---

代码链接：https://github.com/WongKinYiu/yolov9/tree/main

论文链接：YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

---

大量文字及图片来袭！

本文整理了YOLOv9中的创新模块，附代码和结构图，快收藏使用吧！

---

1.Silence

       Silence 代码：

class Silence(nn.Module):def __init__(self):super(Silence, self).__init__()def forward(self, x):    return x

        Silence 模块位于yolov9网络的第一层，从Silence的代码中我们可以看到，YOLOv9的Silence 模块的作用就是返回输入的图片变量，并不包含其余操作。这个操作可以将x保存在网络的结构中，极大的方便双主干（在YOLOv9中是辅助分支）的调用及其他工作。

---

2.RepNCSPELAN4

       RepNCSPELAN4代码：

class RepNCSPELAN4(nn.Module):# csp-elandef __init__(self, c1, c2, c3, c4, c5=1):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__()self.c = c3//2self.cv1 = Conv(c1, c3, 1, 1)self.cv2 = nn.Sequential(RepNCSP(c3//2, c4, c5), Conv(c4, c4, 3, 1))self.cv3 = nn.Sequential(RepNCSP(c4, c4, c5), Conv(c4, c4, 3, 1))self.cv4 = Conv(c3+(2*c4), c2, 1, 1)def forward(self, x):y = list(self.cv1(x).chunk(2, 1))y.extend((m(y[-1])) for m in [self.cv2, self.cv3])return self.cv4(torch.cat(y, 1))def forward_split(self, x):y = list(self.cv1(x).split((self.c, self.c), 1))y.extend(m(y[-1]) for m in [self.cv2, self.cv3])return self.cv4(torch.cat(y, 1))

        RepNCSPELAN4模块是YOLOv9中的特征提取-融合模块。

---

3.ADown

       ADown代码：

class ADown(nn.Module):def __init__(self, c1, c2):  # ch_in, ch_out, shortcut, kernels, groups, expandsuper().__init__()self.c = c2 // 2self.cv1 = Conv(c1 // 2, self.c, 3, 2, 1)self.cv2 = Conv(c1 // 2, self.c, 1, 1, 0)def forward(self, x):x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)x1,x2 = x.chunk(2, 1)x1 = self.cv1(x1)x2 = torch.nn.functional.max_pool2d(x2, 3, 2, 1)x2 = self.cv2(x2)return torch.cat((x1, x2), 1)

        ADown模块是YOLOv9中的下采样模块。

---

4.CBLinear

       CBLinear代码：

class CBLinear(nn.Module):def __init__(self, c1, c2s, k=1, s=1, p=None, g=1):  # ch_in, ch_outs, kernel, stride, padding, groupssuper(CBLinear, self).__init__()self.c2s = c2sself.conv = nn.Conv2d(c1, sum(c2s), k, s, autopad(k, p), groups=g, bias=True)def forward(self, x):outs = self.conv(x).split(self.c2s, dim=1)return outs

        CBLinear模块是YOLOv9中的特征提取模块。

YOLOv9配置文件

# 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, 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)]