本项目实现功能如下：
1.训练自己的数据集
2.对任意卷积层进行剪枝
3.剪枝后的训练
4.剪枝后的模型预测
将自己制作好的数据集放在dataset文件下，目录形式如下：
dataset |-- Annotations |-- ImageSets |-- images |-- labels
Annotations是存放xml标签文件的，images是存放图像的，ImageSets存放四个txt文件【后面运行代码的时候会自动生成】，labels是将xml转txt文件。
1.运行makeTXT.py。这将会在ImageSets文件夹下生成 trainval.txt，test.txt，train.txt，val.txt四个文件【如果你打开这些txt文件，里面仅有图像的名字】。
2.打开voc_label.py，并修改代码 classes=[""]填入自己的类名，比如你的是训练猫和狗，那么就是classes=["dog","cat"]，然后运行该程序。此时会在labels文件下生成对应每个图像的txt文件，形式如下：【最前面的0是类对应的索引，我这里只有一个类，后面的四个数为box的参数，均归一化以后的，分别表示box的左上和右下坐标，等训练的时候会处理成center_x，center_y，w, h】
0 0.4723557692307693 0.5408653846153847 0.34375 0.8990384615384616 0 0.8834134615384616 0.5793269230769231 0.21875 0.8221153846153847
3.在data文件夹下新建一个mydata.yaml文件。内容如下【你也可以把coco.yaml复制过来】。
你只需要修改nc以及names即可，nc是类的数量，names是类的名字。
train: ./dataset/train.txt val: ./dataset/val.txt test: ./dataset/test.txt # number of classes nc: 1 # class names names: ['target']
4.终端输入参数，开始训练。
以yolov5s为例：
python train.py --weights yolov5s.pt --cfg models/yolov5s.yaml --data data/mydata.yamlfrom n params module arguments0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 2 115712 models.common.C3 [128, 128, 2] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3] 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5] 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False] 20 -1 1 296448 models.common.C3 [256, 256, 1, False] 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] 24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 270 layers, 7022326 parameters, 7022326 gradients, 15.8 GFLOPs
Starting training for 300 epochs...
Epoch gpu_mem box obj cls labels img_size 0/299 0.589G 0.0779 0.03841 0 4 640: 6%|████▋ | 23/359 [00:23<04:15, 1.31it/s]
看到以上信息就开始训练了。
在利用剪枝功能前，需要安装一下剪枝的库。需要安装0.2.7版本，0.2.8有粉丝说有问题。剪枝时的一些log信息会自动保存在logs文件夹下，每个log的大小我设置的为1MB，如果有其他需要大家可以更改。
pip install torch_pruning==0.2.7YOLOv5与我之前写过的剪枝不同，v5在训练保存后的权重本身就保存了完整的model，即用的是torch.save(model,...)，而不是torch.save(model.state_dict(),...)，因此不需要单独在对网络结构保存一次。
模型剪枝代码在tools/prunmodel.py。你只需要找到这部分代码进行修改：我这里是以剪枝整个backbone的卷积层为例，如果你要剪枝的是其他层按需修改.included_layers内就是你要剪枝的层。
"""
这里写要剪枝的层
"""
included_layers = []
for layer in model.model[:10]:
if type(layer) is Conv:
included_layers.append(layer.conv)
elif type(layer) is C3:
included_layers.append(layer.cv1.conv)
included_layers.append(layer.cv2.conv)
included_layers.append(layer.cv3.conv)
elif type(layer) is SPPF:
included_layers.append(layer.cv1.conv)
included_layers.append(layer.cv2.conv)接下来在找到下面这行代码，amount为剪枝率，同样也是按需修改。【这里需要明白的一点，这里的剪枝率仅是对你要剪枝的所有层剪枝这么多，并不是把网络从头到尾全部剪，有些粉丝说我选了一层，剪枝率50%，怎么模型还那么大，没啥变化，这个就是他搞混了，他以为是对整个网络剪枝50%】。
pruning_plan = DG.get_pruning_plan(m, tp.prune_conv, idxs=strategy(m.weight, amount=0.8))接下来调用剪枝函数，传入参数为自己的训练好的权重文件路径。
layer_pruning('../runs/train/exp/weights/best.pt')见到如下形式，就说明剪枝成功了，剪枝以后的权重会保存在model_data下，名字为layer_pruning.pt。
这里需要说明一下，保存的权重文件中不仅包含了网络结构和权值内容，还有优化器的权值，如果仅仅保存网络结构和权值也是可以的，这样pt会更小一点，我这里默认都保存是为了和官方pt格式一致。
-------------
[ <DEP: prune_conv => prune_conv on model.9.cv2.conv (Conv2d(208, 512, kernel_size=(1, 1), stride=(1, 1), bias=False))>, Index=[0, 1, 2, 3, 7, 8, 10, 11, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 65, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 135, 137, 139, 142, 143, 144, 146, 148, 150, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 173, 174, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 232, 233, 234, 235, 236, 237, 239, 240, 241, 242, 243, 246, 247, 248, 249, 251, 252, 253, 254, 257, 258, 259, 260, 263, 264, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 277, 278, 280, 281, 282, 283, 284, 285, 286, 287, 288, 292, 293, 294, 295, 296, 297, 299, 301, 302, 303, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 321, 322, 323, 324, 325, 326, 327, 329, 330, 331, 332, 334, 335, 338, 339, 341, 342, 343, 344, 346, 347, 349, 351, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 368, 369, 370, 372, 373, 374, 375, 378, 379, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 401, 402, 403, 404, 405, 407, 408, 411, 413, 414, 415, 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 441, 442, 443, 444, 445, 446, 448, 449, 451, 452, 453, 454, 455, 456, 457, 458, 459, 461, 463, 465, 466, 468, 470, 472, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 502, 503, 505, 506, 507, 510, 511], NumPruned=85072]
[ <DEP: prune_conv => prune_batchnorm on model.9.cv2.bn (BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True))>, Index=[0, 1, 2, 3, 7, 8, 10, 11, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 65, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 135, 137, 139, 142, 143, 144, 146, 148, 150, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 173, 174, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 232, 233, 234, 235, 236, 237, 239, 240, 241, 242, 243, 246, 247, 248, 249, 251, 252, 253, 254, 257, 258, 259, 260, 263, 264, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 277, 278, 280, 281, 282, 283, 284, 285, 286, 287, 288, 292, 293, 294, 295, 296, 297, 299, 301, 302, 303, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 321, 322, 323, 324, 325, 326, 327, 329, 330, 331, 332, 334, 335, 338, 339, 341, 342, 343, 344, 346, 347, 349, 351, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 368, 369, 370, 372, 373, 374, 375, 378, 379, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 401, 402, 403, 404, 405, 407, 408, 411, 413, 414, 415, 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 441, 442, 443, 444, 445, 446, 448, 449, 451, 452, 453, 454, 455, 456, 457, 458, 459, 461, 463, 465, 466, 468, 470, 472, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 502, 503, 505, 506, 507, 510, 511], NumPruned=818]
[ <DEP: prune_batchnorm => _prune_elementwise_op on _ElementWiseOp()>, Index=[0, 1, 2, 3, 7, 8, 10, 11, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 65, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 135, 137, 139, 142, 143, 144, 146, 148, 150, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 173, 174, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 232, 233, 234, 235, 236, 237, 239, 240, 241, 242, 243, 246, 247, 248, 249, 251, 252, 253, 254, 257, 258, 259, 260, 263, 264, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 277, 278, 280, 281, 282, 283, 284, 285, 286, 287, 288, 292, 293, 294, 295, 296, 297, 299, 301, 302, 303, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 321, 322, 323, 324, 325, 326, 327, 329, 330, 331, 332, 334, 335, 338, 339, 341, 342, 343, 344, 346, 347, 349, 351, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 368, 369, 370, 372, 373, 374, 375, 378, 379, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 401, 402, 403, 404, 405, 407, 408, 411, 413, 414, 415, 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 441, 442, 443, 444, 445, 446, 448, 449, 451, 452, 453, 454, 455, 456, 457, 458, 459, 461, 463, 465, 466, 468, 470, 472, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 502, 503, 505, 506, 507, 510, 511], NumPruned=0]
[ <DEP: _prune_elementwise_op => _prune_elementwise_op on _ElementWiseOp()>, Index=[0, 1, 2, 3, 7, 8, 10, 11, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 65, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 135, 137, 139, 142, 143, 144, 146, 148, 150, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 173, 174, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 232, 233, 234, 235, 236, 237, 239, 240, 241, 242, 243, 246, 247, 248, 249, 251, 252, 253, 254, 257, 258, 259, 260, 263, 264, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 277, 278, 280, 281, 282, 283, 284, 285, 286, 287, 288, 292, 293, 294, 295, 296, 297, 299, 301, 302, 303, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 321, 322, 323, 324, 325, 326, 327, 329, 330, 331, 332, 334, 335, 338, 339, 341, 342, 343, 344, 346, 347, 349, 351, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 368, 369, 370, 372, 373, 374, 375, 378, 379, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 401, 402, 403, 404, 405, 407, 408, 411, 413, 414, 415, 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 441, 442, 443, 444, 445, 446, 448, 449, 451, 452, 453, 454, 455, 456, 457, 458, 459, 461, 463, 465, 466, 468, 470, 472, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 502, 503, 505, 506, 507, 510, 511], NumPruned=0]
[ <DEP: _prune_elementwise_op => prune_related_conv on model.10.conv (Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False))>, Index=[0, 1, 2, 3, 7, 8, 10, 11, 12, 13, 16, 17, 18, 19, 21, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 65, 67, 69, 70, 71, 72, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 95, 96, 97, 99, 100, 102, 103, 104, 105, 106, 107, 109, 110, 111, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 132, 133, 135, 137, 139, 142, 143, 144, 146, 148, 150, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 173, 174, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 215, 216, 217, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 232, 233, 234, 235, 236, 237, 239, 240, 241, 242, 243, 246, 247, 248, 249, 251, 252, 253, 254, 257, 258, 259, 260, 263, 264, 265, 266, 267, 268, 270, 271, 272, 273, 274, 275, 276, 277, 278, 280, 281, 282, 283, 284, 285, 286, 287, 288, 292, 293, 294, 295, 296, 297, 299, 301, 302, 303, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 321, 322, 323, 324, 325, 326, 327, 329, 330, 331, 332, 334, 335, 338, 339, 341, 342, 343, 344, 346, 347, 349, 351, 353, 354, 355, 356, 357, 358, 359, 361, 362, 363, 364, 365, 366, 368, 369, 370, 372, 373, 374, 375, 378, 379, 381, 382, 383, 385, 386, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 401, 402, 403, 404, 405, 407, 408, 411, 413, 414, 415, 416, 418, 419, 420, 421, 422, 423, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 441, 442, 443, 444, 445, 446, 448, 449, 451, 452, 453, 454, 455, 456, 457, 458, 459, 461, 463, 465, 466, 468, 470, 472, 473, 474, 475, 476, 477, 478, 479, 480, 482, 483, 484, 485, 486, 487, 488, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 502, 503, 505, 506, 507, 510, 511], NumPruned=104704]
190594 parameters will be pruned
-------------
2022-09-29 12:30:50.396 | INFO | __main__:layer_pruning:75 - Params: 7022326 => 3056461
2022-09-29 12:30:50.691 | INFO | __main__:layer_pruning:89 - 剪枝完成如果你仅仅就想剪一层，可以这样写：
included_layers = [model.model[3].conv] # 仅仅想剪一个卷积层
这里需要和稀疏训练区别一下，因为很多人在之前项目中问我有没有稀疏训练。我这里的通道剪枝是离线式的，也就是针对已经训练好的模型进行剪枝，而边训练边剪枝是在线式剪枝，这个训练过程也就是稀疏训练，所以还是有区别的。
训练后的剪枝训练与训练部分是一样的，只不过加一个pt参数而已。命令如下：
python train.py --weights model_data/layer_pruning.pt --data data/mydata.yaml --pt 剪枝后的预测，和正常预测一样。
python detect.py --weights model_data/layer_pruning.pt --source [你的图像路径]这里再说明一下！！本文章只是给大家造个轮子，具体最终的剪枝效果，需要根据自己的需求以及实际效果来实现，我对整个backbone剪枝80%后的微调训练反正是效果很不好，对SPPF后其他的层剪枝还稍微好点，网上也有很多人说对backbone剪枝效果不行。
CSDN：https://blog.csdn.net/z240626191s/article/details/127103705