nnUNet_calvingfront_detection
/
nnunet
/experiment_planning
/alternative_experiment_planning
/experiment_planner_pretrained.py
# 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 batchgenerators.utilities.file_and_folder_operations import load_pickle | |
from nnunet.experiment_planning.experiment_planner_baseline_3DUNet_v21 import ExperimentPlanner3D_v21 | |
from nnunet.paths import * | |
class ExperimentPlanner3D_v21_Pretrained(ExperimentPlanner3D_v21): | |
def __init__(self, folder_with_cropped_data, preprocessed_output_folder, pretrained_model_plans_file: str, | |
pretrained_name: str): | |
super().__init__(folder_with_cropped_data, preprocessed_output_folder) | |
self.pretrained_model_plans_file = pretrained_model_plans_file | |
self.pretrained_name = pretrained_name | |
self.data_identifier = "nnUNetData_pretrained_" + pretrained_name | |
self.plans_fname = join(self.preprocessed_output_folder, "nnUNetPlans_pretrained_%s_plans_3D.pkl" % pretrained_name) | |
def load_pretrained_plans(self): | |
classes = self.plans['num_classes'] | |
self.plans = load_pickle(self.pretrained_model_plans_file) | |
self.plans['num_classes'] = classes | |
self.transpose_forward = self.plans['transpose_forward'] | |
self.preprocessor_name = self.plans['preprocessor_name'] | |
self.plans_per_stage = self.plans['plans_per_stage'] | |
self.plans['data_identifier'] = self.data_identifier | |
self.save_my_plans() | |
print(self.plans['plans_per_stage']) | |
def run_preprocessing(self, num_threads): | |
self.load_pretrained_plans() | |
super().run_preprocessing(num_threads) | |