29 lines
1.1 KiB
Python
29 lines
1.1 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import torch
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from torch import nn
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class SELayer(nn.Module):
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def __init__(self, channel, reduction=16):
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super(SELayer, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc_r = nn.Sequential(
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nn.Linear(channel, channel // reduction), nn.ReLU(inplace=True),
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nn.Linear(channel // reduction, channel), nn.Sigmoid())
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self.fc_i = nn.Sequential(
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nn.Linear(channel, channel // reduction), nn.ReLU(inplace=True),
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nn.Linear(channel // reduction, channel), nn.Sigmoid())
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def forward(self, x):
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b, c, _, _, _ = x.size()
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x_r = self.avg_pool(x[:, :, :, :, 0]).view(b, c)
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x_i = self.avg_pool(x[:, :, :, :, 1]).view(b, c)
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y_r = self.fc_r(x_r).view(b, c, 1, 1, 1) - self.fc_i(x_i).view(
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b, c, 1, 1, 1)
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y_i = self.fc_r(x_i).view(b, c, 1, 1, 1) + self.fc_i(x_r).view(
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b, c, 1, 1, 1)
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y = torch.cat([y_r, y_i], 4)
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return x * y
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