手把手带你Yolov5 (v6.1)添加注意力机制（二)（在C3模块中加入注意力机制）

Posted 2022-05-16 迪菲赫尔曼

之前在《手把手带你Yolov5 (v6.1)添加注意力机制（并附上30多种顶会Attention原理图）》文章中已经介绍过了如何在主干网络里添加单独的注意力层，今天这篇将会介绍如何在C3模块里面加入注意力层。

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文章目录

• • 1.添加方式介绍
  • 1.1 C3SE
  • 1.2 C3CA
  • 1.3 C3CBAM
  • 1.4 C3ECA

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1.添加方式介绍

1.1 C3SE

第一步；要把注意力结构代码放到common.py文件中，以C3SE举例，将这段代码粘贴到common.py文件中

class SEBottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, ratio=16):  # ch_in, ch_out, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2
        # self.se=SE(c1,c2,ratio)
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.l1 = nn.Linear(c1, c1 // ratio, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.l2 = nn.Linear(c1 // ratio, c1, bias=False)
        self.sig = nn.Sigmoid()

    def forward(self, x):
        x1 = self.cv2(self.cv1(x))
        b, c, _, _ = x.size()
        y = self.avgpool(x1).view(b, c)
        y = self.l1(y)
        y = self.relu(y)
        y = self.l2(y)
        y = self.sig(y)
        y = y.view(b, c, 1, 1)
        out = x1 * y.expand_as(x1)

        # out=self.se(x1)*x1
        return x + out if self.add else out


class C3SE(C3):
    # C3 module with SEBottleneck()
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(SEBottleneck(c_, c_, shortcut) for _ in range(n)))

第二步；找到yolo.py文件里的parse_model函数，将类名加入进去

第三步；修改配置文件（我这里拿yolov5s.yaml举例子），将C3层替换为我们新引入的C3SE层
yolov5s_C3SE.yaml

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3SE, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3SE, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3SE, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3SE, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

# YOLOv5 v6.0 head
head:
  [[-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, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

其它注意力机制同理

1.2 C3CA

class CABottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, ratio=32):  # ch_in, ch_out, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2
        # self.ca=CoordAtt(c1,c2,ratio)
        self.pool_h = nn.AdaptiveAvgPool2d((None, 1))
        self.pool_w = nn.AdaptiveAvgPool2d((1, None))
        mip = max(8, c1 // ratio)
        self.conv1 = nn.Conv2d(c1, mip, kernel_size=1, stride=1, padding=0)
        self.bn1 = nn.BatchNorm2d(mip)
        self.act = h_swish()
        self.conv_h = nn.Conv2d(mip, c2, kernel_size=1, stride=1, padding=0)
        self.conv_w = nn.Conv2d(mip, c2, kernel_size=1, stride=1, padding=0)
        
    def forward(self, x):
        x1=self.cv2(self.cv1(x))
        n, c, h, w = x.size()
        # c*1*W
        x_h = self.pool_h(x1)
        # c*H*1
        # C*1*h
        x_w = self.pool_w(x1).permute(0, 1, 3, 2)
        y = torch.cat([x_h, x_w], dim=2)
        # C*1*(h+w)
        y = self.conv1(y)
        y = self.bn1(y)
        y = self.act(y)
        x_h, x_w = torch.split(y, [h, w], dim=2)
        x_w = x_w.permute(0, 1, 3, 2)
        a_h = self.conv_h(x_h).sigmoid()
        a_w = self.conv_w(x_w).sigmoid()
        out = x1 * a_w * a_h

        # out=self.ca(x1)*x1
        return x + out if self.add else out


class C3CA(C3):
    # C3 module with CABottleneck()
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(CABottleneck(c_, c_,shortcut) for _ in range(n)))

1.3 C3CBAM

class CBAMBottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5,ratio=16,kernel_size=7):  # ch_in, ch_out, shortcut, groups, expansion
        super(CBAMBottleneck,self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2
        self.channel_attention = ChannelAttention(c2, ratio)
        self.spatial_attention = SpatialAttention(kernel_size)
        #self.cbam=CBAM(c1,c2,ratio,kernel_size)

    def forward(self, x):
        x1 = self.cv2(self.cv1(x))
        out = self.channel_attention(x1) * x1
        # print('outchannels:'.format(out.shape))
        out = self.spatial_attention(out) * out
        return x + out if self.add else out


class C3CBAM(C3):
    # C3 module with CBAMBottleneck()
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(CBAMBottleneck(c_, c_, shortcut) for _ in range(n)))

1.4 C3ECA

class ECABottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, ratio=16, k_size=3):  # ch_in, ch_out, shortcut, groups, expansion
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_, c2, 3, 1, g=g)
        self.add = shortcut and c1 == c2
        # self.eca=ECA(c1,c2)
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x1 = self.cv2(self.cv1(x))
        # out=self.eca(x1)*x1
        y = self.avg_pool(x1)
        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
        y = self.sigmoid(y)
        out = x1 * y.expand_as(x1)

        return x + out if self.add else out


class C3ECA(C3):
    # C3 module with ECABottleneck()
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(ECABottleneck(c_, c_, shortcut) for _ in range(n)))

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本人更多Yolov5(v6.1)实战内容导航🍀

1.手把手带你调参Yolo v5 (v6.1)（一）

2.手把手带你调参Yolo v5 (v6.1)（二）

3.手把手带你Yolov5 (v6.1)添加注意力机制（并附上30多种顶会Attention原理图）

4.Yolov5如何更换激活函数？

5.如何快速使用自己的数据集训练Yolov5模型

6.连夜看了30多篇改进YOLO的中文核心期刊 我似乎发现了一个能发论文的规律

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