YOLOv9有效改进|使用空间和通道重建卷积SCConv改进RepNCSPELAN4-编程知识

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

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、RepNCSPELAN4_SCConv模块详解

2.1 模块简介

RepNCSPELAN4_SCConv的主要思想：使用SCConv替换RepNCSPELAN4中的Conv模块。

三、 RepNCSPELAN4_SCConv模块使用教程

3.1 RepNCSPELAN4_SCConv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 RepNCSPELAN4SCConv1(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), SCConv(c4, c4, 3, 1))self.cv3 = nn.Sequential(RepNCSP_SCConv(c4, c4, c5), SCConv(c4, c4, 3, 1))self.cv4 = Conv(c3 + (2 * c4), c2, 1, 1)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 out

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, RepNCSPELAN4SCConv1}:

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, RepNCSPELAN4SCConv1, [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)]