| # 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 numpy as np | |
| import torch | |
| from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss | |
| class TopKLoss(RobustCrossEntropyLoss): | |
| """ | |
| Network has to have NO LINEARITY! | |
| """ | |
| def __init__(self, weight=None, ignore_index=-100, k=10): | |
| self.k = k | |
| super(TopKLoss, self).__init__(weight, False, ignore_index, reduce=False) | |
| def forward(self, inp, target): | |
| target = target[:, 0].long() | |
| res = super(TopKLoss, self).forward(inp, target) | |
| num_voxels = np.prod(res.shape, dtype=np.int64) | |
| res, _ = torch.topk(res.view((-1, )), int(num_voxels * self.k / 100), sorted=False) | |
| return res.mean() | |