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。

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。

# parametersnc: 2  # number of classesdepth_multiple: 1.0  # model depth multiplewidth_multiple: 1.0  # layer channel multiple# anchorsanchors:  - [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 backbonebackbone:  # [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 headhead:  [[-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

ELAN2

利用Conv构件围城的模块，在head中通道数减半

   # [[-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

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。

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

来源地址：https://blog.csdn.net/qq_41398619/article/details/129738783