58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
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import torch
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from torch import nn
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class ChannelAttention(nn.Module):
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def __init__(self, in_planes, ratio=16):
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super(ChannelAttention, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.max_pool = nn.AdaptiveMaxPool2d(1)
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self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False)
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self.relu1 = nn.ReLU()
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self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
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max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
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out = avg_out + max_out
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return self.sigmoid(out)
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class SpatialAttention(nn.Module):
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def __init__(self, kernel_size=7):
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super(SpatialAttention, self).__init__()
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assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
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padding = 3 if kernel_size == 7 else 1
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self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) # 7,3 3,1
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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avg_out = torch.mean(x, dim=1, keepdim=True)
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max_out, _ = torch.max(x, dim=1, keepdim=True)
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x = torch.cat([avg_out, max_out], dim=1)
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x = self.conv1(x)
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return self.sigmoid(x)
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class CBAM(nn.Module):
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def __init__(self, in_planes, ratio=16, kernel_size=7):
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super(CBAM, self).__init__()
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self.ca = ChannelAttention(in_planes, ratio)
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self.sa = SpatialAttention(kernel_size)
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def forward(self, x):
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out = x * self.ca(x)
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result = out * self.sa(out)
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return result
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# 输入 N C H W, 输出 N C H W
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if __name__ == '__main__':
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block = CBAM(64)
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input = torch.rand(1, 64, 64, 64)
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output = block(input)
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print(input.size(), output.size())
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