详解YOLOV7 网络结构

YOLOv7 网络结构图详解

    • yolo.py 输出结构
    • 整体图
    • yolov7.yaml
    • 组件结构
      • CBS 模块
      • ELAN1
      • ELAN2
      • MP1&2
        • MP1
        • MP2
      • SPPCSPC
  • 参考

yolo.py 输出结构

输出的 arguments 和yaml文件的区别就是 多了第一列Conv输入的通道数

YOLOR  v0.1-112-g55b90e1 torch 1.7.0 CUDA:0 (Quadro RTX 4000, 8191.6875MB)


                 from  n    params  module                                  arguments                     
  0                -1  1       928  models.common.Conv                      [3, 32, 3, 1]                 
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  1      8320  models.common.Conv                      [128, 64, 1, 1]               
  5                -2  1      8320  models.common.Conv                      [128, 64, 1, 1]               
  6                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
  7                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
  8                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
  9                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
 10  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           
 11                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              
 12                -1  1         0  models.common.MP                        []                            
 13                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 14                -3  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 16          [-1, -3]  1         0  models.common.Concat                    [1]                           
 17                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 18                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 20                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 21                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 22                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 23  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           
 24                -1  1    263168  models.common.Conv                      [512, 512, 1, 1]              
 25                -1  1         0  models.common.MP                        []                            
 26                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 27                -3  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 28                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 29          [-1, -3]  1         0  models.common.Concat                    [1]                           
 30                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 31                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 32                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 33                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 34                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 35                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 36  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           
 37                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            
 38                -1  1         0  models.common.MP                        []                            
 39                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 40                -3  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 41                -1  1   2360320  models.common.Conv                      [512, 512, 3, 2]              
 42          [-1, -3]  1         0  models.common.Concat                    [1]                           
 43                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 44                -2  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 45                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 46                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 47                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 48                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 49  [-1, -3, -5, -6]  1         0  models.common.Concat                    [1]                           
 50                -1  1   1050624  models.common.Conv                      [1024, 1024, 1, 1]            
 51                -1  1   7609344  models.common.SPPCSPC                   [1024, 512, 1]                
 52                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 53                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 54                37  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 55          [-1, -2]  1         0  models.common.Concat                    [1]                           
 56                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 57                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 58                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              
 59                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 60                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 61                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 62[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           
 63                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 64                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 65                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 66                24  1     65792  models.common.Conv                      [512, 128, 1, 1]              
 67          [-1, -2]  1         0  models.common.Concat                    [1]                           
 68                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 69                -2  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 70                -1  1     73856  models.common.Conv                      [128, 64, 3, 1]               
 71                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
 72                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
 73                -1  1     36992  models.common.Conv                      [64, 64, 3, 1]                
 74[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           
 75                -1  1     65792  models.common.Conv                      [512, 128, 1, 1]              
 76                -1  1         0  models.common.MP                        []                            
 77                -1  1     16640  models.common.Conv                      [128, 128, 1, 1]              
 78                -3  1     16640  models.common.Conv                      [128, 128, 1, 1]              
 79                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 80      [-1, -3, 63]  1         0  models.common.Concat                    [1]                           
 81                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 82                -2  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 83                -1  1    295168  models.common.Conv                      [256, 128, 3, 1]              
 84                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 85                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 86                -1  1    147712  models.common.Conv                      [128, 128, 3, 1]              
 87[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           
 88                -1  1    262656  models.common.Conv                      [1024, 256, 1, 1]             
 89                -1  1         0  models.common.MP                        []                            
 90                -1  1     66048  models.common.Conv                      [256, 256, 1, 1]              
 91                -3  1     66048  models.common.Conv                      [256, 256, 1, 1]              
 92                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 93      [-1, -3, 51]  1         0  models.common.Concat                    [1]                           
 94                -1  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 95                -2  1    525312  models.common.Conv                      [1024, 512, 1, 1]             
 96                -1  1   1180160  models.common.Conv                      [512, 256, 3, 1]              
 97                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 98                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
 99                -1  1    590336  models.common.Conv                      [256, 256, 3, 1]              
100[-1, -2, -3, -4, -5, -6]  1         0  models.common.Concat                    [1]                           
101                -1  1   1049600  models.common.Conv                      [2048, 512, 1, 1]             
102                75  1    328704  models.common.RepConv                   [128, 256, 3, 1]              
103                88  1   1312768  models.common.RepConv                   [256, 512, 3, 1]              
104               101  1   5246976  models.common.RepConv                   [512, 1024, 3, 1]             
105   [102, 103, 104]  1     39550  IDetect                                 [2, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]
Model Summary: 415 layers, 37201950 parameters, 37201950 gradients, 105.1 GFLOPS

整体图

整体图如下所示,这个有有yaml层数,下一张有具体输出,第三张b导的简洁一些,结合3张图起来看配合yaml文件,基本就很好理解了。
详解YOLOV7 网络结构_第1张图片
详解YOLOV7 网络结构_第2张图片
详解YOLOV7 网络结构_第3张图片

yolov7.yaml

[-1, 1, Conv, [32, 3, 1] 其中的[32, 3, 1] 表示输出通道数为32 ,卷积核为3*3,步长为2
边看整体网络结构图,边看yaml文件,对着看。
注意:
backbone 和 head中的模块MP-1和MP-2区别,backbone中尺寸减半通道数不变,head中尺寸减半通道数变成两倍
backbone 和 head中的模块ELAN-1和ELAN-2的区别,banbone中通道数变成两倍,head中减半

ELAN在backbone中扩张我估计是为了更好的提取特征,而MP-1通道数减半,可以把它理解为改进版本的下采样。

# parameters
nc: 2  # number of classes
depth_multiple: 1.0  # model depth multiple
width_multiple: 1.0  # layer channel multiple

# anchors
anchors:
  - [12,16, 19,36, 40,28]  # P3/8
  - [36,75, 76,55, 72,146]  # P4/16
  - [142,110, 192,243, 459,401]  # P5/32

# yolov7 backbone
backbone:
  # [from, number, module, args]             640*640*3
  [[-1, 1, Conv, [32, 3, 1]],  # 0           640*640*32
  
   [-1, 1, Conv, [64, 3, 2]],  # 1-P1/2      320*320*64
   [-1, 1, Conv, [64, 3, 1]],  #             320*320*64
   
   [-1, 1, Conv, [128, 3, 2]],  # 3-P2/4     160*160*128

   # ELAN1
   [-1, 1, Conv, [64, 1, 1]],
   [-2, 1, Conv, [64, 1, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]],  # 11         160*160*256

   # MPConv
   [-1, 1, MP, []],
   [-1, 1, Conv, [128, 1, 1]],
   [-3, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [128, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 16-P3/8    80*80*256

   # ELAN1
   [-1, 1, Conv, [128, 1, 1]],
   [-2, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [512, 1, 1]],  # 24         80*80*512

   # MPConv
   [-1, 1, MP, []],
   [-1, 1, Conv, [256, 1, 1]],
   [-3, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 29-P4/16   40*40*512

   # ELAN1
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [1024, 1, 1]],  # 37        40*40*1024

   # MPConv
   [-1, 1, MP, []],
   [-1, 1, Conv, [512, 1, 1]],
   [-3, 1, Conv, [512, 1, 1]],
   [-1, 1, Conv, [512, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 42-P5/32    20*20*1024

   # ELAN1
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [1024, 1, 1]],  # 50         20*20*1024
  ]

# yolov7 head
head:
  [[-1, 1, SPPCSPC, [512]], # 51                        20*20*512
  
   [-1, 1, Conv, [256, 1, 1]],#                         20*20*256
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [37, 1, Conv, [256, 1, 1]], # route backbone P4      40*40*1024->40*40*256
   [[-1, -2], 1, Concat, [1]], #                        40*40*512

   # ELAN2  注意:Head和Backbone的ELAN不一样
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]], # 63                      40*40*256

   [-1, 1, Conv, [128, 1, 1]], #                         80*80*128
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [24, 1, Conv, [128, 1, 1]], # route backbone P3       80*80*512->80*80*128
   [[-1, -2], 1, Concat, [1]],#                          80*80*256

   # ELAN2
   [-1, 1, Conv, [128, 1, 1]],
   [-2, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [128, 1, 1]], # 75                      80*80*128

   # MPConv Channel × 2
   [-1, 1, MP, []],
   [-1, 1, Conv, [128, 1, 1]],
   [-3, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [128, 3, 2]],
   [[-1, -3, 63], 1, Concat, [1]],#                       40*40*256

   # ELAN2
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]], # 88                        40*40*256

    # MPConv Channel × 2
   [-1, 1, MP, []],
   [-1, 1, Conv, [256, 1, 1]],
   [-3, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, -3, 51], 1, Concat, [1]],#                         40*40*512

   # ELAN2
   [-1, 1, Conv, [512, 1, 1]],
   [-2, 1, Conv, [512, 1, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [-1, 1, Conv, [256, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [512, 1, 1]], # 101                         20*20*512
   
   [75, 1, RepConv, [256, 3, 1]],#102                           80*80*256
   [88, 1, RepConv, [512, 3, 1]],#103                           40*40*512
   [101, 1, RepConv, [1024, 3, 1]],#104                         20*20*1024

   [[102,103,104], 1, IDetect, [nc, anchors]],   # Detect(P3, P4, P5)
  ]

组件结构

CBS 模块

yaml 文件中的Conv表示卷积归一化激活

对于CBS模块,我们可以看从图中可以看出它是由一个Conv层,也就是卷积层,一个BN层,也就是Batch normalization层,还有一个Silu层,这是一个激活函数。silu激活函数是swish激活函数的变体。

class Conv(nn.Module):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def fuseforward(self, x):
        return self.act(self.conv(x))

ELAN1

利用Conv构件围城的模块,在backbone中通道数扩张两倍

   #[-1, 1, Conv, [128, 3, 2]],  # 3-P2/4     160*160*128

   # ELAN1
   [-1, 1, Conv, [64, 1, 1]],
   [-2, 1, Conv, [64, 1, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [-1, 1, Conv, [64, 3, 1]],
   [[-1, -3, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]],  # 11         160*160*256

详解YOLOV7 网络结构_第4张图片

ELAN2

利用Conv构件围城的模块,在head中通道数减半
详解YOLOV7 网络结构_第5张图片

   # [[-1, -2], 1, Concat, [1]], #55                    40*40*512

   # ELAN2  注意:Head和Backbone的ELAN不一样
   [-1, 1, Conv, [256, 1, 1]],
   [-2, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [-1, 1, Conv, [128, 3, 1]],
   [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
   [-1, 1, Conv, [256, 1, 1]], # 63                      40*40*256

MP1&2

MP1

[-1, 1, Conv, [128, 3, 2]], 表述输出128,

   #[-1, 1, Conv, [256, 1, 1]],  # 11         160*160*256

   # MPConv-1 backbone 下采样 通道数不变
   [-1, 1, MP, []],
   [-1, 1, Conv, [128, 1, 1]],
   [-3, 1, Conv, [128, 1, 1]],
   [-1, 1, Conv, [128, 3, 2]],
   [[-1, -3], 1, Concat, [1]],  # 16-P3/8    80*80*256

MP2

head部分,尺寸减半,通道数扩张为两倍

  # [-1, 1, Conv, [256, 1, 1]], # 88                        40*40*256

    # MPConv Channel × 2
   [-1, 1, MP, []],
   [-1, 1, Conv, [256, 1, 1]],
   [-3, 1, Conv, [256, 1, 1]],
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, -3, 51], 1, Concat, [1]],#                         40*40*512

详解YOLOV7 网络结构_第6张图片

SPPCSPC

类似于yolov5中的SPPF,不同的是,使用了5×5、9×9、13×13最大池化。
详解YOLOV7 网络结构_第7张图片

class SPPCSPC(nn.Module):
    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
        super(SPPCSPC, self).__init__()
        c_ = int(2 * c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(c_, c_, 3, 1)
        self.cv4 = Conv(c_, c_, 1, 1)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
        self.cv5 = Conv(4 * c_, c_, 1, 1)
        self.cv6 = Conv(c_, c_, 3, 1)
        self.cv7 = Conv(2 * c_, c2, 1, 1)

    def forward(self, x):
        x1 = self.cv4(self.cv3(self.cv1(x)))
        y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
        y2 = self.cv2(x)
        return self.cv7(torch.cat((y1, y2), dim=1))

参考

【YOLOv7_0.1】网络结构与源码解析
https://blog.csdn.net/weixin_43799388/article/details/126164288
YOLOV7详细解读(一)网络架构解读
https://blog.csdn.net/qq128252/article/details/126673493
睿智的目标检测61——Pytorch搭建YoloV7目标检测平台
https://blog.csdn.net/weixin_44791964/article/details/125827160

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