def get_commands(configurations, regular_trainer="nnUNetTrainerV2", cascade_trainer="nnUNetTrainerV2CascadeFullRes", plans="nnUNetPlansv2.1"): node_pool = ["hdf18-gpu%02.0d" % i for i in range(1, 21)] + [ "hdf19-gpu%02.0d" % i for i in range(1, 8) ] + ["hdf19-gpu%02.0d" % i for i in range(11, 16)] ctr = 0 for task in configurations: models = configurations[task] for m in models: if m == "3d_cascade_fullres": trainer = cascade_trainer else: trainer = regular_trainer folder = get_output_folder_name( m, task, trainer, plans, overwrite_training_output_dir= "/datasets/datasets_fabian/results/nnUNet") node = node_pool[ctr % len(node_pool)] print( "bsub -m %s -q gputest -L /bin/bash \"source ~/.bashrc && python postprocessing/" "consolidate_postprocessing.py -f" % node, folder, "\"") ctr += 1
def main(): argparser = argparse.ArgumentParser(usage="Used to determine the postprocessing for a trained model. Useful for " "when the best configuration (2d, 3d_fullres etc) as selected manually.") argparser.add_argument("-m", type=str, required=True, help="U-Net model (2d, 3d_lowres, 3d_fullres or " "3d_cascade_fullres)") argparser.add_argument("-t", type=str, required=True, help="Task name or id") argparser.add_argument("-tr", type=str, required=False, default=None, help="nnUNetTrainer class. Default: %s, unless 3d_cascade_fullres " "(then it's %s)" % (default_trainer, default_cascade_trainer)) argparser.add_argument("-pl", type=str, required=False, default=default_plans_identifier, help="Plans name, Default=%s" % default_plans_identifier) argparser.add_argument("-val", type=str, required=False, default="validation_raw", help="Validation folder name. Default: validation_raw") args = argparser.parse_args() model = args.m task = args.t trainer = args.tr plans = args.pl val = args.val if not task.startswith("Task"): task_id = int(task) task = convert_id_to_task_name(task_id) if trainer is None: if model == "3d_cascade_fullres": trainer = "nnUNetTrainerV2CascadeFullRes" else: trainer = "nnUNetTrainerV2" folder = get_output_folder_name(model, task, trainer, plans, None) consolidate_folds(folder, val)
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") parser.add_argument( "--strict", required=False, default=False, action="store_true", help= "set this flag if you want this script to crash of one of the models is missing" ) args = parser.parse_args() tasks = [int(i) for i in args.task_ids] models = args.models tr = args.tr trc = args.ctr strict = args.strict pl = args.pl 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 each task, run ensembling using all combinations of two models for t in tasks: # first collect pure model performance (postprocessed) results = {} all_results = {} valid_models = [] for m in models: try: 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) assert isdir( output_folder ), "Output folder for model %s is missing, expected: %s" % ( m, output_folder) # we need a postprocessing_json for inference, so that must be present postprocessing_json = join(output_folder, "postprocessing.json") # we need cv_niftis_postprocessed to know the single model performance cv_niftis_folder = join(output_folder, "cv_niftis_raw") 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) except Exception as e: if strict: raise e else: print("WARNING!") print(e) # now run ensembling and add ensembling to results print("\nFound the following valid 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) # ensemble2(network1_folder, network2_folder, output_folder_base, id_task_mapping[t], validation_folder, folds) # # 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'] # for m1, m2, m3 in combinations(valid_models, 3): # # trainer_m1 = trc if m1 == "3d_cascade_fullres" else tr # trainer_m2 = trc if m2 == "3d_cascade_fullres" else tr # trainer_m3 = trc if m3 == "3d_cascade_fullres" else tr # # ensemble_name = "ensemble_" + m1 + "__" + trainer_m1 + "__" + pl + "--" + m2 + "__" + trainer_m2 + "__" + pl + "--" + m3 + "__" + trainer_m3 + "__" + 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) # network3_folder = get_output_folder_name(m3, id_task_mapping[t], trainer_m3, pl) # # print("ensembling", network1_folder, network2_folder, network3_folder) # ensemble3(network1_folder, network2_folder, network3_folder, output_folder_base, id_task_mapping[t], validation_folder, folds) # # 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'] for m1, m2, m3, m4 in combinations(valid_models, 4): trainer_m1 = trc if m1 == "3d_cascade_fullres" else tr trainer_m2 = trc if m2 == "3d_cascade_fullres" else tr trainer_m3 = trc if m3 == "3d_cascade_fullres" else tr trainer_m4 = trc if m4 == "3d_cascade_fullres" else tr ensemble_name = "ensemble_" + m1 + "__" + trainer_m1 + "__" + pl + "--" + m2 + "__" + trainer_m2 + "__" + pl + "--" + m3 + "__" + trainer_m3 + "__" + pl + m4 + "__" + trainer_m4 + "__" + 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) network3_folder = get_output_folder_name( m3, id_task_mapping[t], trainer_m3, pl) network4_folder = get_output_folder_name( m4, id_task_mapping[t], trainer_m4, pl) print("ensembling", network1_folder, network2_folder, network3_folder, network4_folder) ensemble4(network1_folder, network2_folder, network3_folder, network4_folder, output_folder_base, id_task_mapping[t], validation_folder, folds) # 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" 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_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']) with open(join(summary_folder, "summary.csv"), 'w') as f: f.write("model") for c in range(1, num_classes): 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): f.write(",%01.4f" % all_results[m][str(c)]["Dice"]) f.write(",%01.4f" % all_results[m]['mean']["Dice"]) f.write("\n")
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) os.makedirs(output_folder_base, exist_ok=True) 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]) os.makedirs(summary_folder, exist_ok=True) 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")