YOLOv7-tiny

• 整体网络结构图
• yolov7-tiny.yaml
• 组件模块
• • MX
  • CBL
  • SPPCSP
  • • 结构图
    • yaml
    • 构建代码
  • MCB
  • • 结构图
    • yaml文件表示
    • common.py代码
• 参考

整体网络结构图

yolov7-tiny.yaml

# parametersnc: 80# number of classesdepth_multiple: 1.0# model depth multiplewidth_multiple: 1.0# layer channel multiple# anchorsanchors:- [10,13, 16,30, 33,23]# P3/8- [30,61, 62,45, 59,119]# P4/16- [116,90, 156,198, 373,326]# P5/32# yolov7-tiny backbonebackbone:# [from, number, module, args] ch_out, k=1, s=1, p=None, g=1, act=True[[-1, 1, Conv, [32, 3, 2, None, 1, nn.LeakyReLU(0.1)]],# 0-P1/2 [-1, 1, Conv, [64, 3, 2, None, 1, nn.LeakyReLU(0.1)]],# 1-P2/4[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]],#MCB [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 7[-1, 1, MP, []],# 8-P3/8 [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 14[-1, 1, MP, []],# 15-P4/16 [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 21[-1, 1, MP, []],# 22-P5/32 [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [512, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 28]# yolov7-tiny headhead:#SPPCSP[[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, SP, [5]], [-2, 1, SP, [9]], [-3, 1, SP, [13]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -7], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 37 [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [21, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P4 [[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 47 [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [14, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], # route backbone P3 [[-1, -2], 1, Concat, [1]],[-1, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [32, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [32, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 57[-1, 1, Conv, [128, 3, 2, None, 1, nn.LeakyReLU(0.1)]], [[-1, 47], 1, Concat, [1]],[-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 65[-1, 1, Conv, [256, 3, 2, None, 1, nn.LeakyReLU(0.1)]], [[-1, 37], 1, Concat, [1]],[-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-2, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 73 [57, 1, Conv, [128, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [65, 1, Conv, [256, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [73, 1, Conv, [512, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[74,75,76], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)]

组件模块

MX

池化层，默认表示两倍下采样，

class MP(nn.Module):def __init__(self, k=2):super(MP, self).__init__()self.m = nn.MaxPool2d(kernel_size=k, stride=k)def forward(self, x):return self.m(x)

 [-1, 1, MP, []],# 8-P3/8

CBL

就是表示Conv+BN+LeakyReLU
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]]

class Conv(nn.Module):# Standard convolutiondef __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):# ch_in, ch_out, kernel, stride, padding, groupssuper(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))

SPPCSP

结构图

yaml

yaml文件中如下表示，直接看最后一层输出通道数，尺寸不会变化，SP模块默认设置卷积Pading为卷积核的一半大小

#SPPCSP[[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], #20*20*256 [-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], #20*20*256 [-1, 1, SP, [5]], [-2, 1, SP, [9]], [-3, 1, SP, [13]], [[-1, -2, -3, -4], 1, Concat, [1]], #20*20*512 [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]], #20*20*256 [[-1, -7], 1, Concat, [1]], #20*20*512 [-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],#20#20*20*256

构建代码

yaml文件中的SP表示如下

# i+2p-kclass SP(nn.Module):def __init__(self, k=3, s=1):super(SP, self).__init__()self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)def forward(self, x):return self.m(x)

MCB

结构图

yaml文件表示

直接看最后一层输出的通道数看Concat后变化，

 [-1, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], #40*40*64 [-2, 1, Conv, [64, 1, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [-1, 1, Conv, [64, 3, 1, None, 1, nn.LeakyReLU(0.1)]], [[-1, -2, -3, -4], 1, Concat, [1]], [-1, 1, Conv, [128, 1, 1, None, 1, nn.LeakyReLU(0.1)]],# 30 #40*40*128

common.py代码

通过Conv函数构建即可

参考

yolov7-tiny网络结构图
https://blog.csdn.net/weixin_51346544/article/details/129322706