233 lines
13 KiB
Python
233 lines
13 KiB
Python
import torch
|
||
import torch.nn as nn
|
||
from torchvision.ops import nms
|
||
import numpy as np
|
||
|
||
|
||
class DecodeBox():
|
||
def __init__(self, anchors, num_classes, input_shape, anchors_mask=[[6, 7, 8], [3, 4, 5], [0, 1, 2]]):
|
||
super(DecodeBox, self).__init__()
|
||
self.anchors = anchors
|
||
self.num_classes = num_classes
|
||
self.bbox_attrs = 5 + num_classes
|
||
self.input_shape = input_shape
|
||
# -----------------------------------------------------------#
|
||
# 13x13的特征层对应的anchor是[116,90],[156,198],[373,326]
|
||
# 26x26的特征层对应的anchor是[30,61],[62,45],[59,119]
|
||
# 52x52的特征层对应的anchor是[10,13],[16,30],[33,23]
|
||
# -----------------------------------------------------------#
|
||
self.anchors_mask = anchors_mask
|
||
|
||
def decode_box(self, inputs):
|
||
outputs = []
|
||
for i, input in enumerate(inputs):
|
||
# -----------------------------------------------#
|
||
# 输入的input一共有三个，他们的shape分别是
|
||
# batch_size, 255, 13, 13
|
||
# batch_size, 255, 26, 26
|
||
# batch_size, 255, 52, 52
|
||
# -----------------------------------------------#
|
||
batch_size = input.size(0)
|
||
input_height = input.size(2)
|
||
input_width = input.size(3)
|
||
|
||
# -----------------------------------------------#
|
||
# 输入为416x416时
|
||
# stride_h = stride_w = 32、16、8
|
||
# -----------------------------------------------#
|
||
stride_h = self.input_shape[0] / input_height
|
||
stride_w = self.input_shape[1] / input_width
|
||
# -------------------------------------------------#
|
||
# 此时获得的scaled_anchors大小是相对于特征层的
|
||
# -------------------------------------------------#
|
||
scaled_anchors = [(anchor_width / stride_w, anchor_height / stride_h) for anchor_width, anchor_height in
|
||
self.anchors[self.anchors_mask[i]]]
|
||
|
||
# -----------------------------------------------#
|
||
# 输入的input一共有三个，他们的shape分别是
|
||
# batch_size, 3, 13, 13, 85
|
||
# batch_size, 3, 26, 26, 85
|
||
# batch_size, 3, 52, 52, 85
|
||
# -----------------------------------------------#
|
||
prediction = input.view(batch_size, len(self.anchors_mask[i]),
|
||
self.bbox_attrs, input_height, input_width).permute(0, 1, 3, 4, 2).contiguous()
|
||
# 调整为 1，3，13，13，25 的形状
|
||
# -----------------------------------------------#
|
||
# 先验框的中心位置的调整参数
|
||
# -----------------------------------------------#
|
||
x = torch.sigmoid(prediction[..., 0])
|
||
y = torch.sigmoid(prediction[..., 1])
|
||
# -----------------------------------------------#
|
||
# 先验框的宽高调整参数
|
||
# -----------------------------------------------#
|
||
w = prediction[..., 2]
|
||
h = prediction[..., 3]
|
||
# -----------------------------------------------#
|
||
# 获得置信度，是否有物体
|
||
# -----------------------------------------------#
|
||
conf = torch.sigmoid(prediction[..., 4])
|
||
# -----------------------------------------------#
|
||
# 种类置信度
|
||
# -----------------------------------------------#
|
||
pred_cls = torch.sigmoid(prediction[..., 5:])
|
||
|
||
FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
|
||
LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
|
||
|
||
# ----------------------------------------------------------#
|
||
# 生成网格，先验框中心，网格左上角
|
||
# batch_size,3,13,13
|
||
# ----------------------------------------------------------#
|
||
grid_x = torch.linspace(0, input_width - 1, input_width).repeat(input_height, 1).repeat(
|
||
batch_size * len(self.anchors_mask[i]), 1, 1).view(x.shape).type(FloatTensor)
|
||
grid_y = torch.linspace(0, input_height - 1, input_height).repeat(input_width, 1).t().repeat(
|
||
batch_size * len(self.anchors_mask[i]), 1, 1).view(y.shape).type(FloatTensor)
|
||
|
||
# ----------------------------------------------------------#
|
||
# 按照网格格式生成先验框的宽高
|
||
# batch_size,3,13,13
|
||
# ----------------------------------------------------------#
|
||
anchor_w = FloatTensor(scaled_anchors).index_select(1, LongTensor([0]))
|
||
anchor_h = FloatTensor(scaled_anchors).index_select(1, LongTensor([1]))
|
||
anchor_w = anchor_w.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(w.shape)
|
||
anchor_h = anchor_h.repeat(batch_size, 1).repeat(1, 1, input_height * input_width).view(h.shape)
|
||
|
||
# ----------------------------------------------------------#
|
||
# 利用预测结果对先验框进行调整
|
||
# 首先调整先验框的中心，从先验框中心向右下角偏移 # ？从先验框左上角向右下角偏移？
|
||
# 再调整先验框的宽高。
|
||
# ----------------------------------------------------------#
|
||
pred_boxes = FloatTensor(prediction[..., :4].shape)
|
||
pred_boxes[..., 0] = x.data + grid_x
|
||
pred_boxes[..., 1] = y.data + grid_y
|
||
pred_boxes[..., 2] = torch.exp(w.data) * anchor_w
|
||
pred_boxes[..., 3] = torch.exp(h.data) * anchor_h
|
||
|
||
# ----------------------------------------------------------#
|
||
# 将输出结果归一化成小数的形式
|
||
# ----------------------------------------------------------#
|
||
_scale = torch.Tensor([input_width, input_height, input_width, input_height]).type(FloatTensor)
|
||
output = torch.cat((pred_boxes.view(batch_size, -1, 4) / _scale,
|
||
conf.view(batch_size, -1, 1), pred_cls.view(batch_size, -1, self.num_classes)), -1)
|
||
# output的shape是 batch_size, -1, attr(25)
|
||
outputs.append(output.data)
|
||
return outputs
|
||
|
||
def yolo_correct_boxes(self, box_xy, box_wh, input_shape, image_shape, letterbox_image):
|
||
# -----------------------------------------------------------------#
|
||
# 把y轴放前面是因为方便预测框和图像的宽高进行相乘
|
||
# -----------------------------------------------------------------#
|
||
box_yx = box_xy[..., ::-1]
|
||
box_hw = box_wh[..., ::-1]
|
||
input_shape = np.array(input_shape)
|
||
image_shape = np.array(image_shape)
|
||
|
||
if letterbox_image:
|
||
# -----------------------------------------------------------------#
|
||
# 这里求出来的offset是图像有效区域相对于图像左上角的偏移情况
|
||
# new_shape指的是宽高缩放情况
|
||
# -----------------------------------------------------------------#
|
||
new_shape = np.round(image_shape * np.min(input_shape / image_shape))
|
||
offset = (input_shape - new_shape) / 2. / input_shape
|
||
scale = input_shape / new_shape
|
||
|
||
box_yx = (box_yx - offset) * scale
|
||
box_hw *= scale
|
||
|
||
box_mins = box_yx - (box_hw / 2.)
|
||
box_maxes = box_yx + (box_hw / 2.)
|
||
boxes = np.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]],
|
||
axis=-1)
|
||
boxes *= np.concatenate([image_shape, image_shape], axis=-1)
|
||
return boxes
|
||
|
||
def non_max_suppression(self, prediction, num_classes, input_shape, image_shape, letterbox_image, conf_thres=0.5,
|
||
nms_thres=0.4):
|
||
# ----------------------------------------------------------#
|
||
# 将预测结果的格式转换成左上角右下角的格式。
|
||
# prediction [batch_size, num_anchors, 85]
|
||
# ----------------------------------------------------------#
|
||
box_corner = prediction.new(prediction.shape)
|
||
box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
|
||
box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
|
||
box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
|
||
box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
|
||
prediction[:, :, :4] = box_corner[:, :, :4]
|
||
|
||
output = [None for _ in range(len(prediction))]
|
||
for i, image_pred in enumerate(prediction):
|
||
# ----------------------------------------------------------#
|
||
# 对种类预测部分取max。 # image_pred 是在prediction中以0维度迭代
|
||
# class_conf [num_anchors, 1] 种类置信度
|
||
# class_pred [num_anchors, 1] 种类 image_pred[:, 5:5 + num_classes] 是取出类别
|
||
# ----------------------------------------------------------#
|
||
class_conf, class_pred = torch.max(image_pred[:, 5:5 + num_classes], 1, keepdim=True)
|
||
|
||
# ----------------------------------------------------------#
|
||
# 利用置信度进行第一轮筛选
|
||
# ----------------------------------------------------------#
|
||
conf_mask = (image_pred[:, 4] * class_conf[:, 0] >= conf_thres).squeeze()
|
||
|
||
# ----------------------------------------------------------#
|
||
# 根据置信度进行预测结果的筛选
|
||
# ----------------------------------------------------------#
|
||
image_pred = image_pred[conf_mask]
|
||
class_conf = class_conf[conf_mask]
|
||
class_pred = class_pred[conf_mask]
|
||
if not image_pred.size(0):
|
||
continue # 如果没有剩下类别，就判断下一张图片
|
||
# -------------------------------------------------------------------------#
|
||
# detections [num_anchors, 7]
|
||
# 7的内容为：x1, y1, x2, y2, obj_conf, class_conf, class_pred
|
||
# -------------------------------------------------------------------------#
|
||
detections = torch.cat((image_pred[:, :5], class_conf.float(), class_pred.float()), 1)
|
||
|
||
# ------------------------------------------#
|
||
# 获得预测结果中包含的所有种类
|
||
# ------------------------------------------#
|
||
unique_labels = detections[:, -1].cpu().unique()
|
||
|
||
if prediction.is_cuda:
|
||
unique_labels = unique_labels.cuda()
|
||
detections = detections.cuda()
|
||
|
||
for c in unique_labels:
|
||
# ------------------------------------------#
|
||
# 获得某一类得分筛选后全部的预测结果
|
||
# ------------------------------------------#
|
||
detections_class = detections[detections[:, -1] == c]
|
||
|
||
# ------------------------------------------#
|
||
# 使用官方自带的非极大抑制会速度更快一些！
|
||
# ------------------------------------------#
|
||
keep = nms(
|
||
detections_class[:, :4],
|
||
detections_class[:, 4] * detections_class[:, 5],
|
||
nms_thres
|
||
)
|
||
max_detections = detections_class[keep]
|
||
|
||
# # 按照存在物体的置信度排序
|
||
# _, conf_sort_index = torch.sort(detections_class[:, 4]*detections_class[:, 5], descending=True)
|
||
# detections_class = detections_class[conf_sort_index]
|
||
# # 进行非极大抑制
|
||
# max_detections = []
|
||
# while detections_class.size(0):
|
||
# # 取出这一类置信度最高的，一步一步往下判断，判断重合程度是否大于nms_thres，如果是则去除掉
|
||
# max_detections.append(detections_class[0].unsqueeze(0))
|
||
# if len(detections_class) == 1:
|
||
# break
|
||
# ious = bbox_iou(max_detections[-1], detections_class[1:])
|
||
# detections_class = detections_class[1:][ious < nms_thres]
|
||
# # 堆叠
|
||
# max_detections = torch.cat(max_detections).data
|
||
|
||
# Add max detections to outputs
|
||
output[i] = max_detections if output[i] is None else torch.cat((output[i], max_detections))
|
||
|
||
if output[i] is not None:
|
||
output[i] = output[i].cpu().numpy()
|
||
box_xy, box_wh = (output[i][:, 0:2] + output[i][:, 2:4]) / 2, output[i][:, 2:4] - output[i][:, 0:2]
|
||
output[i][:, :4] = self.yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)
|
||
return output
|