Method forward

ProGAN/model.py:207–238

(self, x, alpha, steps)

        return torch.cat([x, batch_statistics], dim=1)

    def forward(self, x, alpha, steps):
        # where we should start in the list of prog_blocks, maybe a bit confusing but
        # the last is for the 4x4. So example let's say steps=1, then we should start
        # at the second to last because input_size will be 8x8. If steps==0 we just
        # use the final block
        cur_step = len(self.prog_blocks) - steps

        # convert from rgb as initial step, this will depend on
        # the image size (each will have it's on rgb layer)
        out = self.leaky(self.rgb_layers[cur_step](x))

        if steps == 0:  # i.e, image is 4x4
            out = self.minibatch_std(out)
            return self.final_block(out).view(out.shape[0], -1)

        # because prog_blocks might change the channels, for down scale we use rgb_layer
        # from previous/smaller size which in our case correlates to +1 in the indexing
        #先下采样之后经过rgb_layers层
        downscaled = self.leaky(self.rgb_layers[cur_step + 1](self.avg_pool(x)))
        #经过convblock之后直接下采样
        out = self.avg_pool(self.prog_blocks[cur_step](out))

        # the fade_in is done first between the downscaled and the input
        # this is opposite from the generator
        out = self.fade_in(alpha, downscaled, out)

        for step in range(cur_step + 1, len(self.prog_blocks)):
            out = self.prog_blocks[step](out)
            out = self.avg_pool(out)

        out = self.minibatch_std(out)
        return self.final_block(out).view(out.shape[0], -1)


if __name__ == "__main__":