first commit

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Lam1360
+
+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.

README.md

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+# YOLOv3-model-pruning
+
+用 YOLOv3 模型在一个开源的人手检测数据集 [oxford hand](http://www.robots.ox.ac.uk/~vgg/data/hands/) 上做人手检测，并在此基础上做模型剪枝。对于该数据集，对 YOLOv3 进行 channel pruning 之后，模型的参数量、模型大小减少 80% ，FLOPs 降低 70%，前向推断的速度可以达到原来的 200%，同时可以保持 mAP 基本不变。
+
+## 环境
+
+Python3.6, Pytorch 1.0及以上
+
+YOLOv3 的实现参考了 eriklindernoren 的 [PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) ，因此代码的依赖环境也可以参考其 repo
+
+## 数据集准备
+
+1. 下载[数据集](http://www.robots.ox.ac.uk/~vgg/data/hands/downloads/hand_dataset.tar.gz)，得到压缩文件
+2. 将压缩文件解压到 data 目录，得到 hand_dataset 文件夹
+3. 在 data 目录下执行 converter.py，生成 images、labels 文件夹和 train.txt、valid.txt 文件。训练集中一共有 4807 张图
+   片，测试集中一共有 821 张图片
+
+## 正常训练（Baseline）
+
+```bash
+python train.py --model_def config/yolov3-hand.cfg
+```
+
+## 剪枝算法介绍
+
+本代码基于论文 [Learning Efficient Convolutional Networks Through Network Slimming (ICCV 2017)](http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Learning_Efficient_Convolutional_ICCV_2017_paper.html) 进行改进实现的 channel pruning算法，类似的代码实现还有这个 [yolov3-network-slimming](https://github.com/talebolano/yolov3-network-slimming)。原始论文中的算法是针对分类模型的，基于 BN 层的 gamma 系数进行剪枝的。
+
+### 剪枝算法的大概步骤
+
+以下只是算法的大概步骤，具体实现过程中还要做 s 参数的尝试或者需要进行迭代式剪枝等。
+
+1. 进行稀疏化训练
+
+   ```bash
+   python train.py --model_def config/yolov3-hand.cfg -sr --s 0.01
+   ```
+
+2. 基于 test_prune.py 文件进行剪枝，得到剪枝后的模型
+
+3. 对剪枝后的模型进行微调
+   ```bash
+   python train.py --model_def config/prune_yolov3-hand.cfg -pre checkpoints/prune_yolov3_ckpt.pth
+   ```
+
+### 剪枝前后的对比
+
+1. 下图为对部分卷积层进行剪枝前后通道数的变化：
+
+   ![](https://raw.githubusercontent.com/Lam1360/md-image/master/img/20190628205342.png)
+   > 部分卷积层的通道数大幅度减少
+
+2. 剪枝前后指标对比：
+
+   |                | 参数数量 | 模型体积 |Flops | 前向推断耗时（2070 TI） |  mAP   |
+   | :------------: | :------:| :-----: | :---: | :-------------------: | :----: |
+   | Baseline (416) |  61.5M  | 246.4MB |32.8B  |         15.0 ms       | 0.7692 |
+   |  Prune (416)   |  10.9M  | 43.6MB  | 9.6B  |         7.7 ms        | 0.7722 |
+   | Finetune (416) |   同上   | 同上    | 同上  |          同上         | 0.7750 |
+   
+   > 加入稀疏正则项之后，mAP 反而更高了（在实验过程中发现，其实 mAP上下波动 0.02 是正常现象），因此可以认为稀疏训练得到的 mAP 与正常训练几乎一致。将 prune 后得到的模型进行 finetune 并没有明显的提升，因此剪枝三步可以直接简化成两步。剪枝前后模型的参数量、模型大小降为原来的 1/6 ，FLOPs 降为原来的 1/3，前向推断的速度可以达到原来的 2 倍，同时可以保持 mAP 基本不变。*需要明确的是，上面表格中剪枝的效果是只是针对该数据集的，不一定能保证在其他数据集上也有同样的效果*
+   
+3. 剪枝后模型的测试：
+
+   Prune 模型的权重已放在百度网盘上 （[提取码: gnzx](https://pan.baidu.com/s/13Ycj7JccBHWYF590bgFRxQ)），可以通过执行以下代码进行测试：
+   ```bash
+   python test.py --model_def config/prune_yolov3-hand.cfg --weights_path weights/prune_yolov3_ckpt.pth --data_config config/oxfordhand.data --class_path data/oxfordhand.names --conf_thres 0.01
+   ```

__pycache__/prune_0.58_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
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+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
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+scales=.1,.1
+
+[convolutional]
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+
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+[convolutional]
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+
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+random=1
+

config/1w_prune_0.6_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=30
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
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+size=3
+stride=2
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+
+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+
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+
+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+[convolutional]
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+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
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+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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config/1w_prune_0.7_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+
+[upsample]
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+
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+
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+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/1w_prune_0.875_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+scales=.1,.1
+
+[convolutional]
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+classes=1
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+jitter=.3
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+
+[route]
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+[upsample]
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+[route]
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+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/1w_prune_0.8_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
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+burn_in=1000
+max_batches=500200
+policy=steps
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+scales=.1,.1
+
+[convolutional]
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+[convolutional]
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+[shortcut]
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+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
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+filters=21
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=5
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=8
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=4
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=65
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
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+filters=79
+activation=leaky
+
+[convolutional]
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+size=1
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+
+[convolutional]
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+
+[convolutional]
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+size=1
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+
+[convolutional]
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+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=6,7,8
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers=-1, 61
+
+[convolutional]
+batch_normalize=1
+filters=125
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
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+filters=90
+activation=leaky
+
+[convolutional]
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+size=1
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+
+[convolutional]
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+stride=1
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+
+[convolutional]
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+
+[convolutional]
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+filters=39
+activation=leaky
+
+[convolutional]
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+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=3,4,5
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers=-1, 36
+
+[convolutional]
+batch_normalize=1
+filters=94
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=49
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=63
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=71
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=85
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=54
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/6.8_1w_prune_0.5_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=31
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=57
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=63
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=106
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=112
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=120
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=122
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=119
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=116
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=124
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=117
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=62
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=145
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=171
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=187
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=174
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=187
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+filters=193
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=102
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=118
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=84
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=85
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=230
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=399
+activation=leaky
+
+[convolutional]
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+filters=207
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=294
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=271
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=305
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=6,7,8
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers=-1, 61
+
+[convolutional]
+batch_normalize=1
+filters=208
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=320
+activation=leaky
+
+[convolutional]
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+filters=185
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=307
+activation=leaky
+
+[convolutional]
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+filters=208
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=191
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=3,4,5
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers=-1, 36
+
+[convolutional]
+batch_normalize=1
+filters=111
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=112
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=107
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=159
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=110
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=142
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/create_hand_model.sh

@@ -0,0 +1,794 @@
+#!/bin/bash
+
+NUM_CLASSES=$1
+
+echo "
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=$(expr 3 \* $(expr $NUM_CLASSES \+ 5))
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=$NUM_CLASSES
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=$(expr 3 \* $(expr $NUM_CLASSES \+ 5))
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=$NUM_CLASSES
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=$(expr 3 \* $(expr $NUM_CLASSES \+ 5))
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=$NUM_CLASSES
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+" >> yolov3-test.cfg

config/ds8_1w_prune_0.0_yolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=6,7,8
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers=-1, 61
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
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+[convolutional]
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+
+[yolo]
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+classes=1
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+jitter=.3
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+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[convolutional]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds8_1w_prune_0.5_best_mAP_yolov3_ckptyolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
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+channels=3
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+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+
+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[yolo]
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+
+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
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+random=1
+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+[convolutional]
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+
+[yolo]
+mask=0,1,2
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds8_1w_prune_0.7_best_mAP_yolov3_ckptyolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
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+size=3
+stride=1
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+activation=leaky
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+filters=64
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+
+[convolutional]
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+activation=leaky
+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
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+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+random=1
+

config/ds8_1w_prune_0.7_ckpt_99_06091301yolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+[convolutional]
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+[convolutional]
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+[shortcut]
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+[convolutional]
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+[yolo]
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+random=1
+

config/ds8_1w_prune_0.8_best_mAP_yolov3_ckptyolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
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+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
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+stride=1
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+activation=leaky
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[shortcut]
+from=-3
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+
+[convolutional]
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+

config/ds8_1w_prune_0.8_ckpt_99_05181112yolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+activation=leaky
+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+
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+
+[convolutional]
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+
+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
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+truth_thresh=1
+random=1
+
+[route]
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+
+[convolutional]
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+size=1
+stride=1
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+activation=leaky
+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[convolutional]
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+activation=linear
+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds8_1w_prune_0.8_ckpt_99_06091301yolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=26
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
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+filters=64
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+
+[convolutional]
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+size=1
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+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
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+
+[convolutional]
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+
+[convolutional]
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+filters=128
+size=3
+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
+batch_normalize=1
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+size=1
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+activation=leaky
+
+[convolutional]
+batch_normalize=1
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+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+filters=256
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+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
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+size=1
+stride=1
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+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+filters=128
+size=1
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+activation=leaky
+
+[ds_conv]
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+filters=256
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+[ds_conv]
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+
+[shortcut]
+from=-3
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+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
+from=-3
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+[convolutional]
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+[ds_conv]
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+
+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[shortcut]
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+[shortcut]
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+[shortcut]
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+[shortcut]
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+[shortcut]
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+
+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
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+random=1
+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
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+
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+
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+
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+
+[convolutional]
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+
+[convolutional]
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+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=3,4,5
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
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+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
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+
+[convolutional]
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+filters=92
+size=1
+stride=1
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+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+
+[convolutional]
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+activation=leaky
+
+[convolutional]
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+size=1
+stride=1
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+filters=18
+activation=linear
+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds_1w_prune_0.1_xs_min_loss_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=28
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=28
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=55
+size=1
+stride=1
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+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+filters=56
+size=1
+stride=1
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+activation=leaky
+
+[convolutional]
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+filters=128
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+filters=256
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+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
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+size=1
+stride=1
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+activation=leaky
+
+[ds_conv]
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+filters=256
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+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
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+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+size=1
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+activation=leaky
+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+

config/ds_1w_prune_0.5_xs_best_mAP_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+[convolutional]
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+[yolo]
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+classes=1
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+jitter=.3
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+random=1
+

config/ds_1w_prune_0.6_xs_best_mAP_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
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config/ds_1w_prune_0.6_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
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+[convolutional]
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+
+[yolo]
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+classes=1
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+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+
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+
+[yolo]
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+classes=1
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+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[convolutional]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds_1w_prune_0.7_ckpt_99_05181112yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
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+filters=27
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+filters=64
+size=3
+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+activation=leaky
+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+activation=leaky
+
+[convolutional]
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+
+[ds_conv]
+batch_normalize=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+activation=leaky
+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+[ds_conv]
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+
+[shortcut]
+from=-3
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+[convolutional]
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+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[shortcut]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[convolutional]
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+[shortcut]
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+
+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
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+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
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+
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+
+[convolutional]
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+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
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+
+[convolutional]
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+filters=99
+size=1
+stride=1
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+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+stride=1
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+filters=18
+activation=linear
+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds_1w_prune_0.7_xs_best_mAP_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=20
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=25
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
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+filters=54
+size=1
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+
+[convolutional]
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+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+filters=53
+size=1
+stride=1
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+activation=leaky
+
+[convolutional]
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+filters=128
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
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+stride=1
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+activation=leaky
+
+[ds_conv]
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+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[ds_conv]
+batch_normalize=1
+filters=256
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+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+filters=128
+size=1
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+pad=1
+activation=leaky
+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+activation=leaky
+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
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+
+[convolutional]
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+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
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+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[ds_conv]
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+
+[shortcut]
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+random=1
+

config/ds_1w_prune_0.85_ckpt_99_05181112yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+scales=.1,.1
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+[convolutional]
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+[convolutional]
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+[shortcut]
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+[shortcut]
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+[shortcut]
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+
+[yolo]
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+
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+[upsample]
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+[convolutional]
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+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds_1w_prune_0.8_ckpt_99_05181112yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
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config/ds_1w_prune_0.8_xs_best_mAP_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
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+
+[yolo]
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+classes=1
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+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+
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+
+[yolo]
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+classes=1
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+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+[convolutional]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds_1w_prune_0.8_xs_min_loss_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
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+stride=1
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+activation=leaky
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
+from=-3
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+
+[convolutional]
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+activation=leaky
+
+[convolutional]
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+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[convolutional]
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+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
+from=-3
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+
+[convolutional]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[convolutional]
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+[ds_conv]
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+[shortcut]
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+[shortcut]
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+[shortcut]
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+
+[yolo]
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+classes=1
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+
+[route]
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+
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+
+[upsample]
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+
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+
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+
+[convolutional]
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+stride=1
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+
+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
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+filters=128
+size=1
+stride=1
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+activation=leaky
+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+stride=1
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+activation=linear
+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/ds_1w_prune_0.9_xs_best_mAP_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
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+size=1
+stride=1
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+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
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+
+[convolutional]
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+size=1
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+activation=leaky
+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+filters=256
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+stride=2
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+activation=leaky
+
+[convolutional]
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+size=1
+stride=1
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+activation=leaky
+
+[ds_conv]
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+filters=256
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+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+filters=128
+size=1
+stride=1
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+activation=leaky
+
+[ds_conv]
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+filters=256
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+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+activation=leaky
+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+
+[convolutional]
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+activation=leaky
+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
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+
+[convolutional]
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+
+[ds_conv]
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+activation=leaky
+
+[shortcut]
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+
+[ds_conv]
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+

config/ds_1w_prune_0.9_xs_min_loss_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+

config/oxfordhand.data

@@ -0,0 +1,4 @@
+classes= 1
+train=data/train.txt
+valid=data/valid.txt
+names=data/oxfordhand.names

config/person.data

@@ -0,0 +1,4 @@
+classes= 1
+train=data/train.txt
+valid=data/valid.txt
+names=data/person.names

config/prune_0.2_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=31
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
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+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
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+size=1
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+activation=leaky
+
+[convolutional]
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+filters=64
+size=3
+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+size=3
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+size=1
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+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
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+
+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
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+
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+[convolutional]
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+
+[shortcut]
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+
+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
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+
+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
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+
+[shortcut]
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+[shortcut]
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+[shortcut]
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+
+[shortcut]
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+[shortcut]
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+[shortcut]
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+[convolutional]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
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+random=1
+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+
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+
+[convolutional]
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+
+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
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+filters=128
+size=1
+stride=1
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+activation=leaky
+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[convolutional]
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+size=1
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+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/prune_0.55_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+filters=64
+size=3
+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+[shortcut]
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+[upsample]
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+[yolo]
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+classes=1
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+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/prune_0.58_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
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+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
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+policy=steps
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+scales=.1,.1
+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[shortcut]
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+[shortcut]
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+[yolo]
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+
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+
+[yolo]
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+classes=1
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+jitter=.3
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+
+[route]
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+
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+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+[convolutional]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/prune_0.5_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
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+size=3
+stride=1
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+activation=leaky
+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+stride=1
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[shortcut]
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config/prune_0.65_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+
+[convolutional]
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+
+[convolutional]
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+
+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/prune_0.6_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
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+width=416
+height=416
+channels=3
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+hue=.1
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+policy=steps
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+scales=.1,.1
+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[shortcut]
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+[shortcut]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
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+
+[route]
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+
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+
+[upsample]
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+[route]
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+
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+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/prune_0.7_yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
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+scales=.1,.1
+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+[shortcut]
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+random=1
+

config/prune_0.85_yolov3-hand.cfg

@@ -0,0 +1,759 @@
+[net]
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+
+[convolutional]
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+
+[yolo]
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+
+[route]
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+[upsample]
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+[route]
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+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/prune_yolov3-hand.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
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+scales=.1,.1
+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+[shortcut]
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+[shortcut]
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+
+[shortcut]
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config/small_ds_1w_prune_0.5_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/small_ds_1w_prune_0.6_yolov3-ds-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+scales=.1,.1
+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[shortcut]
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+[shortcut]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
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+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+
+[convolutional]
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+[convolutional]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/smallperson.data

@@ -0,0 +1,4 @@
+classes= 1
+train=data/train.txt
+valid=data/smallvalid.txt
+names=data/person.names

config/xs_nopretrain_1w_prune_0.5_ckpt_99_06091301yolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+width=416
+height=416
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+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
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+burn_in=1000
+max_batches=500200
+policy=steps
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+scales=.1,.1
+
+[convolutional]
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+[convolutional]
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+[shortcut]
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+[upsample]
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+
+[yolo]
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+classes=1
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+jitter=.3
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+truth_thresh=1
+random=1
+

config/xs_nopretrain_1w_prune_0.6_ckpt_99_06091301yolov3-ds8-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
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+channels=3
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+decay=0.0005
+angle=0
+saturation=1.5
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+hue=.1
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+burn_in=1000
+max_batches=500200
+policy=steps
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+scales=.1,.1
+
+[convolutional]
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+
+[convolutional]
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+[convolutional]
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+[convolutional]
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+
+[shortcut]
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+[convolutional]
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+[convolutional]
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+[convolutional]
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+[shortcut]
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+[convolutional]
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+[convolutional]
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+[shortcut]
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+
+[convolutional]
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+
+[convolutional]
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+
+[ds_conv]
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+
+[shortcut]
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+[convolutional]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[ds_conv]
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+[shortcut]
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+[yolo]
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+
+[yolo]
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+classes=1
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+
+[route]
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+
+[convolutional]
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+
+[upsample]
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+
+[route]
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+[convolutional]
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+
+[yolo]
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+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/xs_nopretrain_1w_prune_0.75_ckpt_99_06161408yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=20
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
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+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
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+size=3
+stride=2
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+activation=leaky
+
+[convolutional]
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+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[convolutional]
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+activation=leaky
+
+[shortcut]
+from=-3
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+
+[convolutional]
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+
+[convolutional]
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+
+[convolutional]
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+activation=leaky
+
+[shortcut]
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+
+[convolutional]
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+
+[convolutional]
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+
+[shortcut]
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+
+[convolutional]
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+
+[convolutional]
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+

config/xs_nopretrain_1w_prune_0.7_ckpt_99_06161408yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
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+[yolo]
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+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers=-1, 36
+
+[convolutional]
+batch_normalize=1
+filters=84
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=136
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=82
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=96
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=58
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=157
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/xs_nopretrain_1w_prune_0.8_ckpt_99_06161408yolov3-person.cfg

@@ -0,0 +1,759 @@
+[net]
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation=1.5
+exposure=1.5
+hue=.1
+learning_rate=0.001
+burn_in=1000
+max_batches=500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=20
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=23
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=43
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=43
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=107
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=109
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=101
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=101
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=93
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=80
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=74
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=70
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=161
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=129
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=107
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=78
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=56
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=54
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=39
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=37
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=57
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=28
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=10
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=6
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=50
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=43
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=35
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=41
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=44
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=24
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=6,7,8
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers=-1, 61
+
+[convolutional]
+batch_normalize=1
+filters=80
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=80
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=71
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=62
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=30
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=3,4,5
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+
+[route]
+layers=-4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers=-1, 36
+
+[convolutional]
+batch_normalize=1
+filters=56
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=64
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=56
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=57
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=48
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=145
+activation=leaky
+
+[convolutional]
+batch_normalize=0
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+[yolo]
+mask=0,1,2
+anchors=10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh=.7
+truth_thresh=1
+random=1
+

config/yolov3-hand.cfg

@@ -0,0 +1,790 @@
+
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+

config/yolov3-person.cfg

@@ -0,0 +1,790 @@
+
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18 
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18 
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+

config/yolov3-test.cfg

@@ -0,0 +1,790 @@
+
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=18
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=1
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+

config/yolov3-tiny.cfg

@@ -0,0 +1,206 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=2
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+# 0
+[convolutional]
+batch_normalize=1
+filters=16
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 1
+[maxpool]
+size=2
+stride=2
+
+# 2
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 3
+[maxpool]
+size=2
+stride=2
+
+# 4
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 5
+[maxpool]
+size=2
+stride=2
+
+# 6
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 7
+[maxpool]
+size=2
+stride=2
+
+# 8
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 9
+[maxpool]
+size=2
+stride=2
+
+# 10
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 11
+[maxpool]
+size=2
+stride=1
+
+# 12
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+###########
+
+# 13
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+# 14
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 15
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+# 16
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+# 17
+[route]
+layers = -4
+
+# 18
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+# 19
+[upsample]
+stride=2
+
+# 20
+[route]
+layers = -1, 8
+
+# 21
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# 22
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+# 23
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1

config/yolov3.cfg

@@ -0,0 +1,797 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=16
+subdivisions=1
+width=416
+height=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+# Downsample
+# res1
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+# res2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+# Downsample
+# res8
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+# route1
+
+# Downsample
+# res8
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+# route2
+
+# Downsample
+# res4
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+from=-3
+activation=linear
+
+######################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+# route3
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 61
+
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+# route
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = -1, 36
+
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1

data/README.md

@@ -0,0 +1,4 @@
+1. 将压缩文件解压到当前目录，得到hand_dataset文件夹
+2. 在该目录下执行converter.py，生成images、labels文件夹和train.txt、valid.txt文件
+3. 新建oxfordhand.names文件，记录类别名称信息
+4. 该目录下有images、labels、converter.py、oxfordhand.names、train.txt、valid.txt、readme.md共7个文件，可以删掉其他文件

data/converter.py

@@ -0,0 +1,115 @@
+import scipy.io as sio
+from PIL import Image
+import os, glob
+import datetime
+import shutil
+
+running_from_path = os.getcwd()
+created_images_dir = 'images'
+created_labels_dir = 'labels'
+data_dir = 'data'   # data_dir为脚本所在的文件夹
+
+def hms_string(sec_elapsed):    # 格式化显示已消耗时间
+    h = int(sec_elapsed / (60 * 60))
+    m = int((sec_elapsed % (60 * 60)) / 60)
+    s = sec_elapsed % 60.
+    return "{}:{:>02}:{:>05.2f}".format(h, m, s)
+
+def generate_dir(set_name, root_path):   # 往images和labels文件夹下生成相应的文件夹
+    images_dir = os.path.join(root_path, 'images')
+    annotation_dir = os.path.join(root_path, 'annotations')
+
+    new_images_dir = os.path.join(created_images_dir, set_name)   # 将图片从原来的文件夹复制到该文件夹下
+    new_annotation_dir = os.path.join(created_labels_dir, set_name)
+
+    if not os.path.exists(new_images_dir):
+        os.makedirs(new_images_dir)
+
+    if not os.path.exists(new_annotation_dir):
+        os.makedirs(new_annotation_dir)
+
+    for img in glob.glob(os.path.join(images_dir, "*.jpg")):    # 将图片从原来的文件夹复制到新文件夹下
+        shutil.copy(img, new_images_dir)
+
+    os.chdir(annotation_dir)        # 切换到annotation的路径下
+    matlab_annotations = glob.glob("*.mat")  # 仅仅包含文件名，不包含路径
+    os.chdir(running_from_path)     # 切换回原来的路径
+
+    for matfile in matlab_annotations:
+        filename = matfile.split(".")[0]
+
+        pil_image = Image.open(os.path.join(images_dir, filename+".jpg"))
+
+        content = sio.loadmat(os.path.join(annotation_dir, matfile), matlab_compatible=False)
+
+        boxes = content["boxes"]
+
+        width, height = pil_image.size
+
+        with open(os.path.join(new_annotation_dir, filename+".txt"), "w") as hs:
+            for box_idx, box in enumerate(boxes.T):
+                a = box[0][0][0][0]
+                b = box[0][0][0][1]
+                c = box[0][0][0][2]
+                d = box[0][0][0][3]
+
+                aXY = (a[0][1], a[0][0])
+                bXY = (b[0][1], b[0][0])
+                cXY = (c[0][1], c[0][0])
+                dXY = (d[0][1], d[0][0])
+
+                maxX = max(aXY[0], bXY[0], cXY[0], dXY[0])
+                minX = min(aXY[0], bXY[0], cXY[0], dXY[0])
+                maxY = max(aXY[1], bXY[1], cXY[1], dXY[1])
+                minY = min(aXY[1], bXY[1], cXY[1], dXY[1])
+
+                # clip,防止超出边界
+                maxX = min(maxX, width-1)
+                minX = max(minX, 0)
+                maxY = min(maxY, height-1)
+                minY = max(minY, 0)
+
+                # (<absolute_x> / <image_width>)
+                norm_width = (maxX - minX) / width
+
+                # (<absolute_y> / <image_height>)
+                norm_height = (maxY - minY) / height
+
+                center_x, center_y = (maxX + minX) / 2, (maxY + minY) / 2
+
+                norm_center_x = center_x / width
+                norm_center_y = center_y / height
+
+                if box_idx != 0:
+                    hs.write("\n")
+
+                hs.write("0 %f %f %f %f" % (norm_center_x, norm_center_y, norm_width, norm_height)) # 0表示类别
+
+def create_txt(dirlist, filename):
+    with open(filename, "w") as txtfile:   # 在data文件夹下生成txt文件
+        imglist = []
+
+        for dir in dirlist:     # dir='images/test'
+            imglist.extend(glob.glob(os.path.join(dir, "*.jpg")))   # img='images/test/abc.jpg'
+
+        for idx, img in enumerate(imglist):
+            if idx != 0:
+                txtfile.write("\n")
+            txtfile.write(os.path.join(data_dir, img))    # 加上前缀data
+
+if __name__ == '__main__':
+    start_time = datetime.datetime.now()
+
+    generate_dir("train", "hand_dataset/training_dataset/training_data")    # 第一个参数表示生成的文件夹的名称
+    generate_dir("test", "hand_dataset/test_dataset/test_data")
+    generate_dir("validation", "hand_dataset/validation_dataset/validation_data")
+
+    create_txt((os.path.join(created_images_dir, 'train'),          # 将train和validation文件夹下的图片合并成train
+                os.path.join(created_images_dir, 'validation')),
+               'train.txt')
+    create_txt((os.path.join(created_images_dir, 'test'), ),
+               'valid.txt')
+
+    end_time = datetime.datetime.now()
+    seconds_elapsed = (end_time - start_time).total_seconds()
+    print("It took {} to execute this".format(hms_string(seconds_elapsed)))

data/oxfordhand.names

@@ -0,0 +1,2 @@
+hand
+

data/person.names

@@ -0,0 +1,2 @@
+person
+

data/smallvalid.txt

@@ -0,0 +1,128 @@
+data/images/0006.jpg
+data/images/0009.jpg
+data/images/0010.jpg
+data/images/0022.jpg
+data/images/0037.jpg
+data/images/0039.jpg
+data/images/0040.jpg
+data/images/0041.jpg
+data/images/0044.jpg
+data/images/0053.jpg
+data/images/0061.jpg
+data/images/0066.jpg
+data/images/0072.jpg
+data/images/0077.jpg
+data/images/0078.jpg
+data/images/0080.jpg
+data/images/0081.jpg
+data/images/0086.jpg
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+data/images/0090.jpg
+data/images/0095.jpg
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+data/images/0232.jpg
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+data/images/0256.jpg
+data/images/0262.jpg
+data/images/0263.jpg
+data/images/0280.jpg
+data/images/0289.jpg
+data/images/0300.jpg
+data/images/0306.jpg
+data/images/0309.jpg
+data/images/0314.jpg
+data/images/0316.jpg
+data/images/0320.jpg
+data/images/0348.jpg
+data/images/0355.jpg
+data/images/0361.jpg
+data/images/0362.jpg
+data/images/0365.jpg
+data/images/0368.jpg
+data/images/0369.jpg
+data/images/0376.jpg
+data/images/0383.jpg
+data/images/0390.jpg
+data/images/0396.jpg
+data/images/0399.jpg
+data/images/0410.jpg
+data/images/0419.jpg
+data/images/0421.jpg
+data/images/0431.jpg
+data/images/0435.jpg
+data/images/0437.jpg
+data/images/0441.jpg
+data/images/0451.jpg
+data/images/0456.jpg
+data/images/0459.jpg
+data/images/0462.jpg
+data/images/0469.jpg
+data/images/0471.jpg
+data/images/0472.jpg
+data/images/0479.jpg
+data/images/0480.jpg
+data/images/0482.jpg
+data/images/0487.jpg
+data/images/0488.jpg
+data/images/0490.jpg
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+data/images/0513.jpg
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+data/images/0598.jpg
+data/images/0600.jpg
+data/images/0603.jpg
+data/images/0606.jpg
+data/images/0614.jpg

data/valid_2000.txt

@@ -0,0 +1,410 @@
+data/images/0006.jpg
+data/images/0009.jpg
+data/images/0010.jpg
+data/images/0022.jpg
+data/images/0037.jpg
+data/images/0039.jpg
+data/images/0040.jpg
+data/images/0041.jpg
+data/images/0044.jpg
+data/images/0053.jpg
+data/images/0061.jpg
+data/images/0066.jpg
+data/images/0072.jpg
+data/images/0077.jpg
+data/images/0078.jpg
+data/images/0080.jpg
+data/images/0081.jpg
+data/images/0086.jpg
+data/images/0089.jpg
+data/images/0090.jpg
+data/images/0095.jpg
+data/images/0109.jpg
+data/images/0110.jpg
+data/images/0120.jpg
+data/images/0122.jpg
+data/images/0123.jpg
+data/images/0124.jpg
+data/images/0125.jpg
+data/images/0127.jpg
+data/images/0135.jpg
+data/images/0137.jpg
+data/images/0140.jpg
+data/images/0141.jpg
+data/images/0146.jpg
+data/images/0157.jpg
+data/images/0159.jpg
+data/images/0162.jpg
+data/images/0164.jpg
+data/images/0165.jpg
+data/images/0169.jpg
+data/images/0178.jpg
+data/images/0183.jpg
+data/images/0189.jpg
+data/images/0193.jpg
+data/images/0196.jpg
+data/images/0198.jpg
+data/images/0203.jpg
+data/images/0214.jpg
+data/images/0218.jpg
+data/images/0219.jpg
+data/images/0223.jpg
+data/images/0229.jpg
+data/images/0232.jpg
+data/images/0244.jpg
+data/images/0248.jpg
+data/images/0249.jpg
+data/images/0256.jpg
+data/images/0262.jpg
+data/images/0263.jpg
+data/images/0280.jpg
+data/images/0289.jpg
+data/images/0300.jpg
+data/images/0306.jpg
+data/images/0309.jpg
+data/images/0314.jpg
+data/images/0316.jpg
+data/images/0320.jpg
+data/images/0348.jpg
+data/images/0355.jpg
+data/images/0361.jpg
+data/images/0362.jpg
+data/images/0365.jpg
+data/images/0368.jpg
+data/images/0369.jpg
+data/images/0376.jpg
+data/images/0383.jpg
+data/images/0390.jpg
+data/images/0396.jpg
+data/images/0399.jpg
+data/images/0410.jpg
+data/images/0419.jpg
+data/images/0421.jpg
+data/images/0431.jpg
+data/images/0435.jpg
+data/images/0437.jpg
+data/images/0441.jpg
+data/images/0451.jpg
+data/images/0456.jpg
+data/images/0459.jpg
+data/images/0462.jpg
+data/images/0469.jpg
+data/images/0471.jpg
+data/images/0472.jpg
+data/images/0479.jpg
+data/images/0480.jpg
+data/images/0482.jpg
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+data/images/0488.jpg
+data/images/0490.jpg
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+data/images/0506.jpg
+data/images/0510.jpg
+data/images/0513.jpg
+data/images/0521.jpg
+data/images/0522.jpg
+data/images/0523.jpg
+data/images/0526.jpg
+data/images/0529.jpg
+data/images/0541.jpg
+data/images/0547.jpg
+data/images/0548.jpg
+data/images/0549.jpg
+data/images/0556.jpg
+data/images/0564.jpg
+data/images/0572.jpg
+data/images/0577.jpg
+data/images/0578.jpg
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+data/images/0584.jpg
+data/images/0586.jpg
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+data/images/0600.jpg
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+data/images/0606.jpg
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+data/images/0615.jpg
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debug_utils.py

@@ -0,0 +1,21 @@
+def inspect_lr(optimizer):
+    cur_lr = optimizer.param_groups[0]['lr']
+    print('Current learning rate: %0.6f' % cur_lr)
+    return cur_lr
+
+# 将学习率衰减为原来的gamma倍数
+def modify_lr(optimizer, gamma):
+    cur_lr = inspect_lr(optimizer)
+    new_lr = cur_lr * gamma
+    print('Learning rate has been changed from %0.6f to %0.6f' % (cur_lr, new_lr))
+    for group in optimizer.param_groups:
+        group['lr'] = new_lr
+    return new_lr
+
+def turn_on_sr(opt):
+    opt.sr = True
+    print('Sr has been turned on!')
+
+def turn_off_sr(opt):
+    opt.sr = False
+    print('Sr has been turned off!')

models.py

@@ -0,0 +1,410 @@
+from __future__ import division
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+
+from utils.parse_config import *
+from utils.utils import build_targets, to_cpu, non_max_suppression
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+# convolutional,maxpool,upsample,route,shortcut,yolo
+
+def create_modules(module_defs):
+    """
+    Constructs module list of layer blocks from module configuration in module_defs
+    """
+    hyperparams = module_defs.pop(0)
+    output_filters = [int(hyperparams["channels"])]
+    module_list = nn.ModuleList()       # 一定要用ModuleList()才能被torch识别为module并进行管理，不能用list！
+    for module_i, module_def in enumerate(module_defs):
+        modules = nn.Sequential()
+
+        if module_def["type"] == "convolutional":
+            bn = int(module_def["batch_normalize"])
+            filters = int(module_def["filters"])
+            kernel_size = int(module_def["size"])
+            pad = (kernel_size - 1) // 2
+            modules.add_module(
+                f"conv_{module_i}",
+                nn.Conv2d(
+                    in_channels=output_filters[-1],
+                    out_channels=filters,
+                    kernel_size=kernel_size,
+                    stride=int(module_def["stride"]),
+                    padding=pad,
+                    bias=not bn,
+                ),
+            )
+            if bn:
+                # modules.add_module(f"batch_norm_{module_i}", nn.BatchNorm2d(filters, momentum=0.9, eps=1e-5))
+                modules.add_module(f"batch_norm_{module_i}", nn.BatchNorm2d(filters))
+            if module_def["activation"] == "leaky":
+                modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1, inplace=True))
+        
+        # 增加了深度可分离卷积
+        elif module_def["type"] == "ds_conv":
+            bn = int(module_def["batch_normalize"])
+            filters = int(module_def["filters"])
+            kernel_size = int(module_def["size"])
+            pad = int(module_def["pad"])
+            # 逐通道
+            modules.add_module(
+                f"ds_conv_d_{module_i}",
+                nn.Conv2d(
+                    in_channels=output_filters[-1],
+                    out_channels=output_filters[-1],
+                    kernel_size=kernel_size,
+                    stride=int(module_def["stride"]),
+                    padding=pad,
+                    groups=output_filters[-1], # 和in_channels相同
+                ),
+            )
+            # 逐点
+            modules.add_module(
+                f"ds_conv_p_{module_i}",
+                nn.Conv2d(
+                    in_channels=output_filters[-1],
+                    out_channels=filters,
+                    kernel_size=1,
+                    stride=1,
+                    padding=0,
+                    groups=1, 
+                ),
+            )
+            
+            if bn:
+                # modules.add_module(f"batch_norm_{module_i}", nn.BatchNorm2d(filters, momentum=0.9, eps=1e-5))
+                modules.add_module(f"batch_norm_{module_i}", nn.BatchNorm2d(filters))
+            if module_def["activation"] == "leaky":
+                modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1, inplace=True))
+            
+
+        elif module_def["type"] == "maxpool":
+            kernel_size = int(module_def["size"])
+            stride = int(module_def["stride"])
+            if kernel_size == 2 and stride == 1:
+                modules.add_module(f"_debug_padding_{module_i}", nn.ZeroPad2d((0, 1, 0, 1)))
+            maxpool = nn.MaxPool2d(kernel_size=kernel_size, stride=stride, padding=int((kernel_size - 1) // 2))
+            modules.add_module(f"maxpool_{module_i}", maxpool)
+
+        elif module_def["type"] == "upsample":
+            upsample = Upsample(scale_factor=int(module_def["stride"]), mode="nearest")
+            modules.add_module(f"upsample_{module_i}", upsample)
+
+        elif module_def["type"] == "route": #路由层进行拼接操作 比如： 输入1:26*26*256 输入2： 26*26*128 输出： 26*26*（256+128）
+            layers = [int(x) for x in module_def["layers"].split(",")]
+            filters = sum([output_filters[1:][i] for i in layers])
+            modules.add_module(f"route_{module_i}", EmptyLayer())
+
+        elif module_def["type"] == "shortcut": #残差连接，并不是通道拼接，而是每个通道的特征图逐元素相加，并不会改变通道数量
+            filters = output_filters[1:][int(module_def["from"])]
+            modules.add_module(f"shortcut_{module_i}", EmptyLayer())
+
+        elif module_def["type"] == "yolo":
+            anchor_idxs = [int(x) for x in module_def["mask"].split(",")]
+            # Extract anchors
+            anchors = [int(x) for x in module_def["anchors"].split(",")]
+            anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
+            anchors = [anchors[i] for i in anchor_idxs]
+            num_classes = int(module_def["classes"])
+            img_size = int(hyperparams["height"])
+            # Define detection layer
+            yolo_layer = YOLOLayer(anchors, num_classes, img_size)
+            modules.add_module(f"yolo_{module_i}", yolo_layer)
+        # Register module list and number of output filters
+        module_list.append(modules)
+        output_filters.append(filters)  # filter保存了输出的维度
+
+    return hyperparams, module_list
+
+
+class Upsample(nn.Module):
+    """ nn.Upsample is deprecated """
+
+    def __init__(self, scale_factor, mode="nearest"):
+        super(Upsample, self).__init__()
+        self.scale_factor = scale_factor
+        self.mode = mode
+
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
+        return x
+
+
+class EmptyLayer(nn.Module):    # 只是为了占位，以便处理route层和shortcut层
+    """Placeholder for 'route' and 'shortcut' layers"""
+
+    def __init__(self):
+        super(EmptyLayer, self).__init__()
+
+
+class YOLOLayer(nn.Module):
+    """Detection layer"""
+
+    def __init__(self, anchors, num_classes, img_dim=416):
+        super(YOLOLayer, self).__init__()
+        self.anchors = anchors
+        self.num_anchors = len(anchors)
+        self.num_classes = num_classes
+        self.ignore_thres = 0.5
+        self.mse_loss = nn.MSELoss()
+        self.bce_loss = nn.BCELoss()
+        self.obj_scale = 1
+        self.noobj_scale = 100
+        self.metrics = {}
+        self.img_dim = img_dim
+        self.grid_size = 0  # grid size
+
+    def compute_grid_offsets(self, grid_size, cuda=True):
+        self.grid_size = grid_size
+        g = self.grid_size
+        FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
+        self.stride = self.img_dim / self.grid_size
+        # Calculate offsets for each grid
+        self.grid_x = torch.arange(g).repeat(g, 1).view([1, 1, g, g]).type(FloatTensor)
+        self.grid_y = torch.arange(g).repeat(g, 1).t().view([1, 1, g, g]).type(FloatTensor)
+        self.scaled_anchors = FloatTensor([(a_w / self.stride, a_h / self.stride) for a_w, a_h in self.anchors])
+        self.anchor_w = self.scaled_anchors[:, 0:1].view((1, self.num_anchors, 1, 1))   # anchor_w的范围是[0, grid_size](416下),浮点型数值
+        self.anchor_h = self.scaled_anchors[:, 1:2].view((1, self.num_anchors, 1, 1))
+
+    def forward(self, x, targets=None, img_dim=None):
+
+        # Tensors for cuda support
+        FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor
+        LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor
+        ByteTensor = torch.cuda.ByteTensor if x.is_cuda else torch.ByteTensor
+
+        self.img_dim = img_dim
+        num_samples = x.size(0)
+        grid_size = x.size(2)
+
+        prediction = (
+            x.view(num_samples, self.num_anchors, self.num_classes + 5, grid_size, grid_size)
+            .permute(0, 1, 3, 4, 2)     # num_samples, num_anchors, grid_size, grid_size, self.num_classes + 5
+            .contiguous()
+        )
+
+        # Get outputs
+        x = torch.sigmoid(prediction[..., 0])  # Center x
+        y = torch.sigmoid(prediction[..., 1])  # Center y
+        w = prediction[..., 2]  # Width
+        h = prediction[..., 3]  # Height
+        pred_conf = torch.sigmoid(prediction[..., 4])  # Conf
+        pred_cls = torch.sigmoid(prediction[..., 5:])  # Cls pred.
+
+        # If grid size does not match current we compute new offsets
+        if grid_size != self.grid_size:     # 不用每次都计算，只有在输入图片大小第一次发生变化时计算
+            self.compute_grid_offsets(grid_size, cuda=x.is_cuda)
+
+        # Add offset and scale with anchors
+        pred_boxes = FloatTensor(prediction[..., :4].shape) # 生成形状与prediction[..., :4]相同的张量
+        pred_boxes[..., 0] = x.data + self.grid_x
+        pred_boxes[..., 1] = y.data + self.grid_y
+        pred_boxes[..., 2] = torch.exp(w.data) * self.anchor_w  # anchor_w的范围是[0,grid_size](416下)，浮点型变量
+        pred_boxes[..., 3] = torch.exp(h.data) * self.anchor_h
+
+        output = torch.cat(
+            (
+                pred_boxes.view(num_samples, -1, 4) * self.stride,
+                pred_conf.view(num_samples, -1, 1),
+                pred_cls.view(num_samples, -1, self.num_classes),   # num_samples, num_anchors*grid_size*grid_size, 85
+            ),
+            -1,
+        )
+
+        if targets is None:
+            return output, 0
+        else:
+            iou_scores, class_mask, obj_mask, noobj_mask, tx, ty, tw, th, tcls, tconf = build_targets(
+                pred_boxes=pred_boxes,
+                pred_cls=pred_cls,
+                target=targets,
+                anchors=self.scaled_anchors,
+                ignore_thres=self.ignore_thres,
+            )
+            
+            obj_mask = obj_mask.bool()  #将int8转换为bool
+            noobj_mask = noobj_mask.bool() #将int8转换为bool
+
+            # Loss : Mask outputs to ignore non-existing objects (except with conf. loss)
+            # TODO：这里没有针对wh的损失进行加权处理
+            loss_x = self.mse_loss(x[obj_mask], tx[obj_mask])
+            loss_y = self.mse_loss(y[obj_mask], ty[obj_mask])
+            loss_w = self.mse_loss(w[obj_mask], tw[obj_mask])
+            loss_h = self.mse_loss(h[obj_mask], th[obj_mask])
+            loss_conf_obj = self.bce_loss(pred_conf[obj_mask], tconf[obj_mask])
+            loss_conf_noobj = self.bce_loss(pred_conf[noobj_mask], tconf[noobj_mask])
+            loss_conf = self.obj_scale * loss_conf_obj + self.noobj_scale * loss_conf_noobj
+            loss_cls = self.bce_loss(pred_cls[obj_mask], tcls[obj_mask])
+            total_loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls
+
+            # Metrics
+            cls_acc = 100 * class_mask[obj_mask].mean()
+            conf_obj = pred_conf[obj_mask].mean()
+            conf_noobj = pred_conf[noobj_mask].mean()
+            conf50 = (pred_conf > 0.5).float()
+            iou50 = (iou_scores > 0.5).float()
+            iou75 = (iou_scores > 0.75).float()
+            detected_mask = conf50 * class_mask * tconf
+            precision = torch.sum(iou50 * detected_mask) / (conf50.sum() + 1e-16)
+            recall50 = torch.sum(iou50 * detected_mask) / (obj_mask.sum() + 1e-16)
+            recall75 = torch.sum(iou75 * detected_mask) / (obj_mask.sum() + 1e-16)
+
+            self.metrics = {
+                "loss": to_cpu(total_loss).item(),
+                "x": to_cpu(loss_x).item(),
+                "y": to_cpu(loss_y).item(),
+                "w": to_cpu(loss_w).item(),
+                "h": to_cpu(loss_h).item(),
+                "conf": to_cpu(loss_conf).item(),
+                "cls": to_cpu(loss_cls).item(),
+                "cls_acc": to_cpu(cls_acc).item(),
+                "recall50": to_cpu(recall50).item(),
+                "recall75": to_cpu(recall75).item(),
+                "precision": to_cpu(precision).item(),
+                "conf_obj": to_cpu(conf_obj).item(),
+                "conf_noobj": to_cpu(conf_noobj).item(),
+                "grid_size": grid_size,
+            }
+
+            return output, total_loss
+
+
+class Darknet(nn.Module):
+    """YOLOv3 object detection model"""
+
+    def __init__(self, config_path, img_size=416):
+        super(Darknet, self).__init__()
+
+        if isinstance(config_path, str): #读取配置文件
+            self.module_defs = parse_model_config(config_path)
+        elif isinstance(config_path, list):
+            self.module_defs = config_path
+
+        self.hyperparams, self.module_list = create_modules(self.module_defs) #创建模型
+        self.yolo_layers = [layer[0] for layer in self.module_list if hasattr(layer[0], "metrics")] # layer是个nn.Sequential()
+        self.img_size = img_size
+        self.seen = 0
+        self.header_info = np.array([0, 0, 0, self.seen, 0], dtype=np.int32)
+
+    def forward(self, x, targets=None):
+        img_dim = x.shape[2]    # 取决于输入图片的大小，因为是正方形输入，所以只考虑height
+        loss = 0
+        layer_outputs, yolo_outputs = [], []
+        for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
+            if module_def["type"] in ["convolutional", "upsample", "maxpool", "ds_conv"]: # 增加了"ds_conv"
+                x = module(x)
+            elif module_def["type"] == "route":
+                x = torch.cat([layer_outputs[int(layer_i)] for layer_i in module_def["layers"].split(",")], 1)
+            elif module_def["type"] == "shortcut":
+                layer_i = int(module_def["from"])
+                # print(f'{layer_i}' + ':' + str(len(layer_outputs[layer_i])))
+                # print(f'{layer_i}' + '-layer_outputs:' + str(len(layer_outputs[-1])))
+                x = layer_outputs[-1] + layer_outputs[layer_i]
+            elif module_def["type"] == "yolo":      # [82, 94, 106] for yolov3
+                x, layer_loss = module[0](x, targets, img_dim)  # module是nn.Sequential()，所以要取[0]
+                loss += layer_loss
+                yolo_outputs.append(x)
+            layer_outputs.append(x)     # 将每个块的output都保存起来
+        yolo_outputs = to_cpu(torch.cat(yolo_outputs, 1))   # 只保存yolo层的output
+        return yolo_outputs if targets is None else (loss, yolo_outputs)
+
+    def load_darknet_weights(self, weights_path):
+        """Parses and loads the weights stored in 'weights_path'"""
+
+        # Open the weights file
+        with open(weights_path, "rb") as f:
+            header = np.fromfile(f, dtype=np.int32, count=5)  # First five are header values
+            self.header_info = header  # Needed to write header when saving weights
+            self.seen = header[3]  # number of images seen during training
+            weights = np.fromfile(f, dtype=np.float32)  # The rest are weights
+
+        # Establish cutoff for loading backbone weights
+        cutoff = None
+        if "darknet53.conv.74" in weights_path:
+            cutoff = 75
+
+        ptr = 0
+        for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):
+            if i == cutoff:
+                break
+            if module_def["type"] == "convolutional":
+                conv_layer = module[0]
+                if module_def["batch_normalize"]:
+                    # Load BN bias, weights, running mean and running variance
+                    bn_layer = module[1]
+                    num_b = bn_layer.bias.numel()  # Number of biases
+                    # Bias
+                    bn_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.bias)
+                    bn_layer.bias.data.copy_(bn_b)
+                    ptr += num_b
+                    # Weight
+                    bn_w = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.weight)
+                    bn_layer.weight.data.copy_(bn_w)
+                    ptr += num_b
+                    # Running Mean
+                    bn_rm = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_mean)
+                    bn_layer.running_mean.data.copy_(bn_rm)
+                    ptr += num_b
+                    # Running Var
+                    bn_rv = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_var)
+                    bn_layer.running_var.data.copy_(bn_rv)
+                    ptr += num_b
+                    # Load conv. weights
+                    num_w = conv_layer.weight.numel()
+                    conv_w = torch.from_numpy(weights[ptr: ptr + num_w]).view_as(conv_layer.weight)
+                    conv_layer.weight.data.copy_(conv_w)
+                    ptr += num_w
+                else:
+                    # 对于yolov3.weights,不带bn的卷积层就是YOLO前的卷积层
+                    if "yolov3.weights" in weights_path:
+                        num_b = 255
+                        ptr += num_b
+                        num_w = int(self.module_defs[i-1]["filters"]) * 255
+                        ptr += num_w
+                    else:
+                        # Load conv. bias
+                        num_b = conv_layer.bias.numel()
+                        conv_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(conv_layer.bias)
+                        conv_layer.bias.data.copy_(conv_b)
+                        ptr += num_b
+                        # Load conv. weights
+                        num_w = conv_layer.weight.numel()
+                        conv_w = torch.from_numpy(weights[ptr : ptr + num_w]).view_as(conv_layer.weight)
+                        conv_layer.weight.data.copy_(conv_w)
+                        ptr += num_w
+        # 确保指针到达权重的最后一个位置
+        assert ptr == len(weights)
+
+    def save_darknet_weights(self, path, cutoff=-1):
+        """
+            @:param path    - path of the new weights file
+            @:param cutoff  - save layers between 0 and cutoff (cutoff = -1 -> all are saved)
+        """
+        fp = open(path, "wb")
+        self.header_info[3] = self.seen
+        self.header_info.tofile(fp)
+
+        # Iterate through layers
+        for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):
+            if module_def["type"] == "convolutional":
+                conv_layer = module[0]
+                # If batch norm, load bn first
+                if module_def["batch_normalize"]:
+                    bn_layer = module[1]
+                    bn_layer.bias.data.cpu().numpy().tofile(fp)
+                    bn_layer.weight.data.cpu().numpy().tofile(fp)
+                    bn_layer.running_mean.data.cpu().numpy().tofile(fp)
+                    bn_layer.running_var.data.cpu().numpy().tofile(fp)
+                # Load conv bias
+                else:
+                    conv_layer.bias.data.cpu().numpy().tofile(fp)
+                # Load conv weights
+                conv_layer.weight.data.cpu().numpy().tofile(fp)
+
+        fp.close()

test.py

@@ -0,0 +1,116 @@
+from __future__ import division
+
+from models import *
+from utils.utils import *
+from utils.datasets import *
+from utils.parse_config import *
+
+import os
+import sys
+import time
+import datetime
+import argparse
+import tqdm
+
+import torch
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision import transforms
+from torch.autograd import Variable
+import torch.optim as optim
+import time
+
+
+def evaluate(model, path, iou_thres, conf_thres, nms_thres, img_size, batch_size):
+    model.eval()
+
+    # Get dataloader
+    dataset = ListDataset(path, img_size=img_size, augment=False, multiscale=False)
+    dataloader = torch.utils.data.DataLoader(
+        dataset, batch_size=batch_size, shuffle=False, num_workers=1, collate_fn=dataset.collate_fn
+    )
+
+    Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
+
+    labels = []
+    sample_metrics = []  # List of tuples (TP, confs, pred)
+    for batch_i, (_, imgs, targets) in enumerate(tqdm.tqdm(dataloader, desc="Detecting objects")):
+
+        # Extract labels
+        labels += targets[:, 1].tolist()
+        # Rescale target
+        targets[:, 2:] = xywh2xyxy(targets[:, 2:])
+        targets[:, 2:] *= img_size
+
+        imgs = dataset.resize_imgs(imgs)
+        imgs = Variable(imgs.type(Tensor), requires_grad=False)
+
+        with torch.no_grad():
+            outputs = model(imgs)
+            outputs = non_max_suppression(outputs, conf_thres=conf_thres, nms_thres=nms_thres)
+
+        sample_metrics += get_batch_statistics(outputs, targets, iou_threshold=iou_thres)
+
+    # Concatenate sample statistics
+    assert sample_metrics != []
+    true_positives, pred_scores, pred_labels = [np.concatenate(x, 0) for x in list(zip(*sample_metrics))]
+    precision, recall, AP, f1, ap_class = ap_per_class(true_positives, pred_scores, pred_labels, labels)
+
+    return precision, recall, AP, f1, ap_class
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch_size", type=int, default=8, help="size of each image batch")
+    parser.add_argument("--model_def", type=str, default="config/yolov3.cfg", help="path to model definition file")
+    parser.add_argument("--data_config", type=str, default="config/coco.data", help="path to data config file")
+    parser.add_argument("--weights_path", type=str, default="weights/yolov3.weights", help="path to weights file")
+    parser.add_argument("--class_path", type=str, default="data/coco.names", help="path to class label file")
+    parser.add_argument("--iou_thres", type=float, default=0.5, help="iou threshold required to qualify as detected")
+    parser.add_argument("--conf_thres", type=float, default=0.001, help="object confidence threshold")
+    parser.add_argument("--nms_thres", type=float, default=0.5, help="iou thresshold for non-maximum suppression")
+    parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
+    parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
+    opt = parser.parse_args()
+    print(opt)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    data_config = parse_data_config(opt.data_config)
+    valid_path = data_config["valid"]
+    class_names = load_classes(data_config["names"])
+
+    # Initiate model
+    model = Darknet(opt.model_def).to(device)
+    if opt.weights_path.endswith(".weights"):
+        # Load darknet weights
+        model.load_darknet_weights(opt.weights_path)
+    else:
+        # Load checkpoint weights
+        model.load_state_dict(torch.load(opt.weights_path))
+
+    print("Compute mAP...")
+
+    precision, recall, AP, f1, ap_class = evaluate(
+        model,
+        path=valid_path,
+        iou_thres=opt.iou_thres,
+        conf_thres=opt.conf_thres,
+        nms_thres=opt.nms_thres,
+        img_size=opt.img_size,
+        batch_size=8,
+    )
+    
+    obtain_num_parameters = lambda model:sum([param.nelement() for param in model.parameters()])
+    parameters = obtain_num_parameters(model)
+    print("Parameters : ", f"{parameters}")
+
+    print("Average Precisions:")
+    for i, c in enumerate(ap_class):
+        print(f"+ Class '{c}' ({class_names[c]}) - AP: {AP[i]}")
+        print(f"+ Class '{c}' ({class_names[c]}) - precision: {precision[i]}")
+        print(f"+ Class '{c}' ({class_names[c]}) - recall: {recall[i]}")
+        print(f"+ Class '{c}' ({class_names[c]}) - f1: {f1[i]}")
+
+
+    print(f"mAP: {AP.mean()}")

test.sh

@@ -0,0 +1,4 @@
+#!bash
+CARD=0,1
+
+CUDA_VISIBLE_DEVICES=$CARD python test.py --model_def config/ds_1w_prune_0.8_xs_min_loss_yolov3-ds-person.cfg --weights_path weights/ds_1w_prune_0.8_xs_min_loss_yolov3_ckpt.pth --data_config config/person.data --class_path data/person.names --conf_thres 0.1

test_prune.py

@@ -0,0 +1,277 @@
+from cgi import print_directory
+from models import *
+from utils.utils import *
+import torch
+import numpy as np
+from copy import deepcopy
+from test import evaluate
+from terminaltables import AsciiTable
+import time
+from utils.prune_utils import *
+
+class opt():
+    model_def = "config/yolov3-ds-person.cfg" # yolov3-ds8-person.cfg
+    data_config = "config/smallperson.data"  # smallperson.data
+    model = 'checkpoints/yolov3_ckpt_99_05181112.pth'  # checkpoints/yolov3_ckpt.pth'   # res8 yolov3_ckpt_99_06081725.pth  # 2*res8+res4 yolov3_ckpt_99_05181112
+
+
+#%%
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = Darknet(opt.model_def).to(device) #加载模型
+model.load_state_dict(torch.load(opt.model)) #加载权重
+# print(model)
+# for name in model.state_dict():
+#   print(name)
+
+data_config = parse_data_config(opt.data_config)
+valid_path = data_config["valid"]
+class_names = load_classes(data_config["names"])
+
+# 用lambda表达式创建函数
+eval_model = lambda model:evaluate(model, path=valid_path, iou_thres=0.5, conf_thres=0.01,
+    nms_thres=0.1, img_size=model.img_size, batch_size=8)
+# print(eval_model)
+
+obtain_num_parameters = lambda model:sum([param.nelement() for param in model.parameters()])
+
+# 获取最初的模型评估和参数量
+origin_model_metric = eval_model(model)
+origin_nparameters = obtain_num_parameters(model)
+
+CBL_idx, Conv_idx, prune_idx= parse_module_defs(model.module_defs)
+# print(CBL_idx, Conv_idx, prune_idx)
+# CBL_idx = [0, 1, 2, 3, 5, 6, 7, 9, 10, 12, 13, 16, 19, 22, 25, 28, 31, 34, 37, 38, 41, 44, 47, 50, 53, 56, 59, 62, 63, 66, 
+# 69, 72, 75, 76, 77, 78, 79, 80, 84, 87, 88, 89, 90, 91, 92, 96, 99, 100, 101, 102, 103, 104]
+# 问题是 CBL_idx中没有14就是ds_conv那一层 
+
+# Conv_idx = [81, 93, 105]
+# prune_idx = [0, 2, 6, 9, 75, 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104]
+
+#prune_idx =
+    #[0, 2, 6, 9, 13, 16, 19, 22, 25, 28, 31, 34, 38, 41, 44, 47, 50, 53, 56, 59, 63, 66, 69, 72, 75, 
+    # 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104]
+
+bn_weights = gather_bn_weights(model.module_list, prune_idx)
+# print("model.module_list[0]:",model.module_list[0],"---",len(model.module_list[0]))
+# print('bn_weights = ' ,bn_weights, "---",len(bn_weights))
+
+sorted_bn = torch.sort(bn_weights)[0] # 对bn从小到大排序
+# print('sorted_bn = ' ,sorted_bn)
+
+# 避免剪掉所有channel的最高阈值(每个BN层的gamma的最大值的最小值即为阈值上限)
+highest_thre = []
+for idx in prune_idx:
+    highest_thre.append(model.module_list[idx][1].weight.data.abs().max().item())
+highest_thre = min(highest_thre)
+
+# 找到highest_thre对应的下标对应的百分比
+percent_limit = (sorted_bn==highest_thre).nonzero().item()/len(bn_weights)
+
+print(f'Threshold should be less than {highest_thre:.4f}.')
+print(f'The corresponding prune ratio is {percent_limit:.3f}.')
+
+#%%
+def prune_and_eval(model, sorted_bn, percent=.0):
+    model_copy = deepcopy(model)
+    thre_index = int(len(sorted_bn) * percent)
+    # print('thre_index = ', thre_index) #thre_index = 11369
+    thre = sorted_bn[thre_index]       
+    # print('thre = ', thre)             #thre = tensor(0.0925)
+    # print(model_copy)
+
+    print(f'Channels with Gamma value less than {thre:.4f} are pruned!')
+
+    remain_num = 0
+    for idx in prune_idx:
+        # prune_idx = [0, 2, 6, 9, 75, 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104]
+        # print("idx:",idx)
+        bn_module = model_copy.module_list[idx][1]
+
+        mask = obtain_bn_mask(bn_module, thre) #生成mask
+        # print(idx, ': ', len(mask))
+        # print(idx, ':', len(mask), ':', mask)
+        remain_num += int(mask.sum())
+        # BN层的权重(gamma)乘以这个mask，就相当于剪枝了
+        bn_module.weight.data.mul_(mask) # 用mask对原始权重进行操作
+    # print("remain_num:",remain_num)
+    # print(model_copy)
+    # print(bn_module.weight.data)
+    # model_copy.module_list[0][1] = BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    # print(model_copy.module_list[0][1].weight.data)
+    """
+    model_copy.module_list[0][1].weight.data = 
+    tensor([ 1.6235,  0.3670,  0.6683,  0.2203, -0.2113,  1.6356,  0.0717,  0.5802,
+            0.1437, -0.3640,  0.2322,  0.2651,  0.7316,  0.6135,  1.6100,  0.8620,
+            0.1987,  0.5357,  0.2006,  0.2127,  0.7190, -1.1396, -0.2585, -0.4673,
+            0.0498,  0.5148,  0.7377,  0.3179,  1.2934,  1.1743,  0.2840,  0.2782],
+       device='cuda:0')
+    """
+    
+    mAP = eval_model(model_copy)[2].mean() #  搞明白为什么 剪枝之后sample_metrics = [] 为空列表了 : 剪枝率过高会为空
+    # print(mAP)
+
+    print(f'Number of channels has been reduced from {len(sorted_bn)} to {remain_num}')
+    print(f'Prune ratio: {1-remain_num/len(sorted_bn):.3f}')
+    print(f'mAP of the pruned model is {mAP:.4f}')
+
+    return thre
+
+#调用上面的函数
+percent = 0.85  # 0.85
+threshold = prune_and_eval(model, sorted_bn, percent)
+
+# print(threshold)
+
+#%%
+def obtain_filters_mask(model, thre, CBL_idx, prune_idx):
+
+    pruned = 0
+    total = 0
+    num_filters = []
+    filters_mask = []
+    for idx in CBL_idx:
+        if(model.module_defs[idx]['type'] == 'ds_conv'):
+            bn_module = model.module_list[idx][2]
+        else:
+            bn_module = model.module_list[idx][1]
+        # print("idx",idx,"--bn_module :",model.module_list[idx][1])
+        # 如果idx是在剪枝下标的列表中，就执行剪枝
+        if idx in prune_idx:
+            # prune_idx = [0, 2, 6, 9, 75, 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104]
+
+            mask = obtain_bn_mask(bn_module, thre).cpu().numpy()
+            # 保留的通道数
+            remain = int(mask.sum())
+            # 剪掉的通道数
+            pruned = pruned + mask.shape[0] - remain
+
+            if remain == 0:
+                print("Channels would be all pruned!")
+                raise Exception
+
+            print(f'layer index: {idx:>3d} \t total channel: {mask.shape[0]:>4d} \t '
+                  f'remaining channel: {remain:>4d}')
+        else:
+            # 不用剪枝就全部保留
+            mask = np.ones(bn_module.weight.data.shape)
+            remain = mask.shape[0]
+            
+        # print("idx:",idx,"--mask:",mask,"--lenofmask",len(mask)) # 怀疑这里因为ds_conv有两个卷积层 可能出现问题 但是14层的ds_conv mask长度是256 因为算的是bn层的mask，所以为256，这里值得注意！！明天来检查一下！
+
+        total += mask.shape[0]
+        num_filters.append(remain) # 剪枝后还存在的滤波器
+        filters_mask.append(mask.copy()) # 剪枝的掩码mask
+
+    # print("num_filters:",len(num_filters),"CBL_idx:", len(CBL_idx)) 都是72
+    prune_ratio = pruned / total
+    print(f'Prune channels: {pruned}\tPrune ratio: {prune_ratio:.3f}')
+
+    return num_filters, filters_mask
+
+#调用上面的函数
+num_filters, filters_mask = obtain_filters_mask(model, threshold, CBL_idx, prune_idx)
+# print("num_filters : ", num_filters)
+# num_filters = [29, 64, 29, 64, 128, 59, 128, 61, 128, 256, 128, 256, 128, 256, 128, 256, 128, 256, 128, 256, 128, 256, 128, 256, 128, 256, 512, 
+#                256, 512, 256, 512, 256, 512, 256, 512, 256, 512, 256, 512, 256, 512, 256, 512, 1024, 512, 1024, 512, 1024, 512, 1024, 512, 1024, 
+#                264, 422, 237, 441, 263, 139, 256, 226, 352, 179, 278, 137, 215, 128, 111, 182, 90, 140, 54, 220]
+
+#%%
+# 映射成一个字典，idx->mask
+CBLidx2mask = {idx: mask for idx, mask in zip(CBL_idx, filters_mask)}
+# print("CBLidx2mask:", CBLidx2mask)
+# 获得剪枝后的模型
+pruned_model = prune_model_keep_size(model, prune_idx, CBL_idx, CBLidx2mask)
+# print("pruned_model:",pruned_model)
+# 对剪枝后的模型进行评价
+pruned_model_metric = eval_model(pruned_model)
+print("mAP", f'{origin_model_metric[2].mean():.6f}', f'{pruned_model_metric[2].mean():.6f}')
+# pruned_nparameters = obtain_num_parameters(pruned_model)
+# print("pruned_nparameters:",pruned_nparameters)
+
+#%%
+# 拷贝一份原始模型的参数
+compact_module_defs = deepcopy(model.module_defs)
+# 遍历需要剪枝的CBL模块，将通道数设置为剪枝后的通道数
+for idx, num in zip(CBL_idx, num_filters):
+    # if compact_module_defs[idx]['type'] == 'ds_conv' :
+    #     # continue
+    #     compact_module_defs[idx]['filters'] = str(num)
+    assert compact_module_defs[idx]['type'] == 'convolutional' or compact_module_defs[idx]['type'] == 'ds_conv'
+    compact_module_defs[idx]['filters'] = str(num)
+    # 改了网络结构，在这里相应的更改ds_conv通道数，先试试更改，如果后面不改，就在前面mask那里改改
+    # assert compact_module_defs[idx]['type'] == 'ds_conv'
+    # compact_module_defs[idx]['filters'] = str(num)
+# print("compact_module_defs:",compact_module_defs)
+
+#%%
+#compact_model是剪枝之后的网络的真实结构（注意:上面的剪枝网络只是把那些需要剪枝的卷积层/BN层/激活层通道的权重置0了，并没有保存剪枝后的网络)
+# print("model.hyperparams:",model.hyperparams)
+compact_model = Darknet([model.hyperparams.copy()] + compact_module_defs).to(device)
+# compact_model_metric = eval_model(compact_model)
+# print("compact_model_metric:", "mAP",  f'{compact_model_metric[2].mean():.6f}')
+# print("model.hyperparams:",model.hyperparams)
+# print("compact_model:", compact_model)
+# 计算参数量，MFLOPs
+compact_nparameters = obtain_num_parameters(compact_model)
+# print("compact_nparameters:", compact_nparameters) 18404269已经是剪完枝后的参数了
+# 为剪枝后的真实网络结构重新复制权重参数
+init_weights_from_loose_model(compact_model, pruned_model, CBL_idx, Conv_idx, CBLidx2mask)
+# 对比compact_model 与 prune_model每一层的权重
+# for name, parameters in pruned_model.named_parameters():  
+#     print(name, '1;', parameters.size())
+# for name, parameters in compact_model.named_parameters():  
+#     print(name, '2;', parameters.size())
+
+# print("pruned_model:",pruned_model.state_dict())
+# print("compact_model:",compact_model.state_dict())
+
+#%%
+random_input = torch.rand((1, 3, model.img_size, model.img_size)).to(device)
+
+# 获取模型的推理时间
+def obtain_avg_forward_time(input, model, repeat=200):
+
+    model.eval()
+    start = time.time()
+    with torch.no_grad():
+        for i in range(repeat):
+            output = model(input)
+    avg_infer_time = (time.time() - start) / repeat
+
+    return avg_infer_time, output
+
+# 分别获取原始模型和剪枝后的模型的推理时间和输出
+pruned_forward_time, pruned_output = obtain_avg_forward_time(random_input, pruned_model)
+compact_forward_time, compact_output = obtain_avg_forward_time(random_input, compact_model)
+# print("pruned_forward_time:",pruned_forward_time,"---compact_forward_time:", compact_forward_time)
+# print("pruned_output:",pruned_output,"---compact_output:", compact_output)
+# 计算原始模型推理结果和剪枝后的模型的推理结果，如果差距比较大说明哪里错了
+# 先注释下面几行代码看能不能运行
+diff = (pruned_output-compact_output).abs().gt(0.001).sum().item()
+# print("diff:", diff)
+if diff > 0:
+    print('Something wrong with the pruned model!')
+
+#%%
+# 在测试集上测试剪枝后的模型, 并统计模型的参数数量
+compact_model_metric = eval_model(compact_model)
+
+#%%
+# 比较剪枝前后参数数量的变化、指标性能的变化
+metric_table = [
+    ["Metric", "Before", "After"],
+    ["mAP", f'{origin_model_metric[2].mean():.6f}', f'{compact_model_metric[2].mean():.6f}'],
+    ["Parameters", f"{origin_nparameters}", f"{compact_nparameters}"],
+    ["Inference", f'{pruned_forward_time:.4f}', f'{compact_forward_time:.4f}']
+]
+print(AsciiTable(metric_table).table)
+
+#%%
+# 生成剪枝后的cfg文件并保存模型
+pruned_cfg_name = opt.model_def.replace('/', f'/ds_1w_prune_{percent}_ckpt_99_05181112')
+pruned_cfg_file = write_cfg(pruned_cfg_name, [model.hyperparams.copy()] + compact_module_defs)
+print(f'Config file has been saved: {pruned_cfg_file}')
+
+compact_model_name = opt.model.replace('/', f'/ds_1w_prune_{percent}_')
+torch.save(compact_model.state_dict(), compact_model_name)
+print(f'Compact model has been saved: {compact_model_name}')

test_prune.sh

@@ -0,0 +1,4 @@
+#!bash
+CARD=0,1
+
+CUDA_VISIBLE_DEVICES=$CARD python test_prune.py

testdict.py

@@ -0,0 +1,15 @@
+import torch
+from models import *
+
+class opt():
+    model_def = "config/ds_1w_prune_0.5_yolov3-ds-person.cfg"
+    data_config = "config/smallperson.data"  # person.data
+    model = 'checkpoints/ds_1w_prune_0.5_yolov3_ckpt_99_05181112.pth'  # checkpoints/yolov3_ckpt.pth'  
+
+# 加载模型
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = Darknet(opt.model_def).to(device) #加载模型
+model.load_state_dict(torch.load(opt.model)) #加载权重
+
+# 打印参数
+print(model)

train.py

@@ -0,0 +1,281 @@
+from __future__ import division
+
+from models import *
+from utils.logger import *
+from utils.utils import *
+from utils.datasets import *
+from utils.parse_config import *
+from utils.prune_utils import *
+from test import evaluate
+
+# 调试用的模块，reload用于代码热重载
+from importlib import reload
+import debug_utils
+
+from terminaltables import AsciiTable
+
+import os
+import time
+import datetime
+import argparse
+
+# import logging
+
+import torch
+from torch.utils.data import DataLoader
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--epochs", type=int, default=100, help="number of epochs")
+    parser.add_argument("--batch_size", type=int, default=56, help="size of each image batch") # default=16
+    parser.add_argument("--model_def", type=str, default="config/yolov3-person.cfg", help="path to model definition file") #default="config/yolov3-hand.cfg"
+    parser.add_argument("--data_config", type=str, default="config/person.data", help="path to data config file") #default="config/oxfordhand.data"
+    # parser.add_argument("--pretrained_weights", type=str, default="weights/darknet53.conv.74",
+    parser.add_argument("--pretrained_weights", '-pre', type=str,
+                        default="weights/yolov3.weights", help="if specified starts from checkpoint model")
+    parser.add_argument("--n_cpu", type=int, default=4, help="number of cpu threads to use during batch generation")
+    parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
+    parser.add_argument("--checkpoint_interval", type=int, default=20, help="interval between saving model weights") #default=5
+    parser.add_argument("--evaluation_interval", type=int, default=1, help="interval evaluations on validation set")
+    parser.add_argument("--multiscale_training", default=False, help="allow for multi-scale training")
+    parser.add_argument("--debug_file", type=str, default="debug", help="enter ipdb if dir exists")
+
+    parser.add_argument('--learning_rate', '-lr', dest='lr', type=float, default=1e-3, help='initial learning rate')
+
+    parser.add_argument('--sparsity-regularization', '-sr', dest='sr', action='store_true',
+                        help='train with channel sparsity regularization')
+    parser.add_argument('--s', type=float, default=0.01, help='scale sparse rate') 
+
+    opt = parser.parse_args()
+    print(opt)
+
+    logger = Logger("logs")
+
+    # 设置随机数种子
+    init_seeds()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    timestamp = datetime.datetime.fromtimestamp(time.time()).strftime('%m%d%H%M')
+    
+    os.makedirs("output", exist_ok=True)
+    os.makedirs("checkpoints", exist_ok=True)
+
+    # Get data configuration
+    data_config = parse_data_config(opt.data_config)
+    train_path = data_config["train"]
+    valid_path = data_config["valid"]
+    class_names = load_classes(data_config["names"])
+
+    # Initiate model
+    model = Darknet(opt.model_def).to(device)
+    model.apply(weights_init_normal)
+
+    #打印模型
+    # print(model)
+    
+    # If specified we start from checkpoint
+    #是否加载预训练模型
+    # if opt.pretrained_weights:
+    #     if opt.pretrained_weights.endswith(".pth"):
+    #         model.load_state_dict(torch.load(opt.pretrained_weights))
+    #     else:
+    #         model.load_darknet_weights(opt.pretrained_weights)
+
+    _, _, prune_idx= parse_module_defs(model.module_defs) # 剪枝需要的参数S
+    # prune_idx = \
+    #     [0,
+    #      2,
+    #      6, 9,
+    #      13, 16, 19, 22, 25, 28, 31, 34,
+    #      38, 41, 44, 47, 50, 53, 56, 59,
+    #      63, 66, 69, 72,
+    #      75, 76, 77, 78, 79,     80,
+    #     #84,
+    #      87, 88, 89, 90, 91,     92,
+    #     #96,
+    #      99,100,101,102,103,    104]
+
+    # Get dataloader 导入数据
+    dataset = ListDataset(train_path, augment=True, multiscale=opt.multiscale_training)
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=opt.batch_size,
+        shuffle=True,
+        num_workers=opt.n_cpu,
+        pin_memory=True,
+        collate_fn=dataset.collate_fn
+    )
+
+    optimizer = torch.optim.SGD(model.parameters(), lr=opt.lr, momentum=0.9)
+
+    metrics = [
+        "grid_size",
+        "loss",
+        "x", "y", "w", "h",
+        "conf",
+        "cls", "cls_acc",
+        "recall50", "recall75",
+        "precision",
+        "conf_obj", "conf_noobj",
+    ]
+
+
+    # 配置日志记录器
+    # logger = logging.getLogger('my_logger')
+    # logger.setLevel(logging.INFO)
+    # handler = logging.FileHandler('log.txt')
+    # handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s: %(message)s [in %(pathname)s:%(lineno)d]'))
+    # logger.addHandler(handler)
+
+    
+    # TFLOPS = 0
+    tmp_loss = 0
+    min_loss = 100
+    best_mAP = 0
+
+    for epoch in range(opt.epochs):
+
+        # 进入调试模式
+        if os.path.exists(opt.debug_file):
+            import ipdb
+            ipdb.set_trace()
+
+        model.train()
+        start_time = time.time()
+
+        sr_flag = get_sr_flag(epoch, opt.sr)
+        
+        obtain_num_parameters = lambda model:sum([param.nelement() for param in model.parameters()])
+        origin_nparameters = obtain_num_parameters(model)
+        print("Parameters : ", f"{origin_nparameters}")
+        print("Model size:", sum([p.numel() for name, p in model.named_parameters()])) # 输出模型大小
+
+        for batch_i, (_, imgs, targets) in enumerate(dataloader):
+            batches_done = len(dataloader) * epoch + batch_i
+
+            imgs = imgs.to(device)
+            targets = targets.to(device)
+
+            loss, outputs = model(imgs, targets)
+
+            optimizer.zero_grad()
+            loss.backward()
+
+            BNOptimizer.updateBN(sr_flag, model.module_list, opt.s, prune_idx)
+
+            optimizer.step()
+            
+            # acc = (outputs > 0.5).float().mean()
+            # logger.info('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Acc: {:.4f}'.format(epoch, epoch, i+1, len(dataloader), loss.item(), acc.item()))
+
+            # ----------------
+            #   Log progress
+            # ----------------
+
+            log_str = "\n---- [Epoch %d/%d, Batch %d/%d] ----\n" % (epoch, opt.epochs, batch_i, len(dataloader))
+
+            metric_table = [["Metrics", *[f"YOLO Layer {i}" for i in range(len(model.yolo_layers))]]]
+
+            # Log metrics at each YOLO layer
+            formats = {m: "%.6f" for m in metrics}
+            formats["grid_size"] = "%2d"
+            formats["cls_acc"] = "%.2f%%"
+            for metric in metrics:
+                row_metrics = [formats[metric] % yolo.metrics.get(metric, 0) for yolo in model.yolo_layers]
+                metric_table += [[metric, *row_metrics]]
+
+            log_str += AsciiTable(metric_table).table
+            log_str += f"\nTotal loss {loss.item()}"
+            
+            #记录每个batch的最后一次loss
+            tmp_loss = loss.item()
+
+            # Determine approximate time left for epoch
+            epoch_batches_left = len(dataloader) - (batch_i + 1)
+            time_left = datetime.timedelta(seconds=epoch_batches_left * (time.time() - start_time) / (batch_i + 1))
+            log_str += f"\n---- ETA {time_left}"
+
+            print(log_str)
+
+            # Tensorboard logging
+            tensorboard_log = []
+            for i, yolo in enumerate(model.yolo_layers):
+                for name, metric in yolo.metrics.items():
+                    # 选择部分指标写入tensorboard
+                    if name not in {"grid_size", "x", "y", "w", "h", "cls_acc"}:
+                        tensorboard_log += [(f"{name}_{i+1}", metric)]
+            tensorboard_log += [("loss", loss.item())]
+            tensorboard_log += [("lr", optimizer.param_groups[0]['lr'])]
+            logger.list_of_scalars_summary('train', tensorboard_log, batches_done)
+            
+        
+
+        if epoch % opt.evaluation_interval == 0:
+            print("\n---- Evaluating Model ----")
+            # Evaluate the model on the validation set
+            precision, recall, AP, f1, ap_class = evaluate(
+                model,
+                path=valid_path,
+                iou_thres=0.5,
+                conf_thres=0.01,
+                nms_thres=0.5,
+                img_size=opt.img_size,
+                batch_size=8,
+            )
+            evaluation_metrics = [
+                ("val_precision", precision.mean()),
+                ("val_recall", recall.mean()),
+                ("val_mAP", AP.mean()),
+                ("val_f1", f1.mean()),
+            ]
+            logger.list_of_scalars_summary('valid', evaluation_metrics, epoch)
+
+            # Print class APs and mAP
+            ap_table = [["Index", "Class name", "AP"]]
+            for i, c in enumerate(ap_class):
+                ap_table += [[c, class_names[c], "%.5f" % AP[i]]]
+            print(AsciiTable(ap_table).table)
+            print(f"---- mAP {AP.mean()}")
+
+            # 往tensorboard中记录bn权重分布
+            bn_weights = gather_bn_weights(model.module_list, prune_idx)
+            logger.writer.add_histogram('bn_weights/hist', bn_weights.numpy(), epoch, bins='doane')
+            
+        if(best_mAP < AP.mean()):
+            best_mAP = AP.mean()
+            torch.save(model.state_dict(), f"checkpoints/xs_nopretrain_pt_best_mAP_{epoch}_yolov3_ckpt.pth") # 保存mAP最高的模型
+        if(tmp_loss < min_loss):
+            min_loss = tmp_loss
+            torch.save(model.state_dict(), f"checkpoints/xs_nopretrain_pt_min_loss_{epoch}_yolov3_ckpt.pth") # 保存loss最低的模型
+
+        if epoch % opt.checkpoint_interval == 0 or epoch == opt.epochs - 1:
+            
+            torch.save(model.state_dict(), f"checkpoints/xs_nopretrain_yolov3_ckpt_{epoch}_{timestamp}.pth")
+            print(f"model has been saved:checkpoints/xs_nopretrain_yolov3_ckpt_{epoch}_{timestamp}.pth")
+            
+            
+        
+        # obtain_num_parameters = lambda model:sum([param.nelement() for param in model.parameters()])
+        # origin_nparameters = obtain_num_parameters(model)
+        # print("Parameters : ", f"{origin_nparameters}")
+            
+        # GPUs = torch.hub.list_hub_servers()    
+        # flops = [] 
+        # parameters = [] 
+        
+        # for server in GPUs:    
+        #     # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")    
+        #     flops.append(torch.Tensor(np.sum(np.square(model.parameters()[:3])).item()))  
+        #     parameters.append(torch.Tensor(model.parameters()))  
+        #     model.to(device)    
+        # flops = np.array(flops)    
+        # flops = flops.reshape(-1, flops.shape[-1])
+        # TFLOPS += flops.item()
+        # parameters = np.array(parameters)      
+        # parameters = parameters.reshape(-1, parameters.shape[-1])    
+        # print('GPUs:', GPUs)    
+        # print('FLOPS:', flops.item())
+        # print('Params:', parameters.item())  
+        
+    # print('TFLOPS:', TFLOPS)

train_person.sh

@@ -0,0 +1,4 @@
+#!bash
+CARD=0,1
+
+CUDA_VISIBLE_DEVICES=$CARD python train.py --model_def config/yolov3-person.cfg

utils/augmentations.py

@@ -0,0 +1,42 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+import albumentations as A
+
+# boxes = (cls, x, y, w, h)
+def horizontal_flip(images, boxes):
+    images = np.flip(images, [-1])
+    boxes[:, 1] = 1 - boxes[:, 1]
+    return images, boxes
+
+# images[np.unit8], boxes[numpy] = (cls, x, y, w, h)
+def augment(image, boxes):
+    h, w, _ = image.shape
+    labels, boxes_coord = boxes[:, 0], boxes[:, 1:]
+    labels = labels.tolist()
+    boxes_coord = boxes_coord * h     # 得到原图尺寸下的坐标（未归一化的坐标）
+    boxes_coord[:, 0] = np.clip(boxes_coord[:, 0]-boxes_coord[:, 2]/2, a_min=0, a_max=None)   # 确保x_min和y_min有效
+    boxes_coord[:, 1] = np.clip(boxes_coord[:, 1]-boxes_coord[:, 3]/2, a_min=0, a_max=None)
+    boxes_coord = boxes_coord.tolist()      # [x_min, y_min, width, height]
+
+    # 在这里设置数据增强的方法
+    aug = A.Compose([
+        A.HorizontalFlip(p=0.5),
+        # A.HueSaturationValue(hue_shift_limit=10, sat_shift_limit=10, val_shift_limit=10, p=0.5),
+        # A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.1, rotate_limit=5, border_mode=0, p=0.5)
+    ], bbox_params={'format':'coco', 'label_fields': ['category_id']})
+
+    augmented = aug(image=image, bboxes=boxes_coord, category_id=labels)
+
+    # 经过aug之后，如果把boxes变没了，则返回原来的图片
+    if augmented['bboxes']:
+        image = augmented['image']
+
+        boxes_coord = np.array(augmented['bboxes']) # x_min, y_min, w, h → x, y, w, h
+        boxes_coord[:, 0] = boxes_coord[:, 0] + boxes_coord[:, 2]/2
+        boxes_coord[:, 1] = boxes_coord[:, 1] + boxes_coord[:, 3]/2
+        boxes_coord = boxes_coord / h
+        labels = np.array(augmented['category_id'])[:, None]
+        boxes = np.concatenate((labels, boxes_coord), 1)
+
+    return image, boxes

utils/datasets.py

@@ -0,0 +1,173 @@
+import glob
+import random
+import os
+import sys
+import numpy as np
+from PIL import Image
+import torch
+import torch.nn.functional as F
+
+from utils.augmentations import augment
+from torch.utils.data import Dataset
+import torchvision.transforms as transforms
+
+# 图像的转换流程:PIL→numpy→pad→transform→tensor
+
+# 对numpy格式的img进行padding([0,255])
+def pad_to_square(img, pad_value):
+    h, w, _ = img.shape
+    dim_diff = np.abs(h - w)
+    # (upper / left) padding and (lower / right) padding
+    pad1, pad2 = dim_diff // 2, dim_diff - dim_diff // 2
+    # Determine padding
+    pad = ((pad1, pad2), (0,0), (0,0)) if h <= w else ((0,0), (pad1, pad2), (0,0))  # 分别对应h,w,c的padding
+    # Add padding
+    img = np.pad(img, pad, 'constant', constant_values=pad_value)
+
+    return img, (*pad[1], *pad[0])  # 返回w，c的padding
+
+
+# 对tensor格式的img进行resize
+def resize(image, size):
+    image = F.interpolate(image.unsqueeze(0), size=size, mode="nearest").squeeze(0)
+    return image
+
+
+class ImageFolder(Dataset):
+    def __init__(self, folder_path, img_size=416):
+        self.files = sorted(glob.glob("%s/*.*" % folder_path))
+        self.img_size = img_size
+
+    def __getitem__(self, index):
+        img_path = self.files[index % len(self.files)]
+
+        img = Image.open(img_path)
+        img = np.array(img)
+
+        # Pad to square resolution
+        img, _ = pad_to_square(img, 0)
+
+        img = transforms.ToTensor()(img)    # img为np.uint8格式
+
+        # Resize
+        img = resize(img, self.img_size)
+
+        return img_path, img
+
+    def __len__(self):
+        return len(self.files)
+
+
+class ListDataset(Dataset):
+    def __init__(self, list_path, img_size=416, augment=True, multiscale=True, normalized_labels=True):
+        with open(list_path, "r") as file:
+            self.img_files = file.readlines()
+
+        self.label_files = [
+            path.replace("images", "labels").replace(".png", ".txt").replace(".jpg", ".txt")
+            for path in self.img_files
+        ]
+        self.img_size = img_size
+        self.augment = augment
+        self.multiscale = multiscale
+        self.normalized_labels = normalized_labels
+        self.min_size = self.img_size - 3 * 32
+        self.max_size = self.img_size + 3 * 32
+        self.batch_count = 0
+
+    def __getitem__(self, index):
+
+        # ---------
+        #  Image
+        # ---------
+
+        img_path = self.img_files[index % len(self.img_files)].rstrip()
+
+        img = Image.open(img_path).convert('RGB')
+        img = np.array(img)
+
+        # Handle images with less than three channels
+        if len(img.shape) != 3:
+            img = img[None, :, :]
+            img = img.repeat(3, 0)
+
+        h, w, _ = img.shape  # np格式的img是H*W*C
+        h_factor, w_factor = (h, w) if self.normalized_labels else (1, 1)
+        # Pad to square resolution
+        img, pad = pad_to_square(img, 0)
+        padded_h, padded_w, _ = img.shape
+
+        # ---------
+        #  Label
+        # ---------
+
+        label_path = self.label_files[index % len(self.img_files)].rstrip()
+        # print(label_path)
+
+        assert os.path.exists(label_path)   # 确保label_path必定存在，即图片必定存在label
+        boxes = np.loadtxt(label_path).reshape(-1, 5)
+        # Extract coordinates for unpadded + unscaled image
+        x1 = w_factor * (boxes[:, 1] - boxes[:, 3] / 2)
+        y1 = h_factor * (boxes[:, 2] - boxes[:, 4] / 2)
+        x2 = w_factor * (boxes[:, 1] + boxes[:, 3] / 2)
+        y2 = h_factor * (boxes[:, 2] + boxes[:, 4] / 2)
+        # Adjust for added padding
+        x1 += pad[0]    # pad是从低维到高维的，感觉这样写是有问题的，应该只与pad[0][2]有关，不过一般都是相等的
+        y1 += pad[2]
+        x2 += pad[0]
+        y2 += pad[2]
+        # Returns (x, y, w, h)
+        boxes[:, 1] = ((x1 + x2) / 2) / padded_w
+        boxes[:, 2] = ((y1 + y2) / 2) / padded_h
+        boxes[:, 3] *= w_factor / padded_w      # 原来的数值是boxw_ori/imgw_ori, 现在变成了(boxw_ori/imgw_ori)*imgw_ori/imgw_pad=boxw_ori/imgw_pad
+        boxes[:, 4] *= h_factor / padded_h
+
+        # Apply augmentations
+        # img, 以最长边为标准进行padding得到的uint8图像
+        # boxes, (cls, x, y, w, h)都以pad后得到的img的高度进行了归一化
+        if self.augment:
+            img, boxes = augment(img, boxes)
+
+        img = transforms.ToTensor()(img)   # ToTensor已经将像素值进行了归一化
+
+        targets = torch.zeros((len(boxes), 6))
+        targets[:, 1:] = torch.from_numpy(boxes)  # 0维在collate_fn中是作为idx用了，用于指定target对应的图片
+
+        return img_path, img, targets
+
+    def collate_fn(self, batch):
+        paths, imgs, targets = list(zip(*batch))
+        # Remove empty placeholder targets
+        # 确保每一张图片都有box，如果某张图片没有标签就会报错！
+        for boxes in targets:
+            assert (boxes is not None)
+        targets = [boxes for boxes in targets if boxes is not None]  # 注意注意！！！这里并没有处理对应的imgs，imgs有可能与targets匹配不上
+        # Add sample index to targets
+        for i, boxes in enumerate(targets):
+            boxes[:, 0] = i
+        targets = torch.cat(targets, 0)
+        # Selects new image size every tenth batch
+        # 因为多线程并行读取的原因，self.batch_count和self.img_size的操作是不对的，
+        # 个人觉得更好的处理方法将尺度的变化放到训练阶段，即读取数据之后再做resize
+        # if self.multiscale:
+        #     self.img_size = random.choice(range(self.min_size, self.max_size + 1, 32))
+        # Resize images to input shape
+        if self.multiscale:
+            imgs = torch.stack([resize(img, self.max_size) for img in imgs])    # 先将img统一resize到最大值
+        else:
+            imgs = torch.stack([resize(img, self.img_size) for img in imgs])
+        return paths, imgs, targets
+
+    def __len__(self):
+        return len(self.img_files)
+
+    # 重新设置img_size
+    def select_new_img_size(self):
+        self.img_size = random.choice(range(self.min_size, self.max_size + 1, 32))
+
+    # 对tensor格式的img进行resize
+    def resize_imgs(self, images):
+        if self.multiscale:
+            images = F.interpolate(images, size=self.img_size, mode="nearest")
+
+        return images

utils/logger.py

@@ -0,0 +1,32 @@
+# import tensorflow as tf
+
+# class Logger(object):
+#     def __init__(self, log_dir):
+#         """Create a summary writer logging to log_dir."""
+#         self.writer = tf.summary.FileWriter(log_dir)
+#
+#     def scalar_summary(self, tag, value, step):
+#         """Log a scalar variable."""
+#         summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
+#         self.writer.add_summary(summary, step)
+#
+#     def list_of_scalars_summary(self, tag_value_pairs, step):
+#         """Log scalar variables."""
+#         summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value) for tag, value in tag_value_pairs])
+#         self.writer.add_summary(summary, step)
+
+from tensorboardX import SummaryWriter
+import os
+from datetime import datetime
+import time
+
+class Logger(object):
+    def __init__(self, log_dir):
+        """Create a summary writer logging to log_dir."""
+        timestamp = datetime.fromtimestamp(time.time()).strftime('%m%d-%H:%M')
+        self.writer = SummaryWriter(os.path.join(log_dir, timestamp))
+
+    def list_of_scalars_summary(self, prefix, tag_value_pairs, step):
+        """Log scalar variables."""
+        for tag, value in tag_value_pairs:
+            self.writer.add_scalar(prefix+'/'+tag, value, step)

utils/parse_config.py

@@ -0,0 +1,36 @@
+
+
+def parse_model_config(path):
+    """Parses the yolo-v3 layer configuration file and returns module definitions"""
+    file = open(path, 'r')
+    lines = file.read().split('\n')
+    lines = [x for x in lines if x and not x.startswith('#')]
+    lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces
+    module_defs = []
+    for line in lines:
+        if line.startswith('['): # This marks the start of a new block
+            module_defs.append({})
+            module_defs[-1]['type'] = line[1:-1].rstrip()
+            if module_defs[-1]['type'] == 'convolutional':
+                module_defs[-1]['batch_normalize'] = 0
+        else:
+            key, value = line.split("=")
+            value = value.strip()
+            module_defs[-1][key.rstrip()] = value.strip()
+
+    return module_defs
+
+def parse_data_config(path):
+    """Parses the data configuration file"""
+    options = dict()
+    # options['gpus'] = '0,1,2,3'
+    # options['num_workers'] = '10'
+    with open(path, 'r') as fp:
+        lines = fp.readlines()
+    for line in lines:
+        line = line.strip()
+        if line == '' or line.startswith('#'):
+            continue
+        key, value = line.split('=')
+        options[key.strip()] = value.strip()
+    return options

utils/prune_utils.py

@@ -0,0 +1,290 @@
+import torch
+from terminaltables import AsciiTable
+from copy import deepcopy
+import numpy as np
+import torch.nn.functional as F
+
+
+def get_sr_flag(epoch, sr):
+    # return epoch >= 5 and sr
+    return sr
+
+
+def parse_module_defs(module_defs):
+
+    CBL_idx = []
+    Conv_idx = []
+    for i, module_def in enumerate(module_defs):
+        # 添加了 or module_def['type'] == 'ds_conv' 将ds_conv也纳入剪枝范围，如果不考虑的话就在下面的不需要剪枝那里添加
+        if module_def['type'] == 'convolutional' or module_def['type'] == 'ds_conv':
+            if module_def['batch_normalize'] == '1':
+                 CBL_idx.append(i)
+                #CBL_idx=      即为有卷积也有bn的层的idx
+                #[0, 1, 2, 3, 5, 6, 7, 9, 10, 12, 13, 14, 16, 17, 19, 20, 22, 23, 25, 26, 28, 29, 31, 32, 34, 35, 37, 38, 
+                # 39, 41, 42, 44, 45, 47, 48, 50, 51, 53, 54, 56, 57, 59, 60, 62, 63, 64, 66, 67, 69, 70, 72, 73, 75, 76, 77, 
+                # 78, 79, 80, 84, 87, 88, 89, 90, 91, 92, 96, 99, 100, 101, 102, 103, 104]             
+            else:
+                #Conv_idx = [81, 93, 105]     即为有卷积没有bn的层的idx
+                Conv_idx.append(i)
+
+    ignore_idx = set() #哪些层不需要剪枝
+    for i, module_def in enumerate(module_defs):
+        #将ds_conv纳入不剪枝范围
+        if module_def['type'] == 'ds_conv':
+            ignore_idx.add(i-1)
+            
+        if module_def['type'] == 'shortcut':
+            ignore_idx.add(i-1)
+            identity_idx = (i + int(module_def['from']))
+            if module_defs[identity_idx]['type'] == 'convolutional':
+                ignore_idx.add(identity_idx)
+            elif module_defs[identity_idx]['type'] == 'shortcut':
+                ignore_idx.add(identity_idx - 1)
+
+    ignore_idx.add(84)
+    ignore_idx.add(96)
+    # print(ignore_idx)
+    #ignore_idx={1, 3, 5, 7, 10, 12, 14, 17, 20, 23, 26, 29, 32, 35, 37, 39, 42, 45, 48, 51, 54, 57, 60, 62, 64, 67, 70, 73, 84, 96} 改结构前
+    # ignore_idx={1, 3, 5, 7, 10, 12, 13, 14, 16, 17, 19, 20, 22, 23, 25, 26, 28, 29, 31, 32, 34, 35, 37, 38, 39, 41, 42, 44, 45, 47, 
+    #  48, 50, 51, 53, 54, 56, 57, 59, 60, 62, 63, 64, 66, 67, 69, 70, 72, 73, 84, 96}
+    prune_idx = [idx for idx in CBL_idx if idx not in ignore_idx]
+    #prune_idx =[0, 2, 6, 9, 13, 16, 19, 22, 25, 28, 31, 34, 38, 41, 44, 47, 50, 53, 56, 59, 63, 66, 69, 72, 75,
+    # 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104] 改结构前
+    # prune_idx = [0, 2, 6, 9, 75, 76, 77, 78, 79, 80, 87, 88, 89, 90, 91, 92, 99, 100, 101, 102, 103, 104]
+    # 返回CBL组件的id，单独的Conv层的id，以及需要被剪枝的层的id
+    return CBL_idx, Conv_idx, prune_idx
+
+
+def  gather_bn_weights(module_list, prune_idx):
+
+    size_list = [module_list[idx][1].weight.data.shape[0] for idx in prune_idx] # 存储prune_idx对应层的 filter数量
+    # print('module_list[0][1].weight.data.shape[0] = ',module_list[0][1].weight.data.shape[0])
+    # print("size_list:",size_list) 
+    # ds_conv对应的size_list [32, 32, 64, 64, 512, 1024, 512, 1024, 512, 1024, 256, 512, 256, 512, 256, 512, 128, 256, 128, 256, 128, 256]
+    #[32, 32, 64, 64, 128, 128, 128, 128, 128, 128, 128, 128, 256, 256, 256, 256, 256, 256, 256, 256, 512, 512, 512, 512, 512, 
+    # 1024, 512, 1024, 512, 1024, 256, 512, 256, 512, 256, 512, 128, 256, 128, 256, 128, 256] 
+
+    bn_weights = torch.zeros(sum(size_list))
+    # print('bn_weights = ', bn_weights)
+    # bn_weights == tensor([0., 0., 0.,  ..., 0., 0., 0.])  
+    index = 0
+    
+    for idx, size in zip(prune_idx, size_list):
+        # print('idx = ' ,idx, ' | size = ',size)
+        bn_weights[index:(index + size)] = module_list[idx][1].weight.data.abs().clone()
+        index += size
+        # print('bn_weights = ' ,bn_weights, ' | index = ',index)
+    # print('module_list[0][1].weight.data.abs().clone()_len = ',len(module_list[0][1].weight.data.abs().clone()))
+    # 获取CBL组件的BN层的权重，即Gamma参数，我们会根据这个参数来剪枝
+    return bn_weights 
+
+
+def write_cfg(cfg_file, module_defs):
+
+    with open(cfg_file, 'w') as f:
+        for module_def in module_defs:
+            f.write(f"[{module_def['type']}]\n")
+            for key, value in module_def.items():
+                if key != 'type':
+                    f.write(f"{key}={value}\n")
+            f.write("\n")
+    return cfg_file
+
+
+class BNOptimizer():
+
+    @staticmethod
+    def updateBN(sr_flag, module_list, s, prune_idx):
+        if sr_flag:
+            for idx in prune_idx:
+                # Squential(Conv, BN, Lrelu)
+                bn_module = module_list[idx][1]
+                # 将批标准化模块(bn_module)的权重矩阵的梯度乘以一个缩放因子后添加到该权重矩阵的梯度上。
+                bn_module.weight.grad.data.add_(s * torch.sign(bn_module.weight.data))  # L1
+
+
+def obtain_quantiles(bn_weights, num_quantile=5):
+
+    sorted_bn_weights, i = torch.sort(bn_weights)
+    total = sorted_bn_weights.shape[0]
+    quantiles = sorted_bn_weights.tolist()[-1::-total//num_quantile][::-1]
+    print("\nBN weights quantile:")
+    quantile_table = [
+        [f'{i}/{num_quantile}' for i in range(1, num_quantile+1)],
+        ["%.3f" % quantile for quantile in quantiles]
+    ]
+    print(AsciiTable(quantile_table).table)
+
+    return quantiles
+
+
+def get_input_mask(module_defs, idx, CBLidx2mask):
+    # print("CBLidx2mask ： ", CBLidx2mask)
+    if idx == 0:
+        return np.ones(3)
+
+    if module_defs[idx - 1]['type'] == 'convolutional':
+        return CBLidx2mask[idx - 1]
+    elif module_defs[idx - 1]['type'] == 'ds_conv':
+        return CBLidx2mask[idx - 1]
+    elif module_defs[idx - 1]['type'] == 'shortcut':
+        return CBLidx2mask[idx - 2]
+    elif module_defs[idx - 1]['type'] == 'route':
+        route_in_idxs = []
+        for layer_i in module_defs[idx - 1]['layers'].split(","):
+            if int(layer_i) < 0:
+                route_in_idxs.append(idx - 1 + int(layer_i))
+            else:
+                route_in_idxs.append(int(layer_i))
+        if len(route_in_idxs) == 1:
+            return CBLidx2mask[route_in_idxs[0]]
+        elif len(route_in_idxs) == 2:
+            return np.concatenate([CBLidx2mask[in_idx - 1] for in_idx in route_in_idxs])
+        else:
+            print("Something wrong with route module!")
+            raise Exception
+
+
+def init_weights_from_loose_model(compact_model, loose_model, CBL_idx, Conv_idx, CBLidx2mask):
+    
+    # (14): Sequential(
+    #   (ds_conv_d_14): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=128)
+    #   (ds_conv_p_14): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1))
+    #   (batch_norm_14): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+    #   (leaky_14): LeakyReLU(negative_slope=0.1, inplace=True)
+    # )
+
+    for idx in CBL_idx:
+        compact_CBL = compact_model.module_list[idx]
+        loose_CBL = loose_model.module_list[idx]
+        # print("idx:",idx,"--compact_CBL :", compact_CBL, "--compact_CBL[1] :", compact_CBL[1],"--compact_CBL[2] :", compact_CBL[2])
+        # print("idx:",idx,"--loose_CBL :", loose_CBL, "--loose_CBL[1] :", loose_CBL[1],"--loose_CBL[2] :", loose_CBL[2])
+        # np.argwhere返回非0元素的索引，X[:,0]是numpy中数组的一种写法，表示对一个二维数组，取该二维数组第一维中的所有数据，第二维中取第0个数据
+        out_channel_idx = np.argwhere(CBLidx2mask[idx])[:, 0].tolist()
+        # print("idx:",idx,"--out_channel_idx:", len(out_channel_idx))
+        # if (idx == 14):
+        #     print("idx:",idx,"--out_channel_idx:", out_channel_idx, "--len:",len(out_channel_idx))
+        # print("CBLidx2mask[idx] :",CBLidx2mask[idx],"--len:",len(CBLidx2mask[idx]))
+        # print("loose_model.module_list[idx][1]:",loose_model.module_list[idx][1])
+
+
+        # 获取剪枝后的模型当前BN层的权重
+        if(compact_model.module_defs[idx]['type'] == 'ds_conv') :
+            compact_bn, loose_bn         = compact_CBL[2], loose_CBL[2]
+        else:
+            compact_bn, loose_bn         = compact_CBL[1], loose_CBL[1]
+        # if idx==14:
+        #     print("compact_bn.weight.data:",compact_bn.weight.data,"--len",len(compact_bn.weight.data))    
+        #     print("loose_bn.weight.data:",loose_bn.weight.data,"--len",len(loose_bn.weight.data))    
+        #     print("loose_bn.weight.data[out_channel_idx]:", loose_bn.weight.data[out_channel_idx],"--len",len(loose_bn.weight.data[out_channel_idx]))
+        compact_bn.weight.data       = loose_bn.weight.data[out_channel_idx].clone()
+        compact_bn.bias.data         = loose_bn.bias.data[out_channel_idx].clone()
+        compact_bn.running_mean.data = loose_bn.running_mean.data[out_channel_idx].clone()
+        compact_bn.running_var.data  = loose_bn.running_var.data[out_channel_idx].clone()
+
+        # 获取剪枝后的模型当前卷积层的权重，这和上一个卷积层的剪枝情况有关
+        # print("idx:",idx)
+        input_mask = get_input_mask(loose_model.module_defs, idx, CBLidx2mask)
+        in_channel_idx = np.argwhere(input_mask)[:, 0].tolist()
+        # if (idx == 14):
+        #     print("idx:",idx,"--in_channel_idx:", in_channel_idx, "--len:",len(in_channel_idx))
+        # print("idx:",idx,"--in_channel_idx:",len(in_channel_idx))
+        if(compact_model.module_defs[idx]['type'] == 'ds_conv') :
+            compact_conv, loose_conv = compact_CBL[0], loose_CBL[0]
+            # compact_conv, loose_conv = compact_CBL[1], loose_CBL[1]
+            compact_conv2, loose_conv2 = compact_CBL[1], loose_CBL[1]
+            
+            # 拷贝权重到剪枝后的模型中去
+            # print("loose_conv2.weight.data:",loose_conv2.weight.data,"--len:",len(loose_conv2.weight.data))
+            # print("loose_conv2.weight.data[:, in_channel_idx, :, :]:",loose_conv2.weight.data[:, in_channel_idx, :, :],"--len:",len(loose_conv2.weight.data[:, in_channel_idx, :, :]))
+            # print("compact_conv.weight.data:",compact_conv.weight.data,"--len:",len(compact_conv.weight.data))
+            # print("compact_conv2.weight.data:",compact_conv2.weight.data,"--len:",len(compact_conv2.weight.data))
+            # print("loose_conv.weight.data:",loose_conv.weight.data,"--len:",len(loose_conv.weight.data))
+            # print("loose_conv.weight.data[:, in_channel_idx, :, :]:",loose_conv.weight.data[:, in_channel_idx, :, :],"--len:",len(loose_conv.weight.data[:, in_channel_idx, :, :]))
+            # tmp1 = loose_conv.weight.data[:, in_channel_idx, :, :].clone()
+            # compact_conv.weight.data = tmp1[in_channel_idx, :, :, :].clone()
+            compact_conv.weight.data = loose_conv.weight.data.clone()
+            compact_conv.bias.data   = loose_conv.bias.data.clone()
+            # tmp2 = loose_conv2.weight.data[:, in_channel_idx, :, :].clone()
+            # compact_conv2.weight.data = tmp2[out_channel_idx, :, :, :].clone()
+            compact_conv2.weight.data = loose_conv2.weight.data.clone()
+            compact_conv2.bias.data   = loose_conv2.bias.data.clone()
+        else:
+            compact_conv, loose_conv = compact_CBL[0], loose_CBL[0]
+        # print("idx:",idx,"--compact_CBL[0]:",compact_CBL[0],"--loose_CBL[0]",loose_CBL[0])  #还有一个可能就是ds_conv中有两个卷积层都需要复制权重！
+        # print("idx:",idx,"--compact_CBL[1]:",compact_CBL[1],"--loose_CBL[0]",loose_CBL[1])
+        
+            # 拷贝权重到剪枝后的模型中去
+            tmp = loose_conv.weight.data[:, in_channel_idx, :, :].clone()
+            compact_conv.weight.data = tmp[out_channel_idx, :, :, :].clone()
+
+    for idx in Conv_idx:
+        
+        compact_conv = compact_model.module_list[idx][0]
+        loose_conv = loose_model.module_list[idx][0]
+
+        # 虽然当前层是不带BN的卷积层，但仍然和上一个层的剪枝情况是相关的
+        input_mask = get_input_mask(loose_model.module_defs, idx, CBLidx2mask)
+        in_channel_idx = np.argwhere(input_mask)[:, 0].tolist()
+        
+        # 拷贝权重到剪枝后的模型中去
+        compact_conv.weight.data = loose_conv.weight.data[:, in_channel_idx, :, :].clone()
+        compact_conv.bias.data   = loose_conv.bias.data.clone()
+
+
+def prune_model_keep_size(model, prune_idx, CBL_idx, CBLidx2mask):
+# 先拷贝一份原始的模型参数
+    pruned_model = deepcopy(model)
+    # 对需要剪枝的层分别处理
+    for idx in prune_idx:
+        # 需要保留的通道
+        mask = torch.from_numpy(CBLidx2mask[idx]).cuda()
+        # 获取BN层的gamma参数，即BN层的权重
+        bn_module = pruned_model.module_list[idx][1]
+        # print("bn_module:", pruned_model.module_list[idx][1])
+
+        bn_module.weight.data.mul_(mask)
+        # 获取保留下来的通道产生的激活值，注意是每个通道分别获取的
+        activation = F.leaky_relu((1 - mask) * bn_module.bias.data, 0.1)
+
+        # 两个上采样层前的卷积层
+        next_idx_list = [idx + 1]
+        if idx == 79:
+            next_idx_list.append(84)
+        elif idx == 91:
+            next_idx_list.append(96)
+
+        # print("idx:",idx,"--next_idx_list:",next_idx_list)
+        
+         # 对下一层进行处理
+        for next_idx in next_idx_list:
+            # 当前层的BN剪枝之后会对下一个卷积层造成影响
+            next_conv = pruned_model.module_list[next_idx][0]
+            # dim=(2,3)即在(w,h)维度上进行求和，因为是通道剪枝，一个通道对应着(w,h)这个矩形
+            conv_sum = next_conv.weight.data.sum(dim=(2, 3))
+            # 将卷积层的权重和激活值相乘获得剪枝后的每个通道的偏置，以更新下一个BN层或者下一个带偏置的卷积层的偏执（因为单独的卷积层是不会被剪枝的，所以只对偏置有影响
+            # print("idx:", {idx} , "| next_idx:" , {next_idx})
+            # print("conv_sum :", conv_sum)         
+            # print("activation :", activation)
+            # print("--------------------------")
+            offset = conv_sum.matmul(activation.reshape(-1, 1)).reshape(-1)
+            if next_idx in CBL_idx:
+                next_bn = pruned_model.module_list[next_idx][1]
+                next_bn.running_mean.data.sub_(offset)
+            else:
+                next_conv.bias.data.add_(offset)
+
+        bn_module.bias.data.mul_(mask)
+    # 返回剪枝后的模型
+    return pruned_model
+
+
+def obtain_bn_mask(bn_module, thre):
+
+    thre = thre.cuda()
+    # ge(a, b)相当于 a>= b
+    mask = bn_module.weight.data.abs().ge(thre).float()
+    # print('thre = ',thre,"| mask = ", mask)
+    
+# 返回通道是否需要剪枝的通道状态
+    return mask

utils/utils.py

@@ -0,0 +1,323 @@
+from __future__ import division
+import tqdm
+import torch
+import numpy as np
+import random
+
+
+def init_seeds(seed=0):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def to_cpu(tensor):
+    return tensor.detach().cpu()
+
+
+def load_classes(path):
+    """
+    Loads class labels at 'path'
+    """
+    fp = open(path, "r")
+    names = fp.read().split("\n")[:-1]
+    return names
+
+
+def weights_init_normal(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
+    elif classname.find("BatchNorm2d") != -1:
+        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
+        torch.nn.init.constant_(m.bias.data, 0.0)
+
+
+def rescale_boxes(boxes, current_dim, original_shape):
+    """ Rescales bounding boxes to the original shape """
+    orig_h, orig_w = original_shape
+    # The amount of padding that was added
+    pad_x = max(orig_h - orig_w, 0) * (current_dim / max(original_shape))
+    pad_y = max(orig_w - orig_h, 0) * (current_dim / max(original_shape))
+    # Image height and width after padding is removed
+    unpad_h = current_dim - pad_y
+    unpad_w = current_dim - pad_x
+    # Rescale bounding boxes to dimension of original image
+    boxes[:, 0] = ((boxes[:, 0] - pad_x // 2) / unpad_w) * orig_w
+    boxes[:, 1] = ((boxes[:, 1] - pad_y // 2) / unpad_h) * orig_h
+    boxes[:, 2] = ((boxes[:, 2] - pad_x // 2) / unpad_w) * orig_w
+    boxes[:, 3] = ((boxes[:, 3] - pad_y // 2) / unpad_h) * orig_h
+    return boxes
+
+
+def xywh2xyxy(x):
+    y = x.new(x.shape)
+    y[..., 0] = x[..., 0] - x[..., 2] / 2
+    y[..., 1] = x[..., 1] - x[..., 3] / 2
+    y[..., 2] = x[..., 0] + x[..., 2] / 2
+    y[..., 3] = x[..., 1] + x[..., 3] / 2
+    return y
+
+
+def ap_per_class(tp, conf, pred_cls, target_cls):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+    # Arguments
+        tp:    True positives (list).
+        conf:  Objectness value from 0-1 (list).
+        pred_cls: Predicted object classes (list).
+        target_cls: True object classes (list).
+    # 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
+    ap, p, r = [], [], []
+    for c in tqdm.tqdm(unique_classes, desc="Computing AP"):
+        i = pred_cls == c
+        n_gt = (target_cls == c).sum()  # Number of ground truth objects
+        n_p = i.sum()  # Number of predicted objects
+
+        if n_p == 0 and n_gt == 0:
+            continue
+        elif n_p == 0 or n_gt == 0:
+            ap.append(0)
+            r.append(0)
+            p.append(0)
+        else:
+            # Accumulate FPs and TPs
+            fpc = (1 - tp[i]).cumsum()
+            tpc = (tp[i]).cumsum()
+
+            # Recall
+            recall_curve = tpc / (n_gt + 1e-16)
+            r.append(recall_curve[-1])
+
+            # Precision
+            precision_curve = tpc / (tpc + fpc)
+            p.append(precision_curve[-1])
+
+            # AP from recall-precision curve
+            ap.append(compute_ap(recall_curve, precision_curve))
+
+    # Compute F1 score (harmonic mean of precision and recall)
+    p, r, ap = np.array(p), np.array(r), np.array(ap)
+    f1 = 2 * p * r / (p + r + 1e-16)
+
+    return p, r, ap, f1, unique_classes.astype("int32")
+
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves.
+    Code originally from https://github.com/rbgirshick/py-faster-rcnn.
+
+    # Arguments
+        recall:    The recall curve (list).
+        precision: The precision curve (list).
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+    # correct AP calculation
+    # first append sentinel values at the end
+    mrec = np.concatenate(([0.0], recall, [1.0]))
+    mpre = np.concatenate(([0.0], precision, [0.0]))
+
+    # 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 get_batch_statistics(outputs, targets, iou_threshold):
+    """ Compute true positives, predicted scores and predicted labels per sample """
+    batch_metrics = []
+    for sample_i in range(len(outputs)):
+
+        if outputs[sample_i] is None:
+            continue
+
+        output = outputs[sample_i] # 取出结果
+        pred_boxes = output[:, :4] # box
+        pred_scores = output[:, 4] # 置信度
+        pred_labels = output[:, -1]# 类别
+
+        true_positives = np.zeros(pred_boxes.shape[0]) # 统计postive的值
+
+        annotations = targets[targets[:, 0] == sample_i][:, 1:] # 取出对应的图片的 标注【label，x,y,w,h】
+        target_labels = annotations[:, 0] if len(annotations) else [] # 取出图片内的 label  
+        if len(annotations):
+            detected_boxes = []
+            target_boxes = annotations[:, 1:] # 标注bbox
+
+            for pred_i, (pred_box, pred_label) in enumerate(zip(pred_boxes, pred_labels)):
+
+                # If targets are found break
+                if len(detected_boxes) == len(annotations): # 完成图片内的目标检测
+                    break
+
+                # Ignore if label is not one of the target labels
+                if pred_label not in target_labels:
+                    continue
+
+                iou, box_index = bbox_iou(pred_box.unsqueeze(0), target_boxes).max(0) # 返回列最大值
+                if iou >= iou_threshold and box_index not in detected_boxes:          # iou>阈值的
+                    true_positives[pred_i] = 1
+                    detected_boxes += [box_index]
+        batch_metrics.append([true_positives, pred_scores, pred_labels])
+    return batch_metrics
+
+
+def bbox_wh_iou(wh1, wh2):
+    wh2 = wh2.t()
+    w1, h1 = wh1[0], wh1[1]
+    w2, h2 = wh2[0], wh2[1]
+    inter_area = torch.min(w1, w2) * torch.min(h1, h2)
+    union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
+    return inter_area / union_area
+
+
+def bbox_iou(box1, box2, x1y1x2y2=True):
+    """
+    Returns the IoU of two bounding boxes
+    """
+    if not x1y1x2y2:
+        # Transform from center and width to exact coordinates
+        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
+    else:
+        # 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]
+
+    # get the corrdinates of the intersection rectangle
+    inter_rect_x1 = torch.max(b1_x1, b2_x1)
+    inter_rect_y1 = torch.max(b1_y1, b2_y1)
+    inter_rect_x2 = torch.min(b1_x2, b2_x2)
+    inter_rect_y2 = torch.min(b1_y2, b2_y2)
+    # Intersection area
+    inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(
+        inter_rect_y2 - inter_rect_y1 + 1, min=0
+    )
+    # Union Area
+    b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)
+    b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)
+
+    iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)
+
+    return iou
+
+
+def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4):
+    """
+    Removes detections with lower object confidence score than 'conf_thres' and performs
+    Non-Maximum Suppression to further filter detections.
+    Returns detections with shape:
+        (x1, y1, x2, y2, object_conf, class_score, class_pred)
+    """
+
+    # From (center x, center y, width, height) to (x1, y1, x2, y2)
+    prediction[..., :4] = xywh2xyxy(prediction[..., :4])
+    output = [None for _ in range(len(prediction))]
+    for image_i, image_pred in enumerate(prediction):
+        # Filter out confidence scores below threshold
+        image_pred = image_pred[image_pred[:, 4] >= conf_thres]
+        # If none are remaining => process next image
+        if not image_pred.size(0):
+            continue
+        # Object confidence times class confidence
+        score = image_pred[:, 4] * image_pred[:, 5:].max(1)[0]
+        # Sort by it
+        image_pred = image_pred[(-score).argsort()]
+        class_confs, class_preds = image_pred[:, 5:].max(1, keepdim=True)
+        detections = torch.cat((image_pred[:, :5], class_confs.float(), class_preds.float()), 1)
+        # Perform non-maximum suppression
+        keep_boxes = []
+        while detections.size(0):
+            large_overlap = bbox_iou(detections[0, :4].unsqueeze(0), detections[:, :4]) > nms_thres
+            label_match = detections[0, -1] == detections[:, -1]
+            # Indices of boxes with lower confidence scores, large IOUs and matching labels
+            invalid = large_overlap & label_match
+            weights = detections[invalid, 4:5]
+            # Merge overlapping bboxes by order of confidence
+            detections[0, :4] = (weights * detections[invalid, :4]).sum(0) / weights.sum()
+            keep_boxes += [detections[0]]
+            detections = detections[~invalid]
+        if keep_boxes:
+            output[image_i] = torch.stack(keep_boxes)
+
+    return output
+
+
+def build_targets(pred_boxes, pred_cls, target, anchors, ignore_thres):
+
+    ByteTensor = torch.cuda.ByteTensor if pred_boxes.is_cuda else torch.ByteTensor
+    FloatTensor = torch.cuda.FloatTensor if pred_boxes.is_cuda else torch.FloatTensor
+
+    nB = pred_boxes.size(0)     # num_samples, num_anchors, grid_size, grid_size, 4
+    nA = pred_boxes.size(1)
+    nC = pred_cls.size(-1)      # num_samples, num_anchors, grid_size, grid_size, num_classes
+    nG = pred_boxes.size(2)
+
+    # Output tensors
+    obj_mask = ByteTensor(nB, nA, nG, nG).fill_(0)
+    noobj_mask = ByteTensor(nB, nA, nG, nG).fill_(1)
+    class_mask = FloatTensor(nB, nA, nG, nG).fill_(0)
+    iou_scores = FloatTensor(nB, nA, nG, nG).fill_(0)
+    tx = FloatTensor(nB, nA, nG, nG).fill_(0)
+    ty = FloatTensor(nB, nA, nG, nG).fill_(0)
+    tw = FloatTensor(nB, nA, nG, nG).fill_(0)
+    th = FloatTensor(nB, nA, nG, nG).fill_(0)
+    tcls = FloatTensor(nB, nA, nG, nG, nC).fill_(0)
+
+    # Convert to position relative to box
+    target_boxes = target[:, 2:6] * nG      # target的1维是其对应图片的idx
+    gxy = target_boxes[:, :2]
+    gwh = target_boxes[:, 2:]
+    # Get anchors with best iou
+    ious = torch.stack([bbox_wh_iou(anchor, gwh) for anchor in anchors])    # anchors*target
+    best_ious, best_n = ious.max(0)
+    # Separate target values
+    b, target_labels = target[:, :2].long().t() # target = [idx, labels, x, y, w, h]
+    gx, gy = gxy.t()
+    gw, gh = gwh.t()
+    gi, gj = gxy.long().t()   # long是变成长整型
+    # Set masks
+    obj_mask[b, best_n, gj, gi] = 1
+    noobj_mask[b, best_n, gj, gi] = 0
+
+    # Set noobj mask to zero where iou exceeds ignore threshold
+    for i, anchor_ious in enumerate(ious.t()):
+        noobj_mask[b[i], anchor_ious > ignore_thres, gj[i], gi[i]] = 0
+
+    # Coordinates
+    tx[b, best_n, gj, gi] = gx - gx.floor()
+    ty[b, best_n, gj, gi] = gy - gy.floor()
+    # Width and height
+    tw[b, best_n, gj, gi] = torch.log(gw / anchors[best_n][:, 0] + 1e-16)
+    th[b, best_n, gj, gi] = torch.log(gh / anchors[best_n][:, 1] + 1e-16)
+    # One-hot encoding of label
+    tcls[b, best_n, gj, gi, target_labels] = 1
+    # Compute label correctness and iou at best anchor
+    class_mask[b, best_n, gj, gi] = (pred_cls[b, best_n, gj, gi].argmax(-1) == target_labels).float()
+    iou_scores[b, best_n, gj, gi] = bbox_iou(pred_boxes[b, best_n, gj, gi], target_boxes, x1y1x2y2=False)
+
+    tconf = obj_mask.float()
+    return iou_scores, class_mask, obj_mask, noobj_mask, tx, ty, tw, th, tcls, tconf

xs_train.sh

@@ -0,0 +1,4 @@
+#!bash
+CARD=0,1
+
+CUDA_VISIBLE_DEVICES=$CARD python train.py --model_def config/yolov3-person.cfg -sr --s 0.01