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详解YOLOV7 网络结构_yolov7网络结构
作者：凡人多烦事01 | 2024-02-21 13:41:59
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yolov7网络结构
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
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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。
# 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) ]
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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))
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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
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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
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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
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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
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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))
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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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