networks.py 文件源码

def forward(self, x):
        # conv & downsampling
        down_sampled_fmaps = []
        for i in range(self.n_stages-1):
            x = self.down_convs[i](x)
            x = self.max_pooling(x)
            down_sampled_fmaps.insert(0, x)

        # center convs
        x = self.down_convs[self.n_stages-1](x)
        x = self.up_convs[0](x)

        # conv & upsampling
        for i, down_sampled_fmap in enumerate(down_sampled_fmaps):
            x = torch.cat([x, down_sampled_fmap], 1)
            x = self.up_convs[i+1](x)
            x = F.upsample(x, scale_factor=2, mode='bilinear')

        return self.out_conv(x)
        #x = self.out_conv(x)
        #return x if self.out_conv.out_channels == 1 else F.relu(x)