46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
import numpy as np
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import torch
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from torch import nn
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from torch.nn import init
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class SEAttention(nn.Module):
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def __init__(self, channel=512, reduction=16):
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super().__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(channel, channel // reduction, bias=False),
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nn.ReLU(inplace=True),
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nn.Linear(channel // reduction, channel, bias=False),
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nn.Sigmoid()
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)
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, mode='fan_out')
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if m.bias is not None:
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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init.constant_(m.weight, 1)
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init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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init.normal_(m.weight, std=0.001)
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if m.bias is not None:
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init.constant_(m.bias, 0)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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return x * y.expand_as(x)
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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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input = torch.randn(50, 512, 7, 7)
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se = SEAttention(channel=512, reduction=8)
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output = se(input)
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print(output.shape)
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