drsn pytorch

DRSN是一种使用了残差收缩层的ResNet变体，在PyTorch中有相应的实现代码。残差收缩层的结构包括两次卷积操作后取绝对值，然后经过全局平均池化(GAP)将通道数缩减为1，再经过两次全连接层得到z，再经过Sigmoid函数得到一个值。最后，将GAP的结果取平均后与z相乘，得到软阈值化的结果，再与原输入进行阈值化。在PyTorch中的代码实现如下所示：

class Shrinkage(nn.Module):
    def __init__(self, gap_size, channel):
        super(Shrinkage, self).__init__()
        self.gap = nn.AdaptiveAvgPool2d(gap_size)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel),
            nn.BatchNorm1d(channel),
            nn.ReLU(inplace=True),
            nn.Linear(channel, 1),
            nn.Sigmoid(),
        )
        
    def forward(self, x):
        x_raw = x
        x = torch.abs(x)
        x_abs = x
        x = self.gap(x)
        x = torch.flatten(x, 1)
        average = torch.mean(x, dim=1, keepdim=True)
        x = self.fc(x)
        x = torch.mul(average, x)
        x = x.unsqueeze(2).unsqueeze(2)
        sub = x_abs - x
        zeros = sub - sub
        n_sub = torch.max(sub, zeros)
        x = torch.mul(torch.sign(x_raw), n_sub)
        return x

这段代码是对DRSN中残差收缩层的PyTorch实现。其中，输入x经过取绝对值操作后，通过全局平均池化(GAP)得到一个向量，再经过两次全连接层得到z，再通过Sigmoid函数得到一个值。然后，将GAP的结果取平均后与z相乘，得到软阈值化的结果，再与原输入进行阈值化。这段代码可以用于PyTorch中的DRSN模型实现。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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