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import torch.nn as nn | |
class SELayer(nn.Module): | |
def __init__(self, channel, reduction=16): | |
super(SELayer, self).__init__() | |
self.avg_pool = nn.AdaptiveAvgPool2d(1) | |
self.fc = nn.Sequential( | |
nn.Linear(channel, channel // reduction, bias=False), | |
nn.ReLU(inplace=True), | |
nn.Linear(channel // reduction, channel, bias=False), | |
nn.Sigmoid() | |
) | |
def forward(self, x): | |
b, c, _, _ = x.size() | |
y = self.avg_pool(x).view(b, c) | |
y = self.fc(y).view(b, c, 1, 1) | |
res = x * y.expand_as(x) | |
return res | |