最近做yolo添加注意力机制，将找到的关于注意力机制的资料集合到一起。会给出使用原文的链接，感谢各位乐于分享的博主，侵删！

CBAM

import torch
from torch import nn

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.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)
        self.relu1 = nn.ReLU()
        self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
        out = avg_out + max_out
        return self.sigmoid(out)


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.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)  # 7,3     3,1
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        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)
        x = self.conv1(x)
        return self.sigmoid(x)

class CBAM(nn.Module):
    def __init__(self, in_planes, ratio=16, kernel_size=7):
        super(CBAM, self).__init__()
        self.ca = ChannelAttention(in_planes, ratio)
        self.sa = SpatialAttention(kernel_size)
        
    def forward(self, x):
        out = x * self.ca(x)
        result = out * self.sa(out)
        return result

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版权声明：本文为CSDN博主「敲代码的小风」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/m0_46653437/article/details/114829275

原博主还介绍了通道、空间、SE注意力。值得一看。

SE

再写一个SE主要是这位博主给出了添加位置和条件，跟一般增加模块的位置不一样，为了直接cv。

for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):
    m = eval(m) if isinstance(m, str) else m  # eval strings
    for j, a in enumerate(args):
        try:
            args[j] = eval(a) if isinstance(a, str) else a  # eval strings
        except:
            pass
    n = max(round(n * gd), 1) if n > 1 else n  # depth gain
    if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP,
                DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, 
                C3]:
        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]:
            args.insert(2, n)  # number of repeats
            n = 1
    elif m is nn.BatchNorm2d:
        args = [ch[f]]
    elif m is Concat:
        c2 = sum([ch[x] for x in f])
    elif m is Detect:
        args.append([ch[x] for x in f])
        if isinstance(args[1], int):  # number of anchors
            args[1] = [list(range(args[1] * 2))] * len(f)
    elif m is Contract:
        c2 = ch[f] * args[0] ** 2
    elif m is Expand:
        c2 = ch[f] // args[0] ** 2
     #SE添加位置   
    elif m is SELayer: # 这里是修改的部分
        channel, re = args[0], args[1]
        channel = make_divisible(channel * gw, 8) if channel != no else channel 
        args = [channel, re]
    
    else:
        c2 = ch[f]

版权声明：本文为CSDN博主「pprp」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/DD_PP_JJ/article/details/114098169

CoorAttention

原博主介绍了很多注意力机制，还有可以直接cv的，这里只放不重叠的CoorAttention。我也直接cv了。

# 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) / 6
#
#
# class 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()
#         x_h = self.pool_h(x)
#         x_w = self.pool_w(x).permute(0, 1, 3, 2)
#
#         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

版权声明：本文为CSDN博主「调参者」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/zqt321/article/details/121772854