YOLOv5改进--添加CBAM注意力机制

注意力机制包括CBAM、CA、ECA、SE、S2A、SimAM等，接下来介绍具体添加方式。

1.  CBAM代码，在common文件中添加以下模块：

class CBAMC3(nn.Module):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super(CBAMC3, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1)
        self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
        self.channel_attention = ChannelAttention(c2, 16)
        self.spatial_attention = SpatialAttention(7)

        # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])

    def forward(self, x):
   		# 将最后的标准卷积模块改为了注意力机制提取特征
        return self.spatial_attention(
            self.channel_attention(self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))))

 2. 在yolo文件中，定位到parse_model函数，在C3Ghost后面加入CBAMC3模块

 if m in (Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,BottleneckCSP, C3, C3TR, C3SPP, C3Ghost):
     c1, c2 = ch[f], args[0]
     if c2 != no:  # if not output
          c2 = make_divisible(c2 * gw, 8)

     args = [c1, c2, *args[1:]]
     if m in [BottleneckCSP, C3, C3TR, C3Ghost]:
           args.insert(2, n)  # number of repeats
           n = 1

 ３．在yolov5s.yaml文件中修改网络结构，可以在backbone中添加一层

  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, CBAMC3,[1024]]，
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

则下面的head也得修改，p4,p5以及最后的总层数都得+1。

  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 15], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 11], 1,Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)


   [[18, 21, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

正常训练即可。