Create nnUNetTrainerV2_Loss_FL_and_CE.py

nnUNetTrainerV2_Loss_FL_and_CE.py

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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)