YOLOv9独家改进|动态蛇形卷积Dynamic Snake Convolution与空间和通道重建卷积SCConv与RepNCSPELAN4融合

专栏介绍：YOLOv9改进系列 | 包含深度学习最新创新，主力高效涨点！！！

一、改进点介绍

Dynamic Snake Convolution是一种针对细长微弱的局部结构特征与复杂多变的全局形态特征设计的卷积模块。

SCConv是一种即插即用的空间和通道重建卷积。

RepNCSPELAN4是YOLOv9中的特征提取模块，类似YOLOv5和v8中的C2f与C3模块。

二、DS-RepNCSPELAN4模块详解

2.1 模块简介

DS-RepNCSPELAN4的主要思想：使用Dynamic Snake Convolution、SCConv与RepNCSPELAN4中融合。

三、 DS-RepNCSPELAN4模块使用教程

3.1 DS-RepNCSPELAN4模块的代码

class RepConvN_SC(RepConvN):"""RepConv is a basic rep-style block, including training and deploy statusThis code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py"""default_act = nn.SiLU()  # default activationdef __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False):super().__init__(c1, c2, k, s, p, g, d, act, bn, deploy)assert k == 3 and p == 1self.g = gself.c1 = c1self.c2 = c2self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()self.bn = Noneself.conv1 = SCConv(c1, c2, k, s, p=p, g=g)self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False)class RepNBottleneck_SC(RepNBottleneck):# Standard bottleneckdef __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, kernels, groups, expandsuper().__init__( c1, c2, shortcut, g, k, e)c_ = int(c2 * e)  # hidden channelsself.cv1 = RepConvN_SC(c1, c_, k[0], 1)self.cv2 = SCConv(c_, c2, k[1], s=1, g=g)self.add = shortcut and c1 == c2class RepNCSP_SCConv(RepNCSP):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__(c1, c2, n, shortcut, g, e)c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_)self.cv2 = Conv(c1, c_)self.cv3 = Conv(2 * c_, c2)  # optional act=FReLU(c2)self.m = nn.Sequential(*(RepNBottleneck_SC(c_, c_, shortcut, g, e=1.0) for _ in range(n)))class SCConv(nn.Module):"""https://github.com/MCG-NKU/SCNet/blob/master/scnet.py"""def __init__(self, inplanes, planes,k=3, s=1, p=1, dilation=1, g=1, pooling_r=4):super(SCConv, self).__init__()self.k2 = nn.Sequential(nn.AvgPool2d(kernel_size=pooling_r, stride=pooling_r),Conv(inplanes, planes, k=k, s=s, p=p, d=dilation, g=g, act=False))self.k3 = Conv(inplanes, planes, k=k, s=s, p=p, d=dilation, g=g, act=False)self.k4 = Conv(inplanes, planes, k=k, s=s, p=p, d=dilation, g=g, act=False)def forward(self, x):identity = xout = torch.sigmoid(torch.add(identity, F.interpolate(self.k2(x), identity.size()[2:]))) # sigmoid(identity + k2)out = torch.mul(self.k3(x), out)    # k3 * sigmoid(identity + k2)out = self.k4(out)  # k4return outclass DS_RepNCSPELAN4(RepNCSPELAN4):# csp-elandef __init__(self, c1, c2, c3, c4, c5=1):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__(c1, c2, c3, c4, c5)self.cv1 = Conv(c1, c3, k=1, s=1)self.cv2 = nn.Sequential(RepNCSP_SCConv(c3 // 2, c4, c5), DySnakeConv(c4, c4, 3))self.cv3 = nn.Sequential(RepNCSP_SCConv(c4, c4, c5), DySnakeConv(c4, c4, 3))self.cv4 = Conv(c3 + (2 * c4), c2, 1, 1)

3.2 在YOlO v9中的添加教程

阅读YOLOv9添加模块教程或使用下文操作

1. 将YOLOv9工程中models下common.py文件中的最下行（否则可能因类继承报错）增加模块的代码。

2. 将YOLOv9工程中models下yolo.py文件中的第681行（可能因版本变化而变化）增加以下代码。

            RepNCSPELAN4, SPPELAN, DS_RepNCSPELAN4}:

3.3 运行配置文件

# YOLOv9
# Powered bu https://blog.csdn.net/StopAndGoyyy# parameters
nc: 80  # number of classes
#depth_multiple: 0.33  # model depth multiple
depth_multiple: 1  # model depth multiple
#width_multiple: 0.25  # layer channel multiple
width_multiple: 1  # layer channel multiple
#activation: nn.LeakyReLU(0.1)
#activation: nn.ReLU()# anchors
anchors: 3# YOLOv9 backbone
backbone:[[-1, 1, Silence, []],  # conv down[-1, 1, Conv, [64, 3, 2]],  # 1-P1/2# conv down[-1, 1, Conv, [128, 3, 2]],  # 2-P2/4# elan-1 block[-1, 1, DS_RepNCSPELAN4, [256, 128, 64, 1]],  # 3# avg-conv down[-1, 1, ADown, [256]],  # 4-P3/8# elan-2 block[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 5# avg-conv down[-1, 1, ADown, [512]],  # 6-P4/16# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 7# avg-conv down[-1, 1, ADown, [512]],  # 8-P5/32# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 9]# YOLOv9 head
head:[# elan-spp block[-1, 1, SPPELAN, [512, 256]],  # 10# up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 7], 1, Concat, [1]],  # cat backbone P4# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 13# up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 5], 1, Concat, [1]],  # cat backbone P3# elan-2 block[-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 16 (P3/8-small)# avg-conv-down merge[-1, 1, ADown, [256]],[[-1, 13], 1, Concat, [1]],  # cat head P4# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 19 (P4/16-medium)# avg-conv-down merge[-1, 1, ADown, [512]],[[-1, 10], 1, Concat, [1]],  # cat head P5# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 22 (P5/32-large)# multi-level reversible auxiliary branch# routing[5, 1, CBLinear, [[256]]], # 23[7, 1, CBLinear, [[256, 512]]], # 24[9, 1, CBLinear, [[256, 512, 512]]], # 25# conv down[0, 1, Conv, [64, 3, 2]],  # 26-P1/2# conv down[-1, 1, Conv, [128, 3, 2]],  # 27-P2/4# elan-1 block[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 28# avg-conv down fuse[-1, 1, ADown, [256]],  # 29-P3/8[[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30  # elan-2 block[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 31# avg-conv down fuse[-1, 1, ADown, [512]],  # 32-P4/16[[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33 # elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 34# avg-conv down fuse[-1, 1, ADown, [512]],  # 35-P5/32[[25, -1], 1, CBFuse, [[2]]], # 36# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 37# detection head# detect[[31, 34, 37, 16, 19, 22], 1, DualDDetect, [nc]],  # DualDDetect(A3, A4, A5, P3, P4, P5)]