模块整理！YOLOv9中的“Silence”、“RepNCSPELAN4”、“ADown”、“CBLinear”创新模块汇总！

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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

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YOLOv9有效改进系列|包含卷积、主干、注意力等模块

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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！

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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。

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2.RepNCSPELAN4

       RepNCSPELAN4代码：

class RepNCSPELAN4(nn.Module):
    # csp-elan
    def __init__(self, c1, c2, c3, c4, c5=1):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        self.c = c3//2
        self.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。

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3.ADown

       ADown代码：

class ADown(nn.Module):
    def __init__(self, c1, c2):  # ch_in, ch_out, shortcut, kernels, groups, expand
        super().__init__()
        self.c = c2 // 2
        self.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。

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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, groups
        super(CBLinear, self).__init__()
        self.c2s = c2s
        self.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)
  ]