專欄介紹：YOLOv9改進系列 | 包含深度學習最新創新，主力高效漲點！！！
一、改進點介紹
????????SCConv是一種即插即用的空間和通道重建卷積。
????????RepNCSPELAN4是YOLOv9中的特征提取模塊，類似YOLOv5和v8中的C2f與C3模塊。
二、RepNCSPELAN4_SCConv模塊詳解
?2.1 模塊簡介
???????RepNCSPELAN4_SCConv的主要思想：? 使用SCConv替換RepNCSPELAN4中的Conv模塊。
三、?RepNCSPELAN4_SCConv模塊使用教程
3.1 RepNCSPELAN4_SCConv模塊的代碼
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 out3.2 在YOlO v9中的添加教程
閱讀YOLOv9添加模塊教程或使用下文操作
? ? ? ? 1.?將YOLOv9工程中models下common.py文件中的最下行（否則可能因類繼承報錯）增加模塊的代碼。
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?????????2.?將YOLOv9工程中models下yolo.py文件中的第681行（可能因版本變化而變化）增加以下代碼。
RepNCSPELAN4, SPPELAN, RepNCSPELAN4SCConv1}:3.3 運行配置文件
# 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)]
3.4 訓練過程
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