networks.py 文件源码

def forward(self, input):
        if self.gpu_ids and isinstance(input.data, torch.cuda.FloatTensor) and self.use_parallel:
            output = nn.parallel.data_parallel(self.model, input, self.gpu_ids)
        else:
            output = self.model(input)
        if self.learn_residual:
            output = input + output
            output = torch.clamp(output,min = -1,max = 1)
        return output


# Defines the submodule with skip connection.
# X -------------------identity---------------------- X
#   |-- downsampling -- |submodule| -- upsampling --|