目录

  • 0. 添加方法
  • 1. SE
    • 1.1 SE
    • 1.2 C3-SE
  • 2. CBAM
    • 2.1 CBAM
    • 2.2 C3-CBAM
  • 3. ECA
    • 3.1 ECA
    • 3.2 C3-ECA
  • 4. CA
    • 4.1 CA
    • 4.2 C3-CA

0. 添加方法

主要步骤：
（1）在models/common.py中注册注意力模块
（2）在models/yolo.py中的parse_model函数中添加注意力模块
（3）修改配置文件yolov5s.yaml
（4）运行yolo.py进行验证
各个注意力机制模块的添加方法类似，各注意力模块的修改参照SE。
本文添加注意力完整代码：https://github.com/double-vin/yolov5_attention

1. SE

Squeeze-and-Excitation Networks
https://github.com/hujie-frank/SENet

1.1 SE

  1. 在models/common.py中注册SE模块
class SE(nn.Module):    def __init__(self, c1, c2, ratio=16):        super(SE, self).__init__()        #c*1*1        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):        b, c, _, _ = x.size()        y = self.avgpool(x).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)        return x * y.expand_as(x)
  1. 在models/yolo.py中的parse_model函数中添加SE模块
  2. 修改配置文件yolov5s.yaml。
    添加注意力的两种方法：一是在backbone的最后一层添加注意力；二是将backbone中的C3全部替换。
    这里使用第一种，第二种见下文中的C3SE

    注意：SE添加至第9层，第9层之后所有的编号都要+1，则：
    1>两个Concat的from系数分别由[-1, 14]，[-1, 10]改为[-1, 15]，[-1, 11]
    2>Detect的from系数由[17, 20, 23]改为[18,21,24]
  3. 验证：运行yolo.py

1.2 C3-SE

  1. 在models/common.py中注册C3SE模块：
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 outclass 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)))
  1. 在models/yolo.py中的parse_model函数中添加C3SE模块
  2. 修改配置文件yolov5s.yaml。
  3. 验证：运行yolo.py

2. CBAM

《CBAM: Convolutional Block Attention Module》

2.1 CBAM

class ChannelAttention(nn.Module):    def __init__(self, in_planes, ratio=16):        super(ChannelAttention, self).__init__()        self.avg_pool = nn.AdaptiveAvgPool2d(1)        self.max_pool = nn.AdaptiveMaxPool2d(1)        self.f1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)        self.relu = nn.ReLU()        self.f2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)        self.sigmoid = nn.Sigmoid()    def forward(self, x):        avg_out = self.f2(self.relu(self.f1(self.avg_pool(x))))        max_out = self.f2(self.relu(self.f1(self.max_pool(x))))        out = self.sigmoid(avg_out + max_out)        return outclass SpatialAttention(nn.Module):    def __init__(self, kernel_size=7):        super(SpatialAttention, self).__init__()        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'        padding = 3 if kernel_size == 7 else 1        # (特征图的大小-算子的size+2*padding)/步长+1        self.conv = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)        self.sigmoid = nn.Sigmoid()    def forward(self, x):        # 1*h*w        avg_out = torch.mean(x, dim=1, keepdim=True)        max_out, _ = torch.max(x, dim=1, keepdim=True)        x = torch.cat([avg_out, max_out], dim=1)        #2*h*w        x = self.conv(x)        #1*h*w        return self.sigmoid(x)class CBAM(nn.Module):    # CSP Bottleneck with 3 convolutions    def __init__(self, c1, c2, ratio=16, kernel_size=7):  # ch_in, ch_out, number, shortcut, groups, expansion        super(CBAM, self).__init__()        self.channel_attention = ChannelAttention(c1, ratio)        self.spatial_attention = SpatialAttention(kernel_size)    def forward(self, x):        out = self.channel_attention(x) * x        # c*h*w        # c*h*w * 1*h*w        out = self.spatial_attention(out) * out        return out

2.2 C3-CBAM

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 outclass 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)))

3. ECA

《ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks》
https://github.com/BangguWu/ECANet

3.1 ECA

class ECA(nn.Module):    """Constructs a ECA module.    Args:        channel: Number of channels of the input feature map        k_size: Adaptive selection of kernel size    """    def __init__(self, c1, c2, k_size=3):        super(ECA, self).__init__()        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):        # feature descriptor on the global spatial information        y = self.avg_pool(x)        # print(y.shape,y.squeeze(-1).shape,y.squeeze(-1).transpose(-1, -2).shape)        # Two different branches of ECA module        # 50*C*1*1        # 50*C*1        # 50*1*C        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)        # Multi-scale information fusion        y = self.sigmoid(y)        return x * y.expand_as(x)

3.2 C3-ECA

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 outclass 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)))

4. CA

Coordinate Attention for Efficient Mobile Network Design
https://github.com/Andrew-Qibin/CoordAttention

4.1 CA

class h_sigmoid(nn.Module):    def __init__(self, inplace=True):        super(h_sigmoid, self).__init__()        self.relu = nn.ReLU6(inplace=inplace)    def forward(self, x):        return self.relu(x + 3) / 6class h_swish(nn.Module):    def __init__(self, inplace=True):        super(h_swish, self).__init__()        self.sigmoid = h_sigmoid(inplace=inplace)    def forward(self, x):        return x * self.sigmoid(x)class CoordAtt(nn.Module):    def __init__(self, inp, oup, reduction=32):        super(CoordAtt, self).__init__()        self.pool_h = nn.AdaptiveAvgPool2d((None, 1))        self.pool_w = nn.AdaptiveAvgPool2d((1, None))        mip = max(8, inp // reduction)        self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0)        self.bn1 = nn.BatchNorm2d(mip)        self.act = h_swish()        self.conv_h = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)        self.conv_w = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)    def forward(self, x):        identity = x        n, c, h, w = x.size()        # c*1*W        x_h = self.pool_h(x)        # c*H*1        # C*1*h        x_w = self.pool_w(x).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 = identity * a_w * a_h        return out

4.2 C3-CA

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 outclass 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)))


Tips：添加注意力的位置不局限，可以尝试各种排列组合
参考：
多种注意力介绍
添加注意力视频讲解
添加CBAM