torch.py 文件源码

def forward(self, input):
        input_torch = torch.from_numpy(input)
        if self.use_gpu:
            input_torch = input_torch.cuda()
        else:
            input_torch = input_torch.float()

        input_var = Variable(input_torch)

        # forward
        out = self.model.forward(input_var)

        if type(out) is list:
            clean_out = []
            for v in out:
                clean_out.append(v.data.cpu().numpy())
            out = clean_out
        else:
            out = out.data.cpu().numpy()
        self.ready = True

        return out