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nnUNet_calvingfront_detection / nnunet /paths.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.
import os
from batchgenerators.utilities.file_and_folder_operations import maybe_mkdir_p, join
# do not modify these unless you know what you are doing
my_output_identifier = "nnUNet"
default_plans_identifier = "nnUNetPlansv2.1"
default_data_identifier = 'nnUNetData_plans_v2.1'
default_trainer = "nnUNetTrainerV2"
default_cascade_trainer = "nnUNetTrainerV2CascadeFullRes"
"""
PLEASE READ paths.md FOR INFORMATION TO HOW TO SET THIS UP
"""
base = os.environ['nnUNet_raw_data_base'] if "nnUNet_raw_data_base" in os.environ.keys() else None
preprocessing_output_dir = os.environ['nnUNet_preprocessed'] if "nnUNet_preprocessed" in os.environ.keys() else None
network_training_output_dir_base = os.path.join(os.environ['RESULTS_FOLDER']) if "RESULTS_FOLDER" in os.environ.keys() else None
if base is not None:
nnUNet_raw_data = join(base, "nnUNet_raw_data")
nnUNet_cropped_data = join(base, "nnUNet_cropped_data")
maybe_mkdir_p(nnUNet_raw_data)
maybe_mkdir_p(nnUNet_cropped_data)
else:
print("nnUNet_raw_data_base is not defined and nnU-Net can only be used on data for which preprocessed files "
"are already present on your system. nnU-Net cannot be used for experiment planning and preprocessing like "
"this. If this is not intended, please read documentation/setting_up_paths.md for information on how to set this up properly.")
nnUNet_cropped_data = nnUNet_raw_data = None
if preprocessing_output_dir is not None:
maybe_mkdir_p(preprocessing_output_dir)
else:
print("nnUNet_preprocessed is not defined and nnU-Net can not be used for preprocessing "
"or training. If this is not intended, please read documentation/setting_up_paths.md for information on how to set this up.")
preprocessing_output_dir = None
if network_training_output_dir_base is not None:
network_training_output_dir = join(network_training_output_dir_base, my_output_identifier)
maybe_mkdir_p(network_training_output_dir)
else:
print("RESULTS_FOLDER is not defined and nnU-Net cannot be used for training or "
"inference. If this is not intended behavior, please read documentation/setting_up_paths.md for information on how to set this "
"up.")
network_training_output_dir = None