# 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 shutil from itertools import combinations import nnunet from batchgenerators.utilities.file_and_folder_operations import * from nnunet.evaluation.add_mean_dice_to_json import foreground_mean from nnunet.evaluation.evaluator import evaluate_folder from nnunet.evaluation.model_selection.ensemble import ensemble from nnunet.paths import network_training_output_dir import numpy as np from subprocess import call from nnunet.postprocessing.consolidate_postprocessing import consolidate_folds, collect_cv_niftis from nnunet.utilities.folder_names import get_output_folder_name from nnunet.paths import default_cascade_trainer, default_trainer, default_plans_identifier def find_task_name(folder, task_id): candidates = subdirs(folder, prefix="Task%03.0d_" % task_id, join=False) assert len(candidates) > 0, "no candidate for Task id %d found in folder %s" % (task_id, folder) assert len(candidates) == 1, "more than one candidate for Task id %d found in folder %s" % (task_id, folder) return candidates[0] def get_mean_foreground_dice(json_file): results = load_json(json_file) return get_foreground_mean(results) def get_foreground_mean(results): results_mean = results['results']['mean'] dice_scores = [results_mean[i]['Dice'] for i in results_mean.keys() if i != "0" and i != 'mean'] return np.mean(dice_scores) def main(): import argparse parser = argparse.ArgumentParser(usage="This is intended to identify the best model based on the five fold " "cross-validation. Running this script requires all models to have been run " "already. This script will summarize the results of the five folds of all " "models in one json each for easy interpretability") parser.add_argument("-m", '--models', nargs="+", required=False, default=['2d', '3d_lowres', '3d_fullres', '3d_cascade_fullres']) parser.add_argument("-t", '--task_ids', nargs="+", required=True) parser.add_argument("-tr", type=str, required=False, default=default_trainer, help="nnUNetTrainer class. Default: %s" % default_trainer) parser.add_argument("-ctr", type=str, required=False, default=default_cascade_trainer, help="nnUNetTrainer class for cascade model. Default: %s" % default_cascade_trainer) parser.add_argument("-pl", type=str, required=False, default=default_plans_identifier, help="plans name, Default: %s" % default_plans_identifier) parser.add_argument('-f', '--folds', nargs='+', default=(0, 1, 2, 3, 4), help="Use this if you have non-standard " "folds. Experienced users only.") parser.add_argument('--disable_ensembling', required=False, default=False, action='store_true', help='Set this flag to disable the use of ensembling. This will find the best single ' 'configuration for each task.') parser.add_argument("--disable_postprocessing", required=False, default=False, action="store_true", help="Set this flag if you want to disable the use of postprocessing") args = parser.parse_args() tasks = [int(i) for i in args.task_ids] models = args.models tr = args.tr trc = args.ctr pl = args.pl disable_ensembling = args.disable_ensembling disable_postprocessing = args.disable_postprocessing folds = tuple(int(i) for i in args.folds) validation_folder = "validation_raw" # this script now acts independently from the summary jsons. That was unnecessary id_task_mapping = {} for t in tasks: # first collect pure model performance results = {} all_results = {} valid_models = [] for m in models: if m == "3d_cascade_fullres": trainer = trc else: trainer = tr if t not in id_task_mapping.keys(): task_name = find_task_name(get_output_folder_name(m), t) id_task_mapping[t] = task_name output_folder = get_output_folder_name(m, id_task_mapping[t], trainer, pl) if not isdir(output_folder): raise RuntimeError("Output folder for model %s is missing, expected: %s" % (m, output_folder)) if disable_postprocessing: # we need to collect the predicted niftis from the 5-fold cv and evaluate them against the ground truth cv_niftis_folder = join(output_folder, 'cv_niftis_raw') if not isfile(join(cv_niftis_folder, 'summary.json')): print(t, m, ': collecting niftis from 5-fold cv') if isdir(cv_niftis_folder): shutil.rmtree(cv_niftis_folder) collect_cv_niftis(output_folder, cv_niftis_folder, validation_folder, folds) niftis_gt = subfiles(join(output_folder, "gt_niftis"), suffix='.nii.gz', join=False) niftis_cv = subfiles(cv_niftis_folder, suffix='.nii.gz', join=False) if not all([i in niftis_gt for i in niftis_cv]): raise AssertionError("It does not seem like you trained all the folds! Train " \ "all folds first! There are %d gt niftis in %s but only " \ "%d predicted niftis in %s" % (len(niftis_gt), niftis_gt, len(niftis_cv), niftis_cv)) # load a summary file so that we can know what class labels to expect summary_fold0 = load_json(join(output_folder, "fold_%d" % folds[0], validation_folder, "summary.json"))['results']['mean'] # read classes from summary.json classes = tuple((int(i) for i in summary_fold0.keys())) # evaluate the cv niftis print(t, m, ': evaluating 5-fold cv results') evaluate_folder(join(output_folder, "gt_niftis"), cv_niftis_folder, classes) else: postprocessing_json = join(output_folder, "postprocessing.json") cv_niftis_folder = join(output_folder, "cv_niftis_raw") # we need cv_niftis_postprocessed to know the single model performance. And we need the # postprocessing_json. If either of those is missing, rerun consolidate_folds if not isfile(postprocessing_json) or not isdir(cv_niftis_folder): print("running missing postprocessing for %s and model %s" % (id_task_mapping[t], m)) consolidate_folds(output_folder, folds=folds) assert isfile(postprocessing_json), "Postprocessing json missing, expected: %s" % postprocessing_json assert isdir(cv_niftis_folder), "Folder with niftis from CV missing, expected: %s" % cv_niftis_folder # obtain mean foreground dice summary_file = join(cv_niftis_folder, "summary.json") results[m] = get_mean_foreground_dice(summary_file) foreground_mean(summary_file) all_results[m] = load_json(summary_file)['results']['mean'] valid_models.append(m) if not disable_ensembling: # now run ensembling and add ensembling to results print("\nI will now ensemble combinations of the following models:\n", valid_models) if len(valid_models) > 1: for m1, m2 in combinations(valid_models, 2): trainer_m1 = trc if m1 == "3d_cascade_fullres" else tr trainer_m2 = trc if m2 == "3d_cascade_fullres" else tr ensemble_name = "ensemble_" + m1 + "__" + trainer_m1 + "__" + pl + "--" + m2 + "__" + trainer_m2 + "__" + pl output_folder_base = join(network_training_output_dir, "ensembles", id_task_mapping[t], ensemble_name) maybe_mkdir_p(output_folder_base) network1_folder = get_output_folder_name(m1, id_task_mapping[t], trainer_m1, pl) network2_folder = get_output_folder_name(m2, id_task_mapping[t], trainer_m2, pl) print("ensembling", network1_folder, network2_folder) ensemble(network1_folder, network2_folder, output_folder_base, id_task_mapping[t], validation_folder, folds, allow_ensembling=not disable_postprocessing) # ensembling will automatically do postprocessingget_foreground_mean # now get result of ensemble results[ensemble_name] = get_mean_foreground_dice(join(output_folder_base, "ensembled_raw", "summary.json")) summary_file = join(output_folder_base, "ensembled_raw", "summary.json") foreground_mean(summary_file) all_results[ensemble_name] = load_json(summary_file)['results']['mean'] # now print all mean foreground dice and highlight the best foreground_dices = list(results.values()) best = np.max(foreground_dices) for k, v in results.items(): print(k, v) predict_str = "" best_model = None for k, v in results.items(): if v == best: print("%s submit model %s" % (id_task_mapping[t], k), v) best_model = k print("\nHere is how you should predict test cases. Run in sequential order and replace all input and output folder names with your personalized ones\n") if k.startswith("ensemble"): tmp = k[len("ensemble_"):] model1, model2 = tmp.split("--") m1, t1, pl1 = model1.split("__") m2, t2, pl2 = model2.split("__") predict_str += "nnUNet_predict -i FOLDER_WITH_TEST_CASES -o OUTPUT_FOLDER_MODEL1 -tr " + tr + " -ctr " + trc + " -m " + m1 + " -p " + pl + " -t " + \ id_task_mapping[t] + "\n" predict_str += "nnUNet_predict -i FOLDER_WITH_TEST_CASES -o OUTPUT_FOLDER_MODEL2 -tr " + tr + " -ctr " + trc + " -m " + m2 + " -p " + pl + " -t " + \ id_task_mapping[t] + "\n" if not disable_postprocessing: predict_str += "nnUNet_ensemble -f OUTPUT_FOLDER_MODEL1 OUTPUT_FOLDER_MODEL2 -o OUTPUT_FOLDER -pp " + join(network_training_output_dir, "ensembles", id_task_mapping[t], k, "postprocessing.json") + "\n" else: predict_str += "nnUNet_ensemble -f OUTPUT_FOLDER_MODEL1 OUTPUT_FOLDER_MODEL2 -o OUTPUT_FOLDER\n" else: predict_str += "nnUNet_predict -i FOLDER_WITH_TEST_CASES -o OUTPUT_FOLDER_MODEL1 -tr " + tr + " -ctr " + trc + " -m " + k + " -p " + pl + " -t " + \ id_task_mapping[t] + "\n" print(predict_str) summary_folder = join(network_training_output_dir, "ensembles", id_task_mapping[t]) maybe_mkdir_p(summary_folder) with open(join(summary_folder, "prediction_commands.txt"), 'w') as f: f.write(predict_str) num_classes = len([i for i in all_results[best_model].keys() if i != 'mean' and i != '0']) with open(join(summary_folder, "summary.csv"), 'w') as f: f.write("model") for c in range(1, num_classes + 1): f.write(",class%d" % c) f.write(",average") f.write("\n") for m in all_results.keys(): f.write(m) for c in range(1, num_classes + 1): f.write(",%01.4f" % all_results[m][str(c)]["Dice"]) f.write(",%01.4f" % all_results[m]['mean']["Dice"]) f.write("\n") if __name__ == "__main__": main()