Create nnUNetTrainerV2_Loss_CE_checkpoints.py

nnUNetTrainerV2_Loss_CE_checkpoints.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 nnunet.training.network_training.nnUNet_variants.loss_function.nnUNetTrainerV2_Loss_CE import nnUNetTrainerV2_Loss_CE
+
+
+class nnUNetTrainerV2_Loss_CE_checkpoints(nnUNetTrainerV2_Loss_CE):
+    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.save_latest_only = False
+
+
+class nnUNetTrainerV2_Loss_CE_checkpoints2(nnUNetTrainerV2_Loss_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)
+        pass
+
+
+class nnUNetTrainerV2_Loss_CE_checkpoints3(nnUNetTrainerV2_Loss_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)
+        pass