +.main_top_right .main_top_left_top_con_right { + float: right; + height: 48%; + width: 49%; + background: #19CA88; +} + +.main_top_right .main_top_left_top_con_list { + float: left; + height: 48%; + width: 32%; + margin-left: 2%; + margin-top: 1.5%; +} + +.main_top_right .main_top_left_top_con_list:nth-child(3) { + background: #858FF8; + margin-left: 0; +} + +.main_top_right .main_top_left_top_con_list:nth-child(4) { + background: #2E8CFF; +} + +.main_top_right .main_top_left_top_con_list:nth-child(5) { + background: #FD9133; +} + +/* .main_middle { + height: 7.1%; + width: 100%; + +} + +.main_middle_list { + position: relative; + float: left; + width: 18.5%; + height: 100%; + background: rgba(11, 76, 151, 0.10); + margin-left: 1.875%; + box-shadow: 1px 2px 10px 1px rgba(14, 252, 255, 0.53), inset 5px 4px 100px 1px rgba(14, 252, 255, 0.34); +} + +.main_middle_list img { + position: absolute; + width: 500px; + height: 100%; +} + +.main_middle_list:nth-child(1) { + margin-left:0; +} */ + +.main_list_title { + font-size: .75vw; + color: #0EFCFF; + text-indent: .8vw; + padding-top: .5vw; +} + +.main_middle_list span { + display: inline-block; + width: 100%; + font-size: 1.4vw; + font-weight: bold; + color: white; + text-align: center; + letter-spacing: .2vw; + margin-top: -.5vw; +} + +.main_bottom { + height: 55.9%; + width: 100%; +} + +.main_bottom_top { + float: left; + width: 100%; + height: 36.5%; + margin-top: 1.5%; +} + +.main_bottom_top_list { + position: relative; + float: left; + width: 32%; + height: 100%; + margin-left: 2%; +} + +.main_bottom_top_list:nth-child(1) { + margin-left: 0; +} + +.main_bottom_top_list img { + position: absolute; + width: 100%; + height: 100%; +} + +.main_bottom_t_l_main, +.main_bottom_t_l_main2 { + width: 100%; + /* height: 100%; */ +} + +.main_bottom_t_l_main_list { + font-size: .7vw; + line-height: 1.6vw; + height: 1.6vw; + color: white; +} + +.main_bottom_t_l_main2 .main_bottom_t_l_main_list { + font-size: .7vw; + line-height: 1.6vw; + height: 1.6vw; + color: white; +} + +.main_bottom_t_list_title { + float: left; + width: 70%; + height: 100%; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; +} + +.main_bottom_t_list_time { + float: left; + width: 30%; + height: 100%; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + text-align: right; +} + +.main_bottom_bottom { + float: left; + width: 100%; + height: 54%; + margin-top: 1%; +} + +.main_bottom_b_left, +.main_bottom_b_right { + position: relative; + float: left; + width: 17.3%; + height: 100%; +} + +.main_bottom_b_left img { + position: absolute; + width: 100%; + height: 100%; +} + +.main_bottom_b_middle1, +.main_bottom_b_middle2 { + position: relative; + float: left; + width: 30%; + height: 100%; + margin-left: 1.8%; +} + +.main_bottom_b_right { + margin-left: 1.8%; +} + +.main_bottom_b_right img { + position: absolute; + width: 100%; + height: 100%; +} + +.main_bottom_b_middle1 img { + position: absolute; + width: 100%; + height: 100%; +} + +.main_bottom_b_middle2 img { + position: absolute; + width: 100%; + height: 100%; +} + +.main_bottom_b_title { + font-size: .8vw; + text-align: center; + color: #0EFCFF; + padding-top: .3vw; +} + +.main_bottom_b_con { + width: 90%; + height: 75%; + margin: auto; + margin-top: 8%; +} + +.main_bottom_b_con2 { + height: 70%; +} \ No newline at end of file diff --git a/static/css/land_flow.css b/static/css/land_flow.css new file mode 100644 index 0000000..c12cb56 --- /dev/null +++ b/static/css/land_flow.css @@ -0,0 +1,314 @@ +.longBox { + /* position: relative; */ + width: 99.5%; +} +.cardType { + position: relative; + width: 100%; + height: 100%; +} +.landCondition, .landFertility, .landIrrigate, .landDrainage, .landType { + width: 100%; + height: 87%; +} +.percent{ + font-size: .8vw; + color: #fff; +} +.percent .pec1{ + position: absolute; + top: 40%; + left: 60%; +} +.percent .pec2 { + position: absolute; + top: 65%; + left: 58%; +} +.percent .pec3 { + position: absolute; + top: 50%; + left: 32%; +} +.utilizeActuality { + width: 100%; + height: 87%; + display: flex; + color: #0EFCFF; + font-weight: 10; +} +.utilizeActuality table { + /* width: 90%; */ + /* height: 78%; */ + text-align: center; + margin: 0 auto; + border: 0 solid #0EFCFF; +} +.utilizeActuality table thead { + width: 100%; + height: 12%; +} +.trBox { + height: 18%; +} + + +.textBox { + margin-left: 2.5%; + margin-top: .2vw; + width: 95%; + height: 45%; + border: 1px solid #0EFCFF; + color: #39DCF4; +} +.textBox .topText { + display: flex; + align-items: center; + width: 100%; + height: 20%; + font-size: .65vw; +} +.topText .topOne { + margin-left: .5vw; +} +.topText .topTwo { + margin-left: 2.5vw; +} +.textBox .middleText { + + display: flex; + align-items: center; + width: 100%; + height: 2vw; + background-color: rgba(14,252,255,.22); + font-size: .8vw; +} +.middleText span { + /* margin-top: .8vw; */ + position: relative; + display: inline-block; + width: 16.5%; + height: 100%; + line-height: 2vw; + text-align: center; + border-right: 2px solid rgba(14,252,255,.16); +} + .middleText .tipBox { + position: absolute; + top: 36%; + background-color: red; + display: inline-block; + width: 29%; + height: 29%; + border: 1px solid rgba(14,252,255,0.48); + background: rgba(2,57,99,0.77); + color: #0EFCFF; + font-size: .65vw; +} +.middleText .tipOne { + left: -4%; + display: none; +} +.middleText .tipTwo { + left: 12%; + display: none; +} +.middleText .tipThree { + left: 28%; + display: none; +} +.middleText .tipFour { + left: 44%; + display: none; +} +.middleText .tipFive { + left: 59%; + display: none; +} +.middleText .tipSix { + left: 73%; + display: none; +} +.tipBox div { + margin: .25vw; +} +.bottomText { + width: 100%; + height: 50%; + font-size: .65vw; +} +.bottomText .soilContamination { + width: 100%; + margin-left: .5vw; + margin-top: .55vw; +} +.soilContamination .sencondBox { + margin-left: .8vw; +} +.bottomText .soilData { + width: 100%; + margin-left: .5vw; + margin-top: .55vw; +} +.soilData span { + margin-left: .5vw; +} +.videoBox { + margin-top: 2%; + margin-left: 2.5%; + width: 95%; + height: 34%; +} +.videoBox .vedioIn { + display: inline-block; + width: 23.5%; + height: 100%; + +} +.mlRight { + margin-right: 1%; +} +.videoIn video { + width: 100%; + height: 100%; + /* background-color: red; */ +} +/* .tipBox { + position: absolute; + top: 37%; + left: 58%; + width: 29%; + height: 29%; + border: 1px solid rgba(14,252,255,0.48); + background: rgba(2,57,99,0.77); + color: #0EFCFF; + font-size: .65vw; +} +.tipBox div { + margin: .3vw; +} */ + + +.centerBox .topData { + width: 100%; + height: 22%; + border: 1px solid #0EFCFF; +} +.topData .topContent { + width: 95%; + height: 50%; + margin-left: 2.5%; + border-bottom: 1px solid rgba(14,252,255,0.42);; + +} +.topData .content, ul { + width: 95%; + height: 50%; + /* margin-left: 2%; */ + /* border-bottom: 1px solid #0EFCFF; */ + display: -webkit-flex; + -webkit-justify-content: space-around; + -webkit-align-items: center; + color: #fff; +} +.content li { + text-align: center; +} +.content ul P { + font-size: .7VW; + color: #0EFCFF; +} +.content ul .num { + color: #fff; + font-size: 1vw; +} +.content ul .unit { + color: #0EFCFF; + font-size: .6vw; +} +.bottomData { + width: 100%; + height: 75.5%; + margin-top: 3%; + /* background-color: green; */ + background-image: url(../img/centerBg.jpg); + background-size: 100% 100%; +} +/* ========= 地图样式 stat========== */ +.map{ + position: relative; + width: 100%; + height: 44%; + margin-top: 10%; + /* background: white; */ +} +.map img { + width: 80%; + margin-left: 10%; +} +.mapActived1 { + position: absolute; + top: 40%; + left: 43.5%; + width: 1.5vw; + height: 3vw; + border-radius: 90px; + cursor: pointer; +} +.mapActived2 { + position: absolute; + top: 48%; + right: 23%; + width: 3vw; + height: 2vw; + cursor: pointer; + border-radius: 10px; +} +.mapActived3 { + position: absolute; + top: 44%; + right: 13.5%; + width: 1.8vw; + height: 4vw; + border-radius: 10px; + cursor: pointer; +} +.mapContent1 { + position: absolute; + height: 6vw; + width: 8vw; + left: 47%; + top: -5%; + background: rgba(255,255,255, .2); +} +.mapContent2 { + position: absolute; + height: 6vw; + width: 8vw; + left: 70%; + top: 5%; + background: rgba(255,255,255, .2); + display: none; +} +.mapContent3 { + position: absolute; + height: 6vw; + width: 8vw; + right: 15%; + top: 12%; + background: rgba(255,255,255, .2); + display: none; +} +.mapContentFont { + width: 85%; + margin-left: 12%; + font-size: .6vw; + color: #0efcff; + letter-spacing: .05vw; +} +.mapContentFont:nth-child(1) { + margin-top: .6vw; +} +.mapContentFont span { + color: white; +} diff --git a/static/css/new_index.css b/static/css/new_index.css new file mode 100644 index 0000000..4d48cd0 --- /dev/null +++ b/static/css/new_index.css @@ -0,0 +1,690 @@ +/* 左侧第一个盒子start--- */ + +/**/ +/* */ +/**/ + +.firstBox { + width: 90%; + margin-left: 6%; + height: 90%; +} +.firstBox .pic { + width: 100%; + height: 25%; +} +.pic img { + display: inline-block; + width: 40%; + height: 80%; + margin-top: 1vw; + +} +.first_top1 { + margin-left: 1vw; +} +.first_top2 { + margin-left: 1vw; +} +.picText { + color: #0EFCFF; + margin-left: 1vw; +} +.picText .text_second { + margin-left: 6vw; +} + +/* 声波动画start--- */ +.voice_animation { + width: 100%; + height: 20%; + margin-top: 1vw; + background-image: url(../img/voice_pic.png); + background-size: 90% 90%; + background-repeat: no-repeat; + background-position: center; +} +/* 声波动画over--- */ + + +/* 进度条start--- */ +.progress { + width: 93%; + height: 10%; + margin-top: 1vw; + margin-left: .6vw; + background-image: url(../img/progress_pic.png); + background-size: 100% 100%; +} +/* 进度条over--- */ + + +.about_illness { + width: 100%; + height: 25%; + margin-top: 1.5vw; + margin-left: .6vw; + color: #fff; + font-size: .6vw; +} +.about_illness > div { + display: inline-block; + width: 45%; + height: 100%; + background-image: url(../img/illness_pic.png); + background-size: 100% 100%; +} +.prevent { + margin-left: .5vw; + text-indent: .2vw; +} +.symptom { + /* text-indent: .2vw; */ + /* font-size: .5vw; */ +} +.symptom_content, .prevent_content { + color: #0EFCFF; + margin-top: .5vw; + margin: .6vw .2vw .2vw .5vw; +} +.symptom_title .prevent_title { + font-size: .1vw; + color: red; +} +/* 左侧第一个盒子over--- */ + + +/* center部分start--- */ +.maps { + position: relative; + width: 100%; + height: 95%; + margin-top: 2%; + /* background: pink; */ + /* background-image: url(../img/landLevel.png); */ + /* background-size: 95% 100%; */ + /* background-repeat: no-repeat; */ + /* background-position: center; */ +} +.maps .land_level { + width: 95%; + height: 100%; + margin-left: 2.5%; +} +.maps .wifi_gif { + position: absolute; + right: 27%; + top: -3.5%; + width: 5%; + height: 10%; +} +.maps .sun_pic { + position: absolute; + top: -5%; + left: 18%; + width: 10%; + height: 15%; + + animation:mymove 3s infinite; + -webkit-animation:mymove 3s infinite; /*Safari and Chrome*/ + animation-direction:alternate;/*轮流反向播放动画。*/ + animation-timing-function: ease-in-out; /*动画的速度曲线*/ + /* Safari 和 Chrome */ + -webkit-animation:mymove 3s infinite; + -webkit-animation-direction:alternate;/*轮流反向播放动画。*/ + -webkit-animation-timing-function: ease-in-out; +} +@keyframes mymove +{ + 0%{ + transform: scale(1); /*开始为原始大小*/ + } + 25%{ + transform: scale(1.1); /*放大1.1倍*/ + } + 50%{ + transform: scale(1.05); + } + 75%{ + transform: scale(1); + } + +} + +@-webkit-keyframes mymove /*Safari and Chrome*/ +{ + 0%{ + transform: scale(1); /*开始为原始大小*/ + } + 25%{ + transform: scale(1.1); /*放大1.1倍*/ + } + 50%{ + transform: scale(1.05); + } + 75%{ + transform: scale(1); + } +} + +.maps .wrj_pic { + position: absolute; + width: 8%; + height: 8%; + left: 30%; + animation: myfirst 5s infinite; + -moz-animation: myfirst 5s infinite; + /* Firefox */ + -webkit-animation: myfirst 5s infinite; + /* Safari 和 Chrome */ + -o-animation: myfirst 5s infinite; + /* Opera */ + /* animation: btn-load-loop 1s linear infinite; */ +} + + + @keyframes myfirst + { + 0% { left:250px; top:0px;} + 25% {left:300px; top:0px;} + 50% {left:150px; top:300px;} + 75% { left:250px; top:200px;} + 100% {left:250px; top:0px;} + } + + @-moz-keyframes myfirst + /* Firefox */ + { + 0% { left:250px; top:0px;} + 25% {left:300px; top:0px;} + 50% {left:500px; top:200px;} + 75% { left:250px; top:200px;} + 100% {left:250px; top:0px;} + } + + @-webkit-keyframes myfirst + /* Safari 和 Chrome */ + { + 0% { left:250px; top:0px;} + 25% {left:300px; top:0px;} + 50% {left:500px; top:200px;} + 75% { left:250px; top:200px;} + 100% {left:250px; top:0px;} + } + + @-o-keyframes myfirst + /* Opera */ + { + 0% { left:250px; top:0px;} + 25% {left:300px; top:0px;} + 50% {left:500px; top:200px;} + 75% { left:250px; top:200px;} + 100% {left:250px; top:0px;} + } + +.wind_gif { + position: absolute; + top: 25%; + left: 5%; + width: 10%; + height: 19%; +} + +.plant_pic { + position: absolute; + top: 40%; + left: 60%; + width: 8%; + height: 10%; +} + +.windows, .window_two, .window_three, .window_four, .window_five, .window_six{ + padding: 1.5%; + color: #0EFCFF; + background: rgba(40, 229, 233, .2); +} +.windows { + position: absolute; + bottom: 0; + left: 3%; +} +.windows li:nth-of-type(1) { + font-size: .9vw; +} +.windows li { + margin-bottom: .2vw; +} +.window_two { + position: absolute; + right: 5%; + /* right: 1%; */ + top: 5%; +} +.window_two li:nth-of-type(1) { + font-size: .9vw; +} +.window_three { + position: absolute; + bottom: 5%; + left: 60%; +} +.window_four { + position: absolute; + top: 7%; + left: 10%; +} +.window_five { + position: absolute; + bottom: 15%; + left: 20%; +} +.window_six{ + position: absolute; + /* border: 1px solid red; */ + right: 40%; + top: 0%; +} +.peasant { + position: absolute; + right: 17%; + top: 27%; + width: 5.5%; + height:9%; + cursor: pointer; + /* background-color: #fff; */ +} + +.display_box { + display: none; +} + +.land_box1 { + position: absolute; + left: 13%; + top: 32%; + width: 13%; + height: 30%; + cursor: pointer; +} +.land_box2 { + position: absolute; + left: 45%; + top: 65%; + width: 20%; + height: 15%; + transform:rotate(150deg); + cursor: pointer; +} +.land_box3 { + position: absolute; + right: 5%; + top: 40%; + width: 20%; + height: 15%; + transform:rotate(150deg); + cursor: pointer; +} +.land_box4 { + position: absolute; + right: 41%; + top: 0%; + width: 10%; + height: 25%; + transform:rotate(140deg); + cursor: pointer; +} +.plant { + position: absolute; + top: 30%; + left: 35%; + width: 25%; + height: 30%; + cursor: pointer; + /* border: 1px solid red; */ +} +.soil_data { + position: absolute; + bottom:23%; + left: 45%; + width: 55%; + height: 10%; + transform:rotate(150deg); +} +.weather_info { + position: absolute; + top: -5%; + left: 18%; + width: 10%; + height: 15%; +} + +/* center部分over--- */ + + +/* left bottom start--- */ +.leftBottom { + display: inline-block; + position: relative; + width: 32%; + height: 80%; + font-size: .6vw; + color: #0EFCFF; +} +.leftBottom .land_data p { + position: absolute; + left: 52%; + top: 14%; +} +.land_data p:nth-child(2) { + top: 24%; + left: 57%; +} +.land_data p:nth-child(3) { + top: 32%; + left: 67%; +} +.right_box { + float: right; + width: 65%; + height: 70%; + /* margin: 2%; */ + margin-top: 1%; + margin-right: 1%; + /* background-color: red; */ +} +.grow_data { + position: relative; + display: inline-block; + float: left; + width: 45%; + height: 100%; + /* background-color: pink; */ +} +.grow_data img { + display: inline-block; + width: 20%; + height: 80%; + margin-top: .8vw; +} +.grow_data p { + color: #0EFCFF; + position: absolute; +} +.grow_data p:nth-of-type(1) { + top: 3%; + left: 5%; +} +.grow_data p:nth-of-type(2) { + top: 47%; + left: 22%; +} +.grow_data p:nth-of-type(3) { + top: 80%; + left: 5%; +} +.grow_data span { + position: absolute; + top: 18%; + left: 13%; + padding: .15vw .6vw; + border-radius: 1vw; + display: inline-block; + color: #0EFCFF; + background: rgba(40, 229, 233, .2); +} +.specialistSuggest { + position: absolute; + left: 50%; + top: -15%; + display: inline-block; + width: 30%; + padding: 1vw; + color: #0EFCFF; + font-size: .6vw; +} + +.specialistSuggest div:nth-of-type(1) { + font-size: .7vw; +} + +.fertilizationSuggest { + position: absolute; + left: 50%; + top: 55%; + display: inline-block; + width: 35%; + padding: 1vw; + color: #0EFCFF; + font-size: .6vw; +} +.fertilizationSuggest div:nth-of-type(1) { + font-size: .7vw; +} + +.weather_data { + position: relative; + display: inline-block; + width: 50%; + height: 100%; + margin-left: 3%; + margin-top: .5%; + font-size: .6vw; + /* background-color: yellow; */ + background-image: url(../img//bottom_icons.png); + background-size: 90% 80%; + background-repeat: no-repeat; +} +.weather_text { + color: #0EFCFF; +} +.weather_text span { + position: absolute; + top: 35%; +} +.weather_text span:nth-of-type(1) { + left: -5%; +} +.weather_text span:nth-of-type(2) { + left: 24%; +} +.weather_text span:nth-of-type(3) { + left: 50%; +} +.weather_text span:nth-of-type(4) { + left: 75%; +} +.text_two span { + top: 85%; +} +.text_two span:nth-of-type(1) { + left: 0%; +} +.text_two span:nth-of-type(2) { + left: 30%; +} +.text_two span:nth-of-type(3) { + left: 65%; +} +/* .text_one { + margin-top: 14%; + +} +.text_two { + margin-top: 18%; +} */ + + +/* left bottom over--- */ + + +/* 右侧三个内容框start--- */ + + /* 硬件设备展示start--- */ +.boxLis { + width: 68%; + z-index: 9999; + height: 1.5vw; + margin-left: 3.5vw; + margin-top: 1vw; + font-size: .6vw; + /* background-color: pink; */ + border-bottom: .02vw solid rgb(40, 229, 233); +} + +.boxLis>li { + /* width: 21.1%; */ + /* height: 1.4vw; */ + z-index: 9999; + padding: .1vw; + display: block; + background: rgba(40, 229, 233, .5); + margin-right: 2.5%; + line-height: 1.4vw; + text-align: center; + cursor: pointer; + color: #fff; + /* border-bottom: .02vw solid rgb(40, 229, 233); */ +} +.boxLis li:nth-child(4) { + margin-right: 0; +} + +.boxLis > .active { + /* border-bottom: none; */ + /* color: #0EFCFF; */ + border-top: .02vw solid rgb(40, 229, 233); + border-right: .02vw solid rgb(40, 229, 233); + border-left: .02vw solid rgb(40, 229, 233); +} +.equipment_pic { + position: relative; + width: 100%; + height: 100%; + background-color: #031426; +} +.equipment_pic img { + position: absolute; + margin: auto; + left: 0; + top: 0; + right: 0; + bottom: 0; + width: 80%; + height: 80%; +} +.equipment_pic img:nth-child(2) { + display: none; + width: 45%; + height: 50%; +} +.equipment_pic img:nth-child(3) { + width: 40%; + height: 65%; + display: none; +} +.equipment_pic img:nth-child(4) { + width: 50%; + height: 70%; + display: none; +} + +.liSpan { + width: 100%; + margin-top: -1%; + text-align: center; + color: rgb(40, 229, 233); +} + +.liP { + width: 66%; + margin: .3vw auto 0; + text-align: left; + font-size: .6vw; + color: rgb(40, 229, 233); +} +/* 硬件设备展示over--- */ + + +/* 灌溉数据start--- */ +.irrigate_data { + width: 100%; + height: 100%; + /* background-color: yellow; */ +} +.centerList { + /* float: left; */ + display: inline-block; + width: 37%; + height: 80%; + padding-top: 3%; + text-align: center; + margin-left: 2vw; +} + +.centerListFont { + font-size: .8vw; + color: #0EFCFF; +} + +.centerListNum { + font-size: 1.5vw; + color: white; + margin-top: .2vw; +} +.irrigate_bottom { + width: 100%; + height: 40%; + margin-top: .2vw; +} +.irrigate_bottom .every_line { + width: 90%; + height: 25%; + margin-left: 5%; + margin-bottom: 3%; + background-color: rgba(14,252,255,.2); +} +.every_line span { + color: #0EFCFF; + margin-left: .7vw; + font-size: .7vw; +} +.every_line i { + color: #fff; + float: right; + margin-right: .8vw; + font-size: 1vw; +} +/* 灌溉数据over--- */ + +/* 数据日志start--- */ +.data_day { + width: 100%; + height: 100%; + font-size: .55vw; + color: #fff; +} +.data_day table { + height: 10%; + width: 100%; + overflow:hidden; + text-align: center; + margin: auto; + margin-left: 5%; +} +.data_day .head { + color: #0EFCFF; +} +.data_day ul { + display: inline-block; +} + +/* 数据日志over--- */ + + + +/* 右侧三个内容框over--- */ + + + + + diff --git a/static/css/production.css b/static/css/production.css new file mode 100644 index 0000000..f303ebb --- /dev/null +++ b/static/css/production.css @@ -0,0 +1,467 @@ +.main .unit { + position: absolute; + z-index: 999; + top: 3%; + left: 13%; + font-size: 0.8vw; + color: #fff; +} +.dataGet, .dataStorage, .gettingDate, .dataClean, .dataSave, .dataDesensitization{ + width: 100%; + height: 100%; + /* border: 1px solid #ccc; */ + /* padding: 10px; */ + background-color: #080F1F; + + color: #383F4E; + /* margin-top: -10%; */ +} +.gettingDate{ + margin-top: -10%; +} +.textBox { + width: 100%; +} +.textBox .pieText, .pieText2 { + position: absolute; + color: #fff; + font-size: 0.8vw; + left: 18%; + /* margin-top: 20%; */ +} +.textBox .pieText { + top: 50%; +} +.textBox .pieText2 { + top: 95%; +} +.textBox .pieText .text2 { + margin-left: 3.6vw; +} +.textBox .pieText2 .text2 { + margin-left: 3.2vw; +} + +/* 我的 */ +.service{ + overflow: hidden; + margin: .6vw 1vw; + padding: .3vw 0; + border-bottom: 1px solid #0efcff; +} +.service span{ + font-size: .6vw; + color: #0efcff; +} +.service-data{ + overflow: hidden; + margin: .6vw 1vw; + padding: .3vw 0; +} +.service-data .img-box{ + width: 4vw; + height: 4vw; +} +.service-data .img-box img{ + width: 100%; + height: 100%; +} +.service-data span{ + font-size: .6vw; + color: #0efcff; + margin-top: .2vw; +} +.service-data .right ul li{ + float: right; + color: #0efcff; + font-size: .6vw; + padding: .4vw; + text-align: center; +} +.service-data .right ul li p{ + margin: .5vw 0; +} +.boxMain { + position: relative; + width: 100%; + height: 60%; + /* background: red; */ +} + +.centerdiv { + width: 100%; + height: 32.46%; + position: relative; + border: 1px solid #6176AF; + background: rgba(11, 21, 44, 0.60); + border-radius: 5px; +} + +.boxend { + position: relative; + width: 100%; + height: 32.3%; + border: 1px solid #6176AF; + background: rgba(11, 21, 44, 0.60); + border-radius: 5px; +} + +.bottoms { + margin-bottom: 1.5%; +} + +.boxRader { + position: relative; + width: 100%; + height: 80%; +} + + + +/* 生长图片 */ +.boxImg { + width: 85%; + height: 80%; + display: -webkit-flex; + -webkit-flex-direction: row; + -webkit-justify-content: space-between; + -webkit-flex-wrap: wrap; + margin: 3% auto 0; +} + +.boxImg img,.boxImg video { + width: 48%; + height: 5vw; +} + +/* 加工详情 */ +.processing { + width: 90%; + margin: 1% 0 0 8.2%; + font-size: .8vw; + color: #fff; + +} + +.processing>p { + line-height: 1.6vw; +} + +.processing>p>span:nth-child(1) { + color: #0efcff; +} + +/* 工程流程 */ +.boxSize { + width: 100%; + height: 60%; +} + +.boxContent { + width: 89%; + color: #fff; + display: -webkit-flex; + display: -moz-flex; + margin: 2% auto 2%; + font-size: .5vw; +} + +.boxContent>div>p { + line-height: 1vw; + +} + +.boxContent>div>p>span:nth-child(1) { + color: #0efcff; +} + +.boxSizestxt { + color: #fff; + font-size: .5vw; + width: 89%; + margin: auto; +} + +.centerBoxTop { + width: 100%; + height: 10%; + border: .01vw solid #0efcff; +} + + +.colors { + color: #0efcff; +} + +.colorccc { + color: #ccc; +} + +.name { + color: rgb(40, 229, 233); +} + +.clear { + clear: both; +} + +.left { + float: left; +} + +.leftBox_left { + float: left; + width: 49%; + height: 100%; +} + +.boxNums { + width: 90%; + margin: 3% auto 0; +} + +.boxNums ul { + width: 100%; + display: -webkit-flex; + -webkit-flex-direction: row; + -webkit-flex-wrap: wrap; + -webkit-justify-content: space-between; +} + +.boxNumstit { + width: 32%; + height: .8vw; + text-align: center; + line-height: .8vw; + background: #0D8891; + color: #fff; + font-size: .5vw; +} + +.boxNums ul>li { + width: 50%; + margin-bottom: 4%; +} + +.type>img { + width: 25%; + height: 25%; + margin: 10% 10% 0 0; +} + +.type { + width: 100%; + display: -webkit-flex; + color: #fff; +} +.typeSpan{ + font-size: .57vw; +} +.typeSpan>p>span:nth-child(1) { + color: #0efcff; + line-height: 1.1vw; +} + +/* 品种拆分 */ +.varieties { + width: 95%; + height: 90%; + margin: 2% auto 0; +} + +.varieties ul { + width: 100%; + display: -webkit-flex; + -webkit-align-items: center; + -webkit-justify-content:space-around; +} + +.varietiesBotm { + margin-top: 2% +} + +.varietiesBotm>li { + width: 20%; + color: #fff; + text-align: center; + margin-right: 2%; + font-size: .8vw; +} + +.varietiesBotm>li>p { + line-height: 1.1vw; +} + +.varietiesBotm>li>p:nth-child(1) { + color: #0DFCFC; +} + +.boxLis { + width: 13%; + height: 1.8vw; + color: #fff; + text-align: center; + line-height: 1.8vw; + margin: 0 4.5% 0 2.5%; +} +.varietiesUl{ + position: relative; + font-size: .8vw; +} +.boxLisimg{ + position: absolute; + top:.3vw; + right:0vw; + width: 1.3vw; + height: 1.1vw; +} +.boxLis:hover{ + background: #0B7180; + cursor: pointer; +} +.active { + background: #0B7180; + cursor: pointer; +} + +.solid { + width: .08vw; + height: .8vw; + background: #0efcff; +} + +.boxRadius { + width: 3.5vw; + height: 3.5vw; + text-align: center; + line-height: 4vw; + display: -webkit-flex; + -webkit-flex-direction: column; + -webkit-justify-content:center; + align-items: center; + font-size: .9vw; + border: .15vw solid #F98001; + border-radius: 50%; + margin: .5vw auto 0; +} +.boxRadius img{ + width: 30%; + height: 30%; + margin-bottom: .4vw; + +} +.boxRadius span{ + width: 100%; + height: 1vw; + line-height: 1vw; +} + + /* ========= 地图样式 stat========== */ +.map{ + position: relative; + width: 100%; + height: 84%; + /* margin-top: -3%; */ + /* background: white; */ +} +.map img { + width: 70%; + margin-left: 15%; +} +.mapActived1 { + position: absolute; + top: 41.5%; + left: 44%; + width: 1.3vw; + height: 2.1vw; + border-radius: 90px; + cursor: pointer; +} +.mapActived2 { + position: absolute; + top: 52%; + right: 25%; + width: 3vw; + height: 1.5vw; + cursor: pointer; + border-radius: 90px; +} +.mapActived3 { + position: absolute; + top: 43%; + right: 18%; + width: 1.5vw; + height: 4vw; + cursor: pointer; + border-radius: 90px; +} +.mapContent1 { + position: absolute; + height: 6vw; + width: 8vw; + left: 48%; + top: -14%; + background: rgba(255,255,255, .2); +} +.mapContent2 { + position: absolute; + height: 6vw; + width: 8vw; + left: 68%; + top: -2%; + background: rgba(255,255,255, .2); + display: none; +} +.mapContent3 { + position: absolute; + height: 6vw; + width: 8vw; + right: 19%; + top: 0%; + background: rgba(255,255,255, .2); + display: none; +} +.mapContentFont { + width: 85%; + margin-left: 12%; + font-size: .6vw; + color: #0efcff; + letter-spacing: .05vw; +} +.mapContentFont:nth-child(1) { + margin-top: .6vw; +} +.mapContentFont span { + color: white; +} +/* ========= 地图样式 end========== */ +.mapAllNum { + position: absolute; + width: 50%; + left: 1vw; + top: 2.5vw; + color: #0efcff; + font-size: .8vw; + letter-spacing: .05vw; +} +.mapAllNum span { + color: white; + font-size: 1.1vw; +} +.mapfooterFont { + position: absolute; + right: 0vw; + top: 0.7vw; + width: 7vw; + height: 3vw; + color: #0efcff; + font-size: .7vw; +} +.mapfooterFont img { + width: 50%; + padding-top: .3vw; + margin: 0 .3vw; +} +.mapFootertitle { + margin-bottom: .3vw; +} diff --git a/static/css/production_testing.css b/static/css/production_testing.css new file mode 100644 index 0000000..12656c4 --- /dev/null +++ b/static/css/production_testing.css @@ -0,0 +1,378 @@ + +/**/ +/**/ + /**/ + +.clear{ + clear: both; +} +.flexLeft{ + float: left; +} +.flexRight{ + float: right; +} +.boxMain { + position: relative; + width: 100%; + height: 60%; + /* background: red; */ +} + +.boxRader { + position: relative; + width: 100%; + height: 80%; +} + +.centerBox { + background: transparent; +} + +.centerBoxTop { + width: 100%; + height: 10%; + border: .01vw solid #0efcff; +} + +.centerBoxTop ul { + width: 100%; + height: 100%; + display: -webkit-flex; + -webkit-justify-content: space-around; + -webkit-align-items: center; + color: #fff; +} + +.centerBoxTop ul>li { + text-align: center; +} + +.p-li { + color: #0efcff; + font-size: 1.4vw; +} + +.boxBottomtxt { + width: 80%; + margin: 4% auto 0; + padding-left: 4%; + color: rgb(40, 229, 233); + +} + +.boxBottomtxt>span { + float: left; + margin-right: 5%; + text-align: center; +} + +.boxFont { + font-size: 1.2vw; +} + +.boxDatelis { + width: 100%; + height: 80%; + display: -webkit-flex; + -webkit-justify-content: center; + font-size: .6vw; +} + +.boxDatelis ul { + width: 50%; + margin-left:5%; +} + +.boxDatelis ul>li { + line-height: 1.5vw; + text-align: left; +} +/* 种植 */ +.plant{ + width: 90%; + height: 4vw; + margin:8% auto 0; + display: -webkit-flex; + display: -moz-flex; + -webkit-justify-content:space-between; + -moz-justify-content:space-between; + + + +} +.plant li{ + width: 30%; + height: 100%; + background: rgba(0,235,255,.08); + display: -webkit-flex; + -webkit-flex-direction: column; + -webkit-justify-content: space-between; + display: -moz-flex; + -moz-flex-direction: column; + -moz-justify-content: space-between; +} +.plant li>div>img{ + width: .88vw; + height: .88vw; + float: right; +} +.plant li>p{ + width: 80%; + font-size: 1.5vw; + color: #fff; + margin: auto; + +} +.plant li>div{ + width: 80%; + margin:5% auto 0; +} +.plant li>div>span{ + font-size: .8vw; + color: #0efcff; + float: left; +} + +.spans1{ + width: 80%; + height: 8%; + background: #0efcff; +} +.spans2{ + width: 80%; + height: 8%; + background: #FE0000; +} +.spans3{ + width: 80%; + height: 8%; + background: #F78001; +} +/* 地图 */ +.map{ + position: relative; + width: 100%; + height:38%; + margin-top:8% +} +/* 种植地数量 */ +.plantDdetails{ + width: 85%; + height: 25%; + margin: 8% auto 0; +} +.plantDdetails li{ + margin-bottom: 10%; +} + +.plantNum{ + width: 7.97vw; + height: 2.5vw; + text-align: center; + position: relative; + font-size: 1vw; + color: #01FEFE; +} +.plantNum span{ + line-height: 2.5vw; +} +.plantNum img{ + width: 100%; + height: 100%; + position: absolute; + top:0; + left: 0; +} +.plantNumbers{ + display: -webkit-flex; + display: -moz-flex; + margin-left:5%; +} +.plantNumbers>li{ + width:2.2vw; + height: 2.2vw; + position: relative; + font-size: 1.5vw; + color: #01FEFE; + text-align: center; + margin-right:2%; +} +.plantNumbers>li>span{ + line-height: 100%; +} +.plantNumbers>li img{ + width: 100%; + height: 100%; + position: absolute; + top:0; + left:0; +} +.ptlanTxts{ + font-size: 1.2vw; + color: #01FEFE; + margin-left:2%; +} +.boximgs { + width: 90%; + height: 80%; + margin: 1vw auto 0; + display: -webkit-flex; + display: -o-flex; + -webkit-flex-direction: row; + -webkit-flex-wrap: wrap; + -webkit-justify-content: space-between; + -o-flex-direction: row; + -o-flex-wrap: wrap; + -o-justify-content: space-between; +} + +.boximgs img { + width: 47%; + height: 42%; + +} + +.boxVideo { + width: 90%; + height: 60%; + margin: 1vw auto 0; +} + +.boxVideo video { + width: 100%; + height: 100%; + +} + +.colors { + color: rgb(40, 229, 233); +} + +.colorccc { + color: #ccc; +} + +.name { + color: rgb(40, 229, 233); +} + +.left { + float: left; +} + +.boxFont { + font-size: 1.2VW; +} + +.leftBox_left { + float: left; + width: 49%; + height: 100%; +} + +.leftBox_left:nth-child(2) { + margin-left: 2%; +} + +.leftBox_left .baseBox { + width: 100%; +} + +.leftBox_left .baseBox:nth-child(2) { + margin: 3% 0; +} + +.FiveBox { + height: 49.1%; +} + +.boxTable { + width: 95%; + color: #0efcff; + font-size: .6vw; + text-align: center; + margin: auto; + table-layout: fixed; + margin-top: 1vw; +} + +.boxTable tr { + height: 2.2vw; +} + +.boxTable tr:nth-child(1) { + font-size: .8vw; +} + +/* .boxTable tr:nth-child(1) td:nth-child(1) { + text-align: center; + width: 60%; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} */ +.boxTable tr td:nth-child(1) { + text-align: left; + width: 60%; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + padding-left:1vw; +} + +.boxTable tr td:nth-child(2) { + width: 20%; +} + +.boxTable tr td:nth-child(3) { + width: 20%; +} + +.boxTable2 tr td:nth-child(1) { + text-align: left; + width: 50%; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.boxTable2 tr td:nth-child(3) { + width: 30%; + color: #e98732; +} + + +#all { + margin-top: 6%; + width: 100%; +} + +li { + list-style-type: none; +} + +.amount { + /* float: left; */ + position: absolute; + left: 40%; +} +/* 数字滚动 */ +#all .t_num i { + /* float: left; */ + width: 2.2vw; + height: 2.2vw; + display: inline-block; + background: url(./img/number3.png) no-repeat; + /* background-size: 33px 47px; */ + background-position: 0 0; +} + +#all .unit { + position: relative; + top: 20%; + left: 61%; + /* vertical-align: baseline; */ + color: #11F9FD; + font-size: 1.2vw; +} diff --git a/static/css/reset.css b/static/css/reset.css new file mode 100644 index 0000000..84b855b --- /dev/null +++ b/static/css/reset.css @@ -0,0 +1,396 @@ +html, +body, +div, +h1, +h2, +h3, +h4, +h5, +h6, +p, +dl, +dt, +dd, +ol, +ul, +li, +fieldset, +form, +label, +input, +legend, +table, +caption, +tbody, +tfoot, +thead, +tr, +th, +td, +textarea, +article, +aside, +audio, +canvas, +figure, +footer, +header, +mark, +menu, +nav, +section, +time, +video { + margin : 0; + padding: 0; +} + +h1, +h2, +h3, +h4, +h5, +h6 { + font-size : 100%; + font-weight: normal +} + +article, +aside, +dialog, +figure, +footer, +header, +hgroup, +nav, +section, +blockquote { + display: block; +} + +ul, +ol { + list-style: none; +} + +img { + border : 0 none; + vertical-align: top; +} + +blockquote, +q { + quotes: none; +} + +blockquote:before, +blockquote:after, +q:before, +q:after { + content: none; +} + +table { + border-collapse: collapse; + border-spacing : 0; +} + +strong, +em, +i { + font-style : normal; + font-weight: normal; +} + +ins { + text-decoration: underline; +} + +del { + text-decoration: line-through; +} + +mark { + background: none; +} + +input::-ms-clear { + display: none !important; +} + +body { + font : 12px/1.5 \5FAE\8F6F\96C5\9ED1, \5B8B\4F53, "Hiragino Sans GB", STHeiti, "WenQuanYi Micro Hei", "Droid Sans Fallback", SimSun, sans-serif; + background: #fff; +} + +a { + text-decoration: none; + color : #333; +} + +a:hover { + text-decoration: underline; +} + +body, +html, +.main { + width : 100%; + height: 100%; + font-size: 12px; +} + +.main { + position : relative; + background-repeat: no-repeat; + background-size: cover; + background-color: #1D2B56; +} + +.nav { + position : relative; + top : .5vw; + width : 100%; + height : 5vw; + background : url(../img/top.png) no-repeat; + background-size: 100%; + text-align : center; + line-height : 4vw; + color : #0efcff; + font-size : 1.4vw; + letter-spacing : .4vw; +} + +.nav_btn { + position: absolute; + top : 1.5vw; + width : 100%; + height : 2vw; +} + +.btn_left { + float : left; + width : 25%; + margin-left: 5%; + height : 100%; +} + +.btn_right { + float : right; + width : 25%; + margin-right: 5%; + height : 100%; +} + +.btn_list { + position : relative; + float : left; + width : 5.5vw; + height : 1.6vw; + text-align : center; + line-height : 1.6vw; + font-size : .9vw; + color : #0efcff; + letter-spacing: .1vw; + cursor : pointer; +} + +.btn_left a, +.btn_right a { + display: inline-block; +} + +.btn_left a:nth-child(2) { + margin: 0 .6vw; +} + +.btn_left a:nth-child(4) { + margin-left: .6vw; +} + +.btn_right a:nth-child(2) { + margin: 0 .6vw; +} + +.btn_right a:nth-child(4) { + margin-left: .6vw; +} + +.btn_list:before { + content : ''; + position : absolute; + top : 0; + right : 0; + bottom : 0; + left : 0; + border : 1px solid #6176AF; + transform: skewX(-38deg); +} + +.btn_list:hover::before { + border-color: #0efcff; + box-shadow : 1px 1px 3px 1px #0efcff inset; +} + +.listActive:before { + border-color: #0efcff; + box-shadow : 1px 1px 3px 1px #0efcff inset; +} + +.content { + position : relative; + width : 97%; + height : 87%; + margin : auto; + /* background: white; */ +} + +.baseBox { + position : relative; + float : left; + width : 48.8%; + height : 32.3%; + border : 1px solid #6176AF; + background : rgba(11, 21, 44, 0.60); + border-radius: 5px; +} + +.baseHeightBox { + height: 100%; +} + +.baseCenterBox { + margin: 1.5% 0; +} + +.leftBox { + float : left; + height : 100%; + width : 34.5%; + /* background: yellow; */ +} + +.rightBox { + float : left; + height: 100%; + width : 34.5%; +} + +.centerBox { + position : relative; + float : left; + width : 30%; + height : 100%; + margin : 0 .5%; + /* background: red; */ +} + +.marginLeft { + margin-left: 1.5%; +} + +/* 边框描边 */ +.leftTopLine1 { + position : absolute; + top : 0; + left : -1px; + height : 1vw; + width : 2px; + background: #0efcff; +} + +.leftTopLine2 { + position : absolute; + top : -1px; + left : 0; + height : 2px; + width : 1vw; + background: #0efcff; +} + +.rightTopLine1 { + position : absolute; + top : 0; + right : -1px; + height : 1vw; + width : 2px; + background: #0efcff; +} + +.rightTopLine2 { + position : absolute; + top : -1px; + right : 0; + height : 2px; + width : 1vw; + background: #0efcff; +} + +.leftBottomLine1 { + position : absolute; + bottom : 0; + left : -1px; + height : 1vw; + width : 2px; + background: #0efcff; +} + +.leftBottomLine2 { + position : absolute; + bottom : -1px; + left : 0; + height : 2px; + width : 1vw; + background: #0efcff; +} + +.rightBottomLine1 { + position : absolute; + bottom : 0; + right : -1px; + height : 1vw; + width : 2px; + background: #0efcff; +} + +.rightBottomLine2 { + position : absolute; + bottom : -1px; + right : 0; + height : 2px; + width : 1vw; + background: #0efcff; +} + +.boxTitle { + font-size : 1vw; + color : #0efcff; + width : 80%; + margin-left: .8vw; + margin-top : .5vw; +} + +.left { + float: left; +} + +.right { + font: right; +} +/*// */ +/*// */ +/*// */ + +/* 视频新加 */ +.video-js .vjs-control { + width: 1vw !important; +} +.vjs-volume-panel { + display: none !important; +} +.vjs-live-display { + display: none !important; +} +.vjs-audio-button{ + display: none !important; +} diff --git a/static/element.css b/static/element.css new file mode 100644 index 0000000..eddb3a1 --- /dev/null +++ b/static/element.css @@ -0,0 +1 @@ +@charset "UTF-8";.el-pagination--small .arrow.disabled,.el-table .el-table__cell.is-hidden>*,.el-table .hidden-columns,.el-table--hidden{visibility:hidden}.el-dropdown .el-dropdown-selfdefine:focus:active,.el-dropdown 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Iterator",k="values"==v,S=!1,M=e.prototype,$=M[h]||M["@@iterator"]||v&&M[v],E=$||x(v),D=v?k?x("entries"):E:void 0,T="Array"==t?M.entries||$:$;if(T&&(C=d(T.call(new e)))!==Object.prototype&&(c(C,w,!0),n||a(C,h)||o(C,h,p)),k&&$&&"values"!==$.name&&(S=!0,E=function(){return $.call(this)}),n&&!b||!f&&!S&&M[h]||o(M,h,E),l[t]=E,l[w]=p,v)if(y={values:k?E:x("values"),keys:g?E:x("keys"),entries:D},b)for(_ in y)_ in M||r(M,_,y[_]);else s(s.P+s.F*(f||S),t,y);return y}},function(e,t,i){e.exports=i(22)},function(e,t,i){var n=i(36),s=i(299),r=i(57),o=i(55)("IE_PROTO"),a=function(){},l=function(){var e,t=i(79)("iframe"),n=r.length;for(t.style.display="none",i(300).appendChild(t),t.src="javascript:",e=t.contentWindow.document,e.open(),e.write(" + + + 公共安全技术研究中心算法集 + + + + +
+

算法配置

+
+ + + +
+ +
+ + + +
+ +
+ + + +
+ +
+ + +
+

输入源选择

+
+ + + + +
+ +
+ + +
+
+ + + +
+
+ + +
+ +
+ + + diff --git a/templates/index.html b/templates/index.html new file mode 100644 index 0000000..54f4733 --- /dev/null +++ b/templates/index.html @@ -0,0 +1,527 @@ + + + + + + 公共安全技术研究中心算法集 + + + + + + + + + + + + + +
+
+
+
+ +
+ +
上传图片/视频
+
+
+ +
+ + +
+ + + + + + + + + + + + + + +
+
+
+ + + +
+
+ +
+
+ +
摄像机选择
+
+
+ + + + + + + + + + +
+
+
+ + + + + + +
+
+
+
+ +
+ + + + 算法-算法展示 + + + 管理系统 + web网页 +
+ + + + +
+ +
+ + +
+
+ + + +
+ + + + +
+
+ +
算法描述
+
+ + + +
+ +
+
+ +
检测到的结果为
+ +
+ + + +
+ +
+
+ +
+
+ + + +
+ + + + + + + + + + + + + + + diff --git a/templates/index1.html b/templates/index1.html new file mode 100644 index 0000000..e41eecc --- /dev/null +++ b/templates/index1.html @@ -0,0 +1,528 @@ + + + + + + 公共安全技术研究中心算法集 + + + + + + + + + + + + + +
+
+
+
+ +
+ +
输入源选择
+
+
+ +
+ +
+ 模式一 +
+
+ + + + + + + + + + 上传 + + + +
+
+ +
+
+ 模式二 +
+ + + 打开相机RTSP + + +
+ +
+
+ +
+
+ +
摄像机选择
+
+
+ + + + + + + + + + +
+
+
+
+
+ +
+ + + + 算法-算法展示 + + + 管理系统 + web网页 +
+ + + + +
+ +
+ + +
+
+ + + +
+ + + + +
+
+ +
算法描述
+
+
+ + +
+
+
+
+ +
检测到的结果为
+
+ + + +
+ +
+
+ +
+
+ + + +
+
+ + + + + + + + + + + + + + diff --git a/templates/index1226.html b/templates/index1226.html new file mode 100644 index 0000000..0785523 --- /dev/null +++ b/templates/index1226.html @@ -0,0 +1,518 @@ + + + + + + + + 公共安全技术研究中心算法集 + + + + + +
+

算法配置

+
+ + + +
+ +
+ + + + +
+ +
+ + + +
+ +
+ +
+

输入源选择

+
+ + + + +
+
+ + +
+ +
+ + + +
+ +
+
+ + +
+ +
+ +
+
+ 检到结果： + +
+ + +
+ + + + + + diff --git a/templates/index240103.html b/templates/index240103.html new file mode 100644 index 0000000..1b9e933 --- /dev/null +++ b/templates/index240103.html @@ -0,0 +1,581 @@ + + + + + + + + 公共安全技术研究中心算法集 + + + +
+
+

算法配置

+
+ + + + + + 确定 +
+ +
+ + + + +
+ +
+ + + +
+ +
+ +
+

输入源选择

+
+ + + + + 上传 +
+
+ + + + 打开相机RTSP + + + + +
+ +
+
+ +
+
+ + +
+ + +
+ +
+ + +
+ 检测结果： + +
+ + + +
+ + +
+ + + + + + + + + diff --git a/tools/__pycache__/draw_chinese.cpython-38.pyc b/tools/__pycache__/draw_chinese.cpython-38.pyc new file mode 100644 index 0000000..c78b621 Binary files /dev/null and b/tools/__pycache__/draw_chinese.cpython-38.pyc differ diff --git a/tools/__pycache__/draw_chinese.cpython-39.pyc b/tools/__pycache__/draw_chinese.cpython-39.pyc new file mode 100644 index 0000000..4c163d2 Binary files /dev/null and b/tools/__pycache__/draw_chinese.cpython-39.pyc differ diff --git a/tools/draw_chinese.py b/tools/draw_chinese.py new file mode 100644 index 0000000..5409455 --- /dev/null +++ b/tools/draw_chinese.py @@ -0,0 +1,16 @@ +import cv2 +import numpy as np +from PIL import Image, ImageDraw, ImageFont + +def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20): + if (isinstance(img, np.ndarray)): # 判断是否OpenCV图片类型 + img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + # 创建一个可以在给定图像上绘图的对象 + draw = ImageDraw.Draw(img) + # 字体的格式 + fontStyle = ImageFont.truetype( + "simsun.ttc", textSize, encoding="utf-8") + # 绘制文本 + draw.text((left, top), text, textColor, font=fontStyle) + # 转换回OpenCV格式 + return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) diff --git a/voc_to_coco.py b/voc_to_coco.py new file mode 100644 index 0000000..8c980cb --- /dev/null +++ b/voc_to_coco.py @@ -0,0 +1,161 @@ + +# import random +# import os + +# trainval_percent = 0.9 +# train_percent = 0.9 +# xmlfilepath = '/home/ykn/dataset/PCB_DATASET/Annotationss' +# txtsavepath = '/home/ykn/dataset/PCB_DATASET/ImageSets' +# total_xml = os.listdir(xmlfilepath) + +# num = len(total_xml) +# list = range(num) +# tv = int(num * trainval_percent) +# tr = int(tv * train_percent) +# trainval = random.sample(list, tv) +# train = random.sample(trainval, tr) + +# ftrainval = open(txtsavepath+'/trainval.txt', 'w') +# ftest = open(txtsavepath+'/test.txt', 'w') +# ftrain = open(txtsavepath+'/train.txt', 'w') +# fval = open(txtsavepath+'/val.txt', 'w') + +# for i in list: +# name = total_xml[i][:-4] + '\n' +# if i in trainval: +# ftrainval.write(name) +# if i in train: +# ftrain.write(name) +# else: +# fval.write(name) +# else: +# ftest.write(name) + +# ftrainval.close() +# ftrain.close() +# fval.close() +# ftest.close() + + +# xml解析包 + +import xml.etree.ElementTree as ET +import pickle +import os + +# os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表 + +from os import listdir, getcwd +from os.path import join + + +sets = ['train', 'test', 'val'] +classes = ['missing_hole', 'mouse_bite', 'open_circuit', 'short', 'spur', 'spurious_copper'] +label_path = '/home/ykn/dataset/PCB_DATASET/labels' +ImageSets = '/home/ykn/dataset/PCB_DATASET/ImageSets' +images = '/home/ykn/dataset/PCB_DATASET/images' + + +# 进行归一化操作 + +def convert(size, box): # size:(原图w,原图h) , box:(xmin,xmax,ymin,ymax) + dw = 1./size[0] # 1/w + dh = 1./size[1] # 1/h + x = (box[0] + box[1])/2.0 # 物体在图中的中心点x坐标 + y = (box[2] + box[3])/2.0 # 物体在图中的中心点y坐标 + w = box[1] - box[0] # 物体实际像素宽度 + h = box[3] - box[2] # 物体实际像素高度 + x = x*dw # 物体中心点x的坐标比(相当于 x/原图w) + w = w*dw # 物体宽度的宽度比(相当于 w/原图w) + y = y*dh # 物体中心点y的坐标比(相当于 y/原图h) + h = h*dh # 物体宽度的宽度比(相当于 h/原图h) + return (x, y, w, h) # 返回 相对于原图的物体中心点的x坐标比,y坐标比,宽度比,高度比,取值范围[0-1] + + +# year ='2012', 对应图片的id（文件名） + +def convert_annotation(image_id): + ''' + 将对应文件名的xml文件转化为label文件，xml文件包含了对应的bunding框以及图片长款大小等信息， + 通过对其解析，然后进行归一化最终读到label文件中去，也就是说 + 一张图片文件对应一个xml文件，然后通过解析和归一化，能够将对应的信息保存到唯一一个label文件中去 + labal文件中的格式：calss x y w h　　同时，一张图片对应的类别有多个，所以对应的ｂｕｎｄｉｎｇ的信息也有多个 + ''' + # 对应的通过year 找到相应的文件夹，并且打开相应image_id的xml文件，其对应bund文件 + in_file = open('/home/ykn/dataset/PCB_DATASET/Annotations /%s.xml' % (image_id), encoding='utf-8') + # 准备在对应的image_id 中写入对应的label，分别为 + # + out_file = open(label_path+'/%s.txt' % (image_id), 'w', encoding='utf-8') + # 解析xml文件 + tree = ET.parse(in_file) + # 获得对应的键值对 + root = tree.getroot() + # 获得图片的尺寸大小 + size = root.find('size') + # 如果xml内的标记为空，增加判断条件 + if size != None: + # 获得宽 + w = int(size.find('width').text) + # 获得高 + h = int(size.find('height').text) + # 遍历目标obj + for obj in root.iter('object'): + # 获得difficult ？？ + difficult = obj.find('difficult').text + # 获得类别 =string 类型 + cls = obj.find('name').text + # 如果类别不是对应在我们预定好的class文件中，或difficult==1则跳过 + if cls not in classes or int(difficult) == 1: + continue + # 通过类别名称找到id + cls_id = classes.index(cls) + # 找到bndbox 对象 + xmlbox = obj.find('bndbox') + # 获取对应的bndbox的数组 = ['xmin','xmax','ymin','ymax'] + b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), + float(xmlbox.find('ymax').text)) + print(image_id, cls, b) + # 带入进行归一化操作 + # w = 宽, h = 高， b= bndbox的数组 = ['xmin','xmax','ymin','ymax'] + bb = convert((w, h), b) + # bb 对应的是归一化后的(x,y,w,h) + # 生成 calss x y w h 在label文件中 + out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') + + +# 返回当前工作目录 + +wd = getcwd() +print(wd) + + +for image_set in sets: + ''' + 对所有的文件数据集进行遍历 + 做了两个工作： +　　　　１．将所有图片文件都遍历一遍，并且将其所有的全路径都写在对应的txt文件中去，方便定位 +　　　　２．同时对所有的图片文件进行解析和转化，将其对应的bundingbox 以及类别的信息全部解析写到label 文件中去 + 　　　　　最后再通过直接读取文件，就能找到对应的label 信息 + ''' + # 先找labels文件夹如果不存在则创建 + if not os.path.exists(label_path): + os.makedirs(label_path+'/') + # 读取在ImageSets/Main 中的train、test..等文件的内容 + # 包含对应的文件名称 + image_ids = open(ImageSets+'/%s.txt' % (image_set)).read().strip().split() + # 打开对应的2012_train.txt 文件对其进行写入准备 + list_file = open('/home/ykn/dataset/PCB_DATASET/%s.txt' % (image_set), 'w') + # 将对应的文件_id以及全路径写进去并换行 + for image_id in image_ids: + list_file.write(images+'/%s.jpg\n' % (image_id)) + # 调用 year = 年份 image_id = 对应的文件名_id + convert_annotation(image_id) + # 关闭文件 + list_file.close() + +# os.system(‘comand’) 会执行括号中的命令，如果命令成功执行，这条语句返回0，否则返回1 + +# os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt") + +# os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt") +