model.py 文件源码

def forward(self, x):
        upblock = True
        # Downsizing layer - Large Kernel ensures large receptive field on the residual blocks
        h = F.relu(self.b2(self.c1(x)))

        # Residual Layers
        for r in self.rs:
            h = r(h)  # will go through all residual blocks in this loop

        if upblock:
            # Upsampling Layers - improvement suggested by [2] to remove "checkerboard pattern"
            for u in self.up:
                h = u(h)  # will go through all upsampling blocks in this loop
        else:
            # As recommended by [1]
            h = F.relu(self.bc2(self.dc2(h)))
            h = F.relu(self.bc3(self.dc3(h)))

        # Last layer and scaled tanh activation - Scaled from 0 to 1 instead of 0 - 255
        h = F.tanh(self.c3(h))
        h = torch.add(h, 1.)
        h = torch.mul(h, 0.5)
        return h