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模型压缩（二）yolov5剪枝

作者：繁依Fanyi0 | 2024-08-11 02:38:48

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yolov5剪枝

一、yolov5s

在yolov5s.ymal文件中，

depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple。

通道深度（残差数）及宽度（通道数）相对标准的比例。

标准的backbone中的C3的number分别为：3、6、9、3

yolov5s的backbone中的C3的number为：1,、2、3、1  （depth_multiple*number）

同理网络宽度width_multiple*args[0]。

head类似。

  1. -------------------------------------0-P1/2----------------------------------------------
  2. model.0.conv.weight --------- torch.Size([32, 3, 6, 6])
  3. model.0.bn.weight --------- torch.Size([32])
  4. model.0.bn.bias --------- torch.Size([32])
  5. -------------------------------------1-P2/4----------------------------------------------
  6. model.1.conv.weight --------- torch.Size([64, 32, 3, 3])
  7. model.1.bn.weight --------- torch.Size([64])
  8. model.1.bn.bias --------- torch.Size([64])
  9.  
  10. -------------------------------------C3----------------------------------------------
  11. **cv1**
  12. model.2.cv1.conv.weight --------- torch.Size([32, 64, 1, 1])
  13. model.2.cv1.bn.weight --------- torch.Size([32])  ***
  14. model.2.cv1.bn.bias --------- torch.Size([32])    ***
  15. **cv2**
  16. model.2.cv2.conv.weight --------- torch.Size([32, 64, 1, 1])
  17. model.2.cv2.bn.weight --------- torch.Size([32])
  18. model.2.cv2.bn.bias --------- torch.Size([32])
  19. **cv3**
  20. model.2.cv3.conv.weight --------- torch.Size([64, 64, 1, 1])
  21. model.2.cv3.bn.weight --------- torch.Size([64])
  22. model.2.cv3.bn.bias --------- torch.Size([64])
  23.  
  24. bneck:*1
  25. model.2.m.0.cv1.conv.weight --------- torch.Size([32, 32, 1, 1])
  26. model.2.m.0.cv1.bn.weight --------- torch.Size([32])        ***
  27. model.2.m.0.cv1.bn.bias --------- torch.Size([32])          ***
  28. model.2.m.0.cv2.conv.weight --------- torch.Size([32, 32, 3, 3])
  29. model.2.m.0.cv2.bn.weight --------- torch.Size([32])      ***
  30. model.2.m.0.cv2.bn.bias --------- torch.Size([32])        ***
  31.  
  32. -------------------------------------3-P3/8----------------------------------------------
  33. model.3.conv.weight --------- torch.Size([128, 64, 3, 3])
  34. model.3.bn.weight --------- torch.Size([128])
  35. model.3.bn.bias --------- torch.Size([128])
  36.  
  37. -------------------------------------C3----------------------------------------------
  38. **cv1**
  39. model.4.cv1.conv.weight --------- torch.Size([64, 128, 1, 1])
  40. model.4.cv1.bn.weight --------- torch.Size([64])        ***
  41. model.4.cv1.bn.bias --------- torch.Size([64])        ***
  42. **cv2**
  43. model.4.cv2.conv.weight --------- torch.Size([64, 128, 1, 1])
  44. model.4.cv2.bn.weight --------- torch.Size([64])
  45. model.4.cv2.bn.bias --------- torch.Size([64])
  46. **cv3**
  47. model.4.cv3.conv.weight --------- torch.Size([128, 128, 1, 1])
  48. model.4.cv3.bn.weight --------- torch.Size([128])
  49. model.4.cv3.bn.bias --------- torch.Size([128])
  50. **bneck1**
  51. model.4.m.0.cv1.conv.weight --------- torch.Size([64, 64, 1, 1])
  52. model.4.m.0.cv1.bn.weight --------- torch.Size([64])
  53. model.4.m.0.cv1.bn.bias --------- torch.Size([64])
  54. model.4.m.0.cv2.conv.weight --------- torch.Size([64, 64, 3, 3])
  55. model.4.m.0.cv2.bn.weight --------- torch.Size([64])
  56. model.4.m.0.cv2.bn.bias --------- torch.Size([64])
  57. **bneck2**
  58. model.4.m.1.cv1.conv.weight --------- torch.Size([64, 64, 1, 1])
  59. model.4.m.1.cv1.bn.weight --------- torch.Size([64])
  60. model.4.m.1.cv1.bn.bias --------- torch.Size([64])
  61. model.4.m.1.cv2.conv.weight --------- torch.Size([64, 64, 3, 3])
  62. model.4.m.1.cv2.bn.weight --------- torch.Size([64])
  63. model.4.m.1.cv2.bn.bias --------- torch.Size([64])
  64.  
  65. -------------------------------------5-P4/16----------------------------------------------
  66. model.5.conv.weight --------- torch.Size([256, 128, 3, 3])
  67. model.5.bn.weight --------- torch.Size([256])
  68. model.5.bn.bias --------- torch.Size([256])
  69.  
  70.  
  71. 。。。。。。
  72.

二、C3模块

        本文选择yolov5s进行通道剪枝，同样根据BN层稀疏化达到剪枝效果。在yolov5s结构中存在shortcut与cat，主路与支路合并操作。其中shortcut是将前层与后层特征相加，cat是通道连接，而shortcut必须保证前后层的通道数一致才可相加。如果shortcut的前后层参与剪枝，就无法保证前后层的通道数一致，所以剪枝过程中必须剔除参与shortcut操作的卷积层，而cat操作则不影响。

yolov5s的C3模块的Bottleneck结构中存在shortcut操作。为了避免BN层稀疏后，通道数不匹配，所以所有的残差结构都不剪枝。

C3：

  1. class Bottleneck(nn.Module):
  2.     # Standard bottleneck
  3.     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
  4.         super().__init__()
  5.         c_ = int(c2 * e)  # hidden channels
  6.         self.cv1 = Conv(c1, c_, 1, 1)
  7.         self.cv2 = Conv(c_, c2, 3, 1, g=g)
  8.         self.add = shortcut and c1 == c2  #通道相同直接相加。
  9.  
  10.     def forward(self, x):
  11.         return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
  12.  
  13.  
  14. class C3(nn.Module):
  15.     # CSP Bottleneck with 3 convolutions
  16.     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
  17.         super().__init__()
  18.         c_ = int(c2 * e)  # hidden channels
  19.         self.cv1 = Conv(c1, c_, 1, 1)
  20.         self.cv2 = Conv(c1, c_, 1, 1)#支路
  21.         self.cv3 = Conv(2 * c_, c2, 1)  # act=FReLU(c2)
  22.         self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
  23.         # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
  24.  
  25.     def forward(self, x):
  26.         return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))

 C3结构：

所以C3结构中cv1、cv2参与剪枝。

三、剪枝操作

1、稀疏训练

剔除C3结构中不参与剪枝的卷积层 。

  1.  #-------------------------------parse---------------------------
  2.             srtmp=opt.sr*(1-0.9*epoch/epochs)
  3.             if opt.st:
  4.                 ignore_bn_list=[]
  5.                 #记录bottleneck中所有bn层
  6.                 #C3结构中第一个卷积层与bneck中conv层不剪枝
  7.                 #即参与add操作有三层conv
  8.                 for k,m in model.named_modules():
  9.                     if isinstance(m,Bottleneck):
  10.                         if m.add:
  11.                             ignore_bn_list.append(k.split('.',2)[0]+'.cv1.bn')
  12.                             ignore_bn_list.append(k+ '.cv1.bn')
  13.                             ignore_bn_list.append(k + '.cv2.bn')
  14.                     if isinstance(k,nn.BatchNorm2d) and (k  not in ignore_bn_list):
  15.                         m.weight.grad.data.add_(srtmp*torch.sign(m.weight.data))
  16.                         m.bias.grad.data.add_(opt.sr*10 * torch.sign(m.weight.bias))
  17.                 print(ignore_bn_list)

2、剪枝操作

规整剪枝与正常剪枝。

正常剪枝

需剪枝的bn层

  1.    bn_layers= {}
  2.     ignore_bn_layers=[]
  3.     for layer_name,layer_model in model.named_modules():
  4.         if isinstance(layer_model,Bottleneck):
  5.             if layer_model.add:
  6.                 ignore_bn_layers.append(layer_name.rsplit('.',2)[0]+'.cv1.bn')#C3中第一个conv
  7.                 ignore_bn_layers.append(layer_name+'.cv1.bn')#bottleneck中第一个conv
  8.                 ignore_bn_layers.append(layer_name+'.cv2.bn')#bottleneck中第一个conv
  9.         if isinstance(layer_model,nn.BatchNorm2d) and (layer_name not in ignore_bn_layers):
  10.                 # print(ignore_bn_layers,layer_name)
  11.                 #未剔除全，主要是每次遍历进入C3中时，cv1没剔除，直到bneck中才开始。
  12.                 bn_layers[layer_name]=layer_model
  13.  
  14.     # print(ignore_bn_layers,)
  15.     # print(len(ignore_bn_layers))
  16.     # print(bn_layers)
  17.     # print(len(bn_layers))
  18.     # exit()
  19.     #再次过滤4个C3中的第一个cv层
  20.     bn_layers= {k:v for k,v in bn_layers.items() if k not in ignore_bn_layers}
  21.     # print(bn_names)
  22.     # print(len(bn_names))
  23.     # exit()

统计所有BN层通道数量及各通道的权重值，对权重进行排序，并计算得到索引阈值。

  1.   bn_size=[da.weight.data.shape[0] for da in bn_layers.values()]
  2.     total_size=sum(bn_size)
  3.     print(total_size)
  4.     bn_weights=torch.zeros(total_size)
  5.     start=0
  6.     for i,w in enumerate(bn_layers.values()):
  7.         size=w.weight.data.shape[0]
  8.         bn_weights[start:(start+size)] = w.weight.data.abs().clone()
  9.         start+=bn_size[i]
  10.     print(bn_weights,bn_weights.shape)
  11.  
  12.     bn_data,id=torch.sort(bn_weights)
  13.  
  14.     thresh_index=int(percent*total_size)
  15.     thresh_weight=bn_data[thresh_index]
  16.     print(thresh_index,thresh_weight)
  17.     print(f'Gamma value that less than {thresh_weight:.4f} are set to zero!')
  18.     print("=" * 94)
  19.     print(f"|\t{'layer name':<25}{'|':<10}{'origin channels':<20}{'|':<10}{'remaining channels':<20}|")

存在问题：

根据阈值来分隔，可能存在某一BN层所有通道均小于阈值，如果将其过滤掉，会造成层层之间的断开，此时需要做判断进行限制，使得每层最少有一个通道得以保留。

解决方法：获取每个bn层的权重的最大值，然后在这些最大值中取最小值与设定的阈值进行对比，如果小于阈值，则提示修改。
 

  1.  # 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)
  2.     highest_thre = []
  3.     for bnlayer in bn_layers.values():
  4.         highest_thre.append(bnlayer.weight.data.abs().max().item())
  5.     # print("highest_thre:",highest_thre)
  6.     highest_thre = min(highest_thre)
  7.     # 找到highest_thre对应的下标对应的百分比
  8.     percent_limit = (bn_data == highest_thre).nonzero()[0, 0].item() / len(bn_weights)
  9.  
  10.     print(f'Suggested Gamma threshold should be less than {highest_thre:.4f}.')
  11.     print(f'The corresponding prune ratio is {percent_limit:.3f}, but you can set higher.')
  12.

重新设置模型文件

  1.   pruned_num=0
  2.     pruned_yaml = {}
  3.     nc = model.model[-1].nc
  4.     with open(cfg, encoding='ascii', errors='ignore') as f:
  5.         model_yamls = yaml.safe_load(f)  # model dict
  6.     # # Define model
  7.     pruned_yaml["nc"] = model.model[-1].nc
  8.     pruned_yaml["depth_multiple"] = model_yamls["depth_multiple"]
  9.     pruned_yaml["width_multiple"] = model_yamls["width_multiple"]
  10.     pruned_yaml["anchors"] = model_yamls["anchors"]
  11.     anchors = model_yamls["anchors"]
  12.     pruned_yaml["backbone"] = [
  13.         [-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
  14.         [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
  15.         [-1, 3, C3Pruned, [128]],
  16.         [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
  17.         [-1, 6, C3Pruned, [256]],
  18.         [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
  19.         [-1, 9, C3Pruned, [512]],
  20.         [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
  21.         [-1, 3, C3Pruned, [1024]],
  22.         [-1, 1, SPPFPruned, [1024, 5]],  # 9
  23.     ]
  24.     pruned_yaml["head"] = [
  25.         [-1, 1, Conv, [512, 1, 1]],
  26.         [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  27.         [[-1, 6], 1, Concat, [1]],  # cat backbone P4
  28.         [-1, 3, C3Pruned, [512, False]],  # 13
  29.  
  30.         [-1, 1, Conv, [256, 1, 1]],
  31.         [-1, 1, nn.Upsample, [None, 2, 'nearest']],
  32.         [[-1, 4], 1, Concat, [1]],  # cat backbone P3
  33.         [-1, 3, C3Pruned, [256, False]],  # 17 (P3/8-small)
  34.  
  35.         [-1, 1, Conv, [256, 3, 2]],
  36.         [[-1, 14], 1, Concat, [1]],  # cat head P4
  37.         [-1, 3, C3Pruned, [512, False]],  # 20 (P4/16-medium)
  38.  
  39.         [-1, 1, Conv, [512, 3, 2]],
  40.         [[-1, 10], 1, Concat, [1]],  # cat head P5
  41.         [-1, 3, C3Pruned, [1024, False]],  # 23 (P5/32-large)
  42.  
  43.         [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  44.     ]

模型重构：

  1.    maskbndict={}
  2.     remain_num=0
  3.     for name,layer in model.named_modules():
  4.         if isinstance(layer,nn.BatchNorm2d):
  5.             bn_model=layer
  6.             mask=obtain_bn_mask(bn_model,thresh_weight)
  7.             # print(mask)
  8.             if name in ignore_bn_layers:
  9.                 # print('-----')
  10.                 mask=torch.ones(layer.weight.data.size()).cuda()
  11.             maskbndict[name]=mask
  12.             # print(mask)
  13.             remain_num+=int(mask.sum())
  14.             bn_model.weight.data.mul_(mask)
  15.             bn_model.bias.data.mul_(mask)
  16.             print(f"|\t{name:<25}{'|':<10}{bn_model.weight.data.size()[0]:<20}{'|':<10}{int(mask.sum()):<20}|")
  17.             assert int(
  18.                 mask.sum()) > 0, "Current remaining channel must greater than 0!!! please set prune percent to lower thesh, or you can retrain a more sparse model..."
  19.  
  20.     print("=" * 94)
  21.  
  22.  
  23.     pruned_model=ModelPruned(maskbndict=maskbndict,cfg=pruned_yaml,ch=3).cuda()
  24.     for m in pruned_model.modules():
  25.         if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
  26.             m.inplace = True  # pytorch 1.7.0 compatibility
  27.         elif type(m) is Conv:
  28.             m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
  29.     from_to_map=pruned_model.from_to_map
  30.     pruned_model_state=pruned_model.state_dict()

参数拷贝：

  1.   #-----------------------------参数拷贝----------------------------
  2.     modelstate = model.state_dict()
  3.     changed_state=[]
  4.     for((layername,layermodel),(pruned_layername,pruned_layermodel)) in zip(model.named_modules(),pruned_model.named_modules()):
  5.         if isinstance(layermodel,nn.Conv2d) and not layername.startswith("model.24"):
  6.             convname=layername[:-4]+"bn"
  7.             if convname in from_to_map.keys():
  8.                 former=from_to_map[convname]
  9.                 if isinstance(former,str):
  10.                     out_idx = np.squeeze(np.argwhere(np.asarray(maskbndict[layername[:-4] + "bn"].cpu().numpy())))
  11.                     in_idx = np.squeeze(np.argwhere(np.asarray(maskbndict[former].cpu().numpy())))
  12.                     w = layermodel.weight.data[:, in_idx, :, :].clone()
  13.  
  14.                     if len(w.shape) == 3:  # remain only 1 channel.
  15.                         w = w.unsqueeze(1)
  16.                     w = w[out_idx, :, :, :].clone()
  17.  
  18.                     pruned_layermodel.weight.data = w.clone()
  19.                     changed_state.append(layername + ".weight")
  20.                 if isinstance(former, list):
  21.                     orignin = [modelstate[i + ".weight"].shape[0] for i in former]
  22.                     formerin = []
  23.                     for it in range(len(former)):
  24.                         name = former[it]
  25.                         tmp = [i for i in range(maskbndict[name].shape[0]) if maskbndict[name][i] == 1]
  26.                         if it > 0:
  27.                             tmp = [k + sum(orignin[:it]) for k in tmp]
  28.                         formerin.extend(tmp)
  29.                     out_idx = np.squeeze(np.argwhere(np.asarray(maskbndict[layername[:-4] + "bn"].cpu().numpy())))
  30.                     w = layermodel.weight.data[out_idx, :, :, :].clone()
  31.                     pruned_layermodel.weight.data = w[:, formerin, :, :].clone()
  32.                     changed_state.append(layername + ".weight")
  33.                 else:
  34.                     out_idx = np.squeeze(np.argwhere(np.asarray(maskbndict[layername[:-4] + "bn"].cpu().numpy())))
  35.                     w = layermodel.weight.data[out_idx, :, :, :].clone()
  36.                     assert len(w.shape) == 4
  37.                     pruned_layermodel.weight.data = w.clone()
  38.                     changed_state.append(layername + ".weight")
  39.  
  40.                 if isinstance(layermodel, nn.BatchNorm2d):
  41.                     out_idx = np.squeeze(np.argwhere(np.asarray(maskbndict[layername].cpu().numpy())))
  42.                     pruned_layermodel.weight.data = layermodel.weight.data[out_idx].clone()
  43.                     pruned_layermodel.bias.data = layermodel.bias.data[out_idx].clone()
  44.                     pruned_layermodel.running_mean = layermodel.running_mean[out_idx].clone()
  45.                     pruned_layermodel.running_var = layermodel.running_var[out_idx].clone()
  46.                     changed_state.append(layername + ".weight")
  47.                     changed_state.append(layername + ".bias")
  48.                     changed_state.append(layername + ".running_mean")
  49.                     changed_state.append(layername + ".running_var")
  50.                     changed_state.append(layername + ".num_batches_tracked")
  51.  
  52.                 if isinstance(layermodel, nn.Conv2d) and layername.startswith("model.24"):
  53.                     former = from_to_map[layername]
  54.                     in_idx = np.squeeze(np.argwhere(np.asarray(maskbndict[former].cpu().numpy())))
  55.                     pruned_layermodel.weight.data = layermodel.weight.data[:, in_idx, :, :]
  56.                     pruned_layermodel.bias.data = layermodel.bias.data
  57.                     changed_state.append(layername + ".weight")
  58.                     changed_state.append(layername + ".bias")
  59.  
  60.     missing = [i for i in pruned_model_state.keys() if i not in changed_state]
  61.  
  62.     pruned_model.eval()
  63.     pruned_model.names = model.names
  64.     # =============================================================================================== #
  65.     torch.save({"model": model}, "weights/pruned_model/orign_model.pt")
  66.     model = pruned_model
  67.     torch.save({"model": model}, "weights/pruned_model/pruned_model.pt")
  68.     model.cuda().eval()

参考：

YOLOv5模型剪枝压缩(2)-YOLOv5模型简介和剪枝层选择_MidasKing的博客-CSDN博客_yolov5剪枝

yolov5模型压缩之模型剪枝_小小小绿叶的博客-CSDN博客_yolov5模型裁剪

GitHub - midasklr/yolov5prune

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