一句话总结CA注意力就是：在通道注意力的基础上兼顾其位置关系，将通道主力注意力与空间注意力联合起来。SE模块只考虑空间注意力，CBAM将空间注意力和通道注意力剥离。

论文链接

源码链接


        SE Module                         CBAM Module                            CA Module

class CA(nn.Module):
    def __init__(self, inp, reduction):
        super(CA, self).__init__()
        # h:height(行)   w:width(列)
        self.pool_h = nn.AdaptiveAvgPool2d((None, 1))  # (b,c,h,w)-->(b,c,h,1)
        self.pool_w = nn.AdaptiveAvgPool2d((1, None))  # (b,c,h,w)-->(b,c,1,w)


         # mip = max(8, inp // reduction)  论文作者所用
        mip =  inp // reduction  # 博主所用   reduction = int(math.sqrt(inp))

        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, inp, kernel_size=1, stride=1, padding=0)
        self.conv_w = nn.Conv2d(mip, inp, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        identity = x

        n, c, h, w = x.size()
        x_h = self.pool_h(x)  # (b,c,h,1)
        x_w = self.pool_w(x).permute(0, 1, 3, 2)  # (b,c,w,1)

        y = torch.cat([x_h, x_w], dim=2)
        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