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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) |