CV中的Attention机制：简单而有效的CBAM模块

1. 什么是注意力机制？

• 通道注意力机制：对通道生成掩码mask，进行打分，代表是senet, Channel Attention Module
• 空间注意力机制：对空间进行掩码的生成，进行打分，代表是Spatial Attention Module
• 混合域注意力机制：同时对通道注意力和空间注意力进行评价打分，代表的有BAM, CBAM

2. CBAM模块的实现

2.1 通道注意力机制

class ChannelAttention(nn.Module):    def __init__(self, in_planes, rotio=16):        super(ChannelAttention, self).__init__()        self.avg_pool = nn.AdaptiveAvgPool2d(1)        self.max_pool = nn.AdaptiveMaxPool2d(1)
        self.sharedMLP = nn.Sequential(            nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False), nn.ReLU(),            nn.Conv2d(in_planes // rotio, in_planes, 1, bias=False))        self.sigmoid = nn.Sigmoid()
    def forward(self, x):        avgout = self.sharedMLP(self.avg_pool(x))        maxout = self.sharedMLP(self.max_pool(x))        return self.sigmoid(avgout + maxout)

2.2 空间注意力机制

class 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
        self.conv = nn.Conv2d(2,1,kernel_size, padding=padding, bias=False)        self.sigmoid = nn.Sigmoid()
    def forward(self, x):        avgout = torch.mean(x, dim=1, keepdim=True)        maxout, _ = torch.max(x, dim=1, keepdim=True)        x = torch.cat([avgout, maxout], dim=1)        x = self.conv(x)        return self.sigmoid(x)

2.3 Convolutional bottleneck attention module

class BasicBlock(nn.Module):    expansion = 1    def __init__(self, inplanes, planes, stride=1, downsample=None):        super(BasicBlock, self).__init__()        self.conv1 = conv3x3(inplanes, planes, stride)        self.bn1 = nn.BatchNorm2d(planes)        self.relu = nn.ReLU(inplace=True)        self.conv2 = conv3x3(planes, planes)        self.bn2 = nn.BatchNorm2d(planes)        self.ca = ChannelAttention(planes)        self.sa = SpatialAttention()        self.downsample = downsample        self.stride = stride    def forward(self, x):        residual = x        out = self.conv1(x)        out = self.bn1(out)        out = self.relu(out)        out = self.conv2(out)        out = self.bn2(out)        out = self.ca(out) * out  # 广播机制        out = self.sa(out) * out  # 广播机制        if self.downsample is not None:            residual = self.downsample(x)        out += residual        out = self.relu(out)        return out

class cbam(nn.Module):   def __init__(self, planes)：        self.ca = ChannelAttention(planes)# planes是feature map的通道个数        self.sa = SpatialAttention()     def forward(self, x):        x = self.ca(out) * x  # 广播机制        x = self.sa(out) * x  # 广播机制

3. 在什么情况下可以使用？

1. 如何更有效地计算channel attention?

1. 如何更有效地计算spatial attention?

1. 如何组织这两个部分？

4. 参考