models.py 文件源码

def forward(self, x):
        # Input
        x = self.input_conv(x)
        # Network
        skips = []
        for i, (block, scale) in enumerate(zip(self.blocks, self.scales)):
            if i < self.n_layers:
                x = concat([block(x), x])
                skips.append(x)
                x = scale(x)
            elif i == self.n_layers:
                x = block(x)
            else:
                x = block(concat([scale(x), skips[2 * self.n_layers - i]]))
        # Output
        x = self.output_conv(x)
        b = self.hps.patch_border
        return F.sigmoid(x[:, :, b:-b, b:-b])