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| # 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. | |
| from torch import nn | |
| from nnunet.training.network_training.nnUNetTrainerV2 import nnUNetTrainerV2 | |
| from nnunet.training.loss_functions.crossentropy import RobustCrossEntropyLoss | |
| from nnunet.training.network_training.nnUNet_variants.loss_function.nnUNetTrainerV2_focalLoss import FocalLoss | |
| # TODO: replace FocalLoss by fixed implemetation (and set smooth=0 in that one?) | |
| class FL_and_CE_loss(nn.Module): | |
| def __init__(self, fl_kwargs=None, ce_kwargs=None, alpha=0.5, aggregate="sum"): | |
| super(FL_and_CE_loss, self).__init__() | |
| if fl_kwargs is None: | |
| fl_kwargs = {} | |
| if ce_kwargs is None: | |
| ce_kwargs = {} | |
| self.aggregate = aggregate | |
| self.fl = FocalLoss(apply_nonlin=nn.Softmax(), **fl_kwargs) | |
| self.ce = RobustCrossEntropyLoss(**ce_kwargs) | |
| self.alpha = alpha | |
| def forward(self, net_output, target): | |
| fl_loss = self.fl(net_output, target) | |
| ce_loss = self.ce(net_output, target) | |
| if self.aggregate == "sum": | |
| result = self.alpha*fl_loss + (1-self.alpha)*ce_loss | |
| else: | |
| raise NotImplementedError("nah son") | |
| return result | |
| class nnUNetTrainerV2_Loss_FL_and_CE_checkpoints(nnUNetTrainerV2): | |
| """ | |
| Set loss to FL + CE and set checkpoints | |
| """ | |
| def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, | |
| unpack_data=True, deterministic=True, fp16=False): | |
| super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, | |
| deterministic, fp16) | |
| self.loss = FL_and_CE_loss(alpha=0.5) | |
| self.save_latest_only = False | |
| class nnUNetTrainerV2_Loss_FL_and_CE_checkpoints2(nnUNetTrainerV2_Loss_FL_and_CE_checkpoints): | |
| """ | |
| Each run is stored in a folder with the training class name in it. This simply creates a new folder, | |
| to allow investigating the variability between restarts. | |
| """ | |
| def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, | |
| unpack_data=True, deterministic=True, fp16=False): | |
| super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, | |
| deterministic, fp16) | |
| class nnUNetTrainerV2_Loss_FL_and_CE_checkpoints3(nnUNetTrainerV2_Loss_FL_and_CE_checkpoints): | |
| """ | |
| Each run is stored in a folder with the training class name in it. This simply creates a new folder, | |
| to allow investigating the variability between restarts. | |
| """ | |
| def __init__(self, plans_file, fold, output_folder=None, dataset_directory=None, batch_dice=True, stage=None, | |
| unpack_data=True, deterministic=True, fp16=False): | |
| super().__init__(plans_file, fold, output_folder, dataset_directory, batch_dice, stage, unpack_data, | |
| deterministic, fp16) |