| # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch | |
| def maybe_to_torch(d): | |
| if isinstance(d, list): | |
| d = [maybe_to_torch(i) if not isinstance(i, torch.Tensor) else i for i in d] | |
| elif not isinstance(d, torch.Tensor): | |
| d = torch.from_numpy(d).float() | |
| return d | |
| def to_cuda(data, non_blocking=True, gpu_id=0): | |
| if isinstance(data, list): | |
| data = [i.cuda(gpu_id, non_blocking=non_blocking) for i in data] | |
| else: | |
| data = data.cuda(gpu_id, non_blocking=non_blocking) | |
| return data | |