From 6e0a4f4d57c2d3c920c73d965724ea46d575fbae Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Fri, 12 Feb 2021 09:08:02 +0100 Subject: [PATCH 01/17] Adapt to adni-to-bids changes (#134) * Minor fixes * Adapt to adni-to-bids changes --- clinicadl/clinicadl/tools/tsv/data_formatting.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/clinicadl/clinicadl/tools/tsv/data_formatting.py b/clinicadl/clinicadl/tools/tsv/data_formatting.py index 421ef4680..11680a8d6 100644 --- a/clinicadl/clinicadl/tools/tsv/data_formatting.py +++ b/clinicadl/clinicadl/tools/tsv/data_formatting.py @@ -366,8 +366,11 @@ def get_labels(merged_tsv, missing_mods, results_path, os.makedirs(results_path, exist_ok=True) # Remove SMC patients - if remove_smc and "diagnosis_bl" in bids_df.columns.values: - bids_df = bids_df[~(bids_df.diagnosis_bl == "SMC")] + if remove_smc: + if "diagnosis_bl" in bids_df.columns.values: # Retro-compatibility + bids_df = bids_df[~(bids_df.diagnosis_bl == "SMC")] + if "diagnosis_sc" in bids_df.columns.values: + bids_df = bids_df[~(bids_df.diagnosis_sc == "SMC")] # Adding the field baseline_diagnosis bids_copy_df = copy(bids_df) From 873d22cf5e1733841dffe8e0a1a8d7306479317d Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Fri, 26 Feb 2021 16:15:32 +0100 Subject: [PATCH 02/17] Multi cohort (#133) Add multi-cohort feature. Multiple cohort can be used at the same time. * Minor fixes * Add multi-cohort * Fix minor issues * Update docs * Advertisement on tutorial * Fix doc appearance * Add multi-cohort * Fix minor issues * Update docs * Advertisement on tutorial * Fix doc appearance * update info.json * Reverse bad merge * Adapt to adni-to-bids changes * Fix gray matter maps files path * Add variational CNN * Add option to train on GM maps * update gradients for variational CNN * Improve diagnosis inference to avoid losing sessions * Remove shepplogan generation * Set multi_cohort default value for random search * add multi_cohort arg to options * Fix minor bug in qc * Minor fix * Add multi-cohort * Fix minor issues * Update docs * Advertisement on tutorial * Fix doc appearance * update info.json * Reverse bad merge * Fix gray matter maps files path * Add variational CNN * Add option to train on GM maps * update gradients for variational CNN * Improve diagnosis inference to avoid losing sessions * Remove shepplogan generation * Set multi_cohort default value for random search * add multi_cohort arg to options * Fix minor bug in qc * Minor fix --- clinicadl/clinicadl/classify/inference.py | 52 ++- clinicadl/clinicadl/cli.py | 50 ++- clinicadl/clinicadl/interpret/gradients.py | 5 +- .../clinicadl/interpret/group_backprop.py | 50 +-- .../interpret/individual_backprop.py | 50 +-- .../quality_check/t1_linear/quality_check.py | 6 +- .../clinicadl/tools/data/generate_data.py | 27 +- clinicadl/clinicadl/tools/data/utils.py | 46 ++- .../tools/deep_learning/cnn_utils.py | 67 +++- .../clinicadl/tools/deep_learning/data.py | 230 ++++++++++--- .../clinicadl/tools/deep_learning/iotools.py | 3 + .../tools/deep_learning/models/__init__.py | 2 +- .../tools/deep_learning/models/image_level.py | 74 ++++ .../tools/deep_learning/models/patch_level.py | 10 - .../tools/deep_learning/models/random.py | 1 + .../clinicadl/tools/tsv/data_formatting.py | 92 +++-- clinicadl/clinicadl/tools/tsv/data_split.py | 318 +++++++----------- .../tools/tsv/demographics_analysis.py | 14 +- clinicadl/clinicadl/tools/tsv/kfold_split.py | 190 +++++------ clinicadl/clinicadl/tools/tsv/tsv_utils.py | 89 ++++- clinicadl/clinicadl/train/random_search.py | 1 + .../clinicadl/train/train_autoencoder.py | 3 +- clinicadl/clinicadl/train/train_multiCNN.py | 3 +- clinicadl/clinicadl/train/train_singleCNN.py | 3 +- clinicadl/tests/test_generate.py | 2 - docs/Classify.md | 2 + docs/Installation.md | 2 +- docs/Interpret.md | 3 + docs/RandomSearch.md | 2 +- docs/Train/Details.md | 19 ++ docs/Train/Introduction.md | 4 + docs/Train/Slice.md | 2 +- docs/index.md | 4 + 33 files changed, 887 insertions(+), 539 deletions(-) diff --git a/clinicadl/clinicadl/classify/inference.py b/clinicadl/clinicadl/classify/inference.py index 3782ca715..6e4569c94 100644 --- a/clinicadl/clinicadl/classify/inference.py +++ b/clinicadl/clinicadl/classify/inference.py @@ -23,7 +23,8 @@ def classify(caps_dir, prepare_dl=True, selection_metrics=None, diagnoses=None, - verbose=0): + verbose=0, + multi_cohort=False): """ This function verifies the input folders, and the existence of the json file then it launch the inference stage from a specific model. @@ -44,30 +45,13 @@ def classify(caps_dir, selection_metrics: list of metrics to find best models to be evaluated. diagnoses: list of diagnoses to be tested if tsv_path is a folder. verbose: level of verbosity. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. """ logger = return_logger(verbose, "classify") - # Verify that paths exist - caps_dir = abspath(caps_dir) - model_path = abspath(model_path) - tsv_path = abspath(tsv_path) - - if not isdir(caps_dir): - logger.error("Folder containing MRIs was not found, please verify its location.") - raise FileNotFoundError( - errno.ENOENT, strerror(errno.ENOENT), caps_dir) - if not isdir(model_path): - logger.error("A valid model in the path was not found. Donwload them from aramislab.inria.fr") - raise FileNotFoundError( - errno.ENOENT, strerror(errno.ENOENT), model_path) - if not exists(tsv_path): - raise FileNotFoundError( - errno.ENOENT, strerror(errno.ENOENT), tsv_path) - # Infer json file from model_path (suppose that json file is at the same # folder) - json_file = join(model_path, 'commandline.json') if not exists(json_file): @@ -88,7 +72,8 @@ def classify(caps_dir, prepare_dl, selection_metrics, diagnoses, - logger + logger, + multi_cohort ) @@ -104,7 +89,8 @@ def inference_from_model(caps_dir, prepare_dl=False, selection_metrics=None, diagnoses=None, - logger=None): + logger=None, + multi_cohort=False): """ Inference from previously trained model. @@ -131,6 +117,7 @@ def inference_from_model(caps_dir, selection_metrics: list of metrics to find best models to be evaluated. diagnoses: list of diagnoses to be tested if tsv_path is a folder. logger: Logger instance. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. Returns: Files written in the output folder with prediction results and metrics. By @@ -178,32 +165,31 @@ def inference_from_model(caps_dir, # loop depending the number of folds found in the model folder for fold_dir in currentDirectory.glob(currentPattern): fold = int(str(fold_dir).split("-")[-1]) - fold_path = join(model_path, fold_dir) - model_path = join(fold_path, 'models') + out_path = join(fold_dir, 'models') for selection_metric in selection_metrics: if options.mode_task == 'multicnn': - for cnn_dir in listdir(model_path): - if not exists(join(model_path, cnn_dir, "best_%s" % selection_metric, 'model_best.pth.tar')): + for cnn_dir in listdir(out_path): + if not exists(join(out_path, cnn_dir, "best_%s" % selection_metric, 'model_best.pth.tar')): raise FileNotFoundError( errno.ENOENT, strerror(errno.ENOENT), - join(model_path, + join(out_path, cnn_dir, "best_%s" % selection_metric, 'model_best.pth.tar') ) else: - full_model_path = join(model_path, "best_%s" % selection_metric) + full_model_path = join(out_path, "best_%s" % selection_metric) if not exists(join(full_model_path, 'model_best.pth.tar')): raise FileNotFoundError( errno.ENOENT, strerror(errno.ENOENT), join(full_model_path, 'model_best.pth.tar')) - performance_dir = join(fold_path, 'cnn_classification', 'best_%s' % selection_metric) + performance_dir = join(fold_dir, 'cnn_classification', 'best_%s' % selection_metric) makedirs(performance_dir, exist_ok=True) @@ -211,7 +197,7 @@ def inference_from_model(caps_dir, inference_from_model_generic( caps_dir, tsv_path, - model_path, + out_path, options, prefix, currentDirectory, @@ -219,7 +205,8 @@ def inference_from_model(caps_dir, "best_%s" % selection_metric, labels=labels, num_cnn=num_cnn, - logger=logger + logger=logger, + multi_cohort=multi_cohort ) # Soft voting @@ -241,7 +228,8 @@ def inference_from_model(caps_dir, def inference_from_model_generic(caps_dir, tsv_path, model_path, model_options, prefix, output_dir, fold, selection, - labels=True, num_cnn=None, logger=None): + labels=True, num_cnn=None, logger=None, + multi_cohort=False): from os.path import join import logging @@ -252,7 +240,7 @@ def inference_from_model_generic(caps_dir, tsv_path, model_path, model_options, _, all_transforms = get_transforms(model_options.mode, model_options.minmaxnormalization) - test_df = load_data_test(tsv_path, model_options.diagnoses) + test_df = load_data_test(tsv_path, model_options.diagnoses, multi_cohort=multi_cohort) # Define loss and optimizer criterion = get_criterion(model_options.loss) diff --git a/clinicadl/clinicadl/cli.py b/clinicadl/clinicadl/cli.py index 80d74af9e..4c65f80d9 100644 --- a/clinicadl/clinicadl/cli.py +++ b/clinicadl/clinicadl/cli.py @@ -138,7 +138,8 @@ def classify_func(args): prepare_dl=args.use_extracted_features, selection_metrics=args.selection_metrics, diagnoses=args.diagnoses, - verbose=args.verbose + verbose=args.verbose, + multi_cohort=args.multi_cohort ) @@ -178,10 +179,11 @@ def tsv_split_func(args): n_test=args.n_test, subset_name=args.subset_name, MCI_sub_categories=args.MCI_sub_categories, - t_val_threshold=args.t_val_threshold, - p_val_threshold=args.p_val_threshold, + p_age_threshold=args.p_age_threshold, + p_sex_threshold=args.p_sex_threshold, ignore_demographics=args.ignore_demographics, - verbose=args.verbose + verbose=args.verbose, + categorical_split_variable=args.categorical_split_variable ) @@ -1027,6 +1029,12 @@ def preprocessing_help(args): "--diagnoses", help="List of participants that will be classified.", nargs="+", type=str, choices=['AD', 'CN', 'MCI', 'sMCI', 'pMCI'], default=None) + classify_specific_group.add_argument( + "--multi_cohort", + help="Performs multi-cohort classification. In this case, caps_dir and tsv_path must be paths to TSV files.", + action="store_true", + default=False + ) classify_parser.set_defaults(func=classify_func) @@ -1090,7 +1098,7 @@ def preprocessing_help(args): tsv_getlabels_subparser.add_argument( "--diagnoses", help="Labels that must be extracted from merged_tsv.", - nargs="+", type=str, choices=['AD', 'CN', 'MCI', 'sMCI', 'pMCI'], default=['AD', 'CN']) + nargs="+", type=str, choices=['AD', 'BV', 'CN', 'MCI', 'sMCI', 'pMCI'], default=['AD', 'CN']) tsv_getlabels_subparser.add_argument( "--time_horizon", help="Time horizon to analyse stability of MCI subjects.", @@ -1116,6 +1124,7 @@ def preprocessing_help(args): 'split', parents=[parent_parser], help='Performs one stratified shuffle split on participant level.') + tsv_split_subparser.add_argument( "formatted_data_path", help="Path to the folder containing data extracted by clinicadl tsvtool getlabels.", @@ -1133,11 +1142,11 @@ def preprocessing_help(args): help="Deactivate default managing of MCI sub-categories to avoid data leakage.", action="store_false", default=True) tsv_split_subparser.add_argument( - "--t_val_threshold", "-t", + "--p_sex_threshold", "-ps", help="The threshold used for the chi2 test on sex distributions.", - default=0.0642, type=float) + default=0.80, type=float) tsv_split_subparser.add_argument( - "--p_val_threshold", "-p", + "--p_age_threshold", "-pa", help="The threshold used for the T-test on age distributions.", default=0.80, type=float) tsv_split_subparser.add_argument( @@ -1149,6 +1158,12 @@ def preprocessing_help(args): help="If True do not use age and sex to create the splits.", default=False, action="store_true" ) + tsv_split_subparser.add_argument( + "--categorical_split_variable", + help="Name of a categorical variable used for a stratified shuffle split " + "(in addition to age and sex selection).", + default=None, type=str + ) tsv_split_subparser.set_defaults(func=tsv_split_func) @@ -1204,7 +1219,7 @@ def preprocessing_help(args): tsv_analysis_subparser.add_argument( "--diagnoses", help="Labels selected for the demographic analysis.", - default=['AD', 'CN'], nargs="+", type=str, choices=['AD', 'CN', 'MCI', 'sMCI', 'pMCI']) + default=['AD', 'CN'], nargs="+", type=str, choices=['AD', 'BV', 'CN', 'MCI', 'sMCI', 'pMCI']) tsv_analysis_subparser.set_defaults(func=tsv_analysis_func) @@ -1256,6 +1271,12 @@ def preprocessing_help(args): interpret_data_group.add_argument( "--caps_dir", type=str, default=None, help="Path to input dir of the MRI (preprocessed CAPS_dir), if different from classification task") + interpret_data_group.add_argument( + "--multi_cohort", + help="Performs multi-cohort interpretation. In this case, caps_dir and tsv_path must be paths to TSV files.", + action="store_true", + default=False + ) interpret_data_group.add_argument( "--diagnosis", "-d", default='AD', type=str, help="The images corresponding to this diagnosis only will be loaded.") @@ -1316,7 +1337,7 @@ def return_train_parent_parser(retrain=False): train_pos_group.add_argument( 'preprocessing', help='Defines the type of preprocessing of CAPS data.', - choices=['t1-linear', 't1-extensive'], type=str) + choices=['t1-linear', 't1-extensive', 't1-volume'], type=str) train_pos_group.add_argument( 'tsv_path', help='TSV path with subjects/sessions to process.', @@ -1361,10 +1382,17 @@ def return_train_parent_parser(retrain=False): "--tsv_path", type=str, default=None, help="TSV path with subjects/sessions to process.") + train_data_group.add_argument( + "--multi_cohort", + help="Performs multi-cohort training. In this case, caps_dir and tsv_path must be paths to TSV files.", + action="store_true", + default=None if retrain else False + ) train_data_group.add_argument( '--diagnoses', '-d', help='List of diagnoses that will be selected for training.', - default=None if retrain else ['AD', 'CN'], nargs='+', type=str, choices=['AD', 'CN', 'MCI', 'sMCI', 'pMCI']) + default=None if retrain else ['AD', 'CN'], nargs='+', type=str, + choices=['AD', 'BV', 'CN', 'MCI', 'sMCI', 'pMCI']) train_data_group.add_argument( '--baseline', help='If provided, only the baseline sessions are used for training.', diff --git a/clinicadl/clinicadl/interpret/gradients.py b/clinicadl/clinicadl/interpret/gradients.py index c4e4956dc..8ed224a4c 100644 --- a/clinicadl/clinicadl/interpret/gradients.py +++ b/clinicadl/clinicadl/interpret/gradients.py @@ -17,7 +17,10 @@ def __init__(self, model, gpu=False): def generate_gradients(self, input_batch, target_class): # Forward input_batch.requires_grad = True - model_output = self.model(input_batch) + if hasattr(self.model, "variational") and self.model.variational: + _, _, _, model_output = self.model(input_batch) + else: + model_output = self.model(input_batch) # Target for backprop one_hot_output = torch.zeros_like(model_output) one_hot_output[:, target_class] = 1 diff --git a/clinicadl/clinicadl/interpret/group_backprop.py b/clinicadl/clinicadl/interpret/group_backprop.py index e3bd53cb2..ae496fca4 100644 --- a/clinicadl/clinicadl/interpret/group_backprop.py +++ b/clinicadl/clinicadl/interpret/group_backprop.py @@ -5,6 +5,7 @@ from torch.utils.data import DataLoader import argparse import matplotlib.pyplot as plt +import warnings from clinicadl.tools.deep_learning.iotools import read_json, commandline_to_json, translate_parameters, return_logger from clinicadl.tools.deep_learning.cnn_utils import get_criterion, sort_predicted @@ -22,37 +23,42 @@ def group_backprop(options): if len(fold_list) == 0: raise ValueError("No folds were found at path %s" % options.model_path) + model_options = argparse.Namespace() + model_options = read_json(model_options, path.join(options.model_path, 'commandline.json')) + model_options = translate_parameters(model_options) + model_options.gpu = options.gpu + + if options.tsv_path is None and options.input_dir is None: + options.multi_cohort = model_options.multi_cohort + if options.tsv_path is None: + options.tsv_path = model_options.tsv_path + if options.input_dir is None: + options.input_dir = model_options.input_dir + if options.target_diagnosis is None: + options.target_diagnosis = options.diagnosis + for fold in fold_list: main_logger.info(fold) for selection in options.selection: results_path = path.join(options.model_path, fold, 'gradients', selection, options.name) - model_options = argparse.Namespace() - model_options = read_json(model_options, path.join(options.model_path, 'commandline.json')) - model_options = translate_parameters(model_options) - model_options.gpu = options.gpu - - if options.tsv_path is None: - options.tsv_path = model_options.tsv_path - if options.input_dir is None: - options.input_dir = model_options.input_dir - if options.target_diagnosis is None: - options.target_diagnosis = options.diagnosis - criterion = get_criterion(model_options.loss) # Data management (remove data not well predicted by the CNN) - training_df = load_data_test(options.tsv_path, [options.diagnosis], baseline=options.baseline) + training_df = load_data_test(options.tsv_path, [options.diagnosis], baseline=options.baseline, + multi_cohort=options.multi_cohort) training_df.reset_index(drop=True, inplace=True) # Model creation _, all_transforms = get_transforms(model_options.mode, minmaxnormalization=model_options.minmaxnormalization) - data_example = return_dataset(model_options.mode, options.input_dir, - training_df, model_options.preprocessing, - train_transformations=None, all_transformations=all_transforms, - params=options) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + data_example = return_dataset(model_options.mode, options.input_dir, + training_df, model_options.preprocessing, + train_transformations=None, all_transformations=all_transforms, + params=options) model = create_model(model_options, data_example.size) model_dir = os.path.join(options.model_path, fold, 'models', selection) @@ -71,10 +77,12 @@ def group_backprop(options): # Save the tsv files used for the saliency maps training_df.to_csv(path.join('data.tsv'), sep='\t', index=False) - data_train = return_dataset(model_options.mode, options.input_dir, - training_df, model_options.preprocessing, - train_transformations=None, all_transformations=all_transforms, - params=options) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + data_train = return_dataset(model_options.mode, options.input_dir, + training_df, model_options.preprocessing, + train_transformations=None, all_transformations=all_transforms, + params=options) train_loader = DataLoader(data_train, batch_size=options.batch_size, diff --git a/clinicadl/clinicadl/interpret/individual_backprop.py b/clinicadl/clinicadl/interpret/individual_backprop.py index 91d95900e..5313b8cae 100644 --- a/clinicadl/clinicadl/interpret/individual_backprop.py +++ b/clinicadl/clinicadl/interpret/individual_backprop.py @@ -5,6 +5,7 @@ from torch.utils.data import DataLoader import argparse import matplotlib.pyplot as plt +import warnings from clinicadl.tools.deep_learning.iotools import read_json, commandline_to_json, translate_parameters, return_logger from clinicadl.tools.deep_learning.cnn_utils import get_criterion, sort_predicted @@ -22,37 +23,42 @@ def individual_backprop(options): if len(fold_list) == 0: raise ValueError("No folds were found at path %s" % options.model_path) + model_options = argparse.Namespace() + model_options.gpu = options.gpu + model_options = read_json(model_options, path.join(options.model_path, 'commandline.json')) + model_options = translate_parameters(model_options) + + if options.tsv_path is None and options.input_dir is None: + options.multi_cohort = model_options.multi_cohort + if options.tsv_path is None: + options.tsv_path = model_options.tsv_path + if options.input_dir is None: + options.input_dir = model_options.input_dir + if options.target_diagnosis is None: + options.target_diagnosis = options.diagnosis + for fold in fold_list: main_logger.info(fold) for selection in options.selection: results_path = path.join(options.model_path, fold, 'gradients', selection, options.name) - model_options = argparse.Namespace() - model_options.gpu = options.gpu - model_options = read_json(model_options, path.join(options.model_path, 'commandline.json')) - model_options = translate_parameters(model_options) - - if options.tsv_path is None: - options.tsv_path = model_options.tsv_path - if options.input_dir is None: - options.input_dir = model_options.input_dir - if options.target_diagnosis is None: - options.target_diagnosis = options.diagnosis - criterion = get_criterion(model_options.loss) # Data management (remove data not well predicted by the CNN) - training_df = load_data_test(options.tsv_path, [options.diagnosis], baseline=options.baseline) + training_df = load_data_test(options.tsv_path, [options.diagnosis], baseline=options.baseline, + multi_cohort=options.multi_cohort) training_df.reset_index(drop=True, inplace=True) # Model creation _, all_transforms = get_transforms(model_options.mode, minmaxnormalization=model_options.minmaxnormalization) - data_example = return_dataset(model_options.mode, options.input_dir, - training_df, model_options.preprocessing, - train_transformations=None, all_transformations=all_transforms, - params=options) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + data_example = return_dataset(model_options.mode, options.input_dir, + training_df, model_options.preprocessing, + train_transformations=None, all_transformations=all_transforms, + params=options) model = create_model(model_options, data_example.size) model_dir = os.path.join(options.model_path, fold, 'models', selection) @@ -71,10 +77,12 @@ def individual_backprop(options): # Save the tsv files used for the saliency maps training_df.to_csv(path.join('data.tsv'), sep='\t', index=False) - data_train = return_dataset(model_options.mode, options.input_dir, - training_df, model_options.preprocessing, - train_transformations=None, all_transformations=all_transforms, - params=options) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + data_train = return_dataset(model_options.mode, options.input_dir, + training_df, model_options.preprocessing, + train_transformations=None, all_transformations=all_transforms, + params=options) train_loader = DataLoader(data_train, batch_size=options.batch_size, diff --git a/clinicadl/clinicadl/quality_check/t1_linear/quality_check.py b/clinicadl/clinicadl/quality_check/t1_linear/quality_check.py index c6ac47b31..851a8312e 100755 --- a/clinicadl/clinicadl/quality_check/t1_linear/quality_check.py +++ b/clinicadl/clinicadl/quality_check/t1_linear/quality_check.py @@ -12,6 +12,7 @@ from .utils import QCDataset, resnet_qc_18 from clinica.utils.inputs import fetch_file, RemoteFileStructure from ...tools.data.utils import load_and_check_tsv +from ...tools.deep_learning.data import MRIDataset def quality_check(caps_dir, output_path, @@ -48,8 +49,11 @@ def quality_check(caps_dir, output_path, if gpu: model.cuda() + # Transform caps_dir in dict + caps_dict = MRIDataset.create_caps_dict(caps_dir, multi_cohort=False) + # Load DataFrame - df = load_and_check_tsv(tsv_path, caps_dir, dirname(abspath(output_path))) + df = load_and_check_tsv(tsv_path, caps_dict, dirname(abspath(output_path))) dataset = QCDataset(caps_dir, df) dataloader = DataLoader( diff --git a/clinicadl/clinicadl/tools/data/generate_data.py b/clinicadl/clinicadl/tools/data/generate_data.py index 6db6a16fa..7322e38d8 100644 --- a/clinicadl/clinicadl/tools/data/generate_data.py +++ b/clinicadl/clinicadl/tools/data/generate_data.py @@ -13,14 +13,15 @@ from clinica.utils.inputs import fetch_file, RemoteFileStructure from .utils import im_loss_roi_gaussian_distribution, find_image_path, load_and_check_tsv, generate_shepplogan_phantom -from ..tsv.tsv_utils import baseline_df +from ..tsv.tsv_utils import extract_baseline +from ..deep_learning.data import MRIDataset from clinicadl.tools.inputs.filename_types import FILENAME_TYPE from clinicadl.tools.deep_learning.iotools import check_and_clean, commandline_to_json import tarfile def generate_random_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, mean=0, - sigma=0.5, preprocessing="t1-linear"): + sigma=0.5, preprocessing="t1-linear", multi_cohort=False): """ Generates a random dataset. @@ -38,6 +39,7 @@ def generate_random_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, mea mean: (float) mean of the gaussian noise sigma: (float) standard deviation of the gaussian noise preprocessing: (str) preprocessing performed. Must be in ['t1-linear', 't1-extensive']. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. Returns: A folder written on the output_dir location (in CAPS format), also a @@ -52,8 +54,11 @@ def generate_random_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, mea "mean": mean, "sigma": sigma }) + # Transform caps_dir in dict + caps_dict = MRIDataset.create_caps_dict(caps_dir, multi_cohort=multi_cohort) + # Read DataFrame - data_df = load_and_check_tsv(tsv_path, caps_dir, output_dir) + data_df = load_and_check_tsv(tsv_path, caps_dict, output_dir) # Create subjects dir makedirs(join(output_dir, 'subjects'), exist_ok=True) @@ -61,8 +66,9 @@ def generate_random_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, mea # Retrieve image of first subject participant_id = data_df.loc[0, 'participant_id'] session_id = data_df.loc[0, 'session_id'] + cohort = data_df.loc[0, 'cohort'] - image_path = find_image_path(caps_dir, participant_id, session_id, preprocessing) + image_path = find_image_path(caps_dict, participant_id, session_id, cohort, preprocessing) image_nii = nib.load(image_path) image = image_nii.get_data() @@ -99,7 +105,7 @@ def generate_random_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, mea def generate_trivial_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, preprocessing="linear", - mask_path=None, atrophy_percent=60): + mask_path=None, atrophy_percent=60, multi_cohort=False): """ Generates a fully separable dataset. @@ -117,6 +123,7 @@ def generate_trivial_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, pr preprocessing: (str) preprocessing performed. Must be in ['linear', 'extensive']. mask_path: (str) path to the extracted masks to generate the two labels. atrophy_percent: (float) percentage of atrophy applied. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. Returns: Folder structure where images are stored in CAPS format. @@ -134,9 +141,12 @@ def generate_trivial_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, pr "atrophy_percent": atrophy_percent }) + # Transform caps_dir in dict + caps_dict = MRIDataset.create_caps_dict(caps_dir, multi_cohort=multi_cohort) + # Read DataFrame - data_df = load_and_check_tsv(tsv_path, caps_dir, output_dir) - data_df = baseline_df(data_df, "None") + data_df = load_and_check_tsv(tsv_path, caps_dict, output_dir) + data_df = extract_baseline(data_df, "None") home = str(Path.home()) cache_clinicadl = join(home, '.cache', 'clinicadl', 'ressources', 'masks') @@ -186,12 +196,13 @@ def generate_trivial_dataset(caps_dir, output_dir, n_subjects, tsv_path=None, pr participant_id = data_df.loc[data_idx, "participant_id"] session_id = data_df.loc[data_idx, "session_id"] + cohort = data_df.loc[data_idx, "cohort"] filename = f'sub-TRIV{i}_ses-M00' + FILENAME_TYPE['cropped'] + '.nii.gz' path_image = join(output_dir, 'subjects', f'sub-TRIV{i}', 'ses-M00', 't1_linear') makedirs(path_image, exist_ok=True) - image_path = find_image_path(caps_dir, participant_id, session_id, preprocessing) + image_path = find_image_path(caps_dict, participant_id, session_id, cohort, preprocessing) image_nii = nib.load(image_path) image = image_nii.get_data() diff --git a/clinicadl/clinicadl/tools/data/utils.py b/clinicadl/clinicadl/tools/data/utils.py index fd0c57cc6..1ba37d436 100755 --- a/clinicadl/clinicadl/tools/data/utils.py +++ b/clinicadl/clinicadl/tools/data/utils.py @@ -7,35 +7,55 @@ from scipy.ndimage import gaussian_filter -def load_and_check_tsv(tsv_path, caps_dir, output_path): +def load_and_check_tsv(tsv_path, caps_dict, output_path): import pandas as pd from os.path import join from clinica.iotools.utils.data_handling import create_subs_sess_list + from ..deep_learning.data import check_multi_cohort_tsv if tsv_path is not None: - df = pd.read_csv(tsv_path, sep='\t') - if ('session_id' not in list(df.columns.values)) or ( - 'participant_id' not in list(df.columns.values)): - raise Exception("the data file is not in the correct format." - "Columns should include ['participant_id', 'session_id']") + if len(caps_dict) == 1: + df = pd.read_csv(tsv_path, sep='\t') + if ('session_id' not in list(df.columns.values)) or ( + 'participant_id' not in list(df.columns.values)): + raise Exception("the data file is not in the correct format." + "Columns should include ['participant_id', 'session_id']") + else: + tsv_df = pd.read_csv(tsv_path, sep='\t') + check_multi_cohort_tsv(tsv_df, "labels") + df = pd.DataFrame() + for idx in range(len(tsv_df)): + cohort_name = tsv_df.loc[idx, 'cohort'] + cohort_path = tsv_df.loc[idx, 'path'] + cohort_df = pd.read_csv(cohort_path, sep="\t") + cohort_df['cohort'] = cohort_name + df = pd.concat([df, cohort_df]) else: - create_subs_sess_list(caps_dir, output_path, - is_bids_dir=False, use_session_tsv=False) - df = pd.read_csv( - join(output_path, 'subjects_sessions_list.tsv'), sep="\t") + df = pd.DataFrame() + for cohort, caps_path in caps_dict.items(): + create_subs_sess_list(caps_path, output_path, + is_bids_dir=False, use_session_tsv=False) + cohort_df = pd.read_csv( + join(output_path, 'subjects_sessions_list.tsv'), sep="\t") + cohort_df['cohort'] = cohort + df = pd.concat([df, cohort_df]) return df -def find_image_path(caps_dir, participant_id, session_id, preprocessing): +def find_image_path(caps_dict, participant_id, session_id, cohort, preprocessing): from os import path + + if cohort not in caps_dict.keys(): + raise ValueError('Cohort names in labels and CAPS definitions do not match.') + if preprocessing == "t1-linear": - image_path = path.join(caps_dir, 'subjects', participant_id, session_id, + image_path = path.join(caps_dict[cohort], 'subjects', participant_id, session_id, 't1_linear', participant_id + '_' + session_id + FILENAME_TYPE['cropped'] + '.nii.gz') elif preprocessing == "t1-extensive": - image_path = path.join(caps_dir, 'subjects', participant_id, session_id, + image_path = path.join(caps_dict[cohort], 'subjects', participant_id, session_id, 't1', 'spm', 'segmentation', 'normalized_space', participant_id + '_' + session_id + FILENAME_TYPE['skull_stripped'] + '.nii.gz') diff --git a/clinicadl/clinicadl/tools/deep_learning/cnn_utils.py b/clinicadl/clinicadl/tools/deep_learning/cnn_utils.py index d586b96a0..c24d0dfed 100644 --- a/clinicadl/clinicadl/tools/deep_learning/cnn_utils.py +++ b/clinicadl/clinicadl/tools/deep_learning/cnn_utils.py @@ -6,10 +6,6 @@ import warnings import pandas as pd from time import time -from torch.nn.modules.loss import _Loss -import torch.nn.functional as F -from sklearn.utils import column_or_1d -import scipy.sparse as sp import logging from torch.nn.modules.loss import _Loss import torch.nn.functional as F @@ -50,6 +46,8 @@ def train(model, train_loader, valid_loader, criterion, optimizer, resume, log_d columns = ['epoch', 'iteration', 'time', 'balanced_accuracy_train', 'loss_train', 'balanced_accuracy_valid', 'loss_valid'] + if hasattr(model, "variational") and model.variational: + columns += ["kl_loss_train", "kl_loss_valid"] filename = os.path.join(os.path.dirname(log_dir), 'training.tsv') if not resume: @@ -101,9 +99,14 @@ def train(model, train_loader, valid_loader, criterion, optimizer, resume, log_d imgs, labels = data['image'].cuda(), data['label'].cuda() else: imgs, labels = data['image'], data['label'] - train_output = model(imgs) - _, predict_batch = train_output.topk(1) - loss = criterion(train_output, labels) + + if hasattr(model, "variational") and model.variational: + z, mu, std, train_output = model(imgs) + kl_loss = kl_divergence(z, mu, std) + loss = criterion(train_output, labels) + kl_loss + else: + train_output = model(imgs) + loss = criterion(train_output, labels) # Back propagation loss.backward() @@ -143,6 +146,9 @@ def train(model, train_loader, valid_loader, criterion, optimizer, resume, log_d row = [epoch, i, t_current, results_train["balanced_accuracy"], mean_loss_train, results_valid["balanced_accuracy"], mean_loss_valid] + if hasattr(model, "variational") and model.variational: + row += [results_train["total_kl_loss"] / (len(train_loader) * train_loader.batch_size), + results_valid["total_kl_loss"] / (len(valid_loader) * valid_loader.batch_size)] row_df = pd.DataFrame([row], columns=columns) with open(filename, 'a') as f: row_df.to_csv(f, header=False, index=False, sep='\t') @@ -186,6 +192,9 @@ def train(model, train_loader, valid_loader, criterion, optimizer, resume, log_d row = [epoch, i, t_current, results_train["balanced_accuracy"], mean_loss_train, results_valid["balanced_accuracy"], mean_loss_valid] + if hasattr(model, "variational") and model.variational: + row += [results_train["total_kl_loss"] / (len(train_loader) * train_loader.batch_size), + results_valid["total_kl_loss"] / (len(valid_loader) * valid_loader.batch_size)] row_df = pd.DataFrame([row], columns=columns) with open(filename, 'a') as f: row_df.to_csv(f, header=False, index=False, sep='\t') @@ -296,6 +305,7 @@ def test(model, dataloader, use_cuda, criterion, mode="image", use_labels=True): softmax = torch.nn.Softmax(dim=1) results_df = pd.DataFrame(columns=columns) total_loss = 0 + total_kl_loss = 0 total_time = 0 tend = time() with torch.no_grad(): @@ -306,7 +316,13 @@ def test(model, dataloader, use_cuda, criterion, mode="image", use_labels=True): inputs, labels = data['image'].cuda(), data['label'].cuda() else: inputs, labels = data['image'], data['label'] - outputs = model(inputs) + + if hasattr(model, "variational") and model.variational: + z, mu, std, outputs = model(inputs) + kl_loss = kl_divergence(z, mu, std) + total_kl_loss += kl_loss.item() + else: + outputs = model(inputs) if use_labels: loss = criterion(outputs, labels) total_loss += loss.item() @@ -336,6 +352,7 @@ def test(model, dataloader, use_cuda, criterion, mode="image", use_labels=True): metrics_dict = evaluate_prediction(results_df.true_label.values.astype(int), results_df.predicted_label.values.astype(int)) metrics_dict['total_loss'] = total_loss + metrics_dict['total_kl_loss'] = total_kl_loss torch.cuda.empty_cache() return results_df, metrics_dict @@ -608,11 +625,11 @@ def forward(self, input, target): def get_criterion(option): """Returns the appropriate loss depending on the option""" if option == "default": - return torch.nn.CrossEntropyLoss() + return torch.nn.CrossEntropyLoss(reduction="sum") elif option == "L1Norm" or option == "L1": - return L1ClassificationLoss(reduction="mean", normalization=(option == "L1Norm")) + return L1ClassificationLoss(reduction="sum", normalization=(option == "L1Norm")) elif option == "SmoothL1Norm" or option == "SmoothL1": - return SmoothL1ClassificationLoss(reduction="mean", normalization=(option == "SmoothL1Norm")) + return SmoothL1ClassificationLoss(reduction="sum", normalization=(option == "SmoothL1Norm")) else: raise ValueError("The option %s is unknown for criterion selection" % option) @@ -642,3 +659,31 @@ def binarize_label(y, classes, pos_label=1, neg_label=0): Y[Y == 0] = neg_label Y = torch.from_numpy(Y).float().to(device) return Y + + +def kl_divergence(z, mu, std): + """ + Monte-Carlo KL divergence from + https://towardsdatascience.com/variational-autoencoder-demystified-with-pytorch-implementation-3a06bee395ed + + Args: + z: The vector sampled from Normal(mu, std) in the variational network + mu: The mean value of the Normal distribution of the variational network + std: The standard deviation of the Normal distribution of the variational network + Returns: + The value of the KL divergence between the Normal distributions of mean 0 and std 1 and of mean mu and std std. + """ + # 1. define the first two probabilities (in this case Normal for both) + p = torch.distributions.Normal(torch.zeros_like(mu), torch.ones_like(std)) + q = torch.distributions.Normal(mu, std) + + # 2. get the probabilities from the equation + log_qzx = q.log_prob(z) + log_pz = p.log_prob(z) + + # kl + kl = (log_qzx - log_pz) + + # go from single dim distribution to multi-dim + kl = kl.mean(-1).sum() + return kl diff --git a/clinicadl/clinicadl/tools/deep_learning/data.py b/clinicadl/clinicadl/tools/deep_learning/data.py index 99a885d31..841024e0c 100644 --- a/clinicadl/clinicadl/tools/deep_learning/data.py +++ b/clinicadl/clinicadl/tools/deep_learning/data.py @@ -22,14 +22,15 @@ class MRIDataset(Dataset): def __init__(self, caps_directory, data_file, preprocessing, transformations, labels, - augmentation_transformations=None): - self.caps_directory = caps_directory + augmentation_transformations=None, multi_cohort=False): + self.caps_dict = self.create_caps_dict(caps_directory, multi_cohort) self.transformations = transformations self.augmentation_transformations = augmentation_transformations self.eval_mode = False self.labels = labels self.diagnosis_code = { 'CN': 0, + 'BV': 1, 'AD': 1, 'sMCI': 0, 'pMCI': 1, @@ -77,25 +78,51 @@ def __init__(self, caps_directory, data_file, def __len__(self): return len(self.df) * self.elem_per_image - def _get_path(self, participant, session, mode="image"): + @staticmethod + def create_caps_dict(caps_directory, multi_cohort): + + from clinica.utils.inputs import check_caps_folder + + if multi_cohort: + if not caps_directory.endswith('.tsv'): + raise ValueError('If multi_cohort is given, the caps_dir argument should be a path to a TSV file.') + else: + caps_df = pd.read_csv(caps_directory, sep="\t") + check_multi_cohort_tsv(caps_df, 'CAPS') + caps_dict = dict() + for idx in range(len(caps_df)): + cohort = caps_df.loc[idx, 'cohort'] + caps_path = caps_df.loc[idx, 'path'] + check_caps_folder(caps_path) + caps_dict[cohort] = caps_path + else: + check_caps_folder(caps_directory) + caps_dict = {'single': caps_directory} + + return caps_dict + + def _get_path(self, participant, session, cohort, mode="image"): + + if cohort not in self.caps_dict.keys(): + raise ValueError('Cohort names in labels and CAPS definitions do not match.') if self.preprocessing == "t1-linear": - image_path = path.join(self.caps_directory, 'subjects', participant, session, + image_path = path.join(self.caps_dict[cohort], 'subjects', participant, session, 'deeplearning_prepare_data', '%s_based' % mode, 't1_linear', participant + '_' + session + FILENAME_TYPE['cropped'] + '.pt') elif self.preprocessing == "t1-extensive": - image_path = path.join(self.caps_directory, 'subjects', participant, session, + image_path = path.join(self.caps_dict[cohort], 'subjects', participant, session, 'deeplearning_prepare_data', '%s_based' % mode, 't1_extensive', participant + '_' + session + FILENAME_TYPE['skull_stripped'] + '.pt') elif self.preprocessing == "t1-volume": - image_path = path.join(self.caps_directory, 'subjects', participant, session, - 'deeplearning_prepare_data', '%s_based' % mode, 't1_volume', + image_path = path.join(self.caps_dict[cohort], 'subjects', participant, session, + 'deeplearning_prepare_data', '%s_based' % mode, 'custom', participant + '_' + session + FILENAME_TYPE['gm_maps'] + '.pt') elif self.preprocessing == "shepplogan": - image_path = path.join(self.caps_directory, 'subjects', + image_path = path.join(self.caps_dict[cohort], 'subjects', '%s_%s%s.pt' % (participant, session, FILENAME_TYPE['shepplogan'])) else: raise NotImplementedError( @@ -107,6 +134,7 @@ def _get_meta_data(self, idx): image_idx = idx // self.elem_per_image participant = self.df.loc[image_idx, 'participant_id'] session = self.df.loc[image_idx, 'session_id'] + cohort = self.df.loc[image_idx, 'cohort'] if self.elem_index is None: elem_idx = idx % self.elem_per_image @@ -121,7 +149,7 @@ def _get_meta_data(self, idx): else: label = self.diagnosis_code['unlabeled'] - return participant, session, elem_idx, label + return participant, session, cohort, elem_idx, label def _get_full_image(self): from ..data.utils import find_image_path as get_nii_path @@ -129,16 +157,20 @@ def _get_full_image(self): participant_id = self.df.loc[0, 'participant_id'] session_id = self.df.loc[0, 'session_id'] + cohort = self.df.loc[0, 'cohort'] try: - image_path = self._get_path(participant_id, session_id, "image") + image_path = self._get_path(participant_id, session_id, cohort, mode="image") + print(image_path) image = torch.load(image_path) except FileNotFoundError: image_path = get_nii_path( - self.caps_directory, + self.caps_dict, participant_id, session_id, + cohort=cohort, preprocessing=self.preprocessing) + print(image_path) image_nii = nib.load(image_path) image_np = image_nii.get_fdata() image = ToTensor()(image_np) @@ -167,7 +199,7 @@ class MRIDatasetImage(MRIDataset): def __init__(self, caps_directory, data_file, preprocessing='t1-linear', train_transformations=None, - labels=True, all_transformations=None): + labels=True, all_transformations=None, multi_cohort=False): """ Args: caps_directory (string): Directory of all the images. @@ -176,17 +208,19 @@ def __init__(self, caps_directory, data_file, train_transformations (callable, optional): Optional transform to be applied only on training mode. labels (bool): If True the diagnosis will be extracted from the given DataFrame. all_transformations (callable, options): Optional transform to be applied during training and evaluation. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. + """ self.elem_index = None self.mode = "image" super().__init__(caps_directory, data_file, preprocessing, augmentation_transformations=train_transformations, labels=labels, - transformations=all_transformations) + transformations=all_transformations, multi_cohort=multi_cohort) def __getitem__(self, idx): - participant, session, _, label = self._get_meta_data(idx) + participant, session, cohort, _, label = self._get_meta_data(idx) - image_path = self._get_path(participant, session, "image") + image_path = self._get_path(participant, session, cohort, "image") image = torch.load(image_path) if self.transformations: @@ -207,7 +241,8 @@ def num_elem_per_image(self): class MRIDatasetPatch(MRIDataset): def __init__(self, caps_directory, data_file, patch_size, stride_size, train_transformations=None, prepare_dl=False, - patch_index=None, preprocessing="t1-linear", labels=True, all_transformations=None): + patch_index=None, preprocessing="t1-linear", labels=True, all_transformations=None, + multi_cohort=False): """ Args: caps_directory (string): Directory of all the images. @@ -221,6 +256,8 @@ def __init__(self, caps_directory, data_file, patch_size, stride_size, train_tra stride_size (int): length between the centers of two patches. labels (bool): If True the diagnosis will be extracted from the given DataFrame. all_transformations (callable, options): Optional transform to be applied during training and evaluation. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. + """ if preprocessing == "shepplogan": raise ValueError("Patch mode is not available for preprocessing %s" % preprocessing) @@ -231,20 +268,20 @@ def __init__(self, caps_directory, data_file, patch_size, stride_size, train_tra self.prepare_dl = prepare_dl super().__init__(caps_directory, data_file, preprocessing, augmentation_transformations=train_transformations, labels=labels, - transformations=all_transformations) + transformations=all_transformations, multi_cohort=multi_cohort) def __getitem__(self, idx): - participant, session, patch_idx, label = self._get_meta_data(idx) + participant, session, cohort, patch_idx, label = self._get_meta_data(idx) if self.prepare_dl: - patch_path = path.join(self._get_path(participant, session, "patch")[0:-7] + patch_path = path.join(self._get_path(participant, session, cohort, "patch")[0:-7] + '_patchsize-' + str(self.patch_size) + '_stride-' + str(self.stride_size) + '_patch-' + str(patch_idx) + '_T1w.pt') image = torch.load(patch_path) else: - image_path = self._get_path(participant, session, "image") + image_path = self._get_path(participant, session, cohort, "image") full_image = torch.load(image_path) image = self.extract_patch_from_mri(full_image, patch_idx) @@ -293,7 +330,8 @@ def extract_patch_from_mri(self, image_tensor, index_patch): class MRIDatasetRoi(MRIDataset): def __init__(self, caps_directory, data_file, roi_index=None, preprocessing="t1-linear", - train_transformations=None, prepare_dl=False, labels=True, all_transformations=None): + train_transformations=None, prepare_dl=False, labels=True, all_transformations=None, + multi_cohort=False): """ Args: caps_directory (string): Directory of all the images. @@ -305,6 +343,8 @@ def __init__(self, caps_directory, data_file, roi_index=None, preprocessing="t1- prepare_dl (bool): If true pre-extracted patches will be loaded. labels (bool): If True the diagnosis will be extracted from the given DataFrame. all_transformations (callable, options): Optional transform to be applied during training and evaluation. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. + """ if preprocessing == "shepplogan": raise ValueError("ROI mode is not available for preprocessing %s" % preprocessing) @@ -312,17 +352,17 @@ def __init__(self, caps_directory, data_file, roi_index=None, preprocessing="t1- self.mode = "roi" self.prepare_dl = prepare_dl super().__init__(caps_directory, data_file, preprocessing, augmentation_transformations=train_transformations, - labels=labels, transformations=all_transformations) + labels=labels, transformations=all_transformations, multi_cohort=multi_cohort) def __getitem__(self, idx): - participant, session, roi_idx, label = self._get_meta_data(idx) + participant, session, cohort, roi_idx, label = self._get_meta_data(idx) if self.prepare_dl: raise NotImplementedError( 'The extraction of ROIs prior to training is not implemented.') else: - image_path = self._get_path(participant, session, "image") + image_path = self._get_path(participant, session, cohort, "image") image = torch.load(image_path) patch = self.extract_roi_from_mri(image, roi_idx) @@ -376,7 +416,8 @@ class MRIDatasetSlice(MRIDataset): def __init__(self, caps_directory, data_file, slice_index=None, preprocessing="t1-linear", train_transformations=None, mri_plane=0, prepare_dl=False, - discarded_slices=20, mixed=False, labels=True, all_transformations=None): + discarded_slices=20, mixed=False, labels=True, all_transformations=None, + multi_cohort=False): """ Args: caps_directory (string): Directory of all the images. @@ -393,6 +434,7 @@ def __init__(self, caps_directory, data_file, slice_index=None, preprocessing="t independently. labels (bool): If True the diagnosis will be extracted from the given DataFrame. all_transformations (callable, options): Optional transform to be applied during training and evaluation. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. """ # Rename MRI plane if preprocessing == "shepplogan": @@ -422,20 +464,20 @@ def __init__(self, caps_directory, data_file, slice_index=None, preprocessing="t self.prepare_dl = prepare_dl super().__init__(caps_directory, data_file, preprocessing, augmentation_transformations=train_transformations, labels=labels, - transformations=all_transformations) + transformations=all_transformations, multi_cohort=multi_cohort) def __getitem__(self, idx): - participant, session, slice_idx, label = self._get_meta_data(idx) + participant, session, cohort, slice_idx, label = self._get_meta_data(idx) slice_idx = slice_idx + self.discarded_slices[0] if self.prepare_dl: # read the slices directly - slice_path = path.join(self._get_path(participant, session, "slice")[0:-7] + slice_path = path.join(self._get_path(participant, session, cohort, "slice")[0:-7] + '_axis-%s' % self.direction_list[self.mri_plane] + '_channel-rgb_slice-%i_T1w.pt' % slice_idx) image = torch.load(slice_path) else: - image_path = self._get_path(participant, session, "image") + image_path = self._get_path(participant, session, cohort, "image") full_image = torch.load(image_path) image = self.extract_slice_from_mri(full_image, slice_idx) @@ -506,7 +548,8 @@ def return_dataset(mode, input_dir, data_df, preprocessing, preprocessing, train_transformations=train_transformations, all_transformations=all_transformations, - labels=labels + labels=labels, + multi_cohort=params.multi_cohort ) elif mode == "patch": return MRIDatasetPatch( @@ -519,7 +562,8 @@ def return_dataset(mode, input_dir, data_df, preprocessing, all_transformations=all_transformations, prepare_dl=params.prepare_dl, patch_index=cnn_index, - labels=labels + labels=labels, + multi_cohort=params.multi_cohort ) elif mode == "roi": return MRIDatasetRoi( @@ -529,7 +573,8 @@ def return_dataset(mode, input_dir, data_df, preprocessing, train_transformations=train_transformations, all_transformations=all_transformations, roi_index=cnn_index, - labels=labels + labels=labels, + multi_cohort=params.multi_cohort ) elif mode == "slice": return MRIDatasetSlice( @@ -542,7 +587,8 @@ def return_dataset(mode, input_dir, data_df, preprocessing, prepare_dl=params.prepare_dl, discarded_slices=params.discarded_slices, slice_index=cnn_index, - labels=labels + labels=labels, + multi_cohort=params.multi_cohort ) else: raise ValueError("Mode %s is not implemented." % mode) @@ -551,11 +597,14 @@ def return_dataset(mode, input_dir, data_df, preprocessing, def compute_num_cnn(input_dir, tsv_path, options, data="train"): _, transformations = get_transforms(options.mode, options.minmaxnormalization) + print(tsv_path) + print(input_dir) if data == "train": - example_df, _ = load_data(tsv_path, options.diagnoses, 0, options.n_splits, options.baseline) + example_df, _ = load_data(tsv_path, options.diagnoses, 0, options.n_splits, options.baseline, + multi_cohort=options.multi_cohort) else: - example_df = load_data_test(tsv_path, options.diagnoses) + example_df = load_data_test(tsv_path, options.diagnoses, multi_cohort=options.multi_cohort) full_dataset = return_dataset(options.mode, input_dir, example_df, options.preprocessing, train_transformations=None, @@ -688,9 +737,60 @@ def get_transforms(mode, minmaxnormalization=True, data_augmentation=None): # tsv files loaders ################################ -def load_data(train_val_path, diagnoses_list, +def load_data(tsv_path, diagnoses_list, split, n_splits=None, baseline=True, - logger=None): + logger=None, multi_cohort=False): + + if logger is None: + logger = logging + + if multi_cohort: + if not tsv_path.endswith(".tsv"): + raise ValueError('If multi_cohort is given, the tsv_path argument should be a path to a TSV file.') + else: + tsv_df = pd.read_csv(tsv_path, sep="\t") + check_multi_cohort_tsv(tsv_df, "labels") + train_df = pd.DataFrame() + valid_df = pd.DataFrame() + found_diagnoses = set() + for idx in range(len(tsv_df)): + cohort_name = tsv_df.loc[idx, 'cohort'] + cohort_path = tsv_df.loc[idx, 'path'] + cohort_diagnoses = tsv_df.loc[idx, 'diagnoses'].replace(' ', '').split(",") + if bool(set(cohort_diagnoses) & set(diagnoses_list)): + target_diagnoses = list(set(cohort_diagnoses) & set(diagnoses_list)) + cohort_train_df, cohort_valid_df = load_data_single(cohort_path, target_diagnoses, split, + n_splits=n_splits, + baseline=baseline, + logger=logger) + cohort_train_df["cohort"] = cohort_name + cohort_valid_df["cohort"] = cohort_name + train_df = pd.concat([train_df, cohort_train_df]) + valid_df = pd.concat([valid_df, cohort_valid_df]) + found_diagnoses = found_diagnoses | (set(cohort_diagnoses) & set(diagnoses_list)) + + if found_diagnoses != set(diagnoses_list): + raise ValueError(f"The diagnoses found in the multi cohort dataset {found_diagnoses} " + f"do not correspond to the diagnoses wanted {set(diagnoses_list)}.") + train_df.reset_index(inplace=True, drop=True) + valid_df.reset_index(inplace=True, drop=True) + else: + if tsv_path.endswith(".tsv"): + raise ValueError('To use multi-cohort framework, please add --multi-cohort flag.') + else: + train_df, valid_df = load_data_single(tsv_path, diagnoses_list, split, + n_splits=n_splits, + baseline=baseline, + logger=logger) + train_df["cohort"] = "single" + valid_df["cohort"] = "single" + + return train_df, valid_df + + +def load_data_single(train_val_path, diagnoses_list, + split, n_splits=None, baseline=True, + logger=None): if logger is None: logger = logging @@ -703,10 +803,8 @@ def load_data(train_val_path, diagnoses_list, valid_path = path.join(train_val_path, 'validation') else: - train_path = path.join(train_val_path, 'train_splits-' + str(n_splits), - 'split-' + str(split)) - valid_path = path.join(train_val_path, 'validation_splits-' + str(n_splits), - 'split-' + str(split)) + train_path = path.join(train_val_path, f'train_splits-{n_splits}', f'split-{split}') + valid_path = path.join(train_val_path, f'validation_splits-{n_splits}', f'split-{split}') logger.debug("Train path %s" % train_path) logger.debug("Valid path %s" % valid_path) @@ -734,7 +832,44 @@ def load_data(train_val_path, diagnoses_list, return train_df, valid_df -def load_data_test(test_path, diagnoses_list, baseline=True): +def load_data_test(test_path, diagnoses_list, baseline=True, multi_cohort=False): + + if multi_cohort: + if not test_path.endswith(".tsv"): + raise ValueError('If multi_cohort is given, the tsv_path argument should be a path to a TSV file.') + else: + tsv_df = pd.read_csv(test_path, sep="\t") + check_multi_cohort_tsv(tsv_df, "labels") + test_df = pd.DataFrame() + found_diagnoses = set() + for idx in range(len(tsv_df)): + cohort_name = tsv_df.loc[idx, 'cohort'] + cohort_path = tsv_df.loc[idx, 'path'] + cohort_diagnoses = tsv_df.loc[idx, 'diagnoses'].replace(' ', '').split(",") + if bool(set(cohort_diagnoses) & set(diagnoses_list)): + target_diagnoses = list(set(cohort_diagnoses) & set(diagnoses_list)) + cohort_test_df = load_data_test_single(cohort_path, target_diagnoses, baseline=baseline) + cohort_test_df["cohort"] = cohort_name + test_df = pd.concat([test_df, cohort_test_df]) + found_diagnoses = found_diagnoses | (set(cohort_diagnoses) & set(diagnoses_list)) + + if found_diagnoses != set(diagnoses_list): + raise ValueError(f"The diagnoses found in the multi cohort dataset {found_diagnoses} " + f"do not correspond to the diagnoses wanted {set(diagnoses_list)}.") + test_df.reset_index(inplace=True, drop=True) + else: + if test_path.endswith(".tsv"): + tsv_df = pd.read_csv(test_path, sep='\t') + multi_col = {"cohort", "path"} + if multi_col.issubset(tsv_df.columns.values): + raise ValueError('To use multi-cohort framework, please add --multi-cohort flag.') + test_df = load_data_test_single(test_path, diagnoses_list, baseline=baseline) + test_df["cohort"] = "single" + + return test_df + + +def load_data_test_single(test_path, diagnoses_list, baseline=True): if test_path.endswith('.tsv'): return pd.read_csv(test_path, sep='\t') @@ -848,4 +983,13 @@ def generate_sampler(dataset, sampler_option='random'): elif sampler_option == 'weighted': return sampler.WeightedRandomSampler(weights, len(weights)) else: - raise NotImplementedError("The option %s for sampler is not implemented" % sampler_option) + raise NotImplementedError(f"The option {sampler_option} for sampler is not implemented") + + +def check_multi_cohort_tsv(tsv_df, purpose): + if purpose.upper() == "CAPS": + mandatory_col = {"cohort", "path"} + else: + mandatory_col = {"cohort", "path", "diagnoses"} + if not mandatory_col.issubset(tsv_df.columns.values): + raise ValueError(f'Columns of the TSV file used for {purpose} location must include {mandatory_col}') diff --git a/clinicadl/clinicadl/tools/deep_learning/iotools.py b/clinicadl/clinicadl/tools/deep_learning/iotools.py index 44152fe54..8ee1838be 100644 --- a/clinicadl/clinicadl/tools/deep_learning/iotools.py +++ b/clinicadl/clinicadl/tools/deep_learning/iotools.py @@ -240,6 +240,9 @@ def read_json(options, json_path=None, test=False): options.dropout = None set_default_dropout(options) + if not hasattr(options, 'multi_cohort'): + options.multi_cohort = False + return options diff --git a/clinicadl/clinicadl/tools/deep_learning/models/__init__.py b/clinicadl/clinicadl/tools/deep_learning/models/__init__.py index f2505520d..dc9d94fa8 100644 --- a/clinicadl/clinicadl/tools/deep_learning/models/__init__.py +++ b/clinicadl/clinicadl/tools/deep_learning/models/__init__.py @@ -1,6 +1,6 @@ from .autoencoder import AutoEncoder, initialize_other_autoencoder, transfer_learning from .iotools import load_model, load_optimizer, save_checkpoint -from .image_level import Conv5_FC3, Conv5_FC3_mni, Conv6_FC3 +from .image_level import Conv5_FC3, Conv5_FC3_mni, Conv6_FC3, VConv5_FC3 from .patch_level import Conv4_FC3 from .slice_level import resnet18, ConvNet from .random import RandomArchitecture diff --git a/clinicadl/clinicadl/tools/deep_learning/models/image_level.py b/clinicadl/clinicadl/tools/deep_learning/models/image_level.py index 89d86878e..bc05f25d9 100755 --- a/clinicadl/clinicadl/tools/deep_learning/models/image_level.py +++ b/clinicadl/clinicadl/tools/deep_learning/models/image_level.py @@ -2,6 +2,7 @@ from .modules import PadMaxPool3d, Flatten import torch.nn as nn +import torch """ All the architectures are built here @@ -68,6 +69,79 @@ def forward(self, x): return x +class VConv5_FC3(nn.Module): + """ + Classifier for a binary classification task + + Image level architecture used on Minimal preprocessing + """ + def __init__(self, dropout=0.5): + super(VConv5_FC3, self).__init__() + + self.features = nn.Sequential( + nn.Conv3d(1, 8, 3, padding=1), + nn.BatchNorm3d(8), + nn.ReLU(), + PadMaxPool3d(2, 2), + + nn.Conv3d(8, 16, 3, padding=1), + nn.BatchNorm3d(16), + nn.ReLU(), + PadMaxPool3d(2, 2), + + nn.Conv3d(16, 32, 3, padding=1), + nn.BatchNorm3d(32), + nn.ReLU(), + PadMaxPool3d(2, 2), + + nn.Conv3d(32, 64, 3, padding=1), + nn.BatchNorm3d(64), + nn.ReLU(), + PadMaxPool3d(2, 2), + + nn.Conv3d(64, 128, 3, padding=1), + nn.BatchNorm3d(128), + nn.ReLU(), + PadMaxPool3d(2, 2), + + ) + + self.fc_mu = nn.Sequential( + Flatten(), + nn.Linear(128 * 6 * 7 * 6, 1300) + ) + + self.fc_var = nn.Sequential( + Flatten(), + nn.Linear(128 * 6 * 7 * 6, 1300) + ) + + self.classifier = nn.Sequential( + nn.Linear(1300, 50), + nn.ReLU(), + + nn.Linear(50, 2) + + ) + + self.flattened_shape = [-1, 128, 6, 7, 6] + self.variational = True + + def forward(self, x): + x = self.features(x) + log_var = self.fc_var(x) + mu = self.fc_mu(x) + std = torch.exp(log_var / 2) + if self.training: + q = torch.distributions.Normal(mu, std) + z = q.rsample() + else: + z = mu + out = self.classifier(z) + + return z, mu, std, out + + class Conv5_FC3_mni(nn.Module): """ Classifier for a binary classification task diff --git a/clinicadl/clinicadl/tools/deep_learning/models/patch_level.py b/clinicadl/clinicadl/tools/deep_learning/models/patch_level.py index b9c5553df..0fd804951 100755 --- a/clinicadl/clinicadl/tools/deep_learning/models/patch_level.py +++ b/clinicadl/clinicadl/tools/deep_learning/models/patch_level.py @@ -6,16 +6,6 @@ from torch import nn from .modules import PadMaxPool3d, Flatten -__author__ = "Junhao Wen" -__copyright__ = "Copyright 2018 The Aramis Lab Team" -__credits__ = ["Junhao Wen"] -__license__ = "See LICENSE.txt file" -__version__ = "0.1.0" -__maintainer__ = "Junhao Wen" -__email__ = "junhao.wen89@gmail.com" -__status__ = "Development" - - class Conv4_FC3(nn.Module): """ Classifier for a binary classification task diff --git a/clinicadl/clinicadl/tools/deep_learning/models/random.py b/clinicadl/clinicadl/tools/deep_learning/models/random.py index 30dfe9a6c..292fac6da 100644 --- a/clinicadl/clinicadl/tools/deep_learning/models/random.py +++ b/clinicadl/clinicadl/tools/deep_learning/models/random.py @@ -64,6 +64,7 @@ def random_sampling(rs_options, options): fixed_values = ["tsv_path", "caps_dir", "epochs", "patience", "tolerance", "diagnoses", "data_augmentation", + "multi_cohort", "channels_limit", "use_extracted_patches", "use_extracted_slices"] diff --git a/clinicadl/clinicadl/tools/tsv/data_formatting.py b/clinicadl/clinicadl/tools/tsv/data_formatting.py index 11680a8d6..2a9bbe5dd 100644 --- a/clinicadl/clinicadl/tools/tsv/data_formatting.py +++ b/clinicadl/clinicadl/tools/tsv/data_formatting.py @@ -11,7 +11,7 @@ pipelines, leading to the removal of some subjects. """ from ..deep_learning.iotools import return_logger -from .tsv_utils import neighbour_session, last_session, after_end_screening, find_label +from .tsv_utils import neighbour_session, last_session, after_end_screening, find_label, first_session import pandas as pd from os import path from copy import copy @@ -24,9 +24,12 @@ def cleaning_nan_diagnoses(bids_df, logger): """ Printing the number of missing diagnoses and filling it partially for ADNI datasets - :param bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] - :param logger: Logger object from logging library - :return: cleaned DataFrame + Args: + bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + logger: Logger object from logging library + + Returns: + cleaned DataFrame """ bids_copy_df = copy(bids_df) @@ -66,9 +69,12 @@ def infer_or_drop_diagnosis(bids_df, logger): Deduce the diagnosis when missing from previous and following sessions of the subject. If not identical, the session is dropped. Sessions with no diagnosis are also dropped when there are the last sessions of the follow-up. - :param bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] - :param logger: Logger object from logging library - :return: cleaned DataFrame + Args: + bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + logger: Logger object from logging library + + Returns: + cleaned DataFrame """ bids_copy_df = copy(bids_df) found_diag_interpol = 0 @@ -86,8 +92,14 @@ def infer_or_drop_diagnosis(bids_df, logger): else: prev_session = neighbour_session(session_nb, session_list, -1) prev_diagnosis = bids_df.loc[(subject, prev_session), 'diagnosis'] + while isinstance(prev_diagnosis, float) and prev_session != first_session(subject_df): + prev_session = neighbour_session(int(prev_session[5::]), session_list, -1) + prev_diagnosis = bids_df.loc[(subject, prev_session), 'diagnosis'] post_session = neighbour_session(session_nb, session_list, +1) post_diagnosis = bids_df.loc[(subject, post_session), 'diagnosis'] + while isinstance(post_diagnosis, float) and post_session != last_session(session_list): + post_session = neighbour_session(int(post_session[5::]), session_list, +1) + post_diagnosis = bids_df.loc[(subject, post_session), 'diagnosis'] if prev_diagnosis == post_diagnosis: found_diag_interpol += 1 bids_copy_df.loc[(subject, session), 'diagnosis'] = prev_diagnosis @@ -103,10 +115,13 @@ def mod_selection(bids_df, missing_mods_dict, mod='t1w'): """ Select only sessions for which the modality is present - :param bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] - :param missing_mods_dict: dictionnary of the DataFrames of missing modalities - :param mod: the modality used for selection - :return: DataFrame + Args: + bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + missing_mods_dict: dictionnary of the DataFrames of missing modalities + mod: the modality used for selection + + Returns: + DataFrame """ bids_copy_df = copy(bids_df) if mod is not None: @@ -125,10 +140,13 @@ def stable_selection(bids_df, diagnosis='AD', logger=None): """ Select only subjects whom diagnosis is identical during the whole follow-up. - :param bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] - :param logger: Logger object from logging library - :param diagnosis: (str) diagnosis selected - :return: DataFrame containing only the patients a the stable diagnosis + Args: + bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + logger: Logger object from logging library + diagnosis: (str) diagnosis selected + + Returns: + DataFrame containing only the patients a the stable diagnosis """ if logger is None: logger = logging @@ -168,10 +186,13 @@ def mci_stability(bids_df, horizon_time=36, logger=None): """ A method to label all MCI sessions depending on their stability on the time horizon - :param bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] - :param horizon_time: (int) time horizon in months - :param logger: Logger object from logging library - :return: DataFrame with new labels + Args: + bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + horizon_time: (int) time horizon in months + logger: Logger object from logging library + + Returns: + DataFrame with new labels """ if logger is None: logger = logging @@ -271,9 +292,12 @@ def diagnosis_removal(MCI_df, diagnosis_list): """ Removes subjects whom last diagnosis is in the list provided (avoid to keep rMCI and pMCI in sMCI lists). - :param MCI_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] - :param diagnosis_list: list of diagnoses that will be removed - :return: cleaned DataFrame + Args: + MCI_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + diagnosis_list: list of diagnoses that will be removed + + Returns: + cleaned DataFrame """ output_df = copy(MCI_df) @@ -293,10 +317,13 @@ def apply_restriction(bids_df, restriction_path): """ Application of a restriction (for example after the removal of some subjects after a preprocessing pipeline) - :param bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] - :param restriction_path: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] including - all the sessions that can be included - :return: The restricted DataFrame + Args: + bids_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + restriction_path: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] including + all the sessions that can be included + + Returns: + The restricted DataFrame """ bids_copy_df = copy(bids_df) @@ -367,7 +394,7 @@ def get_labels(merged_tsv, missing_mods, results_path, # Remove SMC patients if remove_smc: - if "diagnosis_bl" in bids_df.columns.values: # Retro-compatibility + if "diagnosis_bl" in bids_df.columns.values: # Retro-compatibility bids_df = bids_df[~(bids_df.diagnosis_bl == "SMC")] if "diagnosis_sc" in bids_df.columns.values: bids_df = bids_df[~(bids_df.diagnosis_sc == "SMC")] @@ -393,6 +420,17 @@ def get_labels(merged_tsv, missing_mods, results_path, sub_df = diagnosis_df.reset_index().groupby('participant_id')['session_id'].nunique() logger.info('Found %s AD subjects for a total of %s sessions\n' % (len(sub_df), len(diagnosis_df))) + if 'BV' in diagnoses: + logger.info('Beginning the selection of BV label') + output_df = stable_selection(bids_df, diagnosis='BV', logger=logger) + output_df = mod_selection(output_df, missing_mods_dict, modality) + output_df = apply_restriction(output_df, restriction_path) + + diagnosis_df = output_df[variables_list] + diagnosis_df.to_csv(path.join(results_path, 'BV.tsv'), sep='\t') + sub_df = diagnosis_df.reset_index().groupby('participant_id')['session_id'].nunique() + logger.info('Found %s BV subjects for a total of %s sessions\n' % (len(sub_df), len(diagnosis_df))) + if 'CN' in diagnoses: logger.info('Beginning the selection of CN label') output_df = stable_selection(bids_df, diagnosis='CN', logger=logger) diff --git a/clinicadl/clinicadl/tools/tsv/data_split.py b/clinicadl/clinicadl/tools/tsv/data_split.py index 91f9b8ecd..06066b107 100644 --- a/clinicadl/clinicadl/tools/tsv/data_split.py +++ b/clinicadl/clinicadl/tools/tsv/data_split.py @@ -1,6 +1,7 @@ # coding: utf8 -from .tsv_utils import complementary_list, find_label, baseline_df, chi2 +from .tsv_utils import complementary_list, extract_baseline, chi2, category_conversion, remove_unicity, find_label, \ + retrieve_longitudinal, remove_sub_labels from ..deep_learning.iotools import return_logger from scipy.stats import ttest_ind import shutil @@ -9,27 +10,34 @@ import numpy as np import os import logging +from sklearn.model_selection import StratifiedShuffleSplit + sex_dict = {'M': 0, 'F': 1} -def create_split(diagnosis, diagnosis_df, n_test, - pval_threshold_ttest=0.80, t_val_chi2_threshold=0.0642, +def create_split(diagnosis, diagnosis_df, split_label, n_test, + p_age_threshold=0.80, p_sex_threshold=0.80, + supplementary_train_df=None, ignore_demographics=False, logger=None): """ - Split data at the subject-level in training and test set with equivalent age and sex distributions - - :param diagnosis: (str) diagnosis on which the split is done - :param diagnosis_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] - :param n_test: (float) - If >= 1 number of subjects to put in the test set. - If < 1 proportion of subjects to put in the test set. - :param pval_threshold_ttest: (float) threshold for the t-test on age - :param t_val_chi2_threshold: (float) threshold for the chi2 test on sex - :param ignore_demographics: (bool): If True the diagnoses are split without taking into account the demographics - distributions (age, sex). - :param logger: Logger object from logging library - :return: + Split data at the subject-level in training and test set with equivalent age, sex and split_label distributions + + Args: + diagnosis: (str) diagnosis on which the split is done + diagnosis_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + split_label: (str) label on which the split is done (categorical variables) + n_test: (float) + If > 1 number of subjects to put in the test set. + If < 1 proportion of subjects to put in the test set. + p_age_threshold: (float) threshold for the t-test on age. + p_sex_threshold: (float) threshold for the chi2 test on sex. + supplementary_train_df: (DataFrame) Add data that must be included in the train set. + ignore_demographics: (bool): If True the diagnoses are split without taking into account the demographics + distributions (age, sex). + logger: Logger object from logging library + + Returns: train_df (DataFrame) subjects in the train set test_df (DataFrame) subjects in the test set """ @@ -38,75 +46,88 @@ def create_split(diagnosis, diagnosis_df, n_test, logger = logging logger.basicConfig(level=logging.DEBUG) - diagnosis_baseline_df = baseline_df(diagnosis_df) + if supplementary_train_df is not None: + sup_train_sex = [sex_dict[x] for x in supplementary_train_df.sex.values] + sup_train_age = [float(x) for x in supplementary_train_df.age.values] + else: + sup_train_sex = [] + sup_train_age = [] - if n_test >= 1: + baseline_df = extract_baseline(diagnosis_df, diagnosis) + + if n_test > 1: n_test = int(n_test) else: - n_test = int(n_test * len(diagnosis_baseline_df)) + n_test = int(n_test * len(baseline_df)) + + if not {split_label}.issubset(set(baseline_df.columns.values)): + raise ValueError(f"The column {split_label} is missing." + f"Please add it using the --variables_of_interest flag in getlabels.") if not ignore_demographics: try: - sex_label = find_label(diagnosis_baseline_df.columns.values, "sex") - age_label = find_label(diagnosis_baseline_df.columns.values, "age") + sex_label = find_label(baseline_df.columns.values, "sex") + age_label = find_label(baseline_df.columns.values, "age") except ValueError: raise ValueError("This dataset do not have age or sex values. " "Please add the flag --ignore_demographics to split " "without trying to balance age or sex distributions.") - sex = list(diagnosis_baseline_df[sex_label].values) - age = list(diagnosis_baseline_df[age_label].values) - - idx = np.arange(len(diagnosis_baseline_df)) + sex = list(baseline_df[sex_label].values) + age = list(baseline_df[age_label].values) + category = list(baseline_df[split_label].values) + category = category_conversion(category) + category = remove_unicity(category) flag_selection = True n_try = 0 while flag_selection: - idx_test = np.random.choice(idx, size=n_test, replace=False) - idx_test.sort() - idx_train = complementary_list(idx, idx_test) - - # Find similarity of distribution for the age variable - if len(set(age)) != 1: - age_test = [float(age[idx]) for idx in idx_test] - age_train = [float(age[idx]) for idx in idx_train] - - t_age, p_age = ttest_ind(age_test, age_train) - else: - p_age = 1 - - # Find the a similar distribution for the sex variable - if len(set(sex)) != 1: - sex_test = [sex_dict[sex[idx]] for idx in idx_test] - sex_train = [sex_dict[sex[idx]] for idx in idx_train] - T_sex = chi2(sex_test, sex_train) - else: - T_sex = 0 - - logger.debug("p=%.2f, T=%.4f" % (p_age, T_sex)) - if T_sex < t_val_chi2_threshold and p_age > pval_threshold_ttest: - flag_selection = False - test_df = diagnosis_baseline_df.loc[idx_test] - train_df = diagnosis_baseline_df.loc[idx_train] - - n_try += 1 + splits = StratifiedShuffleSplit(n_splits=1, test_size=n_test) + for train_index, test_index in splits.split(category, category): + + # Find the value for different demographics (age & sex) + if len(set(age)) != 1: + age_test = [float(age[idx]) for idx in test_index] + age_train = [float(age[idx]) for idx in train_index] + sup_train_age + _, p_age = ttest_ind(age_test, age_train, nan_policy="omit") + else: + p_age = 1 + + if len(set(sex)) != 1: + sex_test = [sex_dict[sex[idx]] for idx in test_index] + sex_train = [sex_dict[sex[idx]] for idx in train_index] + sup_train_sex + _, p_sex = chi2(sex_test, sex_train) + else: + p_sex = 1 + + logger.debug("p_age=%.2f, p_sex=%.4f" % (p_age, p_sex)) + + if p_sex >= p_sex_threshold and p_age >= p_age_threshold: + flag_selection = False + test_df = baseline_df.loc[test_index] + train_df = baseline_df.loc[train_index] + if supplementary_train_df is not None: + train_df = pd.concat([train_df, supplementary_train_df]) + train_df.reset_index(drop=True, inplace=True) + + n_try += 1 logger.info("Split for diagnosis %s was found after %i trials" % (diagnosis, n_try)) + else: - idx = np.arange(len(diagnosis_baseline_df)) + idx = np.arange(len(baseline_df)) idx_test = np.random.choice(idx, size=n_test, replace=False) idx_test.sort() idx_train = complementary_list(idx, idx_test) - test_df = diagnosis_baseline_df.loc[idx_test] - train_df = diagnosis_baseline_df.loc[idx_train] + test_df = baseline_df.loc[idx_test] + train_df = baseline_df.loc[idx_train] return train_df, test_df -def split_diagnoses(formatted_data_path, - n_test=100, subset_name="test", MCI_sub_categories=True, - t_val_threshold=0.0642, p_val_threshold=0.80, +def split_diagnoses(formatted_data_path, n_test=100, subset_name="test", MCI_sub_categories=True, + p_age_threshold=0.80, p_sex_threshold=0.80, categorical_split_variable=None, ignore_demographics=False, verbose=0): """ Performs a single split for each label independently on the subject level. @@ -123,8 +144,9 @@ def split_diagnoses(formatted_data_path, If 0, no training set is created and the whole dataset is considered as one set with name 'subset_name'. subset_name (str): Name of the subset that is complementary to train. MCI_sub_categories (bool): If True, manages MCI sub-categories to avoid data leakage. - t_val_threshold (float): The threshold used for the chi2 test on sex distributions. - p_val_threshold (float): The threshold used for the T-test on age distributions. + p_age_threshold (float): The threshold used for the T-test on age distributions. + p_sex_threshold (float): The threshold used for the T-test on sex distributions. + categorical_split_variable (str): name of a categorical variable to perform a stratified split. ignore_demographics (bool): If True the diagnoses are split without taking into account the demographics distributions (age, sex). verbose (int): level of verbosity. @@ -146,6 +168,9 @@ def split_diagnoses(formatted_data_path, if n_test > 0: os.makedirs(train_path) + if categorical_split_variable is None: + categorical_split_variable = "diagnosis" + test_path = path.join(results_path, subset_name) if path.exists(test_path): shutil.rmtree(test_path) @@ -171,83 +196,44 @@ def split_diagnoses(formatted_data_path, sep='\t') interest_columns = diagnosis_df.columns.values diagnosis = diagnosis_df_path.split('.')[0] - logger.info("Running split for diagnosis %s" % diagnosis) + logger.info(f"Running split for diagnosis {diagnosis}") if n_test > 0: - train_df, test_df = create_split(diagnosis, diagnosis_df, n_test=n_test, - t_val_chi2_threshold=t_val_threshold, - pval_threshold_ttest=p_val_threshold, + train_df, test_df = create_split(diagnosis, diagnosis_df, categorical_split_variable, n_test=n_test, + p_age_threshold=p_age_threshold, + p_sex_threshold=p_sex_threshold, ignore_demographics=ignore_demographics, logger=logger) # Save baseline splits - train_df = train_df[interest_columns] - train_df.to_csv(path.join(train_path, str(diagnosis) + '_baseline.tsv'), sep='\t', index=False) - test_df = test_df[interest_columns] - test_df.to_csv(path.join(test_path, str(diagnosis) + '_baseline.tsv'), sep='\t', index=False) - - # Retrieve all sessions for the training set - complete_train_df = pd.DataFrame() - for idx in train_df.index.values: - subject = train_df.loc[idx, 'participant_id'] - subject_df = diagnosis_df[diagnosis_df.participant_id == subject] - complete_train_df = pd.concat([complete_train_df, subject_df]) + train_df.to_csv(path.join(train_path, f'{diagnosis}_baseline.tsv'), sep='\t', index=False) + test_df.to_csv(path.join(test_path, f'{diagnosis}_baseline.tsv'), sep='\t', index=False) - complete_train_df.to_csv(path.join(train_path, str(diagnosis) + '.tsv'), sep='\t', index=False) + long_train_df = retrieve_longitudinal(train_df, diagnosis_df) + long_train_df.to_csv(path.join(train_path, f'{diagnosis}.tsv'), sep='\t', index=False) + long_test_df = retrieve_longitudinal(test_df, diagnosis_df) + long_test_df.to_csv(path.join(test_path, f'{diagnosis}.tsv'), sep='\t', index=False) else: - diagnosis_baseline_df = baseline_df(diagnosis_df) - test_df = diagnosis_baseline_df[interest_columns] - test_df.to_csv(path.join(test_path, str(diagnosis) + '_baseline.tsv'), sep='\t', index=False) + baseline_df = extract_baseline(diagnosis_df, diagnosis) + test_df = baseline_df[interest_columns] + test_df.to_csv(path.join(test_path, f'{diagnosis}_baseline.tsv'), sep='\t', index=False) + long_test_df = retrieve_longitudinal(test_df, diagnosis_df) + long_test_df.to_csv(path.join(test_path, f'{diagnosis}.tsv'), sep='\t', index=False) if MCI_special_treatment: # Extraction of MCI subjects without intersection with the sMCI / pMCI train diagnosis_df = pd.read_csv(path.join(results_path, 'MCI.tsv'), sep='\t') MCI_df = diagnosis_df.set_index(['participant_id', 'session_id']) - baseline_MCI_df = baseline_df(MCI_df, set_index=False) - supplementary_diagnoses = [] + baseline_df = extract_baseline(MCI_df, set_index=False, diagnosis="MCI") if n_test > 1: n_test = int(n_test) else: - n_test = int(n_test * len(baseline_MCI_df)) - - logger.debug('Before subjects removal for MCI special treatment') - if n_test > 1: - n_test = int(n_test) - else: - n_test = int(n_test * len(baseline_MCI_df)) - - sub_df = diagnosis_df.reset_index().groupby('participant_id')['session_id'].nunique() - logger.debug('%i subjects, %i scans' % (len(sub_df), len(diagnosis_df))) - - if 'sMCI.tsv' in diagnosis_df_paths: - sMCI_baseline_train_df = pd.read_csv(path.join(train_path, 'sMCI_baseline.tsv'), sep='\t') - sMCI_baseline_test_df = pd.read_csv(path.join(test_path, 'sMCI_baseline.tsv'), sep='\t') - sMCI_baseline_df = pd.concat([sMCI_baseline_train_df, sMCI_baseline_test_df]) - sMCI_baseline_df.reset_index(drop=True, inplace=True) - for idx in sMCI_baseline_df.index.values: - subject = sMCI_baseline_df.loc[idx, 'participant_id'] - MCI_df.drop(subject, inplace=True) - supplementary_diagnoses.append('sMCI') - - logger.debug('Removed %i subjects based on sMCI label' % len(sMCI_baseline_df)) - sub_df = MCI_df.reset_index().groupby('participant_id')['session_id'].nunique() - logger.debug('%i subjects, %i scans' % (len(sub_df), len(MCI_df))) - - if 'pMCI.tsv' in diagnosis_df_paths: - pMCI_baseline_train_df = pd.read_csv(path.join(train_path, 'pMCI_baseline.tsv'), sep='\t') - pMCI_baseline_test_df = pd.read_csv(path.join(test_path, 'pMCI_baseline.tsv'), sep='\t') - pMCI_baseline_df = pd.concat([pMCI_baseline_train_df, pMCI_baseline_test_df]) - pMCI_baseline_df.reset_index(drop=True, inplace=True) - for idx in pMCI_baseline_df.index.values: - subject = pMCI_baseline_df.loc[idx, 'participant_id'] - MCI_df.drop(subject, inplace=True) - supplementary_diagnoses.append('pMCI') - - logger.debug('Removed %i subjects based on pMCI label' % len(pMCI_baseline_df)) - sub_df = MCI_df.reset_index().groupby('participant_id')['session_id'].nunique() - logger.debug('%i subjects, %i scans' % (len(sub_df), len(MCI_df))) + n_test = int(n_test * len(baseline_df)) + MCI_df, supplementary_diagnoses = remove_sub_labels(MCI_df, ["sMCI", "pMCI"], + diagnosis_df_paths, results_path, + logger=logger) if len(supplementary_diagnoses) == 0: raise ValueError('The MCI_sub_categories flag is not needed as there are no intersections with' 'MCI subcategories.') @@ -255,90 +241,28 @@ def split_diagnoses(formatted_data_path, # Construction of supplementary train supplementary_train_df = pd.DataFrame() for diagnosis in supplementary_diagnoses: - sup_baseline_train_df = pd.read_csv(path.join(train_path, diagnosis + '_baseline.tsv'), sep='\t') + sup_baseline_train_df = pd.read_csv(path.join(train_path, f'{diagnosis}_baseline.tsv'), sep='\t') supplementary_train_df = pd.concat([supplementary_train_df, sup_baseline_train_df]) sub_df = supplementary_train_df.reset_index().groupby('participant_id')['session_id'].nunique() - logger.debug('supplementary_train_df %i subjects, %i scans' % (len(sub_df), len(supplementary_train_df))) + logger.debug(f'supplementary_train_df {len(sub_df)} subjects, {len(supplementary_diagnoses)} scans') supplementary_train_df.reset_index(drop=True, inplace=True) # MCI selection MCI_df.reset_index(inplace=True) - diagnosis_baseline_df = baseline_df(MCI_df) - if not ignore_demographics: - sex_label = find_label(diagnosis_baseline_df.columns.values, "sex") - age_label = find_label(diagnosis_baseline_df.columns.values, "age") - - sex = list(diagnosis_baseline_df[sex_label].values) - age = list(diagnosis_baseline_df[age_label].values) - - sup_train_sex = list(supplementary_train_df[sex_label].values) - sup_train_age = list(supplementary_train_df[age_label].values) - - sup_train_sex = [sex_dict[x] for x in sup_train_sex] - sup_train_age = [float(x) for x in sup_train_age] + baseline_df = extract_baseline(MCI_df, 'MCI') - idx = np.arange(len(diagnosis_baseline_df)) - - flag_selection = True - n_try = 0 - - while flag_selection: - idx_test = np.random.choice(idx, size=n_test, replace=False) - idx_test.sort() - idx_train = complementary_list(idx, idx_test) - - # Find similarity of distribution for the age variable - if len(set(age)) != 1: - age_test = [float(age[idx]) for idx in idx_test] - age_train = [float(age[idx]) for idx in idx_train] + sup_train_age - - t_age, p_age = ttest_ind(age_test, age_train) - else: - p_age = 1 - - # Find similarity of distribution for the sex variable - if len(set(sex)) != 1: - sex_test = [sex_dict[sex[idx]] for idx in idx_test] - sex_train = [sex_dict[sex[idx]] for idx in idx_train] + sup_train_sex - T_sex = chi2(sex_test, sex_train) - else: - T_sex = 0 - - logger.debug("p=%.2f, T=%.4f" % (p_age, T_sex)) - if T_sex < t_val_threshold and p_age > p_val_threshold: - flag_selection = False - MCI_baseline_test_df = diagnosis_baseline_df.loc[idx_test] - train_df = diagnosis_baseline_df.loc[idx_train] - MCI_baseline_train_df = pd.concat([train_df, supplementary_train_df]) - logger.debug('Supplementary train df %i' % len(supplementary_train_df)) - MCI_baseline_train_df.reset_index(drop=True, inplace=True) - - n_try += 1 - - logger.info('Split for diagnosis MCI was found after %i trials' % n_try) - else: - idx = np.arange(len(diagnosis_baseline_df)) - idx_test = np.random.choice(idx, size=n_test, replace=False) - idx_test.sort() - idx_train = complementary_list(idx, idx_test) - - MCI_baseline_test_df = diagnosis_baseline_df.loc[idx_test] - train_df = diagnosis_baseline_df.loc[idx_train] - MCI_baseline_train_df = pd.concat([train_df, supplementary_train_df]) - MCI_baseline_train_df.reset_index(drop=True, inplace=True) + train_df, test_df = create_split('MCI', baseline_df, categorical_split_variable, + n_test=n_test, p_age_threshold=p_age_threshold, + p_sex_threshold=p_sex_threshold, ignore_demographics=ignore_demographics, + logger=logger, + supplementary_train_df=supplementary_train_df) # Write selection of MCI - MCI_baseline_train_df = MCI_baseline_train_df[interest_columns] - MCI_baseline_train_df.to_csv(path.join(train_path, 'MCI_baseline.tsv'), sep='\t', index=False) - MCI_baseline_test_df = MCI_baseline_test_df[interest_columns] - MCI_baseline_test_df.to_csv(path.join(test_path, 'MCI_baseline.tsv'), sep='\t', index=False) - - # Retrieve all sessions for the training set - MCI_complete_train_df = pd.DataFrame() - for idx in MCI_baseline_train_df.index.values: - subject = MCI_baseline_train_df.loc[idx, 'participant_id'] - subject_df = diagnosis_df[diagnosis_df.participant_id == subject] - MCI_complete_train_df = pd.concat([MCI_complete_train_df, subject_df]) - - MCI_complete_train_df.to_csv(path.join(train_path, 'MCI.tsv'), sep='\t', index=False) + train_df.to_csv(path.join(train_path, 'MCI_baseline.tsv'), sep='\t', index=False) + test_df.to_csv(path.join(test_path, 'MCI_baseline.tsv'), sep='\t', index=False) + + long_train_df = retrieve_longitudinal(train_df, diagnosis_df) + long_train_df.to_csv(path.join(train_path, 'MCI.tsv'), sep='\t', index=False) + long_test_df = retrieve_longitudinal(test_df, diagnosis_df) + long_test_df.to_csv(path.join(test_path, 'MCI.tsv'), sep='\t', index=False) diff --git a/clinicadl/clinicadl/tools/tsv/demographics_analysis.py b/clinicadl/clinicadl/tools/tsv/demographics_analysis.py index 322c46aca..16741b425 100644 --- a/clinicadl/clinicadl/tools/tsv/demographics_analysis.py +++ b/clinicadl/clinicadl/tools/tsv/demographics_analysis.py @@ -5,6 +5,7 @@ import os from os import path import numpy as np +from warnings import warn def demographics_analysis(merged_tsv, formatted_data_path, results_path, diagnoses): @@ -42,7 +43,7 @@ def demographics_analysis(merged_tsv, formatted_data_path, results_path, diagnos 'n_scans'] results_df = pd.DataFrame(index=diagnoses, columns=columns, data=np.zeros((len(diagnoses), len(columns)))) - # Need all values for mean and variance (age and MMSE) + # Need all values for mean and variance (age, MMSE and scans) diagnosis_dict = dict.fromkeys(diagnoses) for diagnosis in diagnoses: diagnosis_dict[diagnosis] = {'age': [], 'MMSE': [], 'scans': []} @@ -60,7 +61,6 @@ def demographics_analysis(merged_tsv, formatted_data_path, results_path, diagnos first_session_id = first_session(subject_df) feature_absence = isinstance(merged_df.loc[(subject, first_session_id), 'diagnosis'], float) while feature_absence: - print(subject, first_session_id) first_session_id = next_session(subject_df, first_session_id) feature_absence = isinstance(merged_df.loc[(subject, first_session_id), 'diagnosis'], float) demographics_subject_df = merged_df.loc[subject] @@ -94,13 +94,13 @@ def demographics_analysis(merged_tsv, formatted_data_path, results_path, diagnos elif cdr == 3: results_df.loc[diagnosis, 'CDR_3'] += 1 else: - raise ValueError('Patient %s has CDR %f' % (subject, cdr)) + warn(f'Patient {subject} has CDR {cdr}') for diagnosis in diagnoses: results_df.loc[diagnosis, 'mean_age'] = np.nanmean(diagnosis_dict[diagnosis]['age']) results_df.loc[diagnosis, 'std_age'] = np.nanstd(diagnosis_dict[diagnosis]['age']) - results_df.loc[diagnosis, 'min_age'] = np.min(diagnosis_dict[diagnosis]['age']) - results_df.loc[diagnosis, 'max_age'] = np.max(diagnosis_dict[diagnosis]['age']) + results_df.loc[diagnosis, 'min_age'] = np.nanmin(diagnosis_dict[diagnosis]['age']) + results_df.loc[diagnosis, 'max_age'] = np.nanmax(diagnosis_dict[diagnosis]['age']) results_df.loc[diagnosis, 'mean_MMSE'] = np.nanmean(diagnosis_dict[diagnosis]['MMSE']) results_df.loc[diagnosis, 'std_MMSE'] = np.nanstd(diagnosis_dict[diagnosis]['MMSE']) results_df.loc[diagnosis, 'min_MMSE'] = np.nanmin(diagnosis_dict[diagnosis]['MMSE']) @@ -108,6 +108,10 @@ def demographics_analysis(merged_tsv, formatted_data_path, results_path, diagnos results_df.loc[diagnosis, 'mean_scans'] = np.nanmean(diagnosis_dict[diagnosis]['scans']) results_df.loc[diagnosis, 'std_scans'] = np.nanstd(diagnosis_dict[diagnosis]['scans']) + for key in diagnosis_dict[diagnosis]: + if np.isnan(diagnosis_dict[diagnosis][key]).any(): + warn(f"NaN values were found for {key} values associated to diagnosis {diagnosis}") + results_df.index.name = "diagnosis" results_df.to_csv(results_path, sep='\t') diff --git a/clinicadl/clinicadl/tools/tsv/kfold_split.py b/clinicadl/clinicadl/tools/tsv/kfold_split.py index d447cde36..aee183727 100644 --- a/clinicadl/clinicadl/tools/tsv/kfold_split.py +++ b/clinicadl/clinicadl/tools/tsv/kfold_split.py @@ -1,6 +1,6 @@ # coding: utf8 -from .tsv_utils import baseline_df +from .tsv_utils import extract_baseline, retrieve_longitudinal, remove_sub_labels from ..deep_learning.iotools import return_logger import shutil from sklearn.model_selection import StratifiedKFold @@ -12,6 +12,63 @@ sex_dict = {'M': 0, 'F': 1} +def write_splits(diagnosis, diagnosis_df, split_label, n_splits, + train_path, test_path, supplementary_diagnoses=None): + """ + Split data at the subject-level in training and test to have equivalent distributions in split_label. + + Args: + diagnosis: (str) diagnosis on which the split is done + diagnosis_df: DataFrame with columns including ['participant_id', 'session_id', 'diagnosis'] + split_label: (str) label on which the split is done (categorical variables) + n_splits: (int) Number of folds in the k-fold split + train_path: (str) Path to the training data. + test_path: (str) Path to the test data. + supplementary_diagnoses: (list of str) List of supplementary diagnoses to add to the data. + + Returns: + train_df (DataFrame) subjects in the train set + test_df (DataFrame) subjects in the test set + """ + + baseline_df = extract_baseline(diagnosis_df, diagnosis) + if split_label is None: + diagnoses_list = list(baseline_df.diagnosis) + unique = list(set(diagnoses_list)) + y = np.array([unique.index(x) for x in diagnoses_list]) + else: + stratification_list = list(baseline_df[split_label]) + unique = list(set(stratification_list)) + y = np.array([unique.index(x) for x in stratification_list]) + + splits = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=2) + + for i, indices in enumerate(splits.split(np.zeros(len(y)), y)): + print(f'Split {i}') + train_index, test_index = indices + + test_df = baseline_df.iloc[test_index] + train_df = baseline_df.iloc[train_index] + + if supplementary_diagnoses is not None: + for supplementary_diagnosis in supplementary_diagnoses: + sup_train_df = pd.read_csv(path.join(train_path, f'split-{i}', + f'{supplementary_diagnosis}_baseline.tsv'), sep='\t') + train_df = pd.concat([train_df, sup_train_df]) + sup_test_df = pd.read_csv(path.join(test_path, f'split-{i}', + f'{supplementary_diagnosis}_baseline.tsv'), sep='\t') + test_df = pd.concat([test_df, sup_test_df]) + + train_df.reset_index(inplace=True, drop=True) + test_df.reset_index(inplace=True, drop=True) + + train_df.to_csv(path.join(train_path, f'split-{i}', f'{diagnosis}_baseline.tsv'), sep='\t', index=False) + test_df.to_csv(path.join(test_path, f'split-{i}', f'{diagnosis}_baseline.tsv'), sep='\t', index=False) + + long_train_df = retrieve_longitudinal(train_df, diagnosis_df) + long_train_df.to_csv(path.join(train_path, f'split-{i}', f'{diagnosis}.tsv'), sep='\t', index=False) + + def split_diagnoses(formatted_data_path, n_splits=5, subset_name="validation", MCI_sub_categories=True, stratification=None, verbose=0): @@ -39,19 +96,19 @@ def split_diagnoses(formatted_data_path, # Read files results_path = formatted_data_path - train_path = path.join(results_path, 'train_splits-' + str(n_splits)) + train_path = path.join(results_path, f'train_splits-{n_splits}') if path.exists(train_path): shutil.rmtree(train_path) os.makedirs(train_path) for i in range(n_splits): - os.mkdir(path.join(train_path, 'split-' + str(i))) + os.mkdir(path.join(train_path, f'split-{i}')) - test_path = path.join(results_path, subset_name + '_splits-' + str(n_splits)) + test_path = path.join(results_path, f'{subset_name}_splits-{n_splits}') if path.exists(test_path): shutil.rmtree(test_path) os.makedirs(test_path) for i in range(n_splits): - os.mkdir(path.join(test_path, 'split-' + str(i))) + os.mkdir(path.join(test_path, f'split-{i}')) diagnosis_df_paths = os.listdir(results_path) diagnosis_df_paths = [x for x in diagnosis_df_paths if x.endswith('.tsv')] @@ -64,7 +121,7 @@ def split_diagnoses(formatted_data_path, diagnosis_df_paths.remove('MCI.tsv') MCI_special_treatment = True elif 'sMCI.tsv' in diagnosis_df_paths or 'pMCI.tsv' in diagnosis_df_paths: - logger.warning("MCI special treatment was deactivated though MCI subgroups were found." + logger.warning("MCI special treatment was deactivated though MCI subgroups were found. " "Be aware that it may cause data leakage in transfer learning tasks.") # The baseline session must be kept before or we are taking all the sessions to mix them @@ -72,122 +129,25 @@ def split_diagnoses(formatted_data_path, diagnosis = diagnosis_df_path.split('.')[0] diagnosis_df = pd.read_csv(path.join(results_path, diagnosis_df_path), sep='\t') - diagnosis_baseline_df = baseline_df(diagnosis_df, diagnosis) - if stratification is None: - diagnoses_list = list(diagnosis_baseline_df.diagnosis) - unique = list(set(diagnoses_list)) - y = np.array([unique.index(x) for x in diagnoses_list]) - else: - stratification_list = list(diagnosis_baseline_df[stratification]) - unique = list(set(stratification_list)) - y = np.array([unique.index(x) for x in stratification_list]) - - splits = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=2) - - for i, indices in enumerate(splits.split(np.zeros(len(y)), y)): - - train_index, test_index = indices - - test_df = diagnosis_baseline_df.iloc[test_index] - train_df = diagnosis_baseline_df.iloc[train_index] - # Retrieve all sessions for the training set - complete_train_df = pd.DataFrame() - for idx in train_df.index.values: - subject = train_df.loc[idx, 'participant_id'] - subject_df = diagnosis_df[diagnosis_df.participant_id == subject] - complete_train_df = pd.concat([complete_train_df, subject_df]) - - complete_train_df.to_csv(path.join(train_path, 'split-' + str(i), str(diagnosis) + '.tsv'), - sep='\t', index=False) - train_df.to_csv( - path.join(train_path, 'split-' + str(i), str(diagnosis) + '_baseline.tsv'), sep='\t', index=False) - test_df.to_csv( - path.join(test_path, 'split-' + str(i), str(diagnosis) + '_baseline.tsv'), sep='\t', index=False) - - logger.info("K-fold split for diagnosis %s is done" % diagnosis) + write_splits(diagnosis, diagnosis_df, stratification, n_splits, train_path, test_path) + + logger.info(f"K-fold split for diagnosis {diagnosis} is done.") if MCI_special_treatment: # Extraction of MCI subjects without intersection with the sMCI / pMCI train diagnosis_df = pd.read_csv(path.join(results_path, 'MCI.tsv'), sep='\t') MCI_df = diagnosis_df.set_index(['participant_id', 'session_id']) - supplementary_diagnoses = [] - - logger.debug('Before subjects removal') - sub_df = diagnosis_df.reset_index().groupby('participant_id')['session_id'].nunique() - logger.debug('%i subjects, %i scans' % (len(sub_df), len(diagnosis_df))) - - if 'sMCI.tsv' in diagnosis_df_paths: - sMCI_baseline_df = pd.read_csv(path.join(results_path, 'sMCI_baseline.tsv'), sep='\t') - for idx in sMCI_baseline_df.index.values: - subject = sMCI_baseline_df.loc[idx, 'participant_id'] - MCI_df.drop(subject, inplace=True, level=0) - supplementary_diagnoses.append('sMCI') - - logger.debug('Removed %i subjects based on sMCI label' % len(sMCI_baseline_df)) - sub_df = MCI_df.reset_index().groupby('participant_id')['session_id'].nunique() - logger.debug('%i subjects, %i scans' % (len(sub_df), len(MCI_df))) - - if 'pMCI.tsv' in diagnosis_df_paths: - pMCI_baseline_df = pd.read_csv(path.join(results_path, 'pMCI_baseline.tsv'), sep='\t') - for idx in pMCI_baseline_df.index.values: - subject = pMCI_baseline_df.loc[idx, 'participant_id'] - MCI_df.drop(subject, inplace=True, level=0) - supplementary_diagnoses.append('pMCI') - - logger.debug('Removed %i subjects based on pMCI label' % len(pMCI_baseline_df)) - sub_df = MCI_df.reset_index().groupby('participant_id')['session_id'].nunique() - logger.debug('%i subjects, %i scans' % (len(sub_df), len(MCI_df))) + MCI_df, supplementary_diagnoses = remove_sub_labels(MCI_df, ["sMCI", "pMCI"], + diagnosis_df_paths, results_path, + logger=logger) if len(supplementary_diagnoses) == 0: - raise ValueError('The MCI_sub_categories flag is not needed as there are no intersections with' + raise ValueError('The MCI_sub_categories flag is not needed as there are no intersections with ' 'MCI subcategories.') - diagnosis_baseline_df = baseline_df(MCI_df, False) - if stratification is None: - diagnoses_list = list(diagnosis_baseline_df.diagnosis) - unique = list(set(diagnoses_list)) - y = np.array([unique.index(x) for x in diagnoses_list]) - else: - stratification_list = list(diagnosis_baseline_df[stratification]) - unique = list(set(stratification_list)) - y = np.array([unique.index(x) for x in stratification_list]) - - splits = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=2) - - for i, indices in enumerate(splits.split(np.zeros(len(y)), y)): - - train_index, test_index = indices - - test_df = diagnosis_baseline_df.iloc[test_index] - train_df = diagnosis_baseline_df.iloc[train_index] - - # Add the sub categories - for diagnosis in supplementary_diagnoses: - sup_train_df = pd.read_csv(path.join(train_path, 'split-' + str(i), str(diagnosis) + '_baseline.tsv'), - sep='\t') - train_df = pd.concat([train_df, sup_train_df]) - sup_test_df = pd.read_csv(path.join(test_path, 'split-' + str(i), str(diagnosis) + '_baseline.tsv'), - sep='\t') - test_df = pd.concat([test_df, sup_test_df]) - - train_df.reset_index(inplace=True, drop=True) - test_df.reset_index(inplace=True, drop=True) - train_df.diagnosis = ['MCI'] * len(train_df) - test_df.diagnosis = ['MCI'] * len(test_df) - - # Retrieve all sessions for the training set - complete_train_df = pd.DataFrame() - for idx in train_df.index.values: - subject = train_df.loc[idx, 'participant_id'] - subject_df = diagnosis_df[diagnosis_df.participant_id == subject] - complete_train_df = pd.concat([complete_train_df, subject_df]) - - complete_train_df.to_csv(path.join(train_path, 'split-' + str(i), 'MCI.tsv'), - sep='\t', index=False) - train_df.to_csv( - path.join(train_path, 'split-' + str(i), 'MCI_baseline.tsv'), sep='\t', index=False) - test_df.to_csv( - path.join(test_path, 'split-' + str(i), 'MCI_baseline.tsv'), sep='\t', index=False) - - logger.info("K-fold split for diagnosis MCI is done") + MCI_df.reset_index(drop=False, inplace=True) + logger.debug(MCI_df) + write_splits('MCI', MCI_df, stratification, n_splits, train_path, test_path, + supplementary_diagnoses=supplementary_diagnoses) + logger.info("K-fold split for diagnosis MCI is done.") diff --git a/clinicadl/clinicadl/tools/tsv/tsv_utils.py b/clinicadl/clinicadl/tools/tsv/tsv_utils.py index 1418cda8d..f76f55d66 100644 --- a/clinicadl/clinicadl/tools/tsv/tsv_utils.py +++ b/clinicadl/clinicadl/tools/tsv/tsv_utils.py @@ -3,6 +3,7 @@ from copy import copy import numpy as np import pandas as pd +from os import path def neighbour_session(session, session_list, neighbour): @@ -76,39 +77,40 @@ def next_session(subject_df, session_orig): raise ValueError('The argument session is the last session') -def baseline_df(diagnosis_df, set_index=True): +def extract_baseline(diagnosis_df, diagnosis, set_index=True): from copy import deepcopy if set_index: all_df = diagnosis_df.set_index(['participant_id', 'session_id']) else: all_df = deepcopy(diagnosis_df) - columns = ['participant_id', 'session_id'] + all_df.columns.values.tolist() + result_df = pd.DataFrame() for subject, subject_df in all_df.groupby(level=0): - first_session_id = first_session(subject_df) - data_baseline = [[subject, first_session_id] + all_df.loc[(subject, first_session_id)].values.tolist()] - subject_baseline_df = pd.DataFrame(data_baseline, columns=columns) + baseline = first_session(subject_df) + subject_baseline_df = pd.DataFrame(data=[[subject, baseline] + + subject_df.loc[(subject, baseline)].tolist()], + columns=["participant_id", "session_id"] + subject_df.columns.values.tolist()) result_df = pd.concat([result_df, subject_baseline_df]) + result_df["diagnosis"] = [diagnosis] * len(result_df) result_df.reset_index(inplace=True, drop=True) return result_df def chi2(x_test, x_train): - # Look for chi2 computation - p_expectedF = np.sum(x_train) / len(x_train) - p_expectedM = 1 - p_expectedF + from scipy.stats import chisquare - expectedF = p_expectedF * len(x_test) - expectedM = p_expectedM * len(x_test) - observedF = np.sum(x_test) - observedM = len(x_test) - np.sum(x_test) + # Look for chi2 computation + total_categories = np.concatenate([x_test, x_train]) + unique_categories = np.unique(total_categories) + f_obs = [(x_test == category).sum() / len(x_test) for category in unique_categories] + f_exp = [(x_train == category).sum() / len(x_train) for category in unique_categories] - T = (expectedF - observedF) ** 2 / expectedF + (expectedM - observedM) ** 2 / expectedM + T, p = chisquare(f_obs, f_exp) - return T + return T, p def add_demographics(df, demographics_df, diagnosis): @@ -125,6 +127,25 @@ def add_demographics(df, demographics_df, diagnosis): return out_df +def remove_unicity(values_list): + """Count the values of each class and label all the classes with only one label under the same label.""" + unique_classes, counts = np.unique(values_list, return_counts=True) + one_sub_classes = unique_classes[(counts == 1)] + for class_element in one_sub_classes: + values_list[values_list.index(class_element)] = unique_classes.min() + + return values_list + + +def category_conversion(values_list): + values_np = np.array(values_list) + unique_classes = np.unique(values_np) + for index, unique_class in enumerate(unique_classes): + values_np[values_np == unique_class] = index + 1 + + return values_np.astype(int).tolist() + + def find_label(labels_list, target_label): if target_label in labels_list: return target_label @@ -139,3 +160,43 @@ def find_label(labels_list, target_label): raise ValueError(f"No label was found in {labels_list} for target label {target_label}.") return found_label + + +def retrieve_longitudinal(df, diagnosis_df): + final_df = pd.DataFrame() + for idx in df.index.values: + subject = df.loc[idx, 'participant_id'] + row_df = diagnosis_df[diagnosis_df.participant_id == subject] + final_df = pd.concat([final_df, row_df]) + + return final_df + + +def remove_sub_labels(diagnosis_df, sub_labels, diagnosis_df_paths, results_path, + logger=None): + + from ..deep_learning.iotools import return_logger + + if logger is None: + logger = return_logger(2, "remove sub labels") + + supplementary_diagnoses = [] + + logger.debug('Before subjects removal') + sub_df = diagnosis_df.reset_index().groupby('participant_id')['session_id'].nunique() + logger.debug(f'{len(sub_df)} subjects, {len(diagnosis_df)} scans') + + for label in sub_labels: + if f'{label}.tsv' in diagnosis_df_paths: + sub_diag_df = pd.read_csv(path.join(results_path, f'{label}.tsv'), sep='\t') + sub_diag_baseline_df = extract_baseline(sub_diag_df, label) + for idx in sub_diag_baseline_df.index.values: + subject = sub_diag_baseline_df.loc[idx, 'participant_id'] + diagnosis_df.drop(subject, inplace=True, level=0) + supplementary_diagnoses.append(label) + + logger.debug(f'Removed {len(sub_diag_baseline_df)} subjects based on {label} label') + sub_df = diagnosis_df.reset_index().groupby('participant_id')['session_id'].nunique() + logger.debug(f'{len(sub_df)} subjects, {len(diagnosis_df)} scans') + + return diagnosis_df, supplementary_diagnoses diff --git a/clinicadl/clinicadl/train/random_search.py b/clinicadl/clinicadl/train/random_search.py index 7b7d62092..ce276247c 100755 --- a/clinicadl/clinicadl/train/random_search.py +++ b/clinicadl/clinicadl/train/random_search.py @@ -32,6 +32,7 @@ def check_and_complete(rs_options): "dropout": 0, "learning_rate": 4, "loss": "default", + "multi_cohort": False, "n_conv": 1, "network_normalization": "BatchNorm", "optimizer": "Adam", diff --git a/clinicadl/clinicadl/train/train_autoencoder.py b/clinicadl/clinicadl/train/train_autoencoder.py index 4b23f46a2..6fce5c824 100755 --- a/clinicadl/clinicadl/train/train_autoencoder.py +++ b/clinicadl/clinicadl/train/train_autoencoder.py @@ -56,7 +56,8 @@ def train_autoencoder(params): fi, n_splits=params.n_splits, baseline=params.baseline, - logger=main_logger + logger=main_logger, + multi_cohort=params.multi_cohort ) data_train = return_dataset(params.mode, params.input_dir, training_df, params.preprocessing, diff --git a/clinicadl/clinicadl/train/train_multiCNN.py b/clinicadl/clinicadl/train/train_multiCNN.py index a045209fa..54f79fbcd 100755 --- a/clinicadl/clinicadl/train/train_multiCNN.py +++ b/clinicadl/clinicadl/train/train_multiCNN.py @@ -63,7 +63,8 @@ def train_multi_cnn(params): fi, n_splits=params.n_splits, baseline=params.baseline, - logger=main_logger + logger=main_logger, + multi_cohort=params.multi_cohort ) data_train = return_dataset(params.mode, params.input_dir, training_df, params.preprocessing, diff --git a/clinicadl/clinicadl/train/train_singleCNN.py b/clinicadl/clinicadl/train/train_singleCNN.py index 39165dbd8..671a7d89d 100755 --- a/clinicadl/clinicadl/train/train_singleCNN.py +++ b/clinicadl/clinicadl/train/train_singleCNN.py @@ -55,7 +55,8 @@ def train_single_cnn(params): fi, n_splits=params.n_splits, baseline=params.baseline, - logger=main_logger + logger=main_logger, + multi_cohort=params.multi_cohort ) data_train = return_dataset(params.mode, params.input_dir, training_df, params.preprocessing, diff --git a/clinicadl/tests/test_generate.py b/clinicadl/tests/test_generate.py index 5b76237f0..7a5fc9ee1 100644 --- a/clinicadl/tests/test_generate.py +++ b/clinicadl/tests/test_generate.py @@ -4,8 +4,6 @@ import os from os.path import join, exists, abspath -# TODO implement test_shepplogan - @pytest.fixture(params=[ 'generate_trivial', diff --git a/docs/Classify.md b/docs/Classify.md index 52ae1654c..159660155 100644 --- a/docs/Classify.md +++ b/docs/Classify.md @@ -77,6 +77,8 @@ Optional arguments: - `--diagnoses` (list[str]) is the list of participants that will be classified. Default will look for the same labels used during the training task. Choices available are `AD`, `CN`, `MCI`, `sMCI` and `pMCI`. + - `--multi_cohort` (bool) is a flag indicated that [multi-cohort classification](Details.md#multi-cohort) is performed. + In this case, `caps_directory` and `tsv_path` must be paths to TSV files. ## Outputs diff --git a/docs/Installation.md b/docs/Installation.md index 6ca59cb0c..117b83db6 100644 --- a/docs/Installation.md +++ b/docs/Installation.md @@ -154,7 +154,7 @@ launched, in the following order: - **classify** test (`test_classify.py`): this test classifies synthetic (random) MRI obtained in previous test. You can preprocess the dataset obtained during the **generate** test (`clinica run deeplearning-prepare-data - ./dataset/random-example image`) or you can [download it + ./dataset/random_example t1-linear image`) or you can [download it here](https://aramislab.paris.inria.fr/files/data/databases/tuto/RandomCaps.tar.gz). This test verifies that the output file exists. ([the previoulsy trained models are available diff --git a/docs/Interpret.md b/docs/Interpret.md index a2644cc9c..e83a35eff 100644 --- a/docs/Interpret.md +++ b/docs/Interpret.md @@ -79,6 +79,9 @@ Optional arguments: classified by the CNN. Default will not perform any selection. - `--nifti_template_path` (str) is a path to a nifti template to retrieve the affine values needed to write Nifti files for 3D saliency maps. Default will use the identity matrix for the affine. + - `--multi_cohort` (bool) is a flag indicated that [multi-cohort interpretation](Details.md#multi-cohort) is performed. + In this case, `caps_dir` and `tsv_path` must be paths to TSV files. If no new `caps_dir` and `tsv_path` are + given this argument is not taken into account. - **Results display** - `--vmax` (float) is the maximum value used for 2D saliency maps display. Default value: `0.5`. diff --git a/docs/RandomSearch.md b/docs/RandomSearch.md index 935515668..1c61738dd 100644 --- a/docs/RandomSearch.md +++ b/docs/RandomSearch.md @@ -60,7 +60,7 @@ An example of this file can be found in Mandatory variables: -- `mode` (str) is the type of input used. Options are: `image`, `patch`, `roi` and `slice`. +- `mode` (str) is the type of input used. Must be chosen between `image`, `patch`, `roi` and `slice`. Sampling function: `choice`. - `network_type` (str) is the type of network used. The options depend on the type of input used, but at most it can be chosen between `autoencoder`, `cnn` and `multicnn`. diff --git a/docs/Train/Details.md b/docs/Train/Details.md index 95a7abe4c..ba07a32f1 100644 --- a/docs/Train/Details.md +++ b/docs/Train/Details.md @@ -125,3 +125,22 @@ where: - *piAD* is the probability of AD for patch *i*, - *bacci* is the validation balanced accuracy for patch *i*. + +## Multi-cohort + +It is now possible to use fonctions of ClinicaDL on several datasets at the same time. +In this case, the `multi-cohort` flag must be given, and the `caps_directory` and the `tsv_path` +must be TSV files. + +The `caps_directory` variable must lead to a TSV file with two columns: +- `cohort` the name of the cohort (must correspond to the values in `tsv_path`), +- `path` the path to the corresponding [CAPS](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/) hierarchy. + +The `tsv_path` variable must lead to a TSV file with two columns: +- `cohort` the name of the cohort (must correspond to the values in `caps_directory`), +- `path` the path to the corresponding labels list, outputs of [`split`](../TSVTools.md#split---single-split-observing-similar-age-and-sex-distributions) +or [`fkold`](../TSVTools.md#kfold---k-fold-split) methods. +- `diagnoses` the diagnoses that will be used in the cohort. Must correspond to a single string with labels accepted by +`clinicadl train` (`AD`, `BV`, `CN`, `MCI`, `sMCI` or `pMCI`) separated by commas. +See the [dedicated section](./Custom.md#custom-labels) to use custom labels. + \ No newline at end of file diff --git a/docs/Train/Introduction.md b/docs/Train/Introduction.md index 712f09fad..671034e32 100644 --- a/docs/Train/Introduction.md +++ b/docs/Train/Introduction.md @@ -23,9 +23,11 @@ where mandatory arguments are: - `network_type` (str) is the type of network used. The options depend on the type of input used, but at most it can be chosen between `autoencoder`, `cnn` and `multicnn`. - `caps_directory` (str) is the input folder containing the neuroimaging data in a [CAPS](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/) hierarchy. +In case of [multi-cohort training](Details.md#multi-cohort), must be a path to a TSV file. - `preprocessing` (str) corresponds to the preprocessing pipeline whose outputs will be used for training. The current version only supports `t1-linear`, but `t1-extensive` will be implemented in next versions of `clinicadl`. - `tsv_path` (str) is the input folder of a TSV file tree generated by `clinicadl tsvtool {split|kfold}`. +In case of [multi-cohort training](Details.md#multi-cohort), must be a path to a TSV file. - `output_directory` (str) is the folder where the results are stored. - `architecture` (str) is the name of the architecture used (e.g. `Conv5_FC3`). It must correspond to a class that inherits from `nn.Module` imported in `tools/deep_learning/models/__init__.py`. @@ -47,6 +49,8 @@ Options shared for all values of `mode` are organized in groups: Must be chosen in [`None`, `Noise`, `Erasing`, `CropPad`, `Smoothing`]. Default: `False`. - `--sampler` (str) is the sampler used on the training set. It must be chosen in [`random`, `weighted`]. `weighted` will give a stronger weight to underrepresented classes. Default: `random`. + - `--multi_cohort` (bool) is a flag indicated that [multi-cohort training](Details.md#multi-cohort) is performed. + In this case, `caps_directory` and `tsv_path` must be paths to TSV files. - **Cross-validation arguments** - `--n_splits` (int) is a number of splits k to load in the case of a k-fold cross-validation. Default will load a single-split. - `--split` (list of int) is a subset of folds that will be used for training. By default all splits available are used. diff --git a/docs/Train/Slice.md b/docs/Train/Slice.md index d17c38e52..d4efb747d 100644 --- a/docs/Train/Slice.md +++ b/docs/Train/Slice.md @@ -266,4 +266,4 @@ results The performance metrics are obtained at two different levels: slice-level and image-level. Slice-level performance corresponds to an evaluation in which all slices are considered to be independent. However it is not the case, and what is more interesting is the evaluation at the image-level, - for which the predictions of slice-level were [assembled](./Details.md#soft-voting). + for which the predictions of slice-level were [assembled](./Details.md#soft-voting). \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 5878d3c12..1a1657e1e 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,5 +1,9 @@ # `clinicadl` Documentation +!!! tip "ClinicaDL tutorial" + Visit our [hands-on tutorial web site](https://aramislab.paris.inria.fr/clinicadl/tuto/intro.html) + to try **ClinicaDL** directly in a Google Colab instance! + ## Installation `clinicadl` can be installed on Mac OS X and Linux machines, and possibly on Windows computers with a Linux Virtual Machine. From 90c8da93a48427bb3b28ec4a8eed885aed0d97c8 Mon Sep 17 00:00:00 2001 From: Elina Thibeau-Sutre Date: Tue, 23 Mar 2021 15:03:14 +0100 Subject: [PATCH 03/17] Add verbose to generate parser --- clinicadl/clinicadl/cli.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/clinicadl/clinicadl/cli.py b/clinicadl/clinicadl/cli.py index 4c65f80d9..025a71364 100644 --- a/clinicadl/clinicadl/cli.py +++ b/clinicadl/clinicadl/cli.py @@ -297,6 +297,7 @@ def parse_command_line(): generate_random_parser = generate_subparser.add_parser( "random", parents=[ + parent_parser, generate_rs_parent_parser], help="Generate a random dataset in which gaussian noise is added to brain images.") generate_random_parser.add_argument( @@ -316,6 +317,7 @@ def parse_command_line(): generate_trivial_parser = generate_subparser.add_parser( "trivial", parents=[ + parent_parser, generate_rs_parent_parser], help="Generate a trivial dataset in which gaussian half of the brain is atrophied.") generate_trivial_parser.add_argument( From 7fd162fae24ebfbebfde61c5dc55127185e3dd4c Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Wed, 24 Mar 2021 11:47:35 +0100 Subject: [PATCH 04/17] Roi management (#122) * Add new mode_tasks for roi and slice * Fix minor errors in parsers * Fix test * Update doc according to new parsers * Fix multicnn for ROI and slice parsers * Ensure retro-compatibility * Update docs * Fix typo * Implement first dragt of ROI reader * PEP8 correction * Fix default roi dataset * Fix custom ROI extraction * Add tests on custom ROI * Ensure retro-compatibility * Update docs * Fix typo * Implement first dragt of ROI reader * PEP8 correction * Fix default roi dataset * Fix custom ROI extraction * Add tests on custom ROI * Minor fixes * Add multi-cohort * Add multi-cohort * Fix minor issues * Update docs * Advertisement on tutorial * Fix doc appearance * Add multi-cohort * Fix minor issues * Update docs * Advertisement on tutorial * Fix doc appearance * update info.json * Reverse bad merge * Adapt to adni-to-bids changes * Fix gray matter maps files path * Add variational CNN * Add option to train on GM maps * update gradients for variational CNN * Improve diagnosis inference to avoid losing sessions * Remove shepplogan generation * Set multi_cohort default value for random search * add multi_cohort arg to options * Fix minor bug in qc * Ensure retro-compatibility * Update docs * Fix typo * Implement first dragt of ROI reader * PEP8 correction * Fix default roi dataset * Fix custom ROI extraction * Add tests on custom ROI * Fix test * Fix multicnn for ROI and slice parsers * Add t1-volume extraction * Deal with different level of cropping * Minor fix * Add multi-cohort * Fix minor issues * Update docs * Advertisement on tutorial * Fix doc appearance * update info.json * Reverse bad merge * Fix gray matter maps files path * Add variational CNN * Add option to train on GM maps * update gradients for variational CNN * Improve diagnosis inference to avoid losing sessions * Remove shepplogan generation * Set multi_cohort default value for random search * add multi_cohort arg to options * Fix minor bug in qc * Minor fix * Fix minor errors in parsers * Fix test * Ensure retro-compatibility * Update docs * Fix typo * Implement first dragt of ROI reader * PEP8 correction * Fix default roi dataset * Fix custom ROI extraction * Add tests on custom ROI * Add t1-volume extraction * Deal with different level of cropping * Change roi path finder * Read correctly outputs of clinica * Fix typo in Exception message --- clinicadl/clinicadl/classify/inference.py | 15 +- clinicadl/clinicadl/cli.py | 110 ++++++----- .../clinicadl/interpret/group_backprop.py | 9 +- .../interpret/individual_backprop.py | 11 +- .../preprocessing/t1_extensive/info.json | 2 +- .../clinicadl/tools/deep_learning/data.py | 177 ++++++++++++++---- .../clinicadl/tools/deep_learning/iotools.py | 8 + .../clinicadl/tools/inputs/filename_types.py | 6 + .../clinicadl/train/train_autoencoder.py | 2 + clinicadl/clinicadl/train/train_multiCNN.py | 2 + clinicadl/clinicadl/train/train_singleCNN.py | 2 + clinicadl/tests/test_train_custom_roi.py | 81 ++++++++ docs/Train/Details.md | 10 +- docs/Train/ROI.md | 55 +++++- 14 files changed, 387 insertions(+), 103 deletions(-) create mode 100644 clinicadl/tests/test_train_custom_roi.py diff --git a/clinicadl/clinicadl/classify/inference.py b/clinicadl/clinicadl/classify/inference.py index 6e4569c94..e6585e6c0 100644 --- a/clinicadl/clinicadl/classify/inference.py +++ b/clinicadl/clinicadl/classify/inference.py @@ -8,7 +8,6 @@ from clinicadl.tools.deep_learning.iotools import return_logger, translate_parameters from clinicadl.tools.deep_learning.data import return_dataset, get_transforms, compute_num_cnn, load_data_test from clinicadl.tools.deep_learning.cnn_utils import test, soft_voting_to_tsvs, mode_level_to_tsvs, get_criterion -import torch.nn as nn from torch.utils.data import DataLoader @@ -147,7 +146,6 @@ def inference_from_model(caps_dir, options.use_cpu = not gpu options.nproc = num_workers options.batch_size = batch_size - options.prepare_dl = prepare_dl if diagnoses is not None: options.diagnoses = diagnoses @@ -206,7 +204,8 @@ def inference_from_model(caps_dir, labels=labels, num_cnn=num_cnn, logger=logger, - multi_cohort=multi_cohort + multi_cohort=multi_cohort, + prepare_dl=prepare_dl ) # Soft voting @@ -229,7 +228,7 @@ def inference_from_model(caps_dir, def inference_from_model_generic(caps_dir, tsv_path, model_path, model_options, prefix, output_dir, fold, selection, labels=True, num_cnn=None, logger=None, - multi_cohort=False): + multi_cohort=False, prepare_dl=True): from os.path import join import logging @@ -258,7 +257,9 @@ def inference_from_model_generic(caps_dir, tsv_path, model_path, model_options, all_transformations=all_transforms, params=model_options, cnn_index=n, - labels=labels + labels=labels, + prepare_dl=prepare_dl, + multi_cohort=multi_cohort ) test_loader = DataLoader( @@ -303,7 +304,9 @@ def inference_from_model_generic(caps_dir, tsv_path, model_path, model_options, train_transformations=None, all_transformations=all_transforms, params=model_options, - labels=labels + labels=labels, + prepare_dl=prepare_dl, + multi_cohort=multi_cohort ) # Load the data diff --git a/clinicadl/clinicadl/cli.py b/clinicadl/clinicadl/cli.py index 025a71364..0623955f2 100644 --- a/clinicadl/clinicadl/cli.py +++ b/clinicadl/clinicadl/cli.py @@ -259,7 +259,7 @@ def parse_command_line(): generate_subparser = generate_parser.add_subparsers( title='''Type of synthetic data generated''', description='''What type of synthetic data do you want to generate? - (random, trivial).''', + (random, shepplogan, trivial).''', dest='mode', help='''****** Synthetic datasets proposed by clinicadl ******''') @@ -334,45 +334,45 @@ def parse_command_line(): ) generate_trivial_parser.set_defaults(func=generate_data_func) - # generate_shepplogan_parser = generate_subparser.add_parser( - # "shepplogan", - # help="Generate a dataset of 2D images including 3 subtypes based on Shepp Logan phantom." - # ) - # generate_shepplogan_parser.add_argument( - # 'output_dir', - # help='Folder containing the synthetic dataset.', - # ) - # generate_shepplogan_parser.add_argument( - # '--n_subjects', - # type=int, - # default=300, - # help="Number of subjects in each class of the synthetic dataset." - # ) - # generate_shepplogan_parser.add_argument( - # '--image_size', - # type=int, - # default=128, - # help="Size in pixels of the squared images." - # ) - # generate_shepplogan_parser.add_argument( - # '--CN_subtypes_distribution', '-Csd', - # type=float, nargs='+', - # default=[1.0, 0.0, 0.0], - # help="Probability of each subtype to be drawn in CN label." - # ) - # generate_shepplogan_parser.add_argument( - # '--AD_subtypes_distribution', '-Asd', - # type=float, nargs='+', - # default=[0.05, 0.85, 0.10], - # help="Probability of each subtype to be drawn in AD label." - # ) - # generate_shepplogan_parser.add_argument( - # '--smoothing', - # action='store_true', - # default=False, - # help='Adds random smoothing to generated data.' - # ) - # generate_shepplogan_parser.set_defaults(func=generate_data_func) + generate_shepplogan_parser = generate_subparser.add_parser( + "shepplogan", + help="Generate a dataset of 2D images including 3 subtypes based on Shepp Logan phantom." + ) + generate_shepplogan_parser.add_argument( + 'output_dir', + help='Folder containing the synthetic dataset.', + ) + generate_shepplogan_parser.add_argument( + '--n_subjects', + type=int, + default=300, + help="Number of subjects in each class of the synthetic dataset." + ) + generate_shepplogan_parser.add_argument( + '--image_size', + type=int, + default=128, + help="Size in pixels of the squared images." + ) + generate_shepplogan_parser.add_argument( + '--CN_subtypes_distribution', '-Csd', + type=float, nargs='+', + default=[1.0, 0.0, 0.0], + help="Probability of each subtype to be drawn in CN label." + ) + generate_shepplogan_parser.add_argument( + '--AD_subtypes_distribution', '-Asd', + type=float, nargs='+', + default=[0.05, 0.85, 0.10], + help="Probability of each subtype to be drawn in AD label." + ) + generate_shepplogan_parser.add_argument( + '--smoothing', + action='store_true', + default=False, + help='Adds random smoothing to generated data.' + ) + generate_shepplogan_parser.set_defaults(func=generate_data_func) # Preprocessing from clinica.pipelines.t1_linear.t1_linear_cli import T1LinearCLI @@ -806,6 +806,25 @@ def preprocessing_help(args): "roi", help="Train a ROI-based level network.") + train_roi_parent = argparse.ArgumentParser(add_help=False) + train_roi_group = train_roi_parent.add_argument_group( + TRAIN_CATEGORIES["ROI"]) + train_roi_group.add_argument( + '-rl', '--roi_list', + help='Names of the regions used for the classification task.' + 'Default will use the hippocampi as described in (Wen et al, 2019).', + type=str, nargs="+", default=None) + train_roi_group.add_argument( + '--uncropped_roi', + help='If given the image is as large as the whole image. Default will crop the image' + 'with the smallest bounding box possible.', + action='store_true', default=False) + train_roi_group.add_argument( + '--use_extracted_roi', + help='''If provided the outputs of extract preprocessing are used, else the whole + MRI is loaded.''', + default=False, action="store_true") + train_roi_subparser = train_roi_parser.add_subparsers( title='''Task to be performed''', description='''Autoencoder reconstruction or cnn classification ?''', @@ -818,6 +837,7 @@ def preprocessing_help(args): parents=[ parent_parser, train_parent_parser, + train_roi_parent, autoencoder_parent, transfer_learning_parent ], @@ -830,6 +850,7 @@ def preprocessing_help(args): parents=[ parent_parser, train_parent_parser, + train_roi_parent, transfer_learning_parent], help="Train a ROI-based CNN.") # /!\ If parents list is changed the arguments won't be in the right group anymore ! @@ -854,6 +875,7 @@ def preprocessing_help(args): parents=[ parent_parser, train_parent_parser, + train_roi_parent, transfer_learning_parent], help="Train a ROI-based multi-CNN (one CNN is trained per patch location).") # /!\ If parents list is changed the arguments won't be in the right group anymore ! @@ -867,8 +889,8 @@ def preprocessing_help(args): train_roi_multicnn_group.add_argument( '--selection_threshold', help='''Threshold on the balanced accuracies to compute the - subject-level performance. Patches are selected if their balanced - accuracy > threshold. Default corresponds to no selection.''', + subject-level performance. Patches are selected if their balanced + accuracy > threshold. Default corresponds to no selection.''', type=float, default=0.0) train_roi_multicnn_parser.set_defaults(func=train_func) @@ -932,8 +954,8 @@ def preprocessing_help(args): train_slice_cnn_group.add_argument( '--selection_threshold', help='''Threshold on the balanced accuracies to compute the - subject-level performance. Slices are selected if their balanced - accuracy > threshold. Default corresponds to no selection.''', + subject-level performance. Slices are selected if their balanced + accuracy > threshold. Default corresponds to no selection.''', type=float, default=0.0) train_slice_cnn_parser.set_defaults(func=train_func) diff --git a/clinicadl/clinicadl/interpret/group_backprop.py b/clinicadl/clinicadl/interpret/group_backprop.py index ae496fca4..65e19272d 100644 --- a/clinicadl/clinicadl/interpret/group_backprop.py +++ b/clinicadl/clinicadl/interpret/group_backprop.py @@ -28,6 +28,9 @@ def group_backprop(options): model_options = translate_parameters(model_options) model_options.gpu = options.gpu + if model_options.network_type == "multicnn": + raise NotImplementedError("The interpretation of multi-CNN is not implemented.") + if options.tsv_path is None and options.input_dir is None: options.multi_cohort = model_options.multi_cohort if options.tsv_path is None: @@ -58,7 +61,8 @@ def group_backprop(options): data_example = return_dataset(model_options.mode, options.input_dir, training_df, model_options.preprocessing, train_transformations=None, all_transformations=all_transforms, - params=options) + prepare_dl=options.prepare_dl, multi_cohort=options.multi_cohort, + params=model_options) model = create_model(model_options, data_example.size) model_dir = os.path.join(options.model_path, fold, 'models', selection) @@ -82,7 +86,8 @@ def group_backprop(options): data_train = return_dataset(model_options.mode, options.input_dir, training_df, model_options.preprocessing, train_transformations=None, all_transformations=all_transforms, - params=options) + prepare_dl=options.prepare_dl, multi_cohort=options.multi_cohort, + params=model_options) train_loader = DataLoader(data_train, batch_size=options.batch_size, diff --git a/clinicadl/clinicadl/interpret/individual_backprop.py b/clinicadl/clinicadl/interpret/individual_backprop.py index 5313b8cae..800bddde9 100644 --- a/clinicadl/clinicadl/interpret/individual_backprop.py +++ b/clinicadl/clinicadl/interpret/individual_backprop.py @@ -24,9 +24,12 @@ def individual_backprop(options): raise ValueError("No folds were found at path %s" % options.model_path) model_options = argparse.Namespace() - model_options.gpu = options.gpu model_options = read_json(model_options, path.join(options.model_path, 'commandline.json')) model_options = translate_parameters(model_options) + model_options.gpu = options.gpu + + if model_options.network_type == "multicnn": + raise NotImplementedError("The interpretation of multi-CNN is not implemented.") if options.tsv_path is None and options.input_dir is None: options.multi_cohort = model_options.multi_cohort @@ -58,7 +61,8 @@ def individual_backprop(options): data_example = return_dataset(model_options.mode, options.input_dir, training_df, model_options.preprocessing, train_transformations=None, all_transformations=all_transforms, - params=options) + prepare_dl=options.prepare_dl, multi_cohort=options.multi_cohort, + params=model_options) model = create_model(model_options, data_example.size) model_dir = os.path.join(options.model_path, fold, 'models', selection) @@ -82,7 +86,8 @@ def individual_backprop(options): data_train = return_dataset(model_options.mode, options.input_dir, training_df, model_options.preprocessing, train_transformations=None, all_transformations=all_transforms, - params=options) + prepare_dl=options.prepare_dl, multi_cohort=options.multi_cohort, + params=model_options) train_loader = DataLoader(data_train, batch_size=options.batch_size, diff --git a/clinicadl/clinicadl/preprocessing/t1_extensive/info.json b/clinicadl/clinicadl/preprocessing/t1_extensive/info.json index 8a4694772..bc80b5e7e 100644 --- a/clinicadl/clinicadl/preprocessing/t1_extensive/info.json +++ b/clinicadl/clinicadl/preprocessing/t1_extensive/info.json @@ -1,6 +1,6 @@ { "id": "aramislab/t1-extensive", - "author": "Alexandre Routier", + "author": ["Alexandre Routier"], "version": "0.1.0", "space_caps": "3M", "space_wd": "3M", diff --git a/clinicadl/clinicadl/tools/deep_learning/data.py b/clinicadl/clinicadl/tools/deep_learning/data.py index 841024e0c..6cc7029a5 100644 --- a/clinicadl/clinicadl/tools/deep_learning/data.py +++ b/clinicadl/clinicadl/tools/deep_learning/data.py @@ -9,7 +9,7 @@ import abc import logging import warnings -from clinicadl.tools.inputs.filename_types import FILENAME_TYPE +from clinicadl.tools.inputs.filename_types import FILENAME_TYPE, MASK_PATTERN ################################# @@ -53,9 +53,14 @@ def __init__(self, caps_directory, data_file, else: raise Exception('The argument data_file is not of correct type.') + if not multi_cohort: + self.df["cohort"] = "single" + mandatory_col = {"participant_id", "session_id"} if self.labels: mandatory_col.add("diagnosis") + if multi_cohort: + mandatory_col.add("cohort") if self.elem_index == "mixed": mandatory_col.add("%s_id" % self.mode) @@ -329,13 +334,15 @@ def extract_patch_from_mri(self, image_tensor, index_patch): class MRIDatasetRoi(MRIDataset): - def __init__(self, caps_directory, data_file, roi_index=None, preprocessing="t1-linear", - train_transformations=None, prepare_dl=False, labels=True, all_transformations=None, - multi_cohort=False): + def __init__(self, caps_directory, data_file, roi_list=None, cropped_roi=True, roi_index=None, + preprocessing="t1-linear", train_transformations=None, prepare_dl=False, labels=True, + all_transformations=None, multi_cohort=False): """ Args: caps_directory (string): Directory of all the images. data_file (string or DataFrame): Path to the tsv file or DataFrame containing the subject/session list. + roi_list (list): Defines the regions used in the classification. + cropped_roi (bool): If True the image is cropped according to the smallest bounding box possible. roi_index (int, optional): If a value is given the same region will be extracted for each image. else the dataset will load all the regions possible for one image. preprocessing (string): Defines the path to the data in CAPS. @@ -350,7 +357,10 @@ def __init__(self, caps_directory, data_file, roi_index=None, preprocessing="t1- raise ValueError("ROI mode is not available for preprocessing %s" % preprocessing) self.elem_index = roi_index self.mode = "roi" + self.roi_list = roi_list + self.cropped_roi = cropped_roi self.prepare_dl = prepare_dl + self.mask_list = self.find_masks(caps_directory, preprocessing) super().__init__(caps_directory, data_file, preprocessing, augmentation_transformations=train_transformations, labels=labels, transformations=all_transformations, multi_cohort=multi_cohort) @@ -358,8 +368,15 @@ def __getitem__(self, idx): participant, session, cohort, roi_idx, label = self._get_meta_data(idx) if self.prepare_dl: - raise NotImplementedError( - 'The extraction of ROIs prior to training is not implemented.') + if self.roi_list is None: + raise NotImplementedError( + 'The extraction of ROIs prior to training is not implemented for default ROIs.' + 'Please disable --use_extracted_rois or precise the regions in --roi_names.') + + # read the regions directly + roi_path = self._get_path(participant, session, cohort, "roi") + roi_path = self.compute_roi_filename(roi_path, roi_idx) + patch = torch.load(roi_path) else: image_path = self._get_path(participant, session, cohort, "image") @@ -381,35 +398,116 @@ def __getitem__(self, idx): def num_elem_per_image(self): if self.elem_index is not None: return 1 - return 2 + if self.roi_list is None: + return 2 + else: + return len(self.roi_list) - def extract_roi_from_mri(self, image_tensor, left_is_odd): + def extract_roi_from_mri(self, image_tensor, roi_idx): """ + :param image_tensor: (Tensor) the tensor of the image. - :param left_is_odd: (int) if 1 the left hippocampus is extracted, else the right one. - :return: Tensor of the extracted hippocampus + :param roi_idx: (int) Region index. + :return: Tensor of the extracted region. """ - if self.preprocessing == "t1-linear": - if left_is_odd == 1: - # the center of the left hippocampus - crop_center = (61, 96, 68) + if self.roi_list is None: + + if self.preprocessing == "t1-linear": + if roi_idx == 1: + # the center of the left hippocampus + crop_center = (61, 96, 68) + else: + # the center of the right hippocampus + crop_center = (109, 96, 68) + else: + raise NotImplementedError("The extraction of hippocampi was not implemented for " + "preprocessing %s" % self.preprocessing) + crop_size = (50, 50, 50) # the output cropped hippocampus size + + if self.cropped_roi: + + extracted_roi = image_tensor[ + :, + crop_center[0] - crop_size[0] // 2: crop_center[0] + crop_size[0] // 2:, + crop_center[1] - crop_size[1] // 2: crop_center[1] + crop_size[1] // 2:, + crop_center[2] - crop_size[2] // 2: crop_center[2] + crop_size[2] // 2: + ].clone() + else: - # the center of the right hippocampus - crop_center = (109, 96, 68) + raise NotImplementedError("The uncropped option for the default ROI was not implemented.") + + else: + roi_mask = self.mask_list[roi_idx] + extracted_roi = image_tensor * roi_mask + if self.cropped_roi: + extracted_roi = extracted_roi[np.ix_(roi_mask.any((1, 2, 3)), + roi_mask.any((0, 2, 3)), + roi_mask.any((0, 1, 3)), + roi_mask.any((0, 1, 2)))] + + return extracted_roi.float() + + def find_masks(self, caps_directory, preprocessing): + """Loads the masks necessary to regions extraction""" + import nibabel as nib + + # TODO replace with import in clinica as soon as the version of clinica is stable + templates_dict = { + "t1-linear": "MNI152NLin2009cSym", + "t1-volume": "Ixi549Space", + "t1-extensive": "Ixi549Space" + } + + if self.prepare_dl or self.roi_list is None: + return None else: - raise NotImplementedError("The extraction of hippocampi was not implemented for " - "preprocessing %s" % self.preprocessing) - crop_size = (50, 50, 50) # the output cropped hippocampus size + mask_list = [] + for roi in self.roi_list: + template = templates_dict[preprocessing] + if preprocessing == "t1-linear": + mask_pattern = MASK_PATTERN['cropped'] + elif preprocessing == "t1-volume": + mask_pattern = MASK_PATTERN['gm_maps'] + elif preprocessing == "t1-extensive": + mask_pattern = MASK_PATTERN['skull_stripped'] + else: + raise NotImplementedError("Roi extraction for %s preprocessing was not implemented." + % preprocessing) + + mask_path = path.join(caps_directory, "masks", "roi_based", "tpl-%s" % template, + "tpl-%s%s_roi-%s_mask.nii.gz" % (template, mask_pattern, roi)) + mask_nii = nib.load(mask_path) + mask_list.append(mask_nii.get_fdata()) + + return mask_list + + def compute_roi_filename(self, image_path, roi_index): + from os import path + + image_dir = path.dirname(image_path) + image_filename = path.basename(image_path) + image_descriptors = image_filename.split("_") + if "desc-Crop" not in image_descriptors and self.cropped_roi: + image_descriptors = self.insert_descriptor(image_descriptors, "desc-CropRoi", "space") + + elif "desc-Crop" in image_descriptors: + image_descriptors = [descriptor for descriptor in image_descriptors if descriptor != "desc-Crop"] + if self.cropped_roi: + image_descriptors = self.insert_descriptor(image_descriptors, "desc-CropRoi", "space") + else: + image_descriptors = self.insert_descriptor(image_descriptors, "desc-CropImage", "space") - extracted_roi = image_tensor[ - :, - crop_center[0] - crop_size[0] // 2: crop_center[0] + crop_size[0] // 2:, - crop_center[1] - crop_size[1] // 2: crop_center[1] + crop_size[1] // 2:, - crop_center[2] - crop_size[2] // 2: crop_center[2] + crop_size[2] // 2: - ].clone() + return path.join(image_dir, "_".join(image_descriptors))[0:-7] + f"_roi-{self.roi_list[roi_index]}_T1w.pt" - return extracted_roi + @staticmethod + def insert_descriptor(image_descriptors, descriptor_to_add, key_to_follow): + + for i, desc in enumerate(image_descriptors): + if key_to_follow in desc: + image_descriptors.insert(i+1, descriptor_to_add) + + return image_descriptors class MRIDatasetSlice(MRIDataset): @@ -521,9 +619,11 @@ def extract_slice_from_mri(self, image, index_slice): def return_dataset(mode, input_dir, data_df, preprocessing, all_transformations, params, train_transformations=None, - cnn_index=None, labels=True): + cnn_index=None, labels=True, multi_cohort=False, + prepare_dl=False): """ Return appropriate Dataset according to given options. + Args: mode: (str) input used by the network. Chosen from ['image', 'patch', 'roi', 'slice']. input_dir: (str) path to a directory containing a CAPS structure. @@ -534,6 +634,9 @@ def return_dataset(mode, input_dir, data_df, preprocessing, params: (Namespace) options used by specific modes. cnn_index: (int) Index of the CNN in a multi-CNN paradigm (optional). labels (bool): If True the diagnosis will be extracted from the given DataFrame. + multi_cohort (bool): If True caps_directory is the path to a TSV file linking cohort names and paths. + prepare_dl (bool): If true pre-extracted slices / patches / regions will be loaded. + Returns: (Dataset) the corresponding dataset. """ @@ -549,7 +652,7 @@ def return_dataset(mode, input_dir, data_df, preprocessing, train_transformations=train_transformations, all_transformations=all_transformations, labels=labels, - multi_cohort=params.multi_cohort + multi_cohort=multi_cohort ) elif mode == "patch": return MRIDatasetPatch( @@ -560,21 +663,24 @@ def return_dataset(mode, input_dir, data_df, preprocessing, preprocessing=preprocessing, train_transformations=train_transformations, all_transformations=all_transformations, - prepare_dl=params.prepare_dl, + prepare_dl=prepare_dl, patch_index=cnn_index, labels=labels, - multi_cohort=params.multi_cohort + multi_cohort=multi_cohort ) elif mode == "roi": return MRIDatasetRoi( input_dir, data_df, + roi_list=params.roi_list, + cropped_roi=not params.uncropped_roi, preprocessing=preprocessing, train_transformations=train_transformations, all_transformations=all_transformations, + prepare_dl=prepare_dl, roi_index=cnn_index, labels=labels, - multi_cohort=params.multi_cohort + multi_cohort=multi_cohort ) elif mode == "slice": return MRIDatasetSlice( @@ -584,11 +690,11 @@ def return_dataset(mode, input_dir, data_df, preprocessing, train_transformations=train_transformations, all_transformations=all_transformations, mri_plane=params.mri_plane, - prepare_dl=params.prepare_dl, + prepare_dl=prepare_dl, discarded_slices=params.discarded_slices, slice_index=cnn_index, labels=labels, - multi_cohort=params.multi_cohort + multi_cohort=multi_cohort ) else: raise ValueError("Mode %s is not implemented." % mode) @@ -699,6 +805,7 @@ def __call__(self, image): def get_transforms(mode, minmaxnormalization=True, data_augmentation=None): """ Outputs the transformations that will be applied to the dataset + :param mode: (str) input used by the network. Chosen from ['image', 'patch', 'roi', 'slice']. :param minmaxnormalization: (bool) if True will perform MinMaxNormalization :param data_augmentation: (list[str]) list of data augmentation performed on the training set. @@ -776,7 +883,7 @@ def load_data(tsv_path, diagnoses_list, valid_df.reset_index(inplace=True, drop=True) else: if tsv_path.endswith(".tsv"): - raise ValueError('To use multi-cohort framework, please add --multi-cohort flag.') + raise ValueError('To use multi-cohort framework, please add --multi_cohort flag.') else: train_df, valid_df = load_data_single(tsv_path, diagnoses_list, split, n_splits=n_splits, @@ -862,7 +969,7 @@ def load_data_test(test_path, diagnoses_list, baseline=True, multi_cohort=False) tsv_df = pd.read_csv(test_path, sep='\t') multi_col = {"cohort", "path"} if multi_col.issubset(tsv_df.columns.values): - raise ValueError('To use multi-cohort framework, please add --multi-cohort flag.') + raise ValueError('To use multi-cohort framework, please add --multi_cohort flag.') test_df = load_data_test_single(test_path, diagnoses_list, baseline=baseline) test_df["cohort"] = "single" diff --git a/clinicadl/clinicadl/tools/deep_learning/iotools.py b/clinicadl/clinicadl/tools/deep_learning/iotools.py index 8ee1838be..2e0b54305 100644 --- a/clinicadl/clinicadl/tools/deep_learning/iotools.py +++ b/clinicadl/clinicadl/tools/deep_learning/iotools.py @@ -84,6 +84,8 @@ def translate_parameters(args): args.prepare_dl = args.use_extracted_slices elif hasattr(args, "use_extracted_roi") and args.mode == "roi": args.prepare_dl = args.use_extracted_roi + else: + args.prepare_dl = False return args @@ -240,6 +242,12 @@ def read_json(options, json_path=None, test=False): options.dropout = None set_default_dropout(options) + if not hasattr(options, 'uncropped_roi'): + options.uncropped_roi = False + + if not hasattr(options, 'roi_list'): + options.roi_list = None + if not hasattr(options, 'multi_cohort'): options.multi_cohort = False diff --git a/clinicadl/clinicadl/tools/inputs/filename_types.py b/clinicadl/clinicadl/tools/inputs/filename_types.py index f2a9c8cf0..5775aacc5 100644 --- a/clinicadl/clinicadl/tools/inputs/filename_types.py +++ b/clinicadl/clinicadl/tools/inputs/filename_types.py @@ -5,3 +5,9 @@ 'skull_stripped': '_space-Ixi549Space_desc-skullstripped_T1w', 'gm_maps': '_T1w_segm-graymatter_space-Ixi549Space_modulated-off_probability', 'shepplogan': '_phantom-SheppLogan'} + +MASK_PATTERN = {'full': '_res-1x1x1', + 'cropped': '_desc-Crop_res-1x1x1', + 'skull_stripped': '', + 'gm_maps': '', + 'shepplogan': ''} diff --git a/clinicadl/clinicadl/train/train_autoencoder.py b/clinicadl/clinicadl/train/train_autoencoder.py index 6fce5c824..ad38f43fe 100755 --- a/clinicadl/clinicadl/train/train_autoencoder.py +++ b/clinicadl/clinicadl/train/train_autoencoder.py @@ -62,9 +62,11 @@ def train_autoencoder(params): data_train = return_dataset(params.mode, params.input_dir, training_df, params.preprocessing, train_transformations=train_transforms, all_transformations=all_transforms, + prepare_dl=params.prepare_dl, multi_cohort=params.multi_cohort, params=params) data_valid = return_dataset(params.mode, params.input_dir, valid_df, params.preprocessing, train_transformations=train_transforms, all_transformations=all_transforms, + prepare_dl=params.prepare_dl, multi_cohort=params.multi_cohort, params=params) train_sampler = generate_sampler(data_train, params.sampler) diff --git a/clinicadl/clinicadl/train/train_multiCNN.py b/clinicadl/clinicadl/train/train_multiCNN.py index 54f79fbcd..e8a5b8c63 100755 --- a/clinicadl/clinicadl/train/train_multiCNN.py +++ b/clinicadl/clinicadl/train/train_multiCNN.py @@ -69,9 +69,11 @@ def train_multi_cnn(params): data_train = return_dataset(params.mode, params.input_dir, training_df, params.preprocessing, train_transformations=train_transforms, all_transformations=all_transforms, + prepare_dl=params.prepare_dl, multi_cohort=params.multi_cohort, params=params, cnn_index=cnn_index) data_valid = return_dataset(params.mode, params.input_dir, valid_df, params.preprocessing, train_transformations=train_transforms, all_transformations=all_transforms, + prepare_dl=params.prepare_dl, multi_cohort=params.multi_cohort, params=params, cnn_index=cnn_index) train_sampler = generate_sampler(data_train, params.sampler) diff --git a/clinicadl/clinicadl/train/train_singleCNN.py b/clinicadl/clinicadl/train/train_singleCNN.py index 671a7d89d..229083ea6 100755 --- a/clinicadl/clinicadl/train/train_singleCNN.py +++ b/clinicadl/clinicadl/train/train_singleCNN.py @@ -61,9 +61,11 @@ def train_single_cnn(params): data_train = return_dataset(params.mode, params.input_dir, training_df, params.preprocessing, train_transformations=train_transforms, all_transformations=all_transforms, + prepare_dl=params.prepare_dl, multi_cohort=params.multi_cohort, params=params) data_valid = return_dataset(params.mode, params.input_dir, valid_df, params.preprocessing, train_transformations=train_transforms, all_transformations=all_transforms, + prepare_dl=params.prepare_dl, multi_cohort=params.multi_cohort, params=params) train_sampler = generate_sampler(data_train, params.sampler) diff --git a/clinicadl/tests/test_train_custom_roi.py b/clinicadl/tests/test_train_custom_roi.py new file mode 100644 index 000000000..2a771b43b --- /dev/null +++ b/clinicadl/tests/test_train_custom_roi.py @@ -0,0 +1,81 @@ +# coding: utf8 + +import pytest +import os +import shutil +from os import path +import numpy as np +import nibabel as nib + +caps_dir = 'data/dataset/random_example' + + +@pytest.fixture(params=[ + 'train_1roi', + 'train_2roi' +]) +def cli_commands(request): + + if request.param == 'train_1roi': + roi_list = ["random1"] + test_input = [ + 'train', + 'roi', + 'cnn', + caps_dir, + 't1-linear', + 'data/labels_list', + 'results', + 'Conv4_FC3', + '--epochs', '1', + '--n_splits', '2', + '--split', '0', + '--roi_list', 'random1' + ] + elif request.param == 'train_2roi': + roi_list = ["random1", "random2"] + test_input = [ + 'train', + 'roi', + 'multicnn', + caps_dir, + 't1-linear', + 'data/labels_list', + 'results', + 'Conv4_FC3', + '--epochs', '1', + '--n_splits', '2', + '--split', '0', + '--roi_list', 'random1 random2' + ] + else: + raise NotImplementedError( + "Test %s is not implemented." % + request.param) + + return roi_list, test_input + + +def test_train(cli_commands): + roi_list, test_input = cli_commands + crop_size = (50, 50, 50) + os.makedirs(path.join(caps_dir, "masks", "roi_based", "tpl-MNI152NLin2009cSym"), exist_ok=True) + for roi in roi_list: + filename = "tpl-MNI152NLin2009cSym_desc-Crop_res-1x1x1_roi-%s_mask.nii.gz" % roi + crop_center = np.random.randint(low=55, high=100, size=3) + mask_np = np.zeros((1, 169, 208, 179)) + mask_np[ + :, + crop_center[0] - crop_size[0] // 2: crop_center[0] + crop_size[0] // 2:, + crop_center[1] - crop_size[1] // 2: crop_center[1] + crop_size[1] // 2:, + crop_center[2] - crop_size[2] // 2: crop_center[2] + crop_size[2] // 2:] = 1 + mask_nii = nib.Nifti1Image(mask_np, affine=np.eye(4)) + nib.save(mask_nii, path.join(caps_dir, "masks", "roi_based", "tpl-MNI152NLin2009cSym", filename)) + + flag_error = not os.system("clinicadl " + " ".join(test_input)) + performances_flag = os.path.exists( + os.path.join("results", "fold-0", "cnn_classification")) + assert flag_error + assert performances_flag + shutil.rmtree("results") + shutil.rmtree(path.join(caps_dir, "masks")) diff --git a/docs/Train/Details.md b/docs/Train/Details.md index ba07a32f1..96547a453 100644 --- a/docs/Train/Details.md +++ b/docs/Train/Details.md @@ -128,19 +128,19 @@ where: ## Multi-cohort -It is now possible to use fonctions of ClinicaDL on several datasets at the same time. +Starting from version 0.2.1, it is possible to use ClinicaDL's functions on several datasets at the same time. In this case, the `multi-cohort` flag must be given, and the `caps_directory` and the `tsv_path` -must be TSV files. +correspond to TSV files. The `caps_directory` variable must lead to a TSV file with two columns: - `cohort` the name of the cohort (must correspond to the values in `tsv_path`), - `path` the path to the corresponding [CAPS](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/) hierarchy. -The `tsv_path` variable must lead to a TSV file with two columns: +The `tsv_path` variable points to a TSV file with two columns: - `cohort` the name of the cohort (must correspond to the values in `caps_directory`), - `path` the path to the corresponding labels list, outputs of [`split`](../TSVTools.md#split---single-split-observing-similar-age-and-sex-distributions) -or [`fkold`](../TSVTools.md#kfold---k-fold-split) methods. +or [`kfold`](../TSVTools.md#kfold---k-fold-split) methods. - `diagnoses` the diagnoses that will be used in the cohort. Must correspond to a single string with labels accepted by `clinicadl train` (`AD`, `BV`, `CN`, `MCI`, `sMCI` or `pMCI`) separated by commas. See the [dedicated section](./Custom.md#custom-labels) to use custom labels. - \ No newline at end of file + diff --git a/docs/Train/ROI.md b/docs/Train/ROI.md index 0573bf13f..fa699c331 100644 --- a/docs/Train/ROI.md +++ b/docs/Train/ROI.md @@ -1,13 +1,14 @@ # `train roi` - Train deep learning networks using predefined regions of interest (ROI) -This option allows training a network on two regions of interest (ROI). -ROI inputs correspond to two patches of size 50x50x50 voxels manually centered on each hippocampus. -This manual centering has only been done for the `t1-linear` pipeline. +This option allows training a network using a set of regions of interest (ROI) defined by +[masks](./ROI.md#definition-of-masks). -![Coronal view of ROI patches](../images/hippocampi.png) +If no ROI is specified, the inputs will correspond to two patches of size 50x50x50 voxels +manually centered on each hippocampus. +This manual centering has only been done for the `t1-linear` pipeline, and is only available if +`--use_extracted_roi` is not enabled. -!!! warning - Contrary to `patch` and `slice`, `roi` inputs cannot be extracted with `clinicadl extract`. +![Coronal view of ROI patches](../images/hippocampi.png) One architecture is implemented in `clinicadl` for the `roi` mode: `Conv4_FC3`, adapted to `t1-linear` pipeline outputs. @@ -16,6 +17,18 @@ One architecture is implemented in `clinicadl` for the `roi` mode: It is possible to add a custom architecture and train it with `clinicadl`. Detailed instructions can be found [here](./Custom.md). +## Definition of masks + +Regions of interest must correspond to masks that are defined in the CAPS directory `caps_directory` +at `/masks/roi_based/tpl-`. Here `tpl` corresponds to the template used for registration +in the preprocessing pipeline wanted. + +The mask corresponding to the region `roi` must be named according to the following pattern: +`tpl-_key1-...keyN-_roi-_mask.nii.gz`. + +Keys that are included in the mask name correspond only to those describing an operation +modifying the size of the image compared to the original template. + ## `train roi autoencoder` - Train autoencoders using ROI The objective of an autoencoder is to learn to reconstruct images given in input while performing a dimension reduction. @@ -39,8 +52,16 @@ where mandatory arguments are: Options that are common to all `train` input and network types can be found in the introduction of [`clinicadl train`](./Introduction.md#running-the-task). -There are two specific options for this task: +The options specific to this task are the following: +- `--roi_list` (list of str) includes the names of the regions wanted. Each region must correspond +to a mask defined in `caps_directory`. See the [dedicated section](./ROI.md#definition-of-masks) for more information. +Default will extract two patches centered on the hippocampi (available for `t1-linear` preprocessing only). +- `--uncropped_roi` (bool) if given the extracted region is not cropped. Default will crop the image +with the smallest bounding box possible. +- `--use_extracted_roi` (bool) if this flag is given, the outputs of `clinicadl extract` are used. +Otherwise, the whole 3D MR volumes are loaded and patches are extracted on-the-fly. +Cannot be used if `--roi_list` is set to default. - `--transfer_learning_path` (str) is the path to a result folder (output of `clinicadl train`). The best model of this folder will be used to initialize the network as explained in the [implementation details](./Details.md#transfer-learning). @@ -49,6 +70,10 @@ If nothing is given the initialization will be random. the validation sets and their corresponding reconstructions are written in `autoencoder_reconstruction`. Inputs are reconstructed based on the model that obtained the [best validation loss](./Details.md#model-selection). +!!! note "Masks" + For more information on the masks needed for ROI extraction please refer to the section on + [`extract`](../Preprocessing/Extract.md). + ### Outputs The complete output file system is the following (the folder `autoencoder_reconstruction` is created only if the @@ -116,6 +141,14 @@ where mandatory arguments are: The options specific to this task are the following: +- `--roi_list` (list of str) includes the names of the targeted regions. Each region corresponds +to a mask defined in `caps_directory`. See the [dedicated section](./ROI.md#definition-of-masks) for more information. +Default behavior will extract two patches centered on the hippocampi (available for `t1-linear` preprocessing only). +- `--uncropped_roi` (bool) if given the extracted region is not cropped. Default will crop the image +with the smallest bounding box possible. +- `--use_extracted_roi` (bool) if this flag is given, the outputs of `clinicadl extract` are used. +Otherwise, the whole 3D MR volumes are loaded and patches are extracted on-the-fly. +Cannot be used if `--roi_list` is set to default. - `--transfer_learning_path` (str) is the path to a result folder (output of `clinicadl train`). The best model of this folder will be used to initialize the network as explained in the [implementation details](./Details.md#transfer-learning). @@ -198,6 +231,14 @@ where mandatory arguments are: The options specific to this task are the following: +- `--roi_list` (list of str) includes the names of the regions wanted. Each region must correspond +to a mask defined in `caps_directory`. See the [dedicated section](./ROI.md#definition-of-masks) for more information. +Default will extract two patches centered on the hippocampi (available for `t1-linear` preprocessing only). +- `--uncropped_roi` (bool) if given the extracted region is not cropped. Default will crop the image +with the smallest bounding box possible. +- `--use_extracted_roi` (bool) if this flag is given, the outputs of `clinicadl extract` are used. +Otherwise, the whole 3D MR volumes are loaded and patches are extracted on-the-fly. +Cannot be used if `--roi_list` is set to default. - `--transfer_learning_path` (str) is the path to a result folder (output of `clinicadl train`). The best model of this folder will be used to initialize the network as explained in the [implementation details](./Details.md#transfer-learning). From 2487e8df92e590018c0f0ea8c842fe06f9c1c1eb Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Mon, 29 Mar 2021 20:20:16 +0200 Subject: [PATCH 05/17] Mkdocs version js script (#139) Add dropdown menu for versions in the documentation * Add mike version * Add menu panel as in bids-specifications * Add readthedocs specifications * Remove RTD file * Remove useless modifications --- .gitignore | 1 - mkdocs.yml | 5 +++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 9d48bd4b0..da1191812 100644 --- a/.gitignore +++ b/.gitignore @@ -35,4 +35,3 @@ clinicadl/notebooks/ # Mask and other files clinicadl/clinicadl/resources/masks/*.nii clinicadl/clinicadl/resources/masks/*.nii.gz - diff --git a/mkdocs.yml b/mkdocs.yml index bc09f4992..4bf46c8a3 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -29,6 +29,10 @@ theme: primary: green accent: green +# Version panel +extra_javascript: + - 'https://code.jquery.com/jquery-3.3.1.min.js' + # Customization extra: feature: @@ -41,6 +45,7 @@ extra: - icon: material/earth link: http://www.aramislab.fr + # Extensions markdown_extensions: - admonition From 2de416410adffd8ed00a26d72cd4a173900fd396 Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Tue, 30 Mar 2021 13:40:33 +0200 Subject: [PATCH 06/17] Add version warning to stable documentation (#140) * Fix broken hyperlinks * Add warning banner --- docs/Classify.md | 3 ++- docs/Interpret.md | 4 ++-- docs/RandomSearch.md | 22 +++++++++++----------- docs/overrides/main.html | 8 ++++++++ mkdocs.yml | 3 +++ 5 files changed, 26 insertions(+), 14 deletions(-) create mode 100644 docs/overrides/main.html diff --git a/docs/Classify.md b/docs/Classify.md index 159660155..62c65aeee 100644 --- a/docs/Classify.md +++ b/docs/Classify.md @@ -77,7 +77,8 @@ Optional arguments: - `--diagnoses` (list[str]) is the list of participants that will be classified. Default will look for the same labels used during the training task. Choices available are `AD`, `CN`, `MCI`, `sMCI` and `pMCI`. - - `--multi_cohort` (bool) is a flag indicated that [multi-cohort classification](Details.md#multi-cohort) is performed. + - `--multi_cohort` (bool) is a flag indicated that [multi-cohort classification](Train/Details.md#multi-cohort) + is performed. In this case, `caps_directory` and `tsv_path` must be paths to TSV files. ## Outputs diff --git a/docs/Interpret.md b/docs/Interpret.md index e83a35eff..a18c8a2d2 100644 --- a/docs/Interpret.md +++ b/docs/Interpret.md @@ -63,7 +63,7 @@ Optional arguments: - `--batch_size` (int) is the size of the batch used in the DataLoader. Default value: `2`. - **Model selection** - `--selection` (list of str) corresponds to the metrics according to which the - [best models](./Details.md#model-selection) of `model_path` will be loaded. + [best models](Train/Details.md#model-selection) of `model_path` will be loaded. Choices are `best_loss` and `best_balanced_accuracy`. Default: `best_balanced_accuracy`. - **Data management** - `--tsv_path` (str) is a path to a directory containing one TSV file per diagnosis @@ -79,7 +79,7 @@ Optional arguments: classified by the CNN. Default will not perform any selection. - `--nifti_template_path` (str) is a path to a nifti template to retrieve the affine values needed to write Nifti files for 3D saliency maps. Default will use the identity matrix for the affine. - - `--multi_cohort` (bool) is a flag indicated that [multi-cohort interpretation](Details.md#multi-cohort) is performed. + - `--multi_cohort` (bool) is a flag indicated that [multi-cohort interpretation](Train/Details.md#multi-cohort) is performed. In this case, `caps_dir` and `tsv_path` must be paths to TSV files. If no new `caps_dir` and `tsv_path` are given this argument is not taken into account. - **Results display** diff --git a/docs/RandomSearch.md b/docs/RandomSearch.md index 1c61738dd..a1aafd1b8 100644 --- a/docs/RandomSearch.md +++ b/docs/RandomSearch.md @@ -23,8 +23,8 @@ Some variables were also added to sample the architecture of the network. ## Prerequisites -You need to execute the [`clinicadl tsvtool getlabels`](../TSVTools.md#getlabels---extract-labels-specific-to-alzheimers-disease) -and [`clinicadl tsvtool {split|kfold}`](../TSVTools.md#split---single-split-observing-similar-age-and-sex-distributions) commands +You need to execute the [`clinicadl tsvtool getlabels`](TSVTools.md#getlabels---extract-labels-specific-to-alzheimers-disease) +and [`clinicadl tsvtool {split|kfold}`](TSVTools.md#split---single-split-observing-similar-age-and-sex-distributions) commands prior to running this task to have the correct TSV file organization. Moreover, there should be a CAPS, obtained running the `t1-linear` pipeline of ClinicaDL. @@ -46,7 +46,7 @@ Optional arguments: - `--use_cpu` (bool) forces to use CPU. Default behaviour is to try to use a GPU and to raise an error if it is not found. - `--nproc` (int) is the number of workers used by the DataLoader. Default value: `2`. - `--batch_size` (int) is the size of the batch used in the DataLoader. Default value: `2`. - - `--evaluation_steps` (int) gives the number of iterations to perform an [evaluation internal to an epoch](Details.md#evaluation). + - `--evaluation_steps` (int) gives the number of iterations to perform an [evaluation internal to an epoch](Train/Details.md#evaluation). Default will only perform an evaluation at the end of each epoch. - **Cross-validation arguments** - `--n_splits` (int) is a number of splits k to load in the case of a k-fold cross-validation. Default will load a single-split. @@ -73,7 +73,7 @@ Sampling function: `choice`. Sampling function: `fixed`. - `diagnoses` (list of str) is the list of the labels that will be used for training. Sampling function: `fixed`. -- `epochs` (int) is the [maximum number of epochs](Details.md#stopping-criterion). +- `epochs` (int) is the [maximum number of epochs](Train/Details.md#stopping-criterion). Sampling function: `fixed`. - `n_convblocks` (int) is the number of convolutional blocks in CNN. Sampling function: `randint`. @@ -116,18 +116,18 @@ Optional variables: Sampling function: `exponent`, conditioned by `wd_bool`. Default: `4` (leading to a value of `1e-4`). - `dropout` (float) is the rate of dropout applied in dropout layers. Sampling function: `uniform`. Default: `0.0`. - - `patience` (int) is the number of epochs for [early stopping](Details.md#stopping-criterion) patience. + - `patience` (int) is the number of epochs for [early stopping](Train/Details.md#stopping-criterion) patience. Sampling function: `fixed`. Default: `0`. - - `tolerance` (float) is the value used for [early stopping](Details.md#stopping-criterion) tolerance. + - `tolerance` (float) is the value used for [early stopping](Train/Details.md#stopping-criterion) tolerance. Sampling function: `fixed`. Default: `0.0`. "accumulation_steps": 1 - **Transfer learning** - `--transfer_learning_path` (str) is the path to a result folder (output of `clinicadl train`). The best model of this folder will be used to initialize the network as - explained in the [implementation details](./Details.md#transfer-learning). + explained in the [implementation details](Train/Details.md#transfer-learning). If nothing is given the initialization will be random. - `--transfer_learning_selection` (str) corresponds to the metric according to which the - [best model](./Details.md#model-selection) of `transfer_learning_path` will be loaded. + [best model](Train/Details.md#model-selection) of `transfer_learning_path` will be loaded. This argument will only be taken into account if the source network is a CNN. Choices are `best_loss` and `best_balanced_accuracy`. Default: `best_balanced_accuracy`. @@ -137,7 +137,7 @@ Mode-dependent variables: - `patch_size` (int) size of the patches in voxels. Sampling function: `randint`. Default: `50`. - `selection_threshold` (float) threshold on the balanced accuracies to compute the - [image-level performance](./Details.md#soft-voting). + [image-level performance](Train/Details.md#soft-voting). Patches are selected if their balanced accuracy is greater than the threshold. Sampling function: `uniform`. Default will perform no selection. - `stride_size` (int) length between the centers of successive patches in voxels. @@ -146,7 +146,7 @@ Mode-dependent variables: Otherwise, the whole 3D MR volumes are loaded and patches are extracted on-the-fly. - `roi` - `selection_threshold` (float) threshold on the balanced accuracies to compute the - [image-level performance](./Details.md#soft-voting). + [image-level performance](Train/Details.md#soft-voting). Patches are selected if their balanced accuracy is greater than the threshold. Sampling function: `uniform`. Default will perform no selection. - `slice` @@ -154,7 +154,7 @@ Mode-dependent variables: If only one argument is given, it will be used for both sides. Sampling function: `randint`. Default: `20`. - `selection_threshold` (float) threshold on the balanced accuracies to compute the - [image-level performance](./Details.md#soft-voting). + [image-level performance](Train/Details.md#soft-voting). Slices are selected if their balanced accuracy is greater than the threshold. Sampling function: `uniform`. Default corresponds to no selection. - `slice_direction` (int) axis along which the MR volume is sliced. diff --git a/docs/overrides/main.html b/docs/overrides/main.html new file mode 100644 index 000000000..6f2da4795 --- /dev/null +++ b/docs/overrides/main.html @@ -0,0 +1,8 @@ +{% extends "base.html" %} + +{% block outdated %} + You're not viewing the latest stable version. + + Click here to go to stable. + +{% endblock %} \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 4bf46c8a3..01cbad2b8 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -22,6 +22,7 @@ google_analytics: # Configuration theme: name: material + custom_dir: docs/overrides icon: repo: material/github language: en @@ -44,6 +45,8 @@ extra: link: https://twitter.com/clinica_run - icon: material/earth link: http://www.aramislab.fr + version: + default: stable # Extensions From 8cc9bfd2acdf29836f23d1114590ae0e3912c309 Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Wed, 31 Mar 2021 10:02:08 +0200 Subject: [PATCH 07/17] Fix interpretability (#141) * Fix data loading for interpretability functions * Fix style --- clinicadl/clinicadl/interpret/group_backprop.py | 4 +--- clinicadl/clinicadl/interpret/individual_backprop.py | 1 + clinicadl/clinicadl/tools/deep_learning/data.py | 10 ++++++++-- 3 files changed, 10 insertions(+), 5 deletions(-) diff --git a/clinicadl/clinicadl/interpret/group_backprop.py b/clinicadl/clinicadl/interpret/group_backprop.py index 65e19272d..2904c4f07 100644 --- a/clinicadl/clinicadl/interpret/group_backprop.py +++ b/clinicadl/clinicadl/interpret/group_backprop.py @@ -119,14 +119,12 @@ def group_backprop(options): mean_map_nii = nib.Nifti1Image(mean_map[0], affine) nib.save(mean_map_nii, path.join(results_path, "map.nii.gz")) - np.save(path.join(results_path, "map.npy"), mean_map[0]) else: jpg_path = path.join(results_path, "map.jpg") plt.imshow(mean_map[0], cmap="coolwarm", vmin=-options.vmax, vmax=options.vmax) plt.colorbar() plt.savefig(jpg_path) plt.close() - numpy_path = path.join(results_path, "map.npy") - np.save(numpy_path, mean_map[0]) + np.save(path.join(results_path, "map.npy"), mean_map[0]) else: main_logger.warn("There are no subjects for the given options") diff --git a/clinicadl/clinicadl/interpret/individual_backprop.py b/clinicadl/clinicadl/interpret/individual_backprop.py index 800bddde9..1e3d35369 100644 --- a/clinicadl/clinicadl/interpret/individual_backprop.py +++ b/clinicadl/clinicadl/interpret/individual_backprop.py @@ -124,3 +124,4 @@ def individual_backprop(options): plt.colorbar() plt.savefig(jpg_path) plt.close() + np.save(path.join(single_path, "map.npy"), map_np[i]) diff --git a/clinicadl/clinicadl/tools/deep_learning/data.py b/clinicadl/clinicadl/tools/deep_learning/data.py index 6cc7029a5..94337eccb 100644 --- a/clinicadl/clinicadl/tools/deep_learning/data.py +++ b/clinicadl/clinicadl/tools/deep_learning/data.py @@ -505,7 +505,7 @@ def insert_descriptor(image_descriptors, descriptor_to_add, key_to_follow): for i, desc in enumerate(image_descriptors): if key_to_follow in desc: - image_descriptors.insert(i+1, descriptor_to_add) + image_descriptors.insert(i + 1, descriptor_to_add) return image_descriptors @@ -979,7 +979,13 @@ def load_data_test(test_path, diagnoses_list, baseline=True, multi_cohort=False) def load_data_test_single(test_path, diagnoses_list, baseline=True): if test_path.endswith('.tsv'): - return pd.read_csv(test_path, sep='\t') + test_df = pd.read_csv(test_path, sep='\t') + if "diagnosis" not in test_df.columns.values: + raise ValueError(f"'diagnosis' column must be present in TSV file {test_path}.") + test_df = test_df[test_df.diagnosis.isin(diagnoses_list)] + if len(test_df) == 0: + raise ValueError(f"Diagnoses wanted {diagnoses_list} were not found in TSV file {test_path}.") + return test_df test_df = pd.DataFrame() From 2f3388e0516526e20dcabf2dbb775c3ef6383fe8 Mon Sep 17 00:00:00 2001 From: Elina Thibeau-Sutre Date: Thu, 8 Apr 2021 11:33:53 +0200 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clinicadl/scripts/preprocessing/test/extract.sh delete mode 100755 clinicadl/scripts/preprocessing/test/preprocessing.sh diff --git a/README.md b/README.md index 3c6d8284a..67a55f2f5 100755 --- a/README.md +++ b/README.md @@ -39,10 +39,7 @@ This repository hosts the source code of a **framework for the reproducible evaluation of deep learning classification experiments using anatomical MRI -data for the computer-aided diagnosis of Alzheimer's disease (AD)**. This work -has been published in [Medical Image -Analysis](https://doi.org/10.1016/j.media.2020.101694) and is also available on -[arXiv](https://arxiv.org/abs/1904.07773). +data for the computer-aided diagnosis of Alzheimer's disease (AD)**. Automatic classification of AD using classical machine learning approaches can be performed using the framework available here: @@ -133,3 +130,8 @@ Updated versions of the models will be published soon. - [Clinica: Software platform for clinical neuroimaging studies](https://github.com/aramis-lab/clinica) - [AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning](https://github.com/aramis-lab/AD-ML) + +## Scientific papers using this framework + +- Wen et al., 2020 [Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation](https://doi.org/10.1016/j.media.2020.101694) +([arXiv version](https://arxiv.org/abs/1904.07773)). \ No newline at end of file diff --git a/clinicadl/clinicadl/preprocessing/T1_postprocessing_extract_hippo.py b/clinicadl/clinicadl/preprocessing/T1_postprocessing_extract_hippo.py deleted file mode 100755 index e4674c7e8..000000000 --- a/clinicadl/clinicadl/preprocessing/T1_postprocessing_extract_hippo.py +++ /dev/null @@ -1,155 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Nov 06 14:17:41 2017 - -@author: Junhao WEN -""" - -__author__ = "Junhao Wen" -__copyright__ = "Copyright 2018 The Aramis Lab Team" -__credits__ = ["Junhao Wen"] -__license__ = "See LICENSE.txt file" -__version__ = "0.1.0" -__maintainer__ = "Junhao Wen" -__email__ = "junhao.wen89@gmail.com" -__status__ = "Development" - - -def postprocessing_t1w_extract_hippo(caps_directory, tsv, working_directory=None, hemi='right'): - """ - This is a postprocessing pipeline to prepare the slice-level and patch-level data from the whole MRI and save them - on disk, so that to facilitate the training process: - - For slice-level CNN, all slices were extracted from the whole MRI from three different axis. The first and last - 15 slice were discarded due to the lack of information. - - For patch-level CNN, the 3D patch (with specific patch size) were extracted by a 3D window. - - :param caps_directory: CAPS directory where stores the output of preprocessing - :param tsv: subject list file containing the participant_id and session_id - :param hemi: chooses which hippocampus is extracted (left or right) - :param working_directory: working directory to store the intermediate files - :return: - """ - - from nipype.interfaces.freesurfer import MRIConvert - import nipype.interfaces.utility as nutil - import nipype.pipeline.engine as npe - import nipype.interfaces.io as nio - import tempfile - from .T1_postprocessing_extract_hippo_utils import get_caps_t1, save_as_pt, compress_nii, get_subid_sesid_datasink - - if working_directory is None: - working_directory = tempfile.mkdtemp() - - inputnode = npe.Node(nutil.IdentityInterface( - fields=['caps_directory', 'tsv']), - name='inputnode') - inputnode.inputs.caps_directory = caps_directory - inputnode.inputs.tsv = tsv - - get_subject_session_list = npe.Node( - name='get_subject_session_list', - interface=nutil.Function( - function=get_caps_t1, - input_names=['caps_directory', 'tsv'], - output_names=[ - 'preprocessed_T1', - 'cropped_hipp_file_name', - 'participant_id', - 'session_id', - 'preprocessed_T1_folder' - ] - ) - ) - - # extract the hippocampus. - hippocampus_patches = npe.MapNode( - name='hippocampus_patches', - iterfield=['in_file', 'out_file'], - interface=MRIConvert() - ) - - hippocampus_patches.inputs.out_type = 'nii' - - # TODO, to decide the position of hippocampus of each hemisphere - if hemi == 'left': - hippocampus_patches.inputs.crop_center = (61, 96, 68) # the center of the right and left hippocampus - hippocampus_patches.inputs.crop_size = (50, 50, 50) # the output cropped hippocampus size - else: - hippocampus_patches.inputs.crop_center = (109, 96, 68) # the center of the right and right hippocampus - hippocampus_patches.inputs.crop_size = (50, 50, 50) # the output cropped hippocampus size - - # zip the result imgs - # in the newest version of nipype for MRIConvert, it seems that they can be saved directly as nii.gz - zip_hippocampus = npe.MapNode( - name='zip_hippocampus', - interface=nutil.Function( - input_names=['in_file'], - output_names=['out_file'], - function=compress_nii - ), - iterfield=['in_file'] - ) - - # save nii.gz into classifiers .pt format. - save_as_pt = npe.MapNode( - name='save_as_pt', - iterfield=['input_img'], - interface=nutil.Function( - function=save_as_pt, - input_names=['input_img'], - output_names=['output_file'] - ) - ) - - # get the information for datasinker. - get_identifiers = npe.MapNode(nutil.Function( - input_names=['participant_id', 'session_id', 'caps_directory', 'hemi'], output_names=['base_directory', 'subst_tuple_list', 'regexp_substitutions'], - function=get_subid_sesid_datasink), iterfield=['participant_id', 'session_id'], name='get_subid_sesid_datasink') - get_identifiers.inputs.caps_directory = caps_directory - get_identifiers.inputs.hemi = hemi - - # datasink - datasink = npe.MapNode( - nio.DataSink( - infields=['output_hippocampus_nii', 'output_hippocampus_pt'] - ), - name='datasinker', - iterfield=[ - 'output_hippocampus_nii', - 'output_hippocampus_pt', - 'base_directory', - 'substitutions', - 'regexp_substitutions' - ] - ) - - outputnode = npe.Node( - nutil.IdentityInterface( - fields=['output_hippocampus_nii', 'output_hippocampus_pt'] - ), - name='outputnode') - - wf = npe.Workflow(name='t1w_postprocessing_dl_extract_hippo') - wf.base_dir = working_directory - - wf.connect([ - (inputnode, get_subject_session_list, [('tsv', 'tsv')]), - (inputnode, get_subject_session_list, [('caps_directory', 'caps_directory')]), - - (get_subject_session_list, hippocampus_patches, [('preprocessed_T1', 'in_file')]), - (get_subject_session_list, hippocampus_patches, [('cropped_hipp_file_name', 'out_file')]), - (hippocampus_patches, zip_hippocampus, [('out_file', 'in_file')]), - (zip_hippocampus, save_as_pt, [('out_file', 'input_img')]), - - # Saving files with datasink: - (get_subject_session_list, get_identifiers, [('participant_id', 'participant_id')]), - (get_subject_session_list, get_identifiers, [('session_id', 'session_id')]), - - (get_identifiers, datasink, [('base_directory', 'base_directory')]), - (get_identifiers, datasink, [('subst_tuple_list', 'substitutions')]), - (get_identifiers, datasink, [('regexp_substitutions', 'regexp_substitutions')]), - (save_as_pt, datasink, [('output_file', 'output_hippocampus_pt')]), - (zip_hippocampus, datasink, [('out_file', 'output_hippocampus_nii')]), - ]) - - return wf diff --git a/clinicadl/clinicadl/preprocessing/T1_postprocessing_extract_hippo_utils.py b/clinicadl/clinicadl/preprocessing/T1_postprocessing_extract_hippo_utils.py deleted file mode 100755 index d2fb652cb..000000000 --- a/clinicadl/clinicadl/preprocessing/T1_postprocessing_extract_hippo_utils.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Nov 06 14:17:41 2017 - -@author: Junhao WEN -""" - -__author__ = "Junhao Wen" -__copyright__ = "Copyright 2018 The Aramis Lab Team" -__credits__ = ["Junhao Wen"] -__license__ = "See LICENSE.txt file" -__version__ = "0.1.0" -__maintainer__ = "Junhao Wen" -__email__ = "junhao.wen89@gmail.com" -__status__ = "Development" - - -def get_caps_t1(caps_directory, tsv): - """ - THis is a function to grab all the cropped files - :param caps_directory: - :param tsv: - :return: - """ - import pandas as pd - import os - - preprocessed_T1 = [] - cropped_hipp_file_name = [] - preprocessed_T1_folder = [] - - df = pd.read_csv(tsv, sep='\t') - if ('session_id' != list(df.columns.values)[1]) and ( - 'participant_id' != list(df.columns.values)[0]): - raise Exception('the data file is not in the correct format.') - participant_id = list(df['participant_id']) - session_id = list(df['session_id']) - - for i in range(len(participant_id)): - img_path = os.path.join(caps_directory, 'subjects', participant_id[i], session_id[i], 't1', 'preprocessing_dl', participant_id[i] + '_' + session_id[i] + '_space-MNI_res-1x1x1.nii.gz') - preprocessed_T1.append(img_path) - preprocessed_T1_folder.append(os.path.join(caps_directory, 'subjects', participant_id[i], session_id[i], 't1', 'preprocessing_dl')) - cropped_hipp_file_name.append(participant_id[i] + '_' + session_id[i] + '_space-MNI_res-1x1x1_hippocampus.nii') - - return preprocessed_T1, cropped_hipp_file_name, participant_id, session_id, preprocessed_T1_folder - - -def save_as_pt(input_img): - """ - This function is to transfer nii.gz file into .pt format, in order to train the classifiers model more efficient when loading the data. - :param input_img: - :return: - """ - - import torch - import os - import nibabel as nib - - image_array = nib.load(input_img).get_fdata() - image_tensor = torch.from_numpy(image_array).unsqueeze(0).float() - # make sure the tensor dtype is torch.float32 - output_file = os.path.join(os.path.dirname(input_img), input_img.split('.nii')[0] + '.pt') - # save - torch.save(image_tensor.clone(), output_file) - - return output_file - - -def compress_nii(in_file, same_dir=True): - """ - This is a function to compress the resulting nii images - Args: - in_file: - - Returns: - - """ - from os import getcwd, remove - from os.path import abspath, join - import gzip - import shutil - from nipype.utils.filemanip import split_filename - - orig_dir, base, ext = split_filename(str(in_file)) - - # Not compressed - if same_dir: - out_file = abspath(join(orig_dir, base + ext + '.gz')) - else: - out_file = abspath(join(getcwd(), base + ext + '.gz')) - - with open(in_file, 'rb') as f_in, gzip.open(out_file, 'wb') as f_out: - shutil.copyfileobj(f_in, f_out) - - return out_file - - -def get_subid_sesid_datasink(participant_id, session_id, caps_directory, hemi): - """ - This is to extract the base_directory for the DataSink including participant_id and sesion_id in CAPS directory, also the tuple_list for substitution - :param participant_id: - :return: base_directory for DataSink - """ - import os - - # for MapNode - base_directory = os.path.join( - caps_directory, 'subjects', participant_id, - session_id, 't1', 'preprocessing_dl') - - subst_tuple_list = [ - (participant_id + '_' + session_id + '_space-MNI_res-1x1x1_hippocampus.nii.gz', - participant_id + '_' + session_id + '_space-MNI_res-1x1x1_hippocampus_hemi-' + hemi + '.nii.gz'), - (participant_id + '_' + session_id + '_space-MNI_res-1x1x1_hippocampus.pt', - participant_id + '_' + session_id + '_space-MNI_res-1x1x1_hippocampus_hemi-' + hemi + '.pt'), - ] - - regexp_substitutions = [ - # I don't know why it's adding this empty folder, so I remove it: - # NOTE, . means any characters and * means any number of repetition in python regex - (r'/output_hippocampus_nii/_hippocampus_patches\d{1,4}/', r'/'), - (r'/output_hippocampus_pt/_hippocampus_patches\d{1,4}/', r'/'), - # I don't know why it's adding this empty folder, so I remove it: - (r'/trait_added/_datasinker\d{1,4}/', r'/') - ] - - return base_directory, subst_tuple_list, regexp_substitutions diff --git a/clinicadl/clinicadl/preprocessing/T1_postprocessing_mean_img_population.py b/clinicadl/clinicadl/preprocessing/T1_postprocessing_mean_img_population.py deleted file mode 100755 index 951106f7c..000000000 --- a/clinicadl/clinicadl/preprocessing/T1_postprocessing_mean_img_population.py +++ /dev/null @@ -1,58 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Mon Nov 06 14:17:41 2017 - -@author: Junhao WEN -""" - -__author__ = "Junhao Wen" -__copyright__ = "Copyright 2018 The Aramis Lab Team" -__credits__ = ["Junhao Wen"] -__license__ = "See LICENSE.txt file" -__version__ = "0.1.0" -__maintainer__ = "Junhao Wen" -__email__ = "junhao.wen89@gmail.com" -__status__ = "Development" - - -def get_mean_image_population(caps_directory, tsv, template_image): - """ - THis is a function to grab all the cropped files - :param caps_directory: - :param tsv: - :return: - """ - import pandas as pd - import os - import nibabel as nib - import numpy as np - - os.makedirs(os.path.join(caps_directory, 'group'), exist_ok=True) - - df = pd.read_csv(tsv, sep='\t') - if ('session_id' != list(df.columns.values)[1]) and ( - 'participant_id' != list(df.columns.values)[0]): - raise Exception('the data file is not in the correct format.') - participant_id = list(df['participant_id']) - session_id = list(df['session_id']) - - # get the template image info: - template_image_data = nib.load(template_image) - template_image_array = template_image_data.get_data() - header = template_image_data.header - affine = template_image_data.affine - - final_array = np.empty((template_image_array.shape[0], template_image_array.shape[1], template_image_array.shape[2], len(participant_id))) - - for i in range(len(participant_id)): - image = os.path.join(caps_directory, 'subjects', participant_id[i], session_id[i], 't1', 'preprocessing_dl', participant_id[i] + '_' + session_id[i] + '_space-MNI_res-1x1x1.nii.gz') - image_data = nib.load(image) - image_array = image_data.get_data() - final_array[..., i] = image_array - - # take the mean of image - final_mean_array = np.mean(final_array, axis=3) - - # save the mean image as nifti - mean_image = nib.Nifti1Image(final_mean_array, affine, header) - nib.save(mean_image, os.path.join(caps_directory, 'group', 'mean_population.nii.gz')) diff --git a/clinicadl/clinicadl/preprocessing/data/mni_icbm152_t1_tal_nlin_sym_09c_0.jpg b/clinicadl/clinicadl/preprocessing/data/mni_icbm152_t1_tal_nlin_sym_09c_0.jpg deleted file mode 100755 index 8ea2e18849cbd1ef6cc8ef4db478c4a59f15fb4b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10538 zcmY*<1yCH%6YfE9f(A(-Sb*RV+=BaI$HCzeJa{g+y9c)ff^)dL69{*>9qvwWLU4cl 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zwmq<{rvwp73+C4~X1yuK*}_V?haIc3W}IPhfm>NnPtt_tX#h!5Ps*AxdKMYP$_$F>uM z-~cHfuB~mY*AvsyMtQ|2u2%K4FoViT??e_TV<<`BcRy+=df(R2NEl8t%|O3qN}a+J Z+r4^{+Mz-@JY&rvH%{gcx{u9&|JgSLX^sE@ diff --git a/clinicadl/clinicadl/tools/deep_learning/data.py b/clinicadl/clinicadl/tools/deep_learning/data.py index 94337eccb..a70c5e512 100644 --- a/clinicadl/clinicadl/tools/deep_learning/data.py +++ b/clinicadl/clinicadl/tools/deep_learning/data.py @@ -166,7 +166,6 @@ def _get_full_image(self): try: image_path = self._get_path(participant_id, session_id, cohort, mode="image") - print(image_path) image = torch.load(image_path) except FileNotFoundError: image_path = get_nii_path( @@ -175,7 +174,6 @@ def _get_full_image(self): session_id, cohort=cohort, preprocessing=self.preprocessing) - print(image_path) image_nii = nib.load(image_path) image_np = image_nii.get_fdata() image = ToTensor()(image_np) @@ -703,8 +701,6 @@ def return_dataset(mode, input_dir, data_df, preprocessing, def compute_num_cnn(input_dir, tsv_path, options, data="train"): _, transformations = get_transforms(options.mode, options.minmaxnormalization) - print(tsv_path) - print(input_dir) if data == "train": example_df, _ = load_data(tsv_path, options.diagnoses, 0, options.n_splits, options.baseline, diff --git a/clinicadl/scripts/patch_level/autoencoder/experiment-5.sh b/clinicadl/scripts/Wen_MedIA_2020/patch_level/autoencoder/experiment-5.sh similarity index 100% rename from clinicadl/scripts/patch_level/autoencoder/experiment-5.sh rename to clinicadl/scripts/Wen_MedIA_2020/patch_level/autoencoder/experiment-5.sh diff --git a/clinicadl/scripts/patch_level/autoencoder/experiment-6.sh b/clinicadl/scripts/Wen_MedIA_2020/patch_level/autoencoder/experiment-6.sh similarity index 100% rename from clinicadl/scripts/patch_level/autoencoder/experiment-6.sh rename to clinicadl/scripts/Wen_MedIA_2020/patch_level/autoencoder/experiment-6.sh diff --git a/clinicadl/scripts/patch_level/autoencoder/experiment-7.sh 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b/clinicadl/scripts/Wen_MedIA_2020/roi_based/evaluation/test6.sh similarity index 100% rename from clinicadl/scripts/roi_based/evaluation/test6.sh rename to clinicadl/scripts/Wen_MedIA_2020/roi_based/evaluation/test6.sh diff --git a/clinicadl/scripts/slice_level/cnn/experiment-18.sh b/clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/experiment-18.sh similarity index 100% rename from clinicadl/scripts/slice_level/cnn/experiment-18.sh rename to clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/experiment-18.sh diff --git a/clinicadl/scripts/slice_level/cnn/experiment-19.sh b/clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/experiment-19.sh similarity index 100% rename from clinicadl/scripts/slice_level/cnn/experiment-19.sh rename to clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/experiment-19.sh diff --git a/clinicadl/scripts/slice_level/cnn/experiment-20.sh b/clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/experiment-20.sh similarity index 100% rename from clinicadl/scripts/slice_level/cnn/experiment-20.sh rename to clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/experiment-20.sh diff --git a/clinicadl/clinicadl/train/train_singleCNN_bad_data_split.py b/clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/train_singleCNN_bad_data_split.py similarity index 57% rename from clinicadl/clinicadl/train/train_singleCNN_bad_data_split.py rename to clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/train_singleCNN_bad_data_split.py index 37a8fc36f..bc1a5d7b5 100755 --- a/clinicadl/clinicadl/train/train_singleCNN_bad_data_split.py +++ b/clinicadl/scripts/Wen_MedIA_2020/slice_level/cnn/train_singleCNN_bad_data_split.py @@ -1,57 +1,32 @@ # coding: utf8 import argparse -import torchvision.transforms as transforms from torch.utils.data import DataLoader import torch import os -from time import time -from clinicadl.tools.deep_learning import commandline_to_json +from clinicadl.tools.deep_learning.iotools import commandline_to_json, return_logger, check_and_clean, \ + write_requirements_version, translate_parameters from clinicadl.tools.deep_learning.models import load_model, init_model from clinicadl.tools.deep_learning.data import (load_data, - MinMaxNormalization, MRIDatasetSlice, - mix_slices) -from clinicadl.tools.deep_learning.cnn_utils import train, test, mode_level_to_tsvs + mix_slices, + generate_sampler, + get_transforms) +from clinicadl.tools.deep_learning.cnn_utils import train, test, mode_level_to_tsvs, get_criterion -__author__ = "Junhao Wen" -__copyright__ = "Copyright 2018-2020 The Aramis Lab Team" -__credits__ = ["Junhao Wen" "Elina Thibeau-Sutre" "Mauricio Diaz"] -__license__ = "See LICENSE.txt file" -__version__ = "0.1.0" -__maintainer__ = "Junhao Wen, Elina Thibeau-Sutre, Mauricio Diaz" -__email__ = "junhao.wen89@gmail.com, mauricio.diaz@inria.fr" -__status__ = "Development" +def test_bad_cnn(model, output_dir, data_loader, subset_name, split, criterion, mode, logger, gpu=False): - -def test_cnn(data_loader, subset_name, split, criterion, options): - - for selection in ["best_acc", "best_loss"]: + for selection in ["best_balanced_accuracy", "best_loss"]: # load the best trained model during the training - model = init_model( - options.model, - gpu=options.gpu, - dropout=options.dropout) - model, best_epoch = load_model(model, os.path.join(options.output_dir, 'best_model_dir', "fold_%i" % split, - 'CNN', selection), - gpu=options.gpu, filename='model_best.pth.tar') - - results_df, metrics = test( - model, data_loader, options.gpu, criterion, options.mode) - print( - "Slice level balanced accuracy is %f" % - metrics['balanced_accuracy']) - - mode_level_to_tsvs( - options.output_dir, - results_df, - metrics, - split, - selection, - options.mode, - dataset=subset_name) + model, best_epoch = load_model(model, os.path.join(output_dir, f'fold-{split}', 'models', selection), + gpu=gpu, filename='model_best.pth.tar') + results_df, metrics = test(model, data_loader, gpu, criterion, mode) + logger.info("%s level %s balanced accuracy is %f for model selected on %s" + % (mode, subset_name, metrics["balanced_accuracy"], selection)) + + mode_level_to_tsvs(output_dir, results_df, metrics, split, selection, mode, dataset=subset_name) # Train 2D CNN - Slice level network @@ -118,7 +93,9 @@ def test_cnn(data_loader, subset_name, split, criterion, options): "--split", default=None, type=int, - help="Define a specific fold in the k-fold, this is very useful to find the optimal model, where you do not want to run your k-fold validation") + nargs="+", + help="Define a specific fold in the k-fold, this is very useful to find the optimal model, " + "when you do not want to run your k-fold validation") parser.add_argument( "--epochs", @@ -189,28 +166,38 @@ def test_cnn(data_loader, subset_name, split, criterion, options): default=0, help="Tolerance of magnitude of performance after each epoch.") - -def train_CNN_bad_data_split(params): - - # Initialize the model - print('Do transfer learning with existed model trained on ImageNet!\n') - print('The chosen network is %s !' % params.model) - - # most of the imagenet pretrained model has this input size - trg_size = (224, 224) - - # All pre-trained models expect input images normalized in the same way, - # i.e. mini-batches of 3-channel RGB images of shape (3 x H x W), where H - # and W are expected to be at least 224. The images have to be loaded in to - # a range of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] - # and std = [0.229, 0.224, 0.225]. - transformations = transforms.Compose([MinMaxNormalization(), - transforms.ToPILImage(), - transforms.Resize(trg_size), - transforms.ToTensor()]) - params.dropout = 0.8 - - total_time = time() +parser.add_argument( + '--verbose', + '-v', + action='count', + default=0) + + +def train_bad_cnn(params): + """ + Trains a single CNN and writes: + - logs obtained with Tensorboard during training, + - best models obtained according to two metrics on the validation set (loss and balanced accuracy), + - for patch and roi modes, the initialization state is saved as it is identical across all folds, + - final performances at the end of the training. + + If the training crashes it is possible to relaunch the training process from the checkpoint.pth.tar and + optimizer.pth.tar files which respectively contains the state of the model and the optimizer at the end + of the last epoch that was completed before the crash. + """ + main_logger = return_logger(params.verbose, "main process") + train_logger = return_logger(params.verbose, "train") + eval_logger = return_logger(params.verbose, "final evaluation") + check_and_clean(params.output_dir) + + params.mode = "slice" + params.network_type = "cnn" + commandline_to_json(params, logger=main_logger) + write_requirements_version(params.output_dir) + params = translate_parameters(params) + train_transforms, all_transforms = get_transforms(params.mode, + minmaxnormalization=True, + data_augmentation=False) if params.split is None: if params.n_splits is None: @@ -218,19 +205,20 @@ def train_CNN_bad_data_split(params): else: fold_iterator = range(params.n_splits) else: - fold_iterator = [params.split] + fold_iterator = params.split for fi in fold_iterator: - print("Running for the %d-th fold" % fi) + main_logger.info("Fold %i" % fi) training_sub_df, valid_sub_df = load_data( params.tsv_path, params.diagnoses, fi, n_splits=params.n_splits, - baseline=params.baseline + baseline=params.baseline, + logger=main_logger, + multi_cohort=params.multi_cohort ) - # split the training + validation by slice training_df, valid_df = mix_slices( training_sub_df, @@ -241,7 +229,8 @@ def train_CNN_bad_data_split(params): data_train = MRIDatasetSlice( params.caps_directory, training_df, - transformations=transformations, + all_transformations=all_transforms, + train_transformations=train_transforms, mri_plane=params.mri_plane, prepare_dl=params.prepare_dl, mixed=True @@ -250,17 +239,19 @@ def train_CNN_bad_data_split(params): data_valid = MRIDatasetSlice( params.caps_directory, valid_df, - transformations=transformations, + all_transformations=all_transforms, + train_transformations=train_transforms, mri_plane=params.mri_plane, prepare_dl=params.prepare_dl, mixed=True ) - # Use argument load to distinguish training and testing + train_sampler = generate_sampler(data_train, params.sampler) + train_loader = DataLoader( data_train, batch_size=params.batch_size, - shuffle=True, + sampler=train_sampler, num_workers=params.num_workers, pin_memory=True ) @@ -274,42 +265,29 @@ def train_CNN_bad_data_split(params): ) # Initialize the model - print('Initialization of the model') - model = init_model( - params.model, - gpu=params.gpu, - dropout=params.dropout) + main_logger.info('Initialization of the model') + model = init_model(params, initial_shape=data_train.size) # Define criterion and optimizer - criterion = torch.nn.CrossEntropyLoss() - optimizer = eval("torch.optim." + params.optimizer)(filter(lambda x: x.requires_grad, model.parameters()), - lr=params.learning_rate, - weight_decay=params.weight_decay) - setattr(params, 'beginning_epoch', 0) + criterion = get_criterion("default") + optimizer = getattr(torch.optim, params.optimizer)(filter(lambda x: x.requires_grad, model.parameters()), + lr=params.learning_rate, + weight_decay=params.weight_decay) # Define output directories log_dir = os.path.join( - params.output_dir, 'fold-%i' % - fi, 'tensorboard_logs') - model_dir = os.path.join(params.output_dir, 'fold-%i' % fi, 'models') - - print('Beginning the training task') - train( - model, - train_loader, - valid_loader, - criterion, - optimizer, - False, - log_dir, - model_dir, - params) + params.output_dir, 'fold-%i' % fi, 'tensorboard_logs') + model_dir = os.path.join( + params.output_dir, 'fold-%i' % fi, 'models') - test_cnn(train_loader, "train", fi, criterion, options) - test_cnn(valid_loader, "validation", fi, criterion, options) + main_logger.debug('Beginning the training task') + train(model, train_loader, valid_loader, criterion, + optimizer, False, log_dir, model_dir, params, train_logger) - total_time = time() - total_time - print("Total time of computation: %d s" % total_time) + test_bad_cnn(model, params.output_dir, train_loader, "train", + fi, criterion, params.mode, eval_logger, gpu=params.gpu) + test_bad_cnn(model, params.output_dir, valid_loader, "validation", + fi, criterion, params.mode, eval_logger, gpu=params.gpu) if __name__ == "__main__": @@ -320,4 +298,4 @@ def train_CNN_bad_data_split(params): raise Exception( "unknown arguments: %s" % (parser.parse_known_args()[1])) - train_CNN_bad_data_split(options) + train_bad_cnn(options) diff --git a/clinicadl/scripts/slice_level/evaluation/test13.sh b/clinicadl/scripts/Wen_MedIA_2020/slice_level/evaluation/test13.sh similarity index 100% rename from clinicadl/scripts/slice_level/evaluation/test13.sh rename to clinicadl/scripts/Wen_MedIA_2020/slice_level/evaluation/test13.sh diff --git a/clinicadl/scripts/slice_level/tests/cnn/experiment-19-bis.sh b/clinicadl/scripts/Wen_MedIA_2020/slice_level/tests/cnn/experiment-19-bis.sh similarity index 100% rename from clinicadl/scripts/slice_level/tests/cnn/experiment-19-bis.sh rename to clinicadl/scripts/Wen_MedIA_2020/slice_level/tests/cnn/experiment-19-bis.sh diff --git 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a/clinicadl/scripts/subject_level/tests/evaluation/evaluation_ADNI_exp4.sh b/clinicadl/scripts/Wen_MedIA_2020/subject_level/tests/evaluation/evaluation_ADNI_exp4.sh similarity index 100% rename from clinicadl/scripts/subject_level/tests/evaluation/evaluation_ADNI_exp4.sh rename to clinicadl/scripts/Wen_MedIA_2020/subject_level/tests/evaluation/evaluation_ADNI_exp4.sh diff --git a/clinicadl/scripts/subject_level/tests/evaluation/example_evaluation_ADNI.sh b/clinicadl/scripts/Wen_MedIA_2020/subject_level/tests/evaluation/example_evaluation_ADNI.sh similarity index 100% rename from clinicadl/scripts/subject_level/tests/evaluation/example_evaluation_ADNI.sh rename to clinicadl/scripts/Wen_MedIA_2020/subject_level/tests/evaluation/example_evaluation_ADNI.sh diff --git a/clinicadl/scripts/svm/apply_validation/experiment-23.sh b/clinicadl/scripts/Wen_MedIA_2020/svm/apply_validation/experiment-23.sh similarity index 83% rename from clinicadl/scripts/svm/apply_validation/experiment-23.sh rename to clinicadl/scripts/Wen_MedIA_2020/svm/apply_validation/experiment-23.sh index 410650400..dfeba30f4 100755 --- a/clinicadl/scripts/svm/apply_validation/experiment-23.sh +++ b/clinicadl/scripts/Wen_MedIA_2020/svm/apply_validation/experiment-23.sh @@ -38,6 +38,5 @@ echo $TASK_NAME NAME="model-svm_task-${TASK_NAME}" -cd /network/lustre/iss01/home/elina.thibeausutre/Code/AD-DL/clinicadl -pwd -python -m clinicadl.svm.$SCRIPT $TSVPATH $IMGPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS --set $SET --train_mode +cd .. +python -m src.$SCRIPT $TSVPATH $IMGPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS --set $SET --train_mode diff --git a/clinicadl/scripts/svm/apply_validation/experiment-26.sh b/clinicadl/scripts/Wen_MedIA_2020/svm/apply_validation/experiment-26.sh similarity index 83% rename from clinicadl/scripts/svm/apply_validation/experiment-26.sh rename to clinicadl/scripts/Wen_MedIA_2020/svm/apply_validation/experiment-26.sh index f3fcc7c0c..8b2f68e52 100755 --- a/clinicadl/scripts/svm/apply_validation/experiment-26.sh +++ b/clinicadl/scripts/Wen_MedIA_2020/svm/apply_validation/experiment-26.sh @@ -38,6 +38,5 @@ echo $TASK_NAME NAME="model-svm_task-${TASK_NAME}" -cd /network/lustre/iss01/home/elina.thibeausutre/Code/AD-DL/clinicadl -pwd -python -m clinicadl.svm.$SCRIPT $TSVPATH $IMGPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS --set $SET --train_mode +cd .. +python -m src.$SCRIPT $TSVPATH $IMGPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS --set $SET --train_mode diff --git a/clinicadl/clinicadl/svm/__init__.py b/clinicadl/scripts/Wen_MedIA_2020/svm/src/__init__.py similarity index 100% rename from clinicadl/clinicadl/svm/__init__.py rename to clinicadl/scripts/Wen_MedIA_2020/svm/src/__init__.py diff --git a/clinicadl/clinicadl/svm/classification_utils.py b/clinicadl/scripts/Wen_MedIA_2020/svm/src/classification_utils.py similarity index 100% rename from clinicadl/clinicadl/svm/classification_utils.py rename to clinicadl/scripts/Wen_MedIA_2020/svm/src/classification_utils.py diff --git a/clinicadl/clinicadl/svm/evaluation.py b/clinicadl/scripts/Wen_MedIA_2020/svm/src/evaluation.py similarity index 98% rename from clinicadl/clinicadl/svm/evaluation.py rename to clinicadl/scripts/Wen_MedIA_2020/svm/src/evaluation.py index b8293c934..51d581c70 100644 --- a/clinicadl/clinicadl/svm/evaluation.py +++ b/clinicadl/scripts/Wen_MedIA_2020/svm/src/evaluation.py @@ -6,7 +6,7 @@ import pandas as pd import numpy as np from .classification_utils import load_data_svm, revert_mask, CAPSVoxelBasedInput, evaluate_prediction, save_data -from ..tools.deep_learning.data import load_data, load_data_test +from clinicadl.tools.deep_learning.data import load_data, load_data_test parser = argparse.ArgumentParser(description="Argparser for Pytorch 3D CNN") diff --git a/clinicadl/clinicadl/svm/model.py b/clinicadl/scripts/Wen_MedIA_2020/svm/src/model.py similarity index 100% rename from clinicadl/clinicadl/svm/model.py rename to clinicadl/scripts/Wen_MedIA_2020/svm/src/model.py diff --git a/clinicadl/clinicadl/svm/train.py b/clinicadl/scripts/Wen_MedIA_2020/svm/src/train.py similarity index 97% rename from clinicadl/clinicadl/svm/train.py rename to clinicadl/scripts/Wen_MedIA_2020/svm/src/train.py index 01e634ca4..6c3553a73 100644 --- a/clinicadl/clinicadl/svm/train.py +++ b/clinicadl/scripts/Wen_MedIA_2020/svm/src/train.py @@ -4,7 +4,7 @@ import os from .classification_utils import extract_indices_from_5_fold from .model import VB_KFold_DualSVM -from ..tools.deep_learning import commandline_to_json +from clinicadl.tools.deep_learning import commandline_to_json parser = argparse.ArgumentParser(description="Argparser for Pytorch 3D CNN") diff --git a/clinicadl/scripts/svm/train/experiment-21.sh b/clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-21.sh similarity index 86% rename from clinicadl/scripts/svm/train/experiment-21.sh rename to clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-21.sh index a5c70ee1b..a6633686f 100755 --- a/clinicadl/scripts/svm/train/experiment-21.sh +++ b/clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-21.sh @@ -42,7 +42,5 @@ TASK_NAME="${TASK_NAME}_baseline-${BASELINE}_final" echo $TASK_NAME NAME="model-svm_task-${TASK_NAME}" - -cd /network/lustre/iss01/home/elina.thibeausutre/Code/AD-DL/clinicadl -pwd -python -m clinicadl.svm.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS +cd .. +python -m src.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS diff --git a/clinicadl/scripts/svm/train/experiment-22.sh b/clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-22.sh similarity index 86% rename from clinicadl/scripts/svm/train/experiment-22.sh rename to clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-22.sh index 9a48bdd03..6acedc4cc 100755 --- a/clinicadl/scripts/svm/train/experiment-22.sh +++ b/clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-22.sh @@ -43,6 +43,5 @@ echo $TASK_NAME NAME="model-svm_task-${TASK_NAME}" -cd /network/lustre/iss01/home/elina.thibeausutre/Code/AD-DL/clinicadl -pwd -python -m clinicadl.svm.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS +cd .. +python -m src.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS diff --git a/clinicadl/scripts/svm/train/experiment-24.sh b/clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-24.sh similarity index 86% rename from clinicadl/scripts/svm/train/experiment-24.sh rename to clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-24.sh index 9787b597f..dd8f26170 100755 --- a/clinicadl/scripts/svm/train/experiment-24.sh +++ b/clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-24.sh @@ -43,6 +43,5 @@ echo $TASK_NAME NAME="model-svm_task-${TASK_NAME}" -cd /network/lustre/iss01/home/elina.thibeausutre/Code/AD-DL/clinicadl -pwd -python -m clinicadl.svm.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS +cd .. +python -m src.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS diff --git a/clinicadl/scripts/svm/train/experiment-25.sh b/clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-25.sh similarity index 86% rename from clinicadl/scripts/svm/train/experiment-25.sh rename to clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-25.sh index 817aefc57..b7bfdf867 100755 --- a/clinicadl/scripts/svm/train/experiment-25.sh +++ b/clinicadl/scripts/Wen_MedIA_2020/svm/train/experiment-25.sh @@ -43,6 +43,5 @@ echo $TASK_NAME NAME="model-svm_task-${TASK_NAME}" -cd /network/lustre/iss01/home/elina.thibeausutre/Code/AD-DL/clinicadl -pwd -python -m clinicadl.svm.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS +cd .. +python -m src.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS diff --git a/clinicadl/scripts/preprocessing/extract_ADNI.sh b/clinicadl/scripts/preprocessing/extract_ADNI.sh deleted file mode 100755 index 20e36b14f..000000000 --- a/clinicadl/scripts/preprocessing/extract_ADNI.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -# Bash script to extract features used in classification algorithms proposed -# by clinicadl - -CAPS_DIR=/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/ADNI_rerun -TSV_FILE=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/tmp/DL/tsv_files/ADNI_after_qc.tsv -WORKING_DIR=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/tmp/DL/working_dir_postproc_rerun -METHOD='patch' - -# Extract patches -# Run clinicadl for extract patches using 42 cores -clinicadl extract --np 42 \ - --patch_size 50 \ - --stride_size 50 \ - $CAPS_DIR \ - $TSV_FILE \ - $WORKING_DIR \ - $METHOD - -# Extract slices -# Run clinicadl for extract sclices using 42 cores -METHOD='patch' -SLICE_MODE='rgb' - -clinicadl extract --np 42 \ - --patch_size 50 \ - --stride_size 50 \ - --slice_mode $SLICE_MODE \ - $CAPS_DIR \ - $TSV_FILE \ - $WORKING_DIR \ - $METHOD diff --git a/clinicadl/scripts/preprocessing/orig/README.txt b/clinicadl/scripts/preprocessing/orig/README.txt deleted file mode 100755 index fa78fb5b8..000000000 --- a/clinicadl/scripts/preprocessing/orig/README.txt +++ /dev/null @@ -1,2 +0,0 @@ -The image preprocessing for DL specific pipeline will be included in the current repository. -However, for the image processing pipeline of CLinica-SPM, we use this version of Clinica: 867bdbca9e86ad6581ce68b8c294d3582c49b151 \ No newline at end of file diff --git a/clinicadl/scripts/preprocessing/orig/download_pretrained_model_resnet.sh b/clinicadl/scripts/preprocessing/orig/download_pretrained_model_resnet.sh deleted file mode 100755 index c68d11c32..000000000 --- a/clinicadl/scripts/preprocessing/orig/download_pretrained_model_resnet.sh +++ /dev/null @@ -1,22 +0,0 @@ -#! /bin/bash -set -e -prefix="https://github.com/vfonov/deep-qc/releases/download/v0/" - -echo "Downloading minimal results, to run pretrained model 227MB in total..." - - -function download { - set -e - for f in $@;do - if [ ! -e ${f} ];then - curl --location "${prefix}/${f}" -o ${f} - fi - done -} - -download models_minimal_01.tar.xz - -echo "Unpacking..." - -mkdir -p results -tar xJf models_minimal_01.tar.xz diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_adni.py b/clinicadl/scripts/preprocessing/orig/run_postprocessing_adni.py deleted file mode 100755 index aea84e4cf..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_adni.py +++ /dev/null @@ -1,24 +0,0 @@ -#################### - -from T1_postprocessing import postprocessing_t1w - -## run the pipeline - # for lustre -caps_directory = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/ADNI' -working_dir = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/run/junhao.wen' - -tsv = '/network/lustre/iss01/home/junhao.wen/Project/AD-DL/tsv_files/tsv_after_qc/ADNI_after_qc.tsv' -patch_size = 50 -stride_size = 50 - -# for original -#wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='slice') -#wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -# for rgb tf learning -wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='slice', slice_mode='rgb') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -## for patch -wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='patch') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_aibl.py b/clinicadl/scripts/preprocessing/orig/run_postprocessing_aibl.py deleted file mode 100755 index 98f8564fc..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_aibl.py +++ /dev/null @@ -1,23 +0,0 @@ -#################### - -from T1_postprocessing import postprocessing_t1w - -## run the pipeline - # for lustre -caps_directory = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/AIBL' -working_dir = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/working_dir/AIBL' -tsv = '/network/lustre/iss01/home/junhao.wen/Project/AD-DL/tsv_files/tsv_after_qc/AIBL_after_qc.tsv' -patch_size = 50 -stride_size = 50 - -# for original -#wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='slice') -#wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -# for rgb tf learning -#wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='slice', slice_mode='rgb') -#wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -## for patch -wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='patch') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_aibl.sh b/clinicadl/scripts/preprocessing/orig/run_postprocessing_aibl.sh deleted file mode 100755 index 282dd3da0..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_aibl.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=4-00:00:00 -#SBATCH --mem=120G -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=28 -#SBATCH --nodes=1 -#SBATCH --workdir=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch -#SBATCH --output=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/patch_aibl_%j.out -#SBATCH --error=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/patch_aibl_%j.err -#SBATCH --mail-type=ALL -#SBATCH --mail-user=junhao.wen@icm-institute.org -#SBATCH --job-name="patch aibl" - -## Load CUDA and python -module load python/2.7 -module load CUDA/9.0 -module load FreeSurfer/5.3.0.lua - -## Begin the training -echo "Begin the image postprocessing:" -python /network/lustre/iss01/home/junhao.wen/Project/AD-DL/Code/image_preprocessing/run_postprocessing_aibl.py -echo "Finish!" - - diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_OASIS.py b/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_OASIS.py deleted file mode 100755 index 49f6deff4..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_OASIS.py +++ /dev/null @@ -1,17 +0,0 @@ -#################### - -from T1_postprocessing_extract_hippo import postprocessing_t1w_extract_hippo - -## run the pipeline -## -caps_directory= '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/OASIS' -tsv='/network/lustre/iss01/home/junhao.wen/Project/AD-DL/tsv_files/tsv_after_qc/OASIS_after_qc.tsv' -working_dir = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/working_dir/OASIS' - -## for the left hippocampus -wf = postprocessing_t1w_extract_hippo(caps_directory, tsv, working_directory=working_dir, hemi='left') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -## for the right hippocampus -wf = postprocessing_t1w_extract_hippo(caps_directory, tsv, working_directory=working_dir, hemi='right') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_OASIS.sh b/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_OASIS.sh deleted file mode 100755 index 3548fd558..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_OASIS.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=4-00:00:00 -#SBATCH --mem=120G -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=28 -#SBATCH --nodes=1 -#SBATCH --workdir=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch -#SBATCH --output=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/oasis_hippo_%j.out -#SBATCH --error=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/oasis_hippo_%j.err -#SBATCH --mail-type=ALL -#SBATCH --mail-user=junhao.wen@icm-institute.org -#SBATCH --job-name="oasis postprocessing extract hippo" - -## Load CUDA and python -module load python/2.7 -module load CUDA/9.0 -module load FreeSurfer/5.3.0 - -#export CUDA_VISIBLE_DEVICES=0,1 -## Begin the training -echo "Begin the image postprocessing:" -python /network/lustre/iss01/home/junhao.wen/Project/AD-DL/Code/image_preprocessing/run_postprocessing_hippo_OASIS.py -echo "Finish!" - - diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_adni.py b/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_adni.py deleted file mode 100755 index dce2b4c39..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_adni.py +++ /dev/null @@ -1,18 +0,0 @@ -#################### - -from T1_postprocessing_extract_hippo import postprocessing_t1w_extract_hippo - -## run the pipeline -## -caps_directory= '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/ADNI' -#tsv= '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/AD_CN_ALL_ADNI.tsv' -tsv='/network/lustre/iss01/home/junhao.wen/Project/AD-DL/tsv_files/tsv_after_qc/ADNI_after_qc.tsv' -working_dir = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/working_dir' - -## for the left hippocampus -wf = postprocessing_t1w_extract_hippo(caps_directory, tsv, working_directory=working_dir, hemi='left') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -## for the right hippocampus -wf = postprocessing_t1w_extract_hippo(caps_directory, tsv, working_directory=working_dir, hemi='right') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_adni.sh b/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_adni.sh deleted file mode 100755 index 18beec421..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_adni.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=4-00:00:00 -#SBATCH --mem=120G -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=28 -#SBATCH --nodes=1 -#SBATCH --workdir=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch -#SBATCH --output=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/image_postprocessing_hippo_%j.out -#SBATCH --error=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/image_postprocessing_hippo_%j.err -#SBATCH --mail-type=ALL -#SBATCH --mail-user=junhao.wen@icm-institute.org -#SBATCH --job-name="image postprocessing extract hippo" - -## Load CUDA and python -module load python/2.7 -module load CUDA/9.0 -module load clinica.all - -#export CUDA_VISIBLE_DEVICES=0,1 -## Begin the training -echo "Begin the image postprocessing:" -python /network/lustre/iss01/home/junhao.wen/Project/AD-DL/Code/image_preprocessing/run_postprocessing_hippo_adni.py -echo "Finish!" - - diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_aibl.py b/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_aibl.py deleted file mode 100755 index 94f7d7716..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_aibl.py +++ /dev/null @@ -1,18 +0,0 @@ -#################### - -from T1_postprocessing_extract_hippo import postprocessing_t1w_extract_hippo - -## run the pipeline -## -caps_directory= '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/AIBL' -#tsv= '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/AD_CN_ALL_ADNI.tsv' -tsv='/network/lustre/iss01/home/junhao.wen/Project/AD-DL/tsv_files/tsv_after_qc/AIBL_after_qc.tsv' -working_dir = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/working_dir/AIBL' - -## for the left hippocampus -wf = postprocessing_t1w_extract_hippo(caps_directory, tsv, working_directory=working_dir, hemi='left') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -## for the right hippocampus -wf = postprocessing_t1w_extract_hippo(caps_directory, tsv, working_directory=working_dir, hemi='right') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_aibl.sh b/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_aibl.sh deleted file mode 100755 index 735b67470..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_hippo_aibl.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=4-00:00:00 -#SBATCH --mem=120G -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=28 -#SBATCH --nodes=1 -#SBATCH --workdir=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch -#SBATCH --output=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/aibl_hippo_%j.out -#SBATCH --error=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/aibl_hippo_%j.err -#SBATCH --mail-type=ALL -#SBATCH --mail-user=junhao.wen@icm-institute.org -#SBATCH --job-name="aibl hippo" - -## Load CUDA and python -module load python/2.7 -module load CUDA/9.0 -module load FreeSurfer/5.3.0.lua - -## Begin the training -echo "Begin the image postprocessing:" -python /network/lustre/iss01/home/junhao.wen/Project/AD-DL/Code/image_preprocessing/run_postprocessing_hippo_aibl.py -echo "Finish!" - - diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_mean_image_population.py b/clinicadl/scripts/preprocessing/orig/run_postprocessing_mean_image_population.py deleted file mode 100755 index cf83075e9..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_mean_image_population.py +++ /dev/null @@ -1,14 +0,0 @@ -#################### - -from T1_postprocessing_mean_img_population import get_mean_image_population - -## run the pipeline -# for test -## -caps_directory= '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/ADNI' -tsv= '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/tsv_after_data_splits/ADNI/lists_by_diagnosis/train/CN_baseline.tsv' -# tsv= '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/test.tsv' -template_image = '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Data/mni_icbm152_nlin_sym_09c/mni_icbm152_t1_tal_nlin_sym_09c_cropped.nii.gz' - - -get_mean_image_population(caps_directory, tsv, template_image) diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_mean_population.sh b/clinicadl/scripts/preprocessing/orig/run_postprocessing_mean_population.sh deleted file mode 100755 index 1636e18d8..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_mean_population.sh +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=4-00:00:00 -#SBATCH --mem=120G -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=28 -#SBATCH --nodes=1 -#SBATCH --workdir=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch -#SBATCH --output=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/image_postprocessing_mean_img_%j.out -#SBATCH --error=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/image_postprocessing_mean_img_%j.err -#SBATCH --mail-type=ALL -#SBATCH --mail-user=junhao.wen@icm-institute.org -#SBATCH --job-name="image postprocessing" - -## Load CUDA and python -module load python/2.7 -module load CUDA/9.0 -#export CUDA_VISIBLE_DEVICES=0,1 -## Begin the training -echo "Begin the image postprocessing:" -python /network/lustre/iss01/home/junhao.wen/Project/AD-DL/Code/image_preprocessing/run_postprocessing_mean_image_population.py -echo "Finish!" - - diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_oasis.py b/clinicadl/scripts/preprocessing/orig/run_postprocessing_oasis.py deleted file mode 100755 index 94d762c69..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_oasis.py +++ /dev/null @@ -1,24 +0,0 @@ -#################### - -from T1_postprocessing import postprocessing_t1w - -## run the pipeline - # for lustre -caps_directory = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/OASIS' -working_dir = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/run/junhao.wen/OASIS' - -tsv = '/network/lustre/iss01/home/junhao.wen/Project/AD-DL/tsv_files/tsv_after_qc/OASIS_after_qc.tsv' -patch_size = 50 -stride_size = 50 - -# for original -#wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='slice') -#wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -# for rgb tf learning -wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='slice', slice_mode='rgb') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -# ## for patch -wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='patch') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) diff --git a/clinicadl/scripts/preprocessing/orig/run_postprocessing_oasis.sh b/clinicadl/scripts/preprocessing/orig/run_postprocessing_oasis.sh deleted file mode 100755 index 315ceef17..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_postprocessing_oasis.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=4-00:00:00 -#SBATCH --mem=120G -#SBATCH --ntasks=1 -#SBATCH --cpus-per-task=28 -#SBATCH --nodes=1 -#SBATCH --workdir=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch -#SBATCH --output=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/image_postprocessing_oasis_%j.out -#SBATCH --error=/network/lustre/iss01/home/junhao.wen/working_dir/pytorch/logs/image_postprocessing_oasis_%j.err -#SBATCH --mail-type=ALL -#SBATCH --mail-user=junhao.wen@icm-institute.org -#SBATCH --job-name="oasis image postprocessing" - -## Load CUDA and python -module load python/2.7 -module load CUDA/9.0 -module load load clinica/05_04_2018.lua - -## Begin the training -echo "Begin the image postprocessing:" -python /network/lustre/iss01/home/junhao.wen/Project/AD-DL/Code/image_preprocessing/run_postprocessing_oasis.py -echo "Finish!" - - diff --git a/clinicadl/scripts/preprocessing/orig/run_preprocessing.py b/clinicadl/scripts/preprocessing/orig/run_preprocessing.py deleted file mode 100755 index 48ce46f73..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_preprocessing.py +++ /dev/null @@ -1,22 +0,0 @@ -#################### - -from T1_preprocessing import preprocessing_t1w - -## run the pipeline -# for test -## -#bids_directory = '/teams/ARAMIS/PROJECTS/CLINICA/CLINICA_datasets/BIDS/ADNI_BIDS_T1_new' -#caps_directory= '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Results/CAPS' -#tsv= '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/image_preprocessing_test.tsv' -#working_dir = '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Results/working_dir' -#ref_template = '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Data/mni_icbm152_nlin_sym_09c/mni_icbm152_t1_tal_nlin_sym_09c.nii' - - -bids_directory = '/network/lustre/dtlake01/aramis/projects/mauricio.diazmelo/tmp/DL/ADNI_BIDS_T1_new' -caps_directory= '/network/lustre/dtlake01/aramis/projects/mauricio.diazmelo/tmp/DL/ADNI_CAPS' -tsv= '/network/lustre/dtlake01/aramis/projects/mauricio.diazmelo/tmp/DL/image_preprocessing_test.tsv' -working_dir = '/localdrive10TB/data/mauricio.diazmelo/tmp/DL/working_dir' -ref_template = '/network/lustre/dtlake01/aramis/projects/mauricio.diazmelo/tmp/DL/mni_icbm152_nlin_sym_09c_nifti/mni_icbm152_nlin_sym_09c/mni_icbm152_t1_tal_nlin_sym_09c.nii' - -wf = preprocessing_t1w(bids_directory, caps_directory, tsv, ref_template, working_directory=working_dir) -wf.run(plugin='MultiProc', plugin_args={'n_procs': 8}) diff --git a/clinicadl/scripts/preprocessing/orig/run_qc_dl_ADNI.sh b/clinicadl/scripts/preprocessing/orig/run_qc_dl_ADNI.sh deleted file mode 100755 index 2494b2633..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_qc_dl_ADNI.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/env bash - -python /teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Code/image_preprocessing/quality_check.py --caps_dir=/teams/ARAMIS/PROJECTS/CLINICA/CLINICA_datasets/temp/CAPS_ADNI_DL --tsv=/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/tsv_before_qc/qc_tsvs/ADNI_all_subjects.tsv diff --git a/clinicadl/scripts/preprocessing/orig/run_qc_dl_AIBLE.sh b/clinicadl/scripts/preprocessing/orig/run_qc_dl_AIBLE.sh deleted file mode 100755 index 42e737422..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_qc_dl_AIBLE.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/env bash - -python /teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Code/image_preprocessing/quality_check.py --caps_dir=/teams/ARAMIS/PROJECTS/CLINICA/CLINICA_datasets/temp/CAPS_AIBL_DL --tsv=/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/qc_tsvs/AIBL_all_subjects.tsv \ No newline at end of file diff --git a/clinicadl/scripts/preprocessing/orig/run_qc_dl_OASIS.sh b/clinicadl/scripts/preprocessing/orig/run_qc_dl_OASIS.sh deleted file mode 100755 index 634981967..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_qc_dl_OASIS.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/env bash - -python /teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Code/image_preprocessing/quality_check.py --caps_dir=/teams/ARAMIS/PROJECTS/CLINICA/CLINICA_datasets/temp/CAPS_OASIS_DL --tsv=/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/qc_tsvs/OASIS_all_subjects.tsv diff --git a/clinicadl/scripts/preprocessing/orig/run_qc_similarity.py b/clinicadl/scripts/preprocessing/orig/run_qc_similarity.py deleted file mode 100755 index 1a5c0feb4..000000000 --- a/clinicadl/scripts/preprocessing/orig/run_qc_similarity.py +++ /dev/null @@ -1,15 +0,0 @@ -#################### - -from Code.image_preprocessing.quality_check_image_similarity import quality_check_image_similarity - -## run the pipeline -# for test -## -caps_directory= '/teams/ARAMIS/PROJECTS/CLINICA/CLINICA_datasets/temp/CAPS_ADNI_DL' -tsv= '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/tsv_files/ADNI_MCI_T1_rest.tsv' -working_dir = '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Results/working_dir' -ref_template = '/teams/ARAMIS/PROJECTS/junhao.wen/PhD/ADNI_classification/gitlabs/AD-DL/Data/mni_icbm152_nlin_sym_09c_nifti/mni_icbm152_nlin_sym_09c/mni_icbm152_t1_tal_nlin_sym_09c.nii' - - -wf = quality_check_image_similarity(caps_directory, tsv, ref_template, working_directory=working_dir) -wf.run(plugin='MultiProc', plugin_args={'n_procs': 8}) diff --git a/clinicadl/scripts/preprocessing/preprocessing_ADNI.sh b/clinicadl/scripts/preprocessing/preprocessing_ADNI.sh deleted file mode 100755 index 805585d7a..000000000 --- a/clinicadl/scripts/preprocessing/preprocessing_ADNI.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash - -BIDS_DIR=/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/BIDS/ADNI_BIDS_T1_new/ -CAPS_DIR=/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/ADNI_rerun -TSV_FILE=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/tmp/DL/tsv_files/ADNI_after_qc.tsv -WORKING_DIR=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/tmp/DL/working_dir_postproc_rerun -REF_TEMPLATE=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/template/mni_icbm152_nlin_sym_09c_nifti/mni_icbm152_nlin_sym_09c/mni_icbm152_t1_tal_nlin_sym_09c.nii - - -# Run clinicadl for preprocessing using 48 cores -clinicadl preprocessing --np 48 \ - $BIDS_DIR \ - $CAPS_DIR \ - $TSV_FILE \ - $REF_TEMPLATE \ - $WORKING_DIR diff --git a/clinicadl/scripts/preprocessing/run_postprocessing_adni.py b/clinicadl/scripts/preprocessing/run_postprocessing_adni.py deleted file mode 100755 index b7081a381..000000000 --- a/clinicadl/scripts/preprocessing/run_postprocessing_adni.py +++ /dev/null @@ -1,24 +0,0 @@ -#################### - -from clinicadl.preprocessing.T1_postprocessing import postprocessing_t1w - -## run the pipeline - # for lustre -caps_directory = '/network/lustre/dtlake01/aramis/projects/clinica/CLINICA_datasets/CAPS/Frontiers_DL/ADNI' -working_dir = '/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/tmp/DL/postprocessing_ADNI' - -tsv = '/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/tmp/DL/tsv_files/ADNI_after_qc.tsv' -patch_size = 50 -stride_size = 50 - -# for original -#wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='slice') -#wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -# for rgb tf learning -wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='slice', slice_mode='rgb') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) - -## for patch -wf = postprocessing_t1w(caps_directory, tsv, patch_size, stride_size, working_directory=working_dir, extract_method='patch') -wf.run(plugin='MultiProc', plugin_args={'n_procs': 28}) diff --git a/clinicadl/scripts/preprocessing/test/extract.sh b/clinicadl/scripts/preprocessing/test/extract.sh deleted file mode 100755 index a1745000e..000000000 --- a/clinicadl/scripts/preprocessing/test/extract.sh +++ /dev/null @@ -1,32 +0,0 @@ -#!/bin/bash -# Bash script for extract features used in classification algorithms proposed -# by clinicadl - -CAPS_DIR=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/CAPS/ADNI -TSV_FILE=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/tsv/2_subjects_test.tsv -WORKING_DIR=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/working_dir -METHOD='patch' - -# Extract patches -# Run clinicadl for extract patches using 32 cores -clinicadl extract --np 32 \ - --patch_size 50 \ - --stride_size 50 \ - $CAPS_DIR \ - $TSV_FILE \ - $WORKING_DIR \ - $METHOD - -# Extract slices -# Run clinicadl for extract sclices using 32 cores -METHOD='patch' -SLICE_MODE='rgb' - -clinicadl extract --np 32 \ - --patch_size 50 \ - --stride_size 50 \ - --slice_mode $SLICE_MODE \ - $CAPS_DIR \ - $TSV_FILE \ - $WORKING_DIR \ - $METHOD diff --git a/clinicadl/scripts/preprocessing/test/preprocessing.sh b/clinicadl/scripts/preprocessing/test/preprocessing.sh deleted file mode 100755 index 7aaf82a7e..000000000 --- a/clinicadl/scripts/preprocessing/test/preprocessing.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash - -BIDS_DIR=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/BIDS/ADNI -CAPS_DIR=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/CAPS/ADNI -TSV_FILE=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/tsv/2_subjects_test.tsv -REF_TEMPLATE=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/template/mni_icbm152_nlin_sym_09c_nifti/mni_icbm152_nlin_sym_09c/mni_icbm152_t1_tal_nlin_sym_09c.nii -WORKING_DIR=/network/lustre/dtlake01/aramis/users/mauricio.diazmelo/data/test_DL/working_dir - - -# Run clinicadl for preprocessing using 32 cores -clinicadl preprocessing --np 32 \ - $BIDS_DIR \ - $CAPS_DIR \ - $TSV_FILE \ - $REF_TEMPLATE \ - $WORKING_DIR From 4019ad90e24d57f99dac3e06ea749da28245a087 Mon Sep 17 00:00:00 2001 From: Elina Thibeau-Sutre Date: Thu, 8 Apr 2021 11:59:18 +0200 Subject: [PATCH 09/17] Fix typo --- README.md | 4 +- data/OASIS_atrophy/AD.tsv | 316 ------------------ data/OASIS_atrophy/CN.tsv | 316 ------------------ data/OASIS_atrophy/test/AD_baseline.tsv | 56 ---- data/OASIS_atrophy/test/CN_baseline.tsv | 56 ---- data/OASIS_atrophy/train/AD.tsv | 261 --------------- data/OASIS_atrophy/train/CN.tsv | 261 --------------- data/OASIS_atrophy/train/train/AD.tsv | 211 ------------ data/OASIS_atrophy/train/train/CN.tsv | 211 ------------ .../train/train_splits-5/split-0/AD.tsv | 211 ------------ .../train/train_splits-5/split-0/CN.tsv | 211 ------------ .../train/train_splits-5/split-1/AD.tsv | 211 ------------ .../train/train_splits-5/split-1/CN.tsv | 211 ------------ .../train/train_splits-5/split-2/AD.tsv | 211 ------------ .../train/train_splits-5/split-2/CN.tsv | 211 ------------ .../train/train_splits-5/split-3/AD.tsv | 211 ------------ .../train/train_splits-5/split-3/CN.tsv | 211 ------------ .../train/train_splits-5/split-4/AD.tsv | 211 ------------ .../train/train_splits-5/split-4/CN.tsv | 211 ------------ .../train/validation/AD_baseline.tsv | 51 --- .../train/validation/CN_baseline.tsv | 51 --- .../split-0/AD_baseline.tsv | 51 --- .../split-0/CN_baseline.tsv | 51 --- .../split-1/AD_baseline.tsv | 51 --- .../split-1/CN_baseline.tsv | 51 --- .../split-2/AD_baseline.tsv | 51 --- .../split-2/CN_baseline.tsv | 51 --- .../split-3/AD_baseline.tsv | 51 --- .../split-3/CN_baseline.tsv | 51 --- .../split-4/AD_baseline.tsv | 51 --- .../split-4/CN_baseline.tsv | 51 --- data/OASIS_random/AD.tsv | 301 ----------------- data/OASIS_random/CN.tsv | 301 ----------------- data/OASIS_random/test/AD_baseline.tsv | 51 --- data/OASIS_random/test/CN_baseline.tsv | 51 --- data/OASIS_random/train/AD.tsv | 251 -------------- data/OASIS_random/train/CN.tsv | 251 -------------- data/OASIS_random/train/train/AD.tsv | 201 ----------- data/OASIS_random/train/train/CN.tsv | 201 ----------- .../train/train_splits-5/split-0/AD.tsv | 201 ----------- .../train/train_splits-5/split-0/CN.tsv | 201 ----------- .../train/train_splits-5/split-1/AD.tsv | 201 ----------- .../train/train_splits-5/split-1/CN.tsv | 201 ----------- .../train/train_splits-5/split-2/AD.tsv | 201 ----------- .../train/train_splits-5/split-2/CN.tsv | 201 ----------- .../train/train_splits-5/split-3/AD.tsv | 201 ----------- .../train/train_splits-5/split-3/CN.tsv | 201 ----------- 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projects/Wen_MedIA_2020/scripts}/svm/src/model.py (100%) rename {clinicadl/scripts/Wen_MedIA_2020 => projects/Wen_MedIA_2020/scripts}/svm/src/train.py (100%) rename {clinicadl/scripts/Wen_MedIA_2020 => projects/Wen_MedIA_2020/scripts}/svm/train/experiment-21.sh (100%) rename {clinicadl/scripts/Wen_MedIA_2020 => projects/Wen_MedIA_2020/scripts}/svm/train/experiment-22.sh (100%) rename {clinicadl/scripts/Wen_MedIA_2020 => projects/Wen_MedIA_2020/scripts}/svm/train/experiment-24.sh (100%) rename {clinicadl/scripts/Wen_MedIA_2020 => projects/Wen_MedIA_2020/scripts}/svm/train/experiment-25.sh (100%) diff --git a/README.md b/README.md index 67a55f2f5..83074df25 100755 --- a/README.md +++ b/README.md @@ -119,7 +119,7 @@ can be performed: ## Pretrained models -Some of the pretained models for the CNN networks described in +Some of the pretrained models for the CNN networks described in ([Wen et al., 2020](https://doi.org/10.1016/j.media.2020.101694)) are available on Zenodo: @@ -134,4 +134,4 @@ Updated versions of the models will be published soon. ## Scientific papers using this framework - Wen et al., 2020 [Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation](https://doi.org/10.1016/j.media.2020.101694) -([arXiv version](https://arxiv.org/abs/1904.07773)). \ No newline at end of file +([arXiv version](https://arxiv.org/abs/1904.07773)). Scripts and related data are available in `projects/Wen_MedIA_2020`. \ No newline at end of file diff --git a/data/OASIS_atrophy/AD.tsv b/data/OASIS_atrophy/AD.tsv deleted file mode 100644 index 00e44960b..000000000 --- a/data/OASIS_atrophy/AD.tsv +++ /dev/null @@ -1,316 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10001 ses-M00 AD -sub-OASIS10002 ses-M00 AD -sub-OASIS10004 ses-M00 AD -sub-OASIS10005 ses-M00 AD -sub-OASIS10006 ses-M00 AD -sub-OASIS10007 ses-M00 AD -sub-OASIS10009 ses-M00 AD -sub-OASIS10010 ses-M00 AD -sub-OASIS10011 ses-M00 AD -sub-OASIS10012 ses-M00 AD -sub-OASIS10013 ses-M00 AD -sub-OASIS10014 ses-M00 AD -sub-OASIS10017 ses-M00 AD -sub-OASIS10018 ses-M00 AD -sub-OASIS10019 ses-M00 AD -sub-OASIS10020 ses-M00 AD -sub-OASIS10025 ses-M00 AD -sub-OASIS10026 ses-M00 AD -sub-OASIS10027 ses-M00 AD -sub-OASIS10029 ses-M00 AD -sub-OASIS10030 ses-M00 AD -sub-OASIS10032 ses-M00 AD -sub-OASIS10033 ses-M00 AD -sub-OASIS10034 ses-M00 AD -sub-OASIS10037 ses-M00 AD 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a/data/OASIS_atrophy/CN.tsv +++ /dev/null @@ -1,316 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20001 ses-M00 CN -sub-OASIS20002 ses-M00 CN -sub-OASIS20004 ses-M00 CN -sub-OASIS20005 ses-M00 CN -sub-OASIS20006 ses-M00 CN -sub-OASIS20007 ses-M00 CN -sub-OASIS20009 ses-M00 CN -sub-OASIS20010 ses-M00 CN -sub-OASIS20011 ses-M00 CN -sub-OASIS20012 ses-M00 CN -sub-OASIS20013 ses-M00 CN -sub-OASIS20014 ses-M00 CN -sub-OASIS20017 ses-M00 CN -sub-OASIS20018 ses-M00 CN -sub-OASIS20019 ses-M00 CN -sub-OASIS20020 ses-M00 CN -sub-OASIS20025 ses-M00 CN -sub-OASIS20026 ses-M00 CN -sub-OASIS20027 ses-M00 CN -sub-OASIS20029 ses-M00 CN -sub-OASIS20030 ses-M00 CN -sub-OASIS20032 ses-M00 CN -sub-OASIS20033 ses-M00 CN -sub-OASIS20034 ses-M00 CN -sub-OASIS20037 ses-M00 CN -sub-OASIS20038 ses-M00 CN -sub-OASIS20040 ses-M00 CN -sub-OASIS20043 ses-M00 CN -sub-OASIS20044 ses-M00 CN -sub-OASIS20045 ses-M00 CN -sub-OASIS20047 ses-M00 CN -sub-OASIS20049 ses-M00 CN -sub-OASIS20050 ses-M00 CN 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-sub-OASIS20422 ses-M00 CN -sub-OASIS20423 ses-M00 CN -sub-OASIS20426 ses-M00 CN -sub-OASIS20428 ses-M00 CN -sub-OASIS20429 ses-M00 CN -sub-OASIS20431 ses-M00 CN -sub-OASIS20433 ses-M00 CN -sub-OASIS20434 ses-M00 CN -sub-OASIS20435 ses-M00 CN -sub-OASIS20437 ses-M00 CN -sub-OASIS20438 ses-M00 CN -sub-OASIS20439 ses-M00 CN -sub-OASIS20442 ses-M00 CN -sub-OASIS20443 ses-M00 CN -sub-OASIS20444 ses-M00 CN -sub-OASIS20445 ses-M00 CN -sub-OASIS20446 ses-M00 CN -sub-OASIS20448 ses-M00 CN -sub-OASIS20449 ses-M00 CN -sub-OASIS20450 ses-M00 CN -sub-OASIS20455 ses-M00 CN -sub-OASIS20456 ses-M00 CN -sub-OASIS20457 ses-M00 CN \ No newline at end of file diff --git a/data/OASIS_atrophy/test/AD_baseline.tsv b/data/OASIS_atrophy/test/AD_baseline.tsv deleted file mode 100644 index a3d3f807a..000000000 --- a/data/OASIS_atrophy/test/AD_baseline.tsv +++ /dev/null @@ -1,56 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10376 ses-M00 AD -sub-OASIS10377 ses-M00 AD -sub-OASIS10378 ses-M00 AD 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a/data/OASIS_atrophy/train/train_splits-5/split-3/CN.tsv b/data/OASIS_atrophy/train/train_splits-5/split-3/CN.tsv deleted file mode 100644 index 773fd2f22..000000000 --- a/data/OASIS_atrophy/train/train_splits-5/split-3/CN.tsv +++ /dev/null @@ -1,211 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20001 ses-M00 CN -sub-OASIS20002 ses-M00 CN -sub-OASIS20004 ses-M00 CN -sub-OASIS20005 ses-M00 CN -sub-OASIS20006 ses-M00 CN -sub-OASIS20007 ses-M00 CN -sub-OASIS20009 ses-M00 CN -sub-OASIS20010 ses-M00 CN -sub-OASIS20011 ses-M00 CN -sub-OASIS20012 ses-M00 CN -sub-OASIS20013 ses-M00 CN -sub-OASIS20014 ses-M00 CN -sub-OASIS20017 ses-M00 CN -sub-OASIS20018 ses-M00 CN -sub-OASIS20019 ses-M00 CN -sub-OASIS20020 ses-M00 CN -sub-OASIS20025 ses-M00 CN -sub-OASIS20026 ses-M00 CN -sub-OASIS20027 ses-M00 CN -sub-OASIS20029 ses-M00 CN -sub-OASIS20030 ses-M00 CN -sub-OASIS20032 ses-M00 CN -sub-OASIS20033 ses-M00 CN -sub-OASIS20034 ses-M00 CN -sub-OASIS20037 ses-M00 CN -sub-OASIS20038 ses-M00 CN 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diff --git a/data/OASIS_atrophy/train/train_splits-5/split-4/AD.tsv b/data/OASIS_atrophy/train/train_splits-5/split-4/AD.tsv deleted file mode 100644 index 2f97a47ff..000000000 --- a/data/OASIS_atrophy/train/train_splits-5/split-4/AD.tsv +++ /dev/null @@ -1,211 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10001 ses-M00 AD -sub-OASIS10002 ses-M00 AD -sub-OASIS10004 ses-M00 AD -sub-OASIS10005 ses-M00 AD -sub-OASIS10006 ses-M00 AD -sub-OASIS10007 ses-M00 AD -sub-OASIS10009 ses-M00 AD -sub-OASIS10010 ses-M00 AD -sub-OASIS10011 ses-M00 AD -sub-OASIS10012 ses-M00 AD -sub-OASIS10013 ses-M00 AD -sub-OASIS10014 ses-M00 AD -sub-OASIS10017 ses-M00 AD -sub-OASIS10018 ses-M00 AD -sub-OASIS10019 ses-M00 AD -sub-OASIS10020 ses-M00 AD -sub-OASIS10025 ses-M00 AD -sub-OASIS10026 ses-M00 AD -sub-OASIS10027 ses-M00 AD -sub-OASIS10029 ses-M00 AD -sub-OASIS10030 ses-M00 AD -sub-OASIS10032 ses-M00 AD -sub-OASIS10033 ses-M00 AD -sub-OASIS10034 ses-M00 AD -sub-OASIS10037 ses-M00 AD -sub-OASIS10038 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end of file diff --git a/data/OASIS_atrophy/train/train_splits-5/split-4/CN.tsv b/data/OASIS_atrophy/train/train_splits-5/split-4/CN.tsv deleted file mode 100644 index 773fd2f22..000000000 --- a/data/OASIS_atrophy/train/train_splits-5/split-4/CN.tsv +++ /dev/null @@ -1,211 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20001 ses-M00 CN -sub-OASIS20002 ses-M00 CN -sub-OASIS20004 ses-M00 CN -sub-OASIS20005 ses-M00 CN -sub-OASIS20006 ses-M00 CN -sub-OASIS20007 ses-M00 CN -sub-OASIS20009 ses-M00 CN -sub-OASIS20010 ses-M00 CN -sub-OASIS20011 ses-M00 CN -sub-OASIS20012 ses-M00 CN -sub-OASIS20013 ses-M00 CN -sub-OASIS20014 ses-M00 CN -sub-OASIS20017 ses-M00 CN -sub-OASIS20018 ses-M00 CN -sub-OASIS20019 ses-M00 CN -sub-OASIS20020 ses-M00 CN -sub-OASIS20025 ses-M00 CN -sub-OASIS20026 ses-M00 CN -sub-OASIS20027 ses-M00 CN -sub-OASIS20029 ses-M00 CN -sub-OASIS20030 ses-M00 CN -sub-OASIS20032 ses-M00 CN -sub-OASIS20033 ses-M00 CN -sub-OASIS20034 ses-M00 CN -sub-OASIS20037 ses-M00 CN 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No newline at end of file diff --git a/data/OASIS_atrophy/train/validation/AD_baseline.tsv b/data/OASIS_atrophy/train/validation/AD_baseline.tsv deleted file mode 100644 index db85ed802..000000000 --- a/data/OASIS_atrophy/train/validation/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10309 ses-M00 AD -sub-OASIS10310 ses-M00 AD -sub-OASIS10311 ses-M00 AD -sub-OASIS10313 ses-M00 AD -sub-OASIS10314 ses-M00 AD -sub-OASIS10317 ses-M00 AD -sub-OASIS10318 ses-M00 AD -sub-OASIS10319 ses-M00 AD -sub-OASIS10321 ses-M00 AD -sub-OASIS10322 ses-M00 AD -sub-OASIS10323 ses-M00 AD -sub-OASIS10325 ses-M00 AD -sub-OASIS10326 ses-M00 AD -sub-OASIS10327 ses-M00 AD -sub-OASIS10328 ses-M00 AD -sub-OASIS10330 ses-M00 AD -sub-OASIS10331 ses-M00 AD -sub-OASIS10332 ses-M00 AD -sub-OASIS10333 ses-M00 AD -sub-OASIS10336 ses-M00 AD -sub-OASIS10337 ses-M00 AD -sub-OASIS10338 ses-M00 AD -sub-OASIS10340 ses-M00 AD -sub-OASIS10341 ses-M00 AD -sub-OASIS10342 ses-M00 AD -sub-OASIS10343 ses-M00 AD -sub-OASIS10344 ses-M00 AD -sub-OASIS10345 ses-M00 AD -sub-OASIS10346 ses-M00 AD -sub-OASIS10348 ses-M00 AD -sub-OASIS10349 ses-M00 AD -sub-OASIS10350 ses-M00 AD -sub-OASIS10353 ses-M00 AD -sub-OASIS10354 ses-M00 AD -sub-OASIS10355 ses-M00 AD -sub-OASIS10356 ses-M00 AD -sub-OASIS10357 ses-M00 AD -sub-OASIS10358 ses-M00 AD -sub-OASIS10359 ses-M00 AD -sub-OASIS10361 ses-M00 AD -sub-OASIS10363 ses-M00 AD -sub-OASIS10365 ses-M00 AD -sub-OASIS10366 ses-M00 AD -sub-OASIS10367 ses-M00 AD -sub-OASIS10368 ses-M00 AD -sub-OASIS10369 ses-M00 AD -sub-OASIS10370 ses-M00 AD -sub-OASIS10371 ses-M00 AD -sub-OASIS10372 ses-M00 AD -sub-OASIS10375 ses-M00 AD \ No newline at end of file diff --git a/data/OASIS_atrophy/train/validation/CN_baseline.tsv b/data/OASIS_atrophy/train/validation/CN_baseline.tsv deleted file mode 100644 index d2d7e993e..000000000 --- a/data/OASIS_atrophy/train/validation/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20309 ses-M00 CN -sub-OASIS20310 ses-M00 CN -sub-OASIS20311 ses-M00 CN -sub-OASIS20313 ses-M00 CN -sub-OASIS20314 ses-M00 CN -sub-OASIS20317 ses-M00 CN -sub-OASIS20318 ses-M00 CN -sub-OASIS20319 ses-M00 CN -sub-OASIS20321 ses-M00 CN -sub-OASIS20322 ses-M00 CN -sub-OASIS20323 ses-M00 CN -sub-OASIS20325 ses-M00 CN -sub-OASIS20326 ses-M00 CN -sub-OASIS20327 ses-M00 CN -sub-OASIS20328 ses-M00 CN -sub-OASIS20330 ses-M00 CN -sub-OASIS20331 ses-M00 CN -sub-OASIS20332 ses-M00 CN -sub-OASIS20333 ses-M00 CN -sub-OASIS20336 ses-M00 CN -sub-OASIS20337 ses-M00 CN -sub-OASIS20338 ses-M00 CN -sub-OASIS20340 ses-M00 CN -sub-OASIS20341 ses-M00 CN -sub-OASIS20342 ses-M00 CN -sub-OASIS20343 ses-M00 CN -sub-OASIS20344 ses-M00 CN -sub-OASIS20345 ses-M00 CN -sub-OASIS20346 ses-M00 CN -sub-OASIS20348 ses-M00 CN -sub-OASIS20349 ses-M00 CN -sub-OASIS20350 ses-M00 CN -sub-OASIS20353 ses-M00 CN -sub-OASIS20354 ses-M00 CN -sub-OASIS20355 ses-M00 CN -sub-OASIS20356 ses-M00 CN -sub-OASIS20357 ses-M00 CN -sub-OASIS20358 ses-M00 CN -sub-OASIS20359 ses-M00 CN -sub-OASIS20361 ses-M00 CN -sub-OASIS20363 ses-M00 CN -sub-OASIS20365 ses-M00 CN -sub-OASIS20366 ses-M00 CN -sub-OASIS20367 ses-M00 CN -sub-OASIS20368 ses-M00 CN -sub-OASIS20369 ses-M00 CN -sub-OASIS20370 ses-M00 CN -sub-OASIS20371 ses-M00 CN -sub-OASIS20372 ses-M00 CN -sub-OASIS20375 ses-M00 CN \ No newline at end of file diff --git a/data/OASIS_atrophy/train/validation_splits-5/split-0/AD_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-0/AD_baseline.tsv deleted file mode 100644 index db85ed802..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-0/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10309 ses-M00 AD -sub-OASIS10310 ses-M00 AD -sub-OASIS10311 ses-M00 AD -sub-OASIS10313 ses-M00 AD -sub-OASIS10314 ses-M00 AD -sub-OASIS10317 ses-M00 AD -sub-OASIS10318 ses-M00 AD -sub-OASIS10319 ses-M00 AD -sub-OASIS10321 ses-M00 AD -sub-OASIS10322 ses-M00 AD 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-sub-OASIS10371 ses-M00 AD -sub-OASIS10372 ses-M00 AD -sub-OASIS10375 ses-M00 AD \ No newline at end of file diff --git a/data/OASIS_atrophy/train/validation_splits-5/split-0/CN_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-0/CN_baseline.tsv deleted file mode 100644 index d2d7e993e..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-0/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20309 ses-M00 CN -sub-OASIS20310 ses-M00 CN -sub-OASIS20311 ses-M00 CN -sub-OASIS20313 ses-M00 CN -sub-OASIS20314 ses-M00 CN -sub-OASIS20317 ses-M00 CN -sub-OASIS20318 ses-M00 CN -sub-OASIS20319 ses-M00 CN -sub-OASIS20321 ses-M00 CN -sub-OASIS20322 ses-M00 CN -sub-OASIS20323 ses-M00 CN -sub-OASIS20325 ses-M00 CN -sub-OASIS20326 ses-M00 CN -sub-OASIS20327 ses-M00 CN -sub-OASIS20328 ses-M00 CN -sub-OASIS20330 ses-M00 CN -sub-OASIS20331 ses-M00 CN -sub-OASIS20332 ses-M00 CN -sub-OASIS20333 ses-M00 CN -sub-OASIS20336 ses-M00 CN 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file mode 100644 index db85ed802..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-1/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10309 ses-M00 AD -sub-OASIS10310 ses-M00 AD -sub-OASIS10311 ses-M00 AD -sub-OASIS10313 ses-M00 AD -sub-OASIS10314 ses-M00 AD -sub-OASIS10317 ses-M00 AD -sub-OASIS10318 ses-M00 AD -sub-OASIS10319 ses-M00 AD -sub-OASIS10321 ses-M00 AD -sub-OASIS10322 ses-M00 AD -sub-OASIS10323 ses-M00 AD -sub-OASIS10325 ses-M00 AD -sub-OASIS10326 ses-M00 AD -sub-OASIS10327 ses-M00 AD -sub-OASIS10328 ses-M00 AD -sub-OASIS10330 ses-M00 AD -sub-OASIS10331 ses-M00 AD -sub-OASIS10332 ses-M00 AD -sub-OASIS10333 ses-M00 AD -sub-OASIS10336 ses-M00 AD -sub-OASIS10337 ses-M00 AD -sub-OASIS10338 ses-M00 AD -sub-OASIS10340 ses-M00 AD -sub-OASIS10341 ses-M00 AD -sub-OASIS10342 ses-M00 AD -sub-OASIS10343 ses-M00 AD -sub-OASIS10344 ses-M00 AD -sub-OASIS10345 ses-M00 AD -sub-OASIS10346 ses-M00 AD -sub-OASIS10348 ses-M00 AD -sub-OASIS10349 ses-M00 AD -sub-OASIS10350 ses-M00 AD -sub-OASIS10353 ses-M00 AD -sub-OASIS10354 ses-M00 AD -sub-OASIS10355 ses-M00 AD -sub-OASIS10356 ses-M00 AD -sub-OASIS10357 ses-M00 AD -sub-OASIS10358 ses-M00 AD -sub-OASIS10359 ses-M00 AD -sub-OASIS10361 ses-M00 AD -sub-OASIS10363 ses-M00 AD -sub-OASIS10365 ses-M00 AD -sub-OASIS10366 ses-M00 AD -sub-OASIS10367 ses-M00 AD -sub-OASIS10368 ses-M00 AD -sub-OASIS10369 ses-M00 AD -sub-OASIS10370 ses-M00 AD -sub-OASIS10371 ses-M00 AD -sub-OASIS10372 ses-M00 AD -sub-OASIS10375 ses-M00 AD \ No newline at end of file diff --git a/data/OASIS_atrophy/train/validation_splits-5/split-1/CN_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-1/CN_baseline.tsv deleted file mode 100644 index d2d7e993e..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-1/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20309 ses-M00 CN -sub-OASIS20310 ses-M00 CN -sub-OASIS20311 ses-M00 CN 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-sub-OASIS20363 ses-M00 CN -sub-OASIS20365 ses-M00 CN -sub-OASIS20366 ses-M00 CN -sub-OASIS20367 ses-M00 CN -sub-OASIS20368 ses-M00 CN -sub-OASIS20369 ses-M00 CN -sub-OASIS20370 ses-M00 CN -sub-OASIS20371 ses-M00 CN -sub-OASIS20372 ses-M00 CN -sub-OASIS20375 ses-M00 CN \ No newline at end of file diff --git a/data/OASIS_atrophy/train/validation_splits-5/split-2/AD_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-2/AD_baseline.tsv deleted file mode 100644 index db85ed802..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-2/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10309 ses-M00 AD -sub-OASIS10310 ses-M00 AD -sub-OASIS10311 ses-M00 AD -sub-OASIS10313 ses-M00 AD -sub-OASIS10314 ses-M00 AD -sub-OASIS10317 ses-M00 AD -sub-OASIS10318 ses-M00 AD -sub-OASIS10319 ses-M00 AD -sub-OASIS10321 ses-M00 AD -sub-OASIS10322 ses-M00 AD -sub-OASIS10323 ses-M00 AD -sub-OASIS10325 ses-M00 AD -sub-OASIS10326 ses-M00 AD 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No newline at end of file diff --git a/data/OASIS_atrophy/train/validation_splits-5/split-2/CN_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-2/CN_baseline.tsv deleted file mode 100644 index d2d7e993e..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-2/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20309 ses-M00 CN -sub-OASIS20310 ses-M00 CN -sub-OASIS20311 ses-M00 CN -sub-OASIS20313 ses-M00 CN -sub-OASIS20314 ses-M00 CN -sub-OASIS20317 ses-M00 CN -sub-OASIS20318 ses-M00 CN -sub-OASIS20319 ses-M00 CN -sub-OASIS20321 ses-M00 CN -sub-OASIS20322 ses-M00 CN -sub-OASIS20323 ses-M00 CN -sub-OASIS20325 ses-M00 CN -sub-OASIS20326 ses-M00 CN -sub-OASIS20327 ses-M00 CN -sub-OASIS20328 ses-M00 CN -sub-OASIS20330 ses-M00 CN -sub-OASIS20331 ses-M00 CN -sub-OASIS20332 ses-M00 CN -sub-OASIS20333 ses-M00 CN -sub-OASIS20336 ses-M00 CN -sub-OASIS20337 ses-M00 CN -sub-OASIS20338 ses-M00 CN -sub-OASIS20340 ses-M00 CN -sub-OASIS20341 ses-M00 CN -sub-OASIS20342 ses-M00 CN -sub-OASIS20343 ses-M00 CN -sub-OASIS20344 ses-M00 CN -sub-OASIS20345 ses-M00 CN -sub-OASIS20346 ses-M00 CN -sub-OASIS20348 ses-M00 CN -sub-OASIS20349 ses-M00 CN -sub-OASIS20350 ses-M00 CN -sub-OASIS20353 ses-M00 CN -sub-OASIS20354 ses-M00 CN -sub-OASIS20355 ses-M00 CN -sub-OASIS20356 ses-M00 CN -sub-OASIS20357 ses-M00 CN -sub-OASIS20358 ses-M00 CN -sub-OASIS20359 ses-M00 CN -sub-OASIS20361 ses-M00 CN -sub-OASIS20363 ses-M00 CN -sub-OASIS20365 ses-M00 CN -sub-OASIS20366 ses-M00 CN -sub-OASIS20367 ses-M00 CN -sub-OASIS20368 ses-M00 CN -sub-OASIS20369 ses-M00 CN -sub-OASIS20370 ses-M00 CN -sub-OASIS20371 ses-M00 CN -sub-OASIS20372 ses-M00 CN -sub-OASIS20375 ses-M00 CN \ No newline at end of file diff --git a/data/OASIS_atrophy/train/validation_splits-5/split-3/AD_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-3/AD_baseline.tsv deleted file mode 100644 index db85ed802..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-3/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10309 ses-M00 AD -sub-OASIS10310 ses-M00 AD -sub-OASIS10311 ses-M00 AD -sub-OASIS10313 ses-M00 AD -sub-OASIS10314 ses-M00 AD -sub-OASIS10317 ses-M00 AD -sub-OASIS10318 ses-M00 AD -sub-OASIS10319 ses-M00 AD -sub-OASIS10321 ses-M00 AD -sub-OASIS10322 ses-M00 AD -sub-OASIS10323 ses-M00 AD -sub-OASIS10325 ses-M00 AD -sub-OASIS10326 ses-M00 AD -sub-OASIS10327 ses-M00 AD -sub-OASIS10328 ses-M00 AD -sub-OASIS10330 ses-M00 AD -sub-OASIS10331 ses-M00 AD -sub-OASIS10332 ses-M00 AD -sub-OASIS10333 ses-M00 AD -sub-OASIS10336 ses-M00 AD -sub-OASIS10337 ses-M00 AD -sub-OASIS10338 ses-M00 AD -sub-OASIS10340 ses-M00 AD -sub-OASIS10341 ses-M00 AD -sub-OASIS10342 ses-M00 AD -sub-OASIS10343 ses-M00 AD -sub-OASIS10344 ses-M00 AD -sub-OASIS10345 ses-M00 AD -sub-OASIS10346 ses-M00 AD -sub-OASIS10348 ses-M00 AD -sub-OASIS10349 ses-M00 AD -sub-OASIS10350 ses-M00 AD -sub-OASIS10353 ses-M00 AD -sub-OASIS10354 ses-M00 AD -sub-OASIS10355 ses-M00 AD -sub-OASIS10356 ses-M00 AD -sub-OASIS10357 ses-M00 AD -sub-OASIS10358 ses-M00 AD -sub-OASIS10359 ses-M00 AD -sub-OASIS10361 ses-M00 AD -sub-OASIS10363 ses-M00 AD -sub-OASIS10365 ses-M00 AD -sub-OASIS10366 ses-M00 AD -sub-OASIS10367 ses-M00 AD -sub-OASIS10368 ses-M00 AD -sub-OASIS10369 ses-M00 AD -sub-OASIS10370 ses-M00 AD -sub-OASIS10371 ses-M00 AD -sub-OASIS10372 ses-M00 AD -sub-OASIS10375 ses-M00 AD \ No newline at end of file diff --git a/data/OASIS_atrophy/train/validation_splits-5/split-3/CN_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-3/CN_baseline.tsv deleted file mode 100644 index d2d7e993e..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-3/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20309 ses-M00 CN -sub-OASIS20310 ses-M00 CN -sub-OASIS20311 ses-M00 CN -sub-OASIS20313 ses-M00 CN -sub-OASIS20314 ses-M00 CN -sub-OASIS20317 ses-M00 CN -sub-OASIS20318 ses-M00 CN -sub-OASIS20319 ses-M00 CN -sub-OASIS20321 ses-M00 CN -sub-OASIS20322 ses-M00 CN -sub-OASIS20323 ses-M00 CN -sub-OASIS20325 ses-M00 CN -sub-OASIS20326 ses-M00 CN -sub-OASIS20327 ses-M00 CN -sub-OASIS20328 ses-M00 CN -sub-OASIS20330 ses-M00 CN -sub-OASIS20331 ses-M00 CN -sub-OASIS20332 ses-M00 CN -sub-OASIS20333 ses-M00 CN -sub-OASIS20336 ses-M00 CN -sub-OASIS20337 ses-M00 CN -sub-OASIS20338 ses-M00 CN -sub-OASIS20340 ses-M00 CN -sub-OASIS20341 ses-M00 CN -sub-OASIS20342 ses-M00 CN -sub-OASIS20343 ses-M00 CN -sub-OASIS20344 ses-M00 CN -sub-OASIS20345 ses-M00 CN -sub-OASIS20346 ses-M00 CN -sub-OASIS20348 ses-M00 CN -sub-OASIS20349 ses-M00 CN -sub-OASIS20350 ses-M00 CN -sub-OASIS20353 ses-M00 CN -sub-OASIS20354 ses-M00 CN -sub-OASIS20355 ses-M00 CN -sub-OASIS20356 ses-M00 CN -sub-OASIS20357 ses-M00 CN -sub-OASIS20358 ses-M00 CN -sub-OASIS20359 ses-M00 CN -sub-OASIS20361 ses-M00 CN -sub-OASIS20363 ses-M00 CN -sub-OASIS20365 ses-M00 CN -sub-OASIS20366 ses-M00 CN -sub-OASIS20367 ses-M00 CN -sub-OASIS20368 ses-M00 CN -sub-OASIS20369 ses-M00 CN -sub-OASIS20370 ses-M00 CN -sub-OASIS20371 ses-M00 CN -sub-OASIS20372 ses-M00 CN -sub-OASIS20375 ses-M00 CN \ No newline at end of file diff --git a/data/OASIS_atrophy/train/validation_splits-5/split-4/AD_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-4/AD_baseline.tsv deleted file mode 100644 index db85ed802..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-4/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10309 ses-M00 AD -sub-OASIS10310 ses-M00 AD -sub-OASIS10311 ses-M00 AD -sub-OASIS10313 ses-M00 AD -sub-OASIS10314 ses-M00 AD -sub-OASIS10317 ses-M00 AD -sub-OASIS10318 ses-M00 AD -sub-OASIS10319 ses-M00 AD -sub-OASIS10321 ses-M00 AD -sub-OASIS10322 ses-M00 AD -sub-OASIS10323 ses-M00 AD -sub-OASIS10325 ses-M00 AD -sub-OASIS10326 ses-M00 AD -sub-OASIS10327 ses-M00 AD -sub-OASIS10328 ses-M00 AD 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a/data/OASIS_atrophy/train/validation_splits-5/split-4/CN_baseline.tsv b/data/OASIS_atrophy/train/validation_splits-5/split-4/CN_baseline.tsv deleted file mode 100644 index d2d7e993e..000000000 --- a/data/OASIS_atrophy/train/validation_splits-5/split-4/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS20309 ses-M00 CN -sub-OASIS20310 ses-M00 CN -sub-OASIS20311 ses-M00 CN -sub-OASIS20313 ses-M00 CN -sub-OASIS20314 ses-M00 CN -sub-OASIS20317 ses-M00 CN -sub-OASIS20318 ses-M00 CN -sub-OASIS20319 ses-M00 CN -sub-OASIS20321 ses-M00 CN -sub-OASIS20322 ses-M00 CN -sub-OASIS20323 ses-M00 CN -sub-OASIS20325 ses-M00 CN -sub-OASIS20326 ses-M00 CN -sub-OASIS20327 ses-M00 CN -sub-OASIS20328 ses-M00 CN -sub-OASIS20330 ses-M00 CN -sub-OASIS20331 ses-M00 CN -sub-OASIS20332 ses-M00 CN -sub-OASIS20333 ses-M00 CN -sub-OASIS20336 ses-M00 CN -sub-OASIS20337 ses-M00 CN -sub-OASIS20338 ses-M00 CN -sub-OASIS20340 ses-M00 CN -sub-OASIS20341 ses-M00 CN -sub-OASIS20342 ses-M00 CN -sub-OASIS20343 ses-M00 CN -sub-OASIS20344 ses-M00 CN -sub-OASIS20345 ses-M00 CN -sub-OASIS20346 ses-M00 CN -sub-OASIS20348 ses-M00 CN -sub-OASIS20349 ses-M00 CN -sub-OASIS20350 ses-M00 CN -sub-OASIS20353 ses-M00 CN -sub-OASIS20354 ses-M00 CN -sub-OASIS20355 ses-M00 CN -sub-OASIS20356 ses-M00 CN -sub-OASIS20357 ses-M00 CN -sub-OASIS20358 ses-M00 CN -sub-OASIS20359 ses-M00 CN -sub-OASIS20361 ses-M00 CN -sub-OASIS20363 ses-M00 CN -sub-OASIS20365 ses-M00 CN -sub-OASIS20366 ses-M00 CN -sub-OASIS20367 ses-M00 CN -sub-OASIS20368 ses-M00 CN -sub-OASIS20369 ses-M00 CN -sub-OASIS20370 ses-M00 CN -sub-OASIS20371 ses-M00 CN -sub-OASIS20372 ses-M00 CN -sub-OASIS20375 ses-M00 CN \ No newline at end of file diff --git a/data/OASIS_random/AD.tsv b/data/OASIS_random/AD.tsv deleted file mode 100644 index 9b1997641..000000000 --- a/data/OASIS_random/AD.tsv +++ /dev/null @@ -1,301 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND0 ses-M00 AD 60 F -sub-RAND1 ses-M00 AD 60 F -sub-RAND2 ses-M00 AD 60 F -sub-RAND3 ses-M00 AD 60 F -sub-RAND4 ses-M00 AD 60 F -sub-RAND5 ses-M00 AD 60 F -sub-RAND6 ses-M00 AD 60 F -sub-RAND7 ses-M00 AD 60 F -sub-RAND8 ses-M00 AD 60 F -sub-RAND9 ses-M00 AD 60 F -sub-RAND10 ses-M00 AD 60 F -sub-RAND11 ses-M00 AD 60 F -sub-RAND12 ses-M00 AD 60 F -sub-RAND13 ses-M00 AD 60 F -sub-RAND14 ses-M00 AD 60 F -sub-RAND15 ses-M00 AD 60 F -sub-RAND16 ses-M00 AD 60 F -sub-RAND17 ses-M00 AD 60 F -sub-RAND18 ses-M00 AD 60 F -sub-RAND19 ses-M00 AD 60 F -sub-RAND20 ses-M00 AD 60 F -sub-RAND21 ses-M00 AD 60 F -sub-RAND22 ses-M00 AD 60 F -sub-RAND23 ses-M00 AD 60 F -sub-RAND24 ses-M00 AD 60 F -sub-RAND25 ses-M00 AD 60 F -sub-RAND26 ses-M00 AD 60 F -sub-RAND27 ses-M00 AD 60 F -sub-RAND28 ses-M00 AD 60 F -sub-RAND29 ses-M00 AD 60 F -sub-RAND30 ses-M00 AD 60 F -sub-RAND31 ses-M00 AD 60 F -sub-RAND32 ses-M00 AD 60 F -sub-RAND33 ses-M00 AD 60 F -sub-RAND34 ses-M00 AD 60 F -sub-RAND35 ses-M00 AD 60 F -sub-RAND36 ses-M00 AD 60 F -sub-RAND37 ses-M00 AD 60 F 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100644 index 776dc4fa8..000000000 --- a/data/OASIS_random/train/train_splits-5/split-0/CN.tsv +++ /dev/null @@ -1,201 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND300 ses-M00 CN 60 F -sub-RAND301 ses-M00 CN 60 F -sub-RAND302 ses-M00 CN 60 F -sub-RAND303 ses-M00 CN 60 F -sub-RAND304 ses-M00 CN 60 F -sub-RAND305 ses-M00 CN 60 F -sub-RAND306 ses-M00 CN 60 F -sub-RAND307 ses-M00 CN 60 F -sub-RAND308 ses-M00 CN 60 F -sub-RAND309 ses-M00 CN 60 F -sub-RAND310 ses-M00 CN 60 F -sub-RAND311 ses-M00 CN 60 F -sub-RAND312 ses-M00 CN 60 F -sub-RAND313 ses-M00 CN 60 F -sub-RAND314 ses-M00 CN 60 F -sub-RAND315 ses-M00 CN 60 F -sub-RAND316 ses-M00 CN 60 F -sub-RAND317 ses-M00 CN 60 F -sub-RAND318 ses-M00 CN 60 F -sub-RAND319 ses-M00 CN 60 F -sub-RAND320 ses-M00 CN 60 F -sub-RAND321 ses-M00 CN 60 F -sub-RAND322 ses-M00 CN 60 F -sub-RAND323 ses-M00 CN 60 F -sub-RAND324 ses-M00 CN 60 F -sub-RAND325 ses-M00 CN 60 F -sub-RAND326 ses-M00 CN 60 F -sub-RAND327 ses-M00 CN 60 F -sub-RAND328 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a/data/OASIS_random/train/train_splits-5/split-1/AD.tsv b/data/OASIS_random/train/train_splits-5/split-1/AD.tsv deleted file mode 100644 index 6d5455a84..000000000 --- a/data/OASIS_random/train/train_splits-5/split-1/AD.tsv +++ /dev/null @@ -1,201 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND0 ses-M00 AD 60 F -sub-RAND1 ses-M00 AD 60 F -sub-RAND2 ses-M00 AD 60 F -sub-RAND3 ses-M00 AD 60 F -sub-RAND4 ses-M00 AD 60 F -sub-RAND5 ses-M00 AD 60 F -sub-RAND6 ses-M00 AD 60 F -sub-RAND7 ses-M00 AD 60 F -sub-RAND8 ses-M00 AD 60 F -sub-RAND9 ses-M00 AD 60 F -sub-RAND10 ses-M00 AD 60 F -sub-RAND11 ses-M00 AD 60 F -sub-RAND12 ses-M00 AD 60 F -sub-RAND13 ses-M00 AD 60 F -sub-RAND14 ses-M00 AD 60 F -sub-RAND15 ses-M00 AD 60 F -sub-RAND16 ses-M00 AD 60 F -sub-RAND17 ses-M00 AD 60 F -sub-RAND18 ses-M00 AD 60 F -sub-RAND19 ses-M00 AD 60 F -sub-RAND20 ses-M00 AD 60 F -sub-RAND21 ses-M00 AD 60 F -sub-RAND22 ses-M00 AD 60 F -sub-RAND23 ses-M00 AD 60 F -sub-RAND24 ses-M00 AD 60 F 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F diff --git a/data/OASIS_random/train/train_splits-5/split-1/CN.tsv b/data/OASIS_random/train/train_splits-5/split-1/CN.tsv deleted file mode 100644 index 776dc4fa8..000000000 --- a/data/OASIS_random/train/train_splits-5/split-1/CN.tsv +++ /dev/null @@ -1,201 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND300 ses-M00 CN 60 F -sub-RAND301 ses-M00 CN 60 F -sub-RAND302 ses-M00 CN 60 F -sub-RAND303 ses-M00 CN 60 F -sub-RAND304 ses-M00 CN 60 F -sub-RAND305 ses-M00 CN 60 F -sub-RAND306 ses-M00 CN 60 F -sub-RAND307 ses-M00 CN 60 F -sub-RAND308 ses-M00 CN 60 F -sub-RAND309 ses-M00 CN 60 F -sub-RAND310 ses-M00 CN 60 F -sub-RAND311 ses-M00 CN 60 F -sub-RAND312 ses-M00 CN 60 F -sub-RAND313 ses-M00 CN 60 F -sub-RAND314 ses-M00 CN 60 F -sub-RAND315 ses-M00 CN 60 F -sub-RAND316 ses-M00 CN 60 F -sub-RAND317 ses-M00 CN 60 F -sub-RAND318 ses-M00 CN 60 F -sub-RAND319 ses-M00 CN 60 F -sub-RAND320 ses-M00 CN 60 F -sub-RAND321 ses-M00 CN 60 F -sub-RAND322 ses-M00 CN 60 F -sub-RAND323 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F -sub-RAND496 ses-M00 CN 60 F -sub-RAND497 ses-M00 CN 60 F -sub-RAND498 ses-M00 CN 60 F -sub-RAND499 ses-M00 CN 60 F diff --git a/data/OASIS_random/train/train_splits-5/split-2/AD.tsv b/data/OASIS_random/train/train_splits-5/split-2/AD.tsv deleted file mode 100644 index 6d5455a84..000000000 --- a/data/OASIS_random/train/train_splits-5/split-2/AD.tsv +++ /dev/null @@ -1,201 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND0 ses-M00 AD 60 F -sub-RAND1 ses-M00 AD 60 F -sub-RAND2 ses-M00 AD 60 F -sub-RAND3 ses-M00 AD 60 F -sub-RAND4 ses-M00 AD 60 F -sub-RAND5 ses-M00 AD 60 F -sub-RAND6 ses-M00 AD 60 F -sub-RAND7 ses-M00 AD 60 F -sub-RAND8 ses-M00 AD 60 F -sub-RAND9 ses-M00 AD 60 F -sub-RAND10 ses-M00 AD 60 F -sub-RAND11 ses-M00 AD 60 F -sub-RAND12 ses-M00 AD 60 F -sub-RAND13 ses-M00 AD 60 F -sub-RAND14 ses-M00 AD 60 F -sub-RAND15 ses-M00 AD 60 F -sub-RAND16 ses-M00 AD 60 F -sub-RAND17 ses-M00 AD 60 F -sub-RAND18 ses-M00 AD 60 F -sub-RAND19 ses-M00 AD 60 F -sub-RAND20 ses-M00 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F -sub-RAND241 ses-M00 AD 60 F -sub-RAND242 ses-M00 AD 60 F -sub-RAND243 ses-M00 AD 60 F -sub-RAND244 ses-M00 AD 60 F -sub-RAND245 ses-M00 AD 60 F -sub-RAND246 ses-M00 AD 60 F -sub-RAND247 ses-M00 AD 60 F -sub-RAND248 ses-M00 AD 60 F -sub-RAND249 ses-M00 AD 60 F diff --git a/data/OASIS_random/train/validation/CN_baseline.tsv b/data/OASIS_random/train/validation/CN_baseline.tsv deleted file mode 100644 index 1e79cb9b4..000000000 --- a/data/OASIS_random/train/validation/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND500 ses-M00 CN 60 F -sub-RAND501 ses-M00 CN 60 F -sub-RAND502 ses-M00 CN 60 F -sub-RAND503 ses-M00 CN 60 F -sub-RAND504 ses-M00 CN 60 F -sub-RAND505 ses-M00 CN 60 F -sub-RAND506 ses-M00 CN 60 F -sub-RAND507 ses-M00 CN 60 F -sub-RAND508 ses-M00 CN 60 F -sub-RAND509 ses-M00 CN 60 F -sub-RAND510 ses-M00 CN 60 F -sub-RAND511 ses-M00 CN 60 F -sub-RAND512 ses-M00 CN 60 F -sub-RAND513 ses-M00 CN 60 F -sub-RAND514 ses-M00 CN 60 F 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ses-M00 CN 60 F diff --git a/data/OASIS_random/train/validation_splits-5/split-0/AD_baseline.tsv b/data/OASIS_random/train/validation_splits-5/split-0/AD_baseline.tsv deleted file mode 100644 index 28496a6f9..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-0/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND200 ses-M00 AD 60 F -sub-RAND201 ses-M00 AD 60 F -sub-RAND202 ses-M00 AD 60 F -sub-RAND203 ses-M00 AD 60 F -sub-RAND204 ses-M00 AD 60 F -sub-RAND205 ses-M00 AD 60 F -sub-RAND206 ses-M00 AD 60 F -sub-RAND207 ses-M00 AD 60 F -sub-RAND208 ses-M00 AD 60 F -sub-RAND209 ses-M00 AD 60 F -sub-RAND210 ses-M00 AD 60 F -sub-RAND211 ses-M00 AD 60 F -sub-RAND212 ses-M00 AD 60 F -sub-RAND213 ses-M00 AD 60 F -sub-RAND214 ses-M00 AD 60 F -sub-RAND215 ses-M00 AD 60 F -sub-RAND216 ses-M00 AD 60 F -sub-RAND217 ses-M00 AD 60 F -sub-RAND218 ses-M00 AD 60 F -sub-RAND219 ses-M00 AD 60 F -sub-RAND220 ses-M00 AD 60 F -sub-RAND221 ses-M00 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index 1e79cb9b4..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-0/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND500 ses-M00 CN 60 F -sub-RAND501 ses-M00 CN 60 F -sub-RAND502 ses-M00 CN 60 F -sub-RAND503 ses-M00 CN 60 F -sub-RAND504 ses-M00 CN 60 F -sub-RAND505 ses-M00 CN 60 F -sub-RAND506 ses-M00 CN 60 F -sub-RAND507 ses-M00 CN 60 F -sub-RAND508 ses-M00 CN 60 F -sub-RAND509 ses-M00 CN 60 F -sub-RAND510 ses-M00 CN 60 F -sub-RAND511 ses-M00 CN 60 F -sub-RAND512 ses-M00 CN 60 F -sub-RAND513 ses-M00 CN 60 F -sub-RAND514 ses-M00 CN 60 F -sub-RAND515 ses-M00 CN 60 F -sub-RAND516 ses-M00 CN 60 F -sub-RAND517 ses-M00 CN 60 F -sub-RAND518 ses-M00 CN 60 F -sub-RAND519 ses-M00 CN 60 F -sub-RAND520 ses-M00 CN 60 F -sub-RAND521 ses-M00 CN 60 F -sub-RAND522 ses-M00 CN 60 F -sub-RAND523 ses-M00 CN 60 F -sub-RAND524 ses-M00 CN 60 F -sub-RAND525 ses-M00 CN 60 F -sub-RAND526 ses-M00 CN 60 F -sub-RAND527 ses-M00 CN 60 F -sub-RAND528 ses-M00 CN 60 F -sub-RAND529 ses-M00 CN 60 F -sub-RAND530 ses-M00 CN 60 F -sub-RAND531 ses-M00 CN 60 F -sub-RAND532 ses-M00 CN 60 F -sub-RAND533 ses-M00 CN 60 F -sub-RAND534 ses-M00 CN 60 F -sub-RAND535 ses-M00 CN 60 F -sub-RAND536 ses-M00 CN 60 F -sub-RAND537 ses-M00 CN 60 F -sub-RAND538 ses-M00 CN 60 F -sub-RAND539 ses-M00 CN 60 F -sub-RAND540 ses-M00 CN 60 F -sub-RAND541 ses-M00 CN 60 F -sub-RAND542 ses-M00 CN 60 F -sub-RAND543 ses-M00 CN 60 F -sub-RAND544 ses-M00 CN 60 F -sub-RAND545 ses-M00 CN 60 F -sub-RAND546 ses-M00 CN 60 F -sub-RAND547 ses-M00 CN 60 F -sub-RAND548 ses-M00 CN 60 F -sub-RAND549 ses-M00 CN 60 F diff --git a/data/OASIS_random/train/validation_splits-5/split-1/AD_baseline.tsv b/data/OASIS_random/train/validation_splits-5/split-1/AD_baseline.tsv deleted file mode 100644 index 28496a6f9..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-1/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND200 ses-M00 AD 60 F -sub-RAND201 ses-M00 AD 60 F -sub-RAND202 ses-M00 AD 60 F -sub-RAND203 ses-M00 AD 60 F -sub-RAND204 ses-M00 AD 60 F -sub-RAND205 ses-M00 AD 60 F -sub-RAND206 ses-M00 AD 60 F -sub-RAND207 ses-M00 AD 60 F -sub-RAND208 ses-M00 AD 60 F -sub-RAND209 ses-M00 AD 60 F -sub-RAND210 ses-M00 AD 60 F -sub-RAND211 ses-M00 AD 60 F -sub-RAND212 ses-M00 AD 60 F -sub-RAND213 ses-M00 AD 60 F -sub-RAND214 ses-M00 AD 60 F -sub-RAND215 ses-M00 AD 60 F -sub-RAND216 ses-M00 AD 60 F -sub-RAND217 ses-M00 AD 60 F -sub-RAND218 ses-M00 AD 60 F -sub-RAND219 ses-M00 AD 60 F -sub-RAND220 ses-M00 AD 60 F -sub-RAND221 ses-M00 AD 60 F -sub-RAND222 ses-M00 AD 60 F -sub-RAND223 ses-M00 AD 60 F -sub-RAND224 ses-M00 AD 60 F -sub-RAND225 ses-M00 AD 60 F -sub-RAND226 ses-M00 AD 60 F -sub-RAND227 ses-M00 AD 60 F -sub-RAND228 ses-M00 AD 60 F -sub-RAND229 ses-M00 AD 60 F -sub-RAND230 ses-M00 AD 60 F -sub-RAND231 ses-M00 AD 60 F -sub-RAND232 ses-M00 AD 60 F -sub-RAND233 ses-M00 AD 60 F -sub-RAND234 ses-M00 AD 60 F -sub-RAND235 ses-M00 AD 60 F -sub-RAND236 ses-M00 AD 60 F -sub-RAND237 ses-M00 AD 60 F -sub-RAND238 ses-M00 AD 60 F -sub-RAND239 ses-M00 AD 60 F -sub-RAND240 ses-M00 AD 60 F -sub-RAND241 ses-M00 AD 60 F -sub-RAND242 ses-M00 AD 60 F -sub-RAND243 ses-M00 AD 60 F -sub-RAND244 ses-M00 AD 60 F -sub-RAND245 ses-M00 AD 60 F -sub-RAND246 ses-M00 AD 60 F -sub-RAND247 ses-M00 AD 60 F -sub-RAND248 ses-M00 AD 60 F -sub-RAND249 ses-M00 AD 60 F diff --git a/data/OASIS_random/train/validation_splits-5/split-1/CN_baseline.tsv b/data/OASIS_random/train/validation_splits-5/split-1/CN_baseline.tsv deleted file mode 100644 index 1e79cb9b4..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-1/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND500 ses-M00 CN 60 F -sub-RAND501 ses-M00 CN 60 F -sub-RAND502 ses-M00 CN 60 F -sub-RAND503 ses-M00 CN 60 F -sub-RAND504 ses-M00 CN 60 F -sub-RAND505 ses-M00 CN 60 F -sub-RAND506 ses-M00 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-sub-RAND541 ses-M00 CN 60 F -sub-RAND542 ses-M00 CN 60 F -sub-RAND543 ses-M00 CN 60 F -sub-RAND544 ses-M00 CN 60 F -sub-RAND545 ses-M00 CN 60 F -sub-RAND546 ses-M00 CN 60 F -sub-RAND547 ses-M00 CN 60 F -sub-RAND548 ses-M00 CN 60 F -sub-RAND549 ses-M00 CN 60 F diff --git a/data/OASIS_random/train/validation_splits-5/split-2/AD_baseline.tsv b/data/OASIS_random/train/validation_splits-5/split-2/AD_baseline.tsv deleted file mode 100644 index 28496a6f9..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-2/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND200 ses-M00 AD 60 F -sub-RAND201 ses-M00 AD 60 F -sub-RAND202 ses-M00 AD 60 F -sub-RAND203 ses-M00 AD 60 F -sub-RAND204 ses-M00 AD 60 F -sub-RAND205 ses-M00 AD 60 F -sub-RAND206 ses-M00 AD 60 F -sub-RAND207 ses-M00 AD 60 F -sub-RAND208 ses-M00 AD 60 F -sub-RAND209 ses-M00 AD 60 F -sub-RAND210 ses-M00 AD 60 F -sub-RAND211 ses-M00 AD 60 F -sub-RAND212 ses-M00 AD 60 F 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ses-M00 AD 60 F -sub-RAND248 ses-M00 AD 60 F -sub-RAND249 ses-M00 AD 60 F diff --git a/data/OASIS_random/train/validation_splits-5/split-2/CN_baseline.tsv b/data/OASIS_random/train/validation_splits-5/split-2/CN_baseline.tsv deleted file mode 100644 index 1e79cb9b4..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-2/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND500 ses-M00 CN 60 F -sub-RAND501 ses-M00 CN 60 F -sub-RAND502 ses-M00 CN 60 F -sub-RAND503 ses-M00 CN 60 F -sub-RAND504 ses-M00 CN 60 F -sub-RAND505 ses-M00 CN 60 F -sub-RAND506 ses-M00 CN 60 F -sub-RAND507 ses-M00 CN 60 F -sub-RAND508 ses-M00 CN 60 F -sub-RAND509 ses-M00 CN 60 F -sub-RAND510 ses-M00 CN 60 F -sub-RAND511 ses-M00 CN 60 F -sub-RAND512 ses-M00 CN 60 F -sub-RAND513 ses-M00 CN 60 F -sub-RAND514 ses-M00 CN 60 F -sub-RAND515 ses-M00 CN 60 F -sub-RAND516 ses-M00 CN 60 F -sub-RAND517 ses-M00 CN 60 F -sub-RAND518 ses-M00 CN 60 F -sub-RAND519 ses-M00 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b/data/OASIS_random/train/validation_splits-5/split-3/AD_baseline.tsv deleted file mode 100644 index 28496a6f9..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-3/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND200 ses-M00 AD 60 F -sub-RAND201 ses-M00 AD 60 F -sub-RAND202 ses-M00 AD 60 F -sub-RAND203 ses-M00 AD 60 F -sub-RAND204 ses-M00 AD 60 F -sub-RAND205 ses-M00 AD 60 F -sub-RAND206 ses-M00 AD 60 F -sub-RAND207 ses-M00 AD 60 F -sub-RAND208 ses-M00 AD 60 F -sub-RAND209 ses-M00 AD 60 F -sub-RAND210 ses-M00 AD 60 F -sub-RAND211 ses-M00 AD 60 F -sub-RAND212 ses-M00 AD 60 F -sub-RAND213 ses-M00 AD 60 F -sub-RAND214 ses-M00 AD 60 F -sub-RAND215 ses-M00 AD 60 F -sub-RAND216 ses-M00 AD 60 F -sub-RAND217 ses-M00 AD 60 F -sub-RAND218 ses-M00 AD 60 F -sub-RAND219 ses-M00 AD 60 F -sub-RAND220 ses-M00 AD 60 F -sub-RAND221 ses-M00 AD 60 F -sub-RAND222 ses-M00 AD 60 F -sub-RAND223 ses-M00 AD 60 F -sub-RAND224 ses-M00 AD 60 F 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a/data/OASIS_random/train/validation_splits-5/split-3/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND500 ses-M00 CN 60 F -sub-RAND501 ses-M00 CN 60 F -sub-RAND502 ses-M00 CN 60 F -sub-RAND503 ses-M00 CN 60 F -sub-RAND504 ses-M00 CN 60 F -sub-RAND505 ses-M00 CN 60 F -sub-RAND506 ses-M00 CN 60 F -sub-RAND507 ses-M00 CN 60 F -sub-RAND508 ses-M00 CN 60 F -sub-RAND509 ses-M00 CN 60 F -sub-RAND510 ses-M00 CN 60 F -sub-RAND511 ses-M00 CN 60 F -sub-RAND512 ses-M00 CN 60 F -sub-RAND513 ses-M00 CN 60 F -sub-RAND514 ses-M00 CN 60 F -sub-RAND515 ses-M00 CN 60 F -sub-RAND516 ses-M00 CN 60 F -sub-RAND517 ses-M00 CN 60 F -sub-RAND518 ses-M00 CN 60 F -sub-RAND519 ses-M00 CN 60 F -sub-RAND520 ses-M00 CN 60 F -sub-RAND521 ses-M00 CN 60 F -sub-RAND522 ses-M00 CN 60 F -sub-RAND523 ses-M00 CN 60 F -sub-RAND524 ses-M00 CN 60 F -sub-RAND525 ses-M00 CN 60 F -sub-RAND526 ses-M00 CN 60 F -sub-RAND527 ses-M00 CN 60 F -sub-RAND528 ses-M00 CN 60 F -sub-RAND529 ses-M00 CN 60 F -sub-RAND530 ses-M00 CN 60 F -sub-RAND531 ses-M00 CN 60 F -sub-RAND532 ses-M00 CN 60 F -sub-RAND533 ses-M00 CN 60 F -sub-RAND534 ses-M00 CN 60 F -sub-RAND535 ses-M00 CN 60 F -sub-RAND536 ses-M00 CN 60 F -sub-RAND537 ses-M00 CN 60 F -sub-RAND538 ses-M00 CN 60 F -sub-RAND539 ses-M00 CN 60 F -sub-RAND540 ses-M00 CN 60 F -sub-RAND541 ses-M00 CN 60 F -sub-RAND542 ses-M00 CN 60 F -sub-RAND543 ses-M00 CN 60 F -sub-RAND544 ses-M00 CN 60 F -sub-RAND545 ses-M00 CN 60 F -sub-RAND546 ses-M00 CN 60 F -sub-RAND547 ses-M00 CN 60 F -sub-RAND548 ses-M00 CN 60 F -sub-RAND549 ses-M00 CN 60 F diff --git a/data/OASIS_random/train/validation_splits-5/split-4/AD_baseline.tsv b/data/OASIS_random/train/validation_splits-5/split-4/AD_baseline.tsv deleted file mode 100644 index 28496a6f9..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-4/AD_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND200 ses-M00 AD 60 F -sub-RAND201 ses-M00 AD 60 F -sub-RAND202 ses-M00 AD 60 F -sub-RAND203 ses-M00 AD 60 F -sub-RAND204 ses-M00 AD 60 F -sub-RAND205 ses-M00 AD 60 F -sub-RAND206 ses-M00 AD 60 F -sub-RAND207 ses-M00 AD 60 F -sub-RAND208 ses-M00 AD 60 F -sub-RAND209 ses-M00 AD 60 F -sub-RAND210 ses-M00 AD 60 F -sub-RAND211 ses-M00 AD 60 F -sub-RAND212 ses-M00 AD 60 F -sub-RAND213 ses-M00 AD 60 F -sub-RAND214 ses-M00 AD 60 F -sub-RAND215 ses-M00 AD 60 F -sub-RAND216 ses-M00 AD 60 F -sub-RAND217 ses-M00 AD 60 F -sub-RAND218 ses-M00 AD 60 F -sub-RAND219 ses-M00 AD 60 F -sub-RAND220 ses-M00 AD 60 F -sub-RAND221 ses-M00 AD 60 F -sub-RAND222 ses-M00 AD 60 F -sub-RAND223 ses-M00 AD 60 F -sub-RAND224 ses-M00 AD 60 F -sub-RAND225 ses-M00 AD 60 F -sub-RAND226 ses-M00 AD 60 F -sub-RAND227 ses-M00 AD 60 F -sub-RAND228 ses-M00 AD 60 F -sub-RAND229 ses-M00 AD 60 F -sub-RAND230 ses-M00 AD 60 F -sub-RAND231 ses-M00 AD 60 F -sub-RAND232 ses-M00 AD 60 F -sub-RAND233 ses-M00 AD 60 F -sub-RAND234 ses-M00 AD 60 F -sub-RAND235 ses-M00 AD 60 F -sub-RAND236 ses-M00 AD 60 F -sub-RAND237 ses-M00 AD 60 F -sub-RAND238 ses-M00 AD 60 F -sub-RAND239 ses-M00 AD 60 F -sub-RAND240 ses-M00 AD 60 F -sub-RAND241 ses-M00 AD 60 F -sub-RAND242 ses-M00 AD 60 F -sub-RAND243 ses-M00 AD 60 F -sub-RAND244 ses-M00 AD 60 F -sub-RAND245 ses-M00 AD 60 F -sub-RAND246 ses-M00 AD 60 F -sub-RAND247 ses-M00 AD 60 F -sub-RAND248 ses-M00 AD 60 F -sub-RAND249 ses-M00 AD 60 F diff --git a/data/OASIS_random/train/validation_splits-5/split-4/CN_baseline.tsv b/data/OASIS_random/train/validation_splits-5/split-4/CN_baseline.tsv deleted file mode 100644 index 1e79cb9b4..000000000 --- a/data/OASIS_random/train/validation_splits-5/split-4/CN_baseline.tsv +++ /dev/null @@ -1,51 +0,0 @@ -participant_id session_id diagnosis age sex -sub-RAND500 ses-M00 CN 60 F -sub-RAND501 ses-M00 CN 60 F -sub-RAND502 ses-M00 CN 60 F -sub-RAND503 ses-M00 CN 60 F -sub-RAND504 ses-M00 CN 60 F -sub-RAND505 ses-M00 CN 60 F -sub-RAND506 ses-M00 CN 60 F -sub-RAND507 ses-M00 CN 60 F -sub-RAND508 ses-M00 CN 60 F -sub-RAND509 ses-M00 CN 60 F -sub-RAND510 ses-M00 CN 60 F -sub-RAND511 ses-M00 CN 60 F -sub-RAND512 ses-M00 CN 60 F -sub-RAND513 ses-M00 CN 60 F -sub-RAND514 ses-M00 CN 60 F -sub-RAND515 ses-M00 CN 60 F -sub-RAND516 ses-M00 CN 60 F -sub-RAND517 ses-M00 CN 60 F -sub-RAND518 ses-M00 CN 60 F -sub-RAND519 ses-M00 CN 60 F -sub-RAND520 ses-M00 CN 60 F -sub-RAND521 ses-M00 CN 60 F -sub-RAND522 ses-M00 CN 60 F -sub-RAND523 ses-M00 CN 60 F -sub-RAND524 ses-M00 CN 60 F -sub-RAND525 ses-M00 CN 60 F -sub-RAND526 ses-M00 CN 60 F -sub-RAND527 ses-M00 CN 60 F -sub-RAND528 ses-M00 CN 60 F -sub-RAND529 ses-M00 CN 60 F -sub-RAND530 ses-M00 CN 60 F -sub-RAND531 ses-M00 CN 60 F -sub-RAND532 ses-M00 CN 60 F -sub-RAND533 ses-M00 CN 60 F -sub-RAND534 ses-M00 CN 60 F -sub-RAND535 ses-M00 CN 60 F -sub-RAND536 ses-M00 CN 60 F -sub-RAND537 ses-M00 CN 60 F -sub-RAND538 ses-M00 CN 60 F -sub-RAND539 ses-M00 CN 60 F -sub-RAND540 ses-M00 CN 60 F -sub-RAND541 ses-M00 CN 60 F -sub-RAND542 ses-M00 CN 60 F -sub-RAND543 ses-M00 CN 60 F -sub-RAND544 ses-M00 CN 60 F -sub-RAND545 ses-M00 CN 60 F -sub-RAND546 ses-M00 CN 60 F -sub-RAND547 ses-M00 CN 60 F -sub-RAND548 ses-M00 CN 60 F -sub-RAND549 ses-M00 CN 60 F diff --git a/data/ADNI/AD.tsv b/projects/Wen_MedIA_2020/data/ADNI/AD.tsv similarity index 100% rename from data/ADNI/AD.tsv rename to projects/Wen_MedIA_2020/data/ADNI/AD.tsv diff --git a/data/ADNI/CN.tsv b/projects/Wen_MedIA_2020/data/ADNI/CN.tsv similarity index 100% rename from data/ADNI/CN.tsv rename to projects/Wen_MedIA_2020/data/ADNI/CN.tsv diff --git a/data/ADNI/MCI.tsv b/projects/Wen_MedIA_2020/data/ADNI/MCI.tsv similarity index 100% rename from data/ADNI/MCI.tsv rename to projects/Wen_MedIA_2020/data/ADNI/MCI.tsv diff --git a/data/ADNI/pMCI.tsv b/projects/Wen_MedIA_2020/data/ADNI/pMCI.tsv similarity index 100% rename from data/ADNI/pMCI.tsv rename to projects/Wen_MedIA_2020/data/ADNI/pMCI.tsv diff --git a/data/ADNI/sMCI.tsv 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projects/Wen_MedIA_2020/scripts/svm/train/experiment-25.sh diff --git a/CHANGELOG b/CHANGELOG index 1e465959f..19b046a0d 100644 --- a/CHANGELOG +++ b/CHANGELOG @@ -26,6 +26,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Security +## ClinicaDL 0.2.1 + +### Changed + +- Update README.md to avoid duplicates. + +### Removed + +- Scripts and data related to MedIA publication. + ## ClinicaDL 0.2.0 ### Added diff --git a/projects/Wen_MedIA_2020/data/ADNI/AD.tsv b/projects/Wen_MedIA_2020/data/ADNI/AD.tsv deleted file mode 100644 index 2bf01e72f..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/AD.tsv +++ /dev/null @@ -1,1107 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S0619 ses-M00 AD -sub-ADNI002S0619 ses-M06 AD -sub-ADNI002S0619 ses-M12 AD -sub-ADNI002S0619 ses-M24 AD -sub-ADNI002S0816 ses-M00 AD -sub-ADNI002S0816 ses-M06 AD -sub-ADNI002S0816 ses-M12 AD -sub-ADNI002S0938 ses-M00 AD -sub-ADNI002S0938 ses-M06 AD -sub-ADNI002S0938 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-sub-ADNI941S1295 ses-M00 MCI -sub-ADNI941S1311 ses-M00 MCI -sub-ADNI941S1363 ses-M00 MCI diff --git a/projects/Wen_MedIA_2020/data/ADNI/train/train/Unlabeled.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/train/Unlabeled.tsv deleted file mode 100755 index 3c4e79f3d..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/train/Unlabeled.tsv +++ /dev/null @@ -1,542 +0,0 @@ -participant_id session_id diagnosis real_diagnosis diagnosis_bl adni_diagnosis_change -sub-ADNI002S2010 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI002S2073 ses-M36 Unlabeled MCI EMCI 2.0 -sub-ADNI002S2073 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI002S2073 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI002S2073 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI002S2073 ses-M03 Unlabeled EMCI -sub-ADNI002S2073 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI002S4270 ses-M06 Unlabeled CN CN 1.0 -sub-ADNI002S4270 ses-M48 Unlabeled CN 4.0 -sub-ADNI002S4270 ses-M12 Unlabeled CN CN 1.0 -sub-ADNI002S4270 ses-M03 Unlabeled CN -sub-ADNI002S4270 ses-M00 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ses-M48 Unlabeled CN CN -sub-ADNI033S0741 ses-M24 Unlabeled CN CN -sub-ADNI033S0741 ses-M36 Unlabeled CN CN -sub-ADNI033S0741 ses-M84 Unlabeled CN CN 1.0 -sub-ADNI033S0741 ses-M06 Unlabeled CN CN -sub-ADNI033S0741 ses-M12 Unlabeled CN CN -sub-ADNI033S0741 ses-M00 Unlabeled CN CN -sub-ADNI033S0741 ses-M108 Unlabeled CN 4.0 -sub-ADNI033S0920 ses-M84 Unlabeled CN 4.0 -sub-ADNI033S0920 ses-M96 Unlabeled MCI CN 2.0 -sub-ADNI033S1098 ses-M96 Unlabeled MCI CN 2.0 -sub-ADNI035S0156 ses-M120 Unlabeled MCI CN 2.0 -sub-ADNI035S0156 ses-M84 Unlabeled CN 4.0 -sub-ADNI035S0156 ses-M96 Unlabeled CN 4.0 -sub-ADNI035S0156 ses-M108 Unlabeled MCI CN 2.0 -sub-ADNI035S0555 ses-M60 Unlabeled MCI CN 2.0 -sub-ADNI035S0555 ses-M48 Unlabeled CN 4.0 -sub-ADNI035S0555 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI035S0555 ses-M96 Unlabeled MCI CN 2.0 -sub-ADNI035S4114 ses-M06 Unlabeled MCI LMCI 2.0 -sub-ADNI035S4114 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI036S2378 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI036S2378 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI036S2378 ses-M03 Unlabeled EMCI -sub-ADNI036S2378 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI036S2378 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI036S4430 ses-M12 Unlabeled MCI LMCI 2.0 -sub-ADNI036S4430 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI036S4899 ses-M12 Unlabeled MCI LMCI 2.0 -sub-ADNI036S4899 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI037S0454 ses-M72 Unlabeled CN 4.0 -sub-ADNI037S0454 ses-M84 Unlabeled MCI CN 2.0 -sub-ADNI037S0454 ses-M96 Unlabeled MCI CN 2.0 -sub-ADNI037S0454 ses-M108 Unlabeled MCI CN 2.0 -sub-ADNI037S0467 ses-M60 Unlabeled MCI CN 2.0 -sub-ADNI037S0467 ses-M48 Unlabeled MCI CN -sub-ADNI037S0566 ses-M24 Unlabeled MCI LMCI -sub-ADNI037S0566 ses-M06 Unlabeled MCI LMCI -sub-ADNI037S0566 ses-M12 Unlabeled MCI LMCI -sub-ADNI037S0566 ses-M00 Unlabeled MCI LMCI -sub-ADNI037S0566 ses-M18 Unlabeled MCI LMCI -sub-ADNI037S4214 ses-M12 Unlabeled LMCI 4.0 -sub-ADNI037S4214 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI037S4214 ses-M24 Unlabeled MCI LMCI 2.0 -sub-ADNI037S4381 ses-M06 Unlabeled MCI LMCI 2.0 -sub-ADNI037S4381 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI037S4381 ses-M24 Unlabeled LMCI 4.0 -sub-ADNI037S4706 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI037S4706 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI037S4706 ses-M24 Unlabeled EMCI 8.0 -sub-ADNI041S4041 ses-M06 Unlabeled CN 4.0 -sub-ADNI041S4041 ses-M12 Unlabeled MCI CN 2.0 -sub-ADNI041S4513 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI041S4513 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI041S4513 ses-M03 Unlabeled EMCI -sub-ADNI041S4513 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI041S4513 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI041S5097 ses-M24 Unlabeled MCI SMC 2.0 -sub-ADNI051S1123 ses-M48 Unlabeled CN 4.0 -sub-ADNI051S1123 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI051S1123 ses-M84 Unlabeled MCI CN 2.0 -sub-ADNI051S5285 ses-M24 Unlabeled SMC 4.0 -sub-ADNI052S1168 ses-M06 Unlabeled MCI LMCI -sub-ADNI052S1168 ses-M12 Unlabeled MCI LMCI -sub-ADNI052S1168 ses-M00 Unlabeled MCI LMCI -sub-ADNI052S1168 ses-M18 Unlabeled MCI LMCI -sub-ADNI052S1352 ses-M06 Unlabeled MCI LMCI -sub-ADNI052S1352 ses-M24 Unlabeled MCI LMCI -sub-ADNI052S1352 ses-M12 Unlabeled MCI LMCI -sub-ADNI052S1352 ses-M00 Unlabeled MCI LMCI -sub-ADNI052S1352 ses-M18 Unlabeled MCI LMCI -sub-ADNI057S0779 ses-M24 Unlabeled MCI CN -sub-ADNI057S0779 ses-M36 Unlabeled MCI CN -sub-ADNI067S0056 ses-M60 Unlabeled CN CN 1.0 -sub-ADNI067S0056 ses-M72 Unlabeled CN CN 1.0 -sub-ADNI067S0056 ses-M36 Unlabeled CN CN -sub-ADNI067S0056 ses-M06 Unlabeled CN CN -sub-ADNI067S0056 ses-M12 Unlabeled CN CN -sub-ADNI067S0056 ses-M00 Unlabeled CN CN -sub-ADNI067S0056 ses-M96 Unlabeled CN 7.0 -sub-ADNI067S0056 ses-M108 Unlabeled CN 7.0 -sub-ADNI067S0059 ses-M60 Unlabeled CN CN 1.0 -sub-ADNI067S0059 ses-M72 Unlabeled CN CN 1.0 -sub-ADNI067S0059 ses-M36 Unlabeled CN CN -sub-ADNI067S0059 ses-M06 Unlabeled CN CN -sub-ADNI067S0059 ses-M12 Unlabeled CN CN -sub-ADNI067S0059 ses-M00 Unlabeled CN CN -sub-ADNI067S0059 ses-M96 Unlabeled CN CN 1.0 -sub-ADNI067S0059 ses-M108 Unlabeled CN CN 1.0 -sub-ADNI067S0077 ses-M06 Unlabeled MCI LMCI -sub-ADNI067S0077 ses-M00 Unlabeled MCI LMCI -sub-ADNI067S2301 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI067S2301 ses-M48 Unlabeled EMCI 4.0 -sub-ADNI067S2301 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI067S2301 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI067S2301 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI067S2304 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI067S2304 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI067S2304 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI067S2304 ses-M36 Unlabeled MCI EMCI 2.0 -sub-ADNI067S2304 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4054 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4054 ses-M03 Unlabeled EMCI -sub-ADNI067S4054 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4072 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4072 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4072 ses-M03 Unlabeled EMCI -sub-ADNI067S4072 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4072 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4184 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4184 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4184 ses-M03 Unlabeled EMCI -sub-ADNI067S4184 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4184 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4310 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4310 ses-M03 Unlabeled EMCI -sub-ADNI067S4310 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI067S4767 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI068S0127 ses-M60 Unlabeled CN 4.0 -sub-ADNI068S0127 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI068S0127 ses-M84 Unlabeled MCI CN 2.0 -sub-ADNI068S0210 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI068S0210 ses-M48 Unlabeled MCI CN -sub-ADNI068S0210 ses-M36 Unlabeled MCI CN -sub-ADNI068S2187 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI068S2187 ses-M48 Unlabeled MCI EMCI 2.0 -sub-ADNI068S2187 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI068S2187 ses-M03 Unlabeled EMCI -sub-ADNI068S2187 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI068S2187 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI068S4061 ses-M06 Unlabeled MCI LMCI 2.0 -sub-ADNI068S4061 ses-M12 Unlabeled MCI LMCI 2.0 -sub-ADNI068S4061 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI068S4061 ses-M24 Unlabeled MCI LMCI 2.0 -sub-ADNI072S5207 ses-M06 Unlabeled SMC 4.0 -sub-ADNI072S5207 ses-M12 Unlabeled MCI SMC 2.0 -sub-ADNI094S1188 ses-M00 Unlabeled MCI LMCI -sub-ADNI094S1241 ses-M12 Unlabeled MCI CN -sub-ADNI094S2367 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI094S2367 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI094S2367 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI094S2367 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI094S4434 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI094S4434 ses-M48 Unlabeled MCI EMCI 8.0 -sub-ADNI094S4434 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI094S4434 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI094S4434 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI098S0171 ses-M60 Unlabeled CN CN 1.0 -sub-ADNI098S0171 ses-M48 Unlabeled CN CN -sub-ADNI098S0171 ses-M72 Unlabeled CN CN 1.0 -sub-ADNI098S0171 ses-M36 Unlabeled CN CN -sub-ADNI098S0171 ses-M06 Unlabeled CN CN -sub-ADNI098S0171 ses-M24 Unlabeled CN CN -sub-ADNI098S0171 ses-M12 Unlabeled CN CN -sub-ADNI098S0171 ses-M00 Unlabeled CN CN -sub-ADNI098S0896 ses-M60 Unlabeled CN CN 1.0 -sub-ADNI098S0896 ses-M48 Unlabeled CN CN 1.0 -sub-ADNI098S0896 ses-M36 Unlabeled CN CN -sub-ADNI098S0896 ses-M06 Unlabeled CN CN -sub-ADNI098S0896 ses-M24 Unlabeled CN CN -sub-ADNI098S0896 ses-M12 Unlabeled CN CN -sub-ADNI098S0896 ses-M00 Unlabeled CN CN -sub-ADNI098S0896 ses-M108 Unlabeled CN 4.0 -sub-ADNI098S4275 ses-M06 Unlabeled CN CN 1.0 -sub-ADNI098S4275 ses-M48 Unlabeled CN 4.0 -sub-ADNI098S4275 ses-M03 Unlabeled CN -sub-ADNI098S4275 ses-M00 Unlabeled CN CN 1.0 -sub-ADNI098S4275 ses-M24 Unlabeled CN CN 1.0 -sub-ADNI098S4275 ses-M18 Unlabeled CN -sub-ADNI098S4506 ses-M06 Unlabeled CN CN 1.0 -sub-ADNI098S4506 ses-M12 Unlabeled CN 4.0 -sub-ADNI098S4506 ses-M03 Unlabeled CN -sub-ADNI098S4506 ses-M00 Unlabeled CN CN 1.0 -sub-ADNI098S4506 ses-M24 Unlabeled CN 5.0 -sub-ADNI099S0551 ses-M00 Unlabeled MCI LMCI -sub-ADNI099S2146 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI099S2146 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI099S2146 ses-M03 Unlabeled EMCI -sub-ADNI099S2146 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI100S0015 ses-M06 Unlabeled CN CN -sub-ADNI100S0015 ses-M12 Unlabeled CN CN -sub-ADNI100S0015 ses-M00 Unlabeled CN CN -sub-ADNI100S0015 ses-M24 Unlabeled CN CN -sub-ADNI100S0015 ses-M36 Unlabeled CN CN -sub-ADNI100S0035 ses-M24 Unlabeled CN CN -sub-ADNI100S0035 ses-M36 Unlabeled CN CN -sub-ADNI100S0035 ses-M06 Unlabeled CN CN -sub-ADNI100S0035 ses-M12 Unlabeled CN CN -sub-ADNI100S0035 ses-M00 Unlabeled CN CN -sub-ADNI100S0069 ses-M60 Unlabeled CN CN 1.0 -sub-ADNI100S0069 ses-M72 Unlabeled CN 4.0 -sub-ADNI100S0069 ses-M24 Unlabeled CN CN -sub-ADNI100S0069 ses-M36 Unlabeled CN CN -sub-ADNI100S0069 ses-M06 Unlabeled CN CN -sub-ADNI100S0069 ses-M12 Unlabeled CN CN -sub-ADNI100S0069 ses-M00 Unlabeled CN CN -sub-ADNI100S0069 ses-M96 Unlabeled CN 7.0 -sub-ADNI100S1226 ses-M06 Unlabeled AD AD -sub-ADNI100S1226 ses-M48 Unlabeled AD 8.0 -sub-ADNI100S1226 ses-M12 Unlabeled AD AD -sub-ADNI100S1226 ses-M00 Unlabeled AD AD -sub-ADNI100S1226 ses-M18 Unlabeled AD AD -sub-ADNI100S1226 ses-M24 Unlabeled AD AD -sub-ADNI100S1226 ses-M36 Unlabeled AD AD -sub-ADNI100S4556 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI100S4556 ses-M03 Unlabeled EMCI -sub-ADNI100S4556 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI100S5096 ses-M12 Unlabeled MCI SMC 2.0 -sub-ADNI109S4380 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI109S4380 ses-M48 Unlabeled MCI EMCI 2.0 -sub-ADNI109S4380 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI109S4380 ses-M36 Unlabeled MCI EMCI 2.0 -sub-ADNI109S4380 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI114S0166 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI114S0173 ses-M72 Unlabeled CN CN 1.0 -sub-ADNI114S0173 ses-M120 Unlabeled CN 4.0 -sub-ADNI114S0173 ses-M24 Unlabeled CN CN -sub-ADNI114S0173 ses-M36 Unlabeled CN CN -sub-ADNI114S0173 ses-M84 Unlabeled CN CN 1.0 -sub-ADNI114S0173 ses-M06 Unlabeled CN CN -sub-ADNI114S0173 ses-M12 Unlabeled CN CN -sub-ADNI114S0173 ses-M00 Unlabeled CN CN -sub-ADNI114S0173 ses-M96 Unlabeled CN CN 1.0 -sub-ADNI114S5234 ses-M24 Unlabeled SMC 4.0 -sub-ADNI116S4092 ses-M48 Unlabeled MCI CN 2.0 -sub-ADNI116S4092 ses-M12 Unlabeled CN 4.0 -sub-ADNI116S4092 ses-M24 Unlabeled MCI CN 2.0 -sub-ADNI123S0050 ses-M00 Unlabeled MCI LMCI -sub-ADNI123S0106 ses-M60 Unlabeled CN 4.0 -sub-ADNI123S0106 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI123S0106 ses-M84 Unlabeled MCI CN 2.0 -sub-ADNI123S0106 ses-M96 Unlabeled MCI CN 2.0 -sub-ADNI123S0162 ses-M06 Unlabeled MCI AD -sub-ADNI123S0162 ses-M12 Unlabeled MCI AD -sub-ADNI126S0605 ses-M84 Unlabeled CN 4.0 -sub-ADNI126S0605 ses-M108 Unlabeled MCI CN 2.0 -sub-ADNI126S0680 ses-M84 Unlabeled CN 4.0 -sub-ADNI126S0680 ses-M108 Unlabeled MCI CN 2.0 -sub-ADNI126S5214 ses-M24 Unlabeled SMC 4.0 -sub-ADNI127S0112 ses-M60 Unlabeled LMCI 4.0 -sub-ADNI127S0112 ses-M84 Unlabeled LMCI 4.0 -sub-ADNI127S0112 ses-M12 Unlabeled MCI LMCI -sub-ADNI127S0112 ses-M00 Unlabeled MCI LMCI -sub-ADNI127S0112 ses-M18 Unlabeled MCI LMCI -sub-ADNI127S0112 ses-M96 Unlabeled MCI LMCI 2.0 -sub-ADNI127S0112 ses-M108 Unlabeled MCI LMCI 2.0 -sub-ADNI127S0259 ses-M60 Unlabeled CN CN 1.0 -sub-ADNI127S0259 ses-M72 Unlabeled CN CN 1.0 -sub-ADNI127S0259 ses-M48 Unlabeled CN CN -sub-ADNI127S0259 ses-M24 Unlabeled CN CN -sub-ADNI127S0259 ses-M36 Unlabeled CN CN -sub-ADNI127S0259 ses-M84 Unlabeled CN CN 1.0 -sub-ADNI127S0259 ses-M06 Unlabeled CN CN -sub-ADNI127S0259 ses-M12 Unlabeled CN CN -sub-ADNI127S0259 ses-M00 Unlabeled CN CN -sub-ADNI127S0259 ses-M96 Unlabeled CN 4.0 -sub-ADNI127S2234 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI127S2234 ses-M03 Unlabeled EMCI -sub-ADNI127S2234 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI127S4624 ses-M48 Unlabeled MCI EMCI 2.0 -sub-ADNI127S4624 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI128S0138 ses-M06 Unlabeled MCI LMCI -sub-ADNI128S0138 ses-M12 Unlabeled MCI LMCI -sub-ADNI128S0138 ses-M00 Unlabeled MCI LMCI -sub-ADNI128S0167 ses-M06 Unlabeled MCI AD -sub-ADNI128S0167 ses-M12 Unlabeled MCI AD -sub-ADNI128S0167 ses-M24 Unlabeled MCI AD -sub-ADNI128S0188 ses-M06 Unlabeled MCI LMCI -sub-ADNI128S0188 ses-M12 Unlabeled MCI LMCI -sub-ADNI128S0188 ses-M00 Unlabeled MCI LMCI -sub-ADNI128S0188 ses-M36 Unlabeled MCI LMCI -sub-ADNI128S0205 ses-M00 Unlabeled MCI LMCI -sub-ADNI128S0229 ses-M72 Unlabeled CN CN 1.0 -sub-ADNI128S0229 ses-M48 Unlabeled CN CN 1.0 -sub-ADNI128S0229 ses-M120 Unlabeled CN 4.0 -sub-ADNI128S0229 ses-M36 Unlabeled CN CN -sub-ADNI128S0229 ses-M84 Unlabeled CN CN 1.0 -sub-ADNI128S0229 ses-M06 Unlabeled CN CN -sub-ADNI128S0229 ses-M24 Unlabeled CN CN -sub-ADNI128S0229 ses-M12 Unlabeled CN CN -sub-ADNI128S0229 ses-M00 Unlabeled CN CN -sub-ADNI128S0229 ses-M96 Unlabeled CN CN 1.0 -sub-ADNI128S0522 ses-M84 Unlabeled MCI CN 2.0 -sub-ADNI128S0545 ses-M120 Unlabeled MCI CN 2.0 -sub-ADNI128S0545 ses-M96 Unlabeled CN 4.0 -sub-ADNI128S1408 ses-M72 Unlabeled MCI LMCI 2.0 -sub-ADNI128S1408 ses-M36 Unlabeled MCI LMCI 2.0 -sub-ADNI128S1408 ses-M06 Unlabeled MCI LMCI -sub-ADNI128S1408 ses-M12 Unlabeled MCI LMCI -sub-ADNI128S1408 ses-M00 Unlabeled MCI LMCI -sub-ADNI128S1408 ses-M96 Unlabeled MCI LMCI 2.0 -sub-ADNI128S2036 ses-M48 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2036 ses-M36 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2036 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2036 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2036 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2036 ses-M03 Unlabeled EMCI -sub-ADNI128S2036 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2123 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2123 ses-M48 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2123 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2123 ses-M36 Unlabeled MCI EMCI 2.0 -sub-ADNI128S2123 ses-M24 Unlabeled MCI EMCI 2.0 -sub-ADNI128S4842 ses-M06 Unlabeled MCI LMCI 2.0 -sub-ADNI128S4842 ses-M12 Unlabeled MCI LMCI 2.0 -sub-ADNI128S4842 ses-M03 Unlabeled LMCI -sub-ADNI128S4842 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI128S4842 ses-M24 Unlabeled MCI LMCI 2.0 -sub-ADNI130S0232 ses-M60 Unlabeled MCI CN 2.0 -sub-ADNI130S0232 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI130S0232 ses-M48 Unlabeled MCI CN -sub-ADNI130S1200 ses-M36 Unlabeled MCI CN -sub-ADNI130S4641 ses-M06 Unlabeled AD AD 3.0 -sub-ADNI130S4641 ses-M12 Unlabeled AD AD 3.0 -sub-ADNI130S4641 ses-M03 Unlabeled AD -sub-ADNI130S4641 ses-M00 Unlabeled AD AD 3.0 -sub-ADNI130S4641 ses-M24 Unlabeled AD 8.0 -sub-ADNI131S0123 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI131S0123 ses-M84 Unlabeled MCI CN 2.0 -sub-ADNI131S0384 ses-M24 Unlabeled MCI LMCI -sub-ADNI131S0384 ses-M06 Unlabeled MCI LMCI -sub-ADNI131S0384 ses-M12 Unlabeled MCI LMCI -sub-ADNI131S0384 ses-M00 Unlabeled MCI LMCI -sub-ADNI131S0384 ses-M18 Unlabeled MCI LMCI -sub-ADNI135S4446 ses-M48 Unlabeled MCI CN 2.0 -sub-ADNI135S4566 ses-M06 Unlabeled CN CN 1.0 -sub-ADNI135S4566 ses-M12 Unlabeled CN CN 1.0 -sub-ADNI135S4566 ses-M03 Unlabeled CN -sub-ADNI135S4566 ses-M00 Unlabeled CN CN 1.0 -sub-ADNI135S4566 ses-M24 Unlabeled CN 4.0 -sub-ADNI135S5273 ses-M24 Unlabeled MCI SMC 2.0 -sub-ADNI136S0429 ses-M06 Unlabeled MCI LMCI -sub-ADNI136S0429 ses-M00 Unlabeled MCI LMCI -sub-ADNI136S0429 ses-M36 Unlabeled MCI LMCI -sub-ADNI136S0429 ses-M24 Unlabeled MCI LMCI -sub-ADNI136S0429 ses-M18 Unlabeled MCI LMCI -sub-ADNI137S0443 ses-M06 Unlabeled MCI LMCI -sub-ADNI137S0443 ses-M00 Unlabeled MCI LMCI -sub-ADNI137S0459 ses-M36 Unlabeled CN CN -sub-ADNI137S0459 ses-M06 Unlabeled CN CN -sub-ADNI137S0459 ses-M24 Unlabeled CN CN -sub-ADNI137S0459 ses-M12 Unlabeled CN CN -sub-ADNI137S0459 ses-M00 Unlabeled CN CN -sub-ADNI137S0669 ses-M12 Unlabeled MCI LMCI -sub-ADNI137S0669 ses-M00 Unlabeled MCI LMCI -sub-ADNI137S0722 ses-M06 Unlabeled MCI LMCI -sub-ADNI137S0722 ses-M00 Unlabeled MCI LMCI -sub-ADNI137S0722 ses-M96 Unlabeled LMCI 4.0 -sub-ADNI137S0972 ses-M60 Unlabeled CN 4.0 -sub-ADNI137S0972 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI137S0972 ses-M84 Unlabeled MCI CN 2.0 -sub-ADNI137S4351 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI137S4351 ses-M03 Unlabeled EMCI -sub-ADNI137S4351 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI141S0717 ses-M60 Unlabeled MCI CN 2.0 -sub-ADNI141S0717 ses-M48 Unlabeled MCI CN 2.0 -sub-ADNI141S0717 ses-M72 Unlabeled MCI CN 2.0 -sub-ADNI141S1245 ses-M00 Unlabeled MCI LMCI -sub-ADNI141S2210 ses-M06 Unlabeled MCI EMCI 2.0 -sub-ADNI141S2210 ses-M12 Unlabeled MCI EMCI 2.0 -sub-ADNI141S2210 ses-M00 Unlabeled MCI EMCI 2.0 -sub-ADNI141S4426 ses-M06 Unlabeled MCI LMCI 2.0 -sub-ADNI141S4426 ses-M12 Unlabeled MCI LMCI 2.0 -sub-ADNI141S4426 ses-M00 Unlabeled MCI LMCI 2.0 -sub-ADNI941S1194 ses-M48 Unlabeled CN 4.0 -sub-ADNI941S1194 ses-M36 Unlabeled MCI CN -sub-ADNI941S1202 ses-M60 Unlabeled MCI CN 2.0 -sub-ADNI941S1202 ses-M48 Unlabeled MCI CN 2.0 -sub-ADNI941S1202 ses-M36 Unlabeled MCI CN -sub-ADNI941S1202 ses-M24 Unlabeled MCI CN -sub-ADNI941S1203 ses-M48 Unlabeled MCI CN 2.0 -sub-ADNI941S1203 ses-M36 Unlabeled MCI CN -sub-ADNI941S4100 ses-M06 Unlabeled CN CN 1.0 -sub-ADNI941S4100 ses-M48 Unlabeled CN 4.0 -sub-ADNI941S4100 ses-M12 Unlabeled CN CN 1.0 -sub-ADNI941S4100 ses-M03 Unlabeled CN -sub-ADNI941S4100 ses-M00 Unlabeled CN CN 1.0 -sub-ADNI941S4100 ses-M24 Unlabeled CN CN 1.0 diff --git a/projects/Wen_MedIA_2020/data/ADNI/train/train/Unlabeled_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/train/Unlabeled_baseline.tsv deleted file mode 100755 index 55182038a..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/train/Unlabeled_baseline.tsv +++ /dev/null @@ -1,155 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S2010 ses-M00 unlabeled -sub-ADNI002S2073 ses-M00 unlabeled -sub-ADNI002S4270 ses-M00 unlabeled -sub-ADNI002S4799 ses-M00 unlabeled -sub-ADNI003S1074 ses-M00 unlabeled -sub-ADNI003S4288 ses-M24 unlabeled -sub-ADNI005S0223 ses-M06 unlabeled -sub-ADNI005S0602 ses-M84 unlabeled -sub-ADNI005S4168 ses-M00 unlabeled -sub-ADNI006S4713 ses-M00 unlabeled -sub-ADNI009S4337 ses-M00 unlabeled -sub-ADNI009S4359 ses-M00 unlabeled -sub-ADNI009S4543 ses-M00 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/dev/null @@ -1,145 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S2010 ses-M00 unlabeled -sub-ADNI002S2073 ses-M00 unlabeled -sub-ADNI002S4746 ses-M00 unlabeled -sub-ADNI003S4288 ses-M24 unlabeled -sub-ADNI005S0223 ses-M06 unlabeled -sub-ADNI005S0602 ses-M84 unlabeled -sub-ADNI005S4168 ses-M00 unlabeled -sub-ADNI009S4337 ses-M00 unlabeled -sub-ADNI009S4359 ses-M00 unlabeled -sub-ADNI009S4388 ses-M06 unlabeled -sub-ADNI009S4543 ses-M00 unlabeled -sub-ADNI009S4741 ses-M00 unlabeled -sub-ADNI010S0420 ses-M48 unlabeled -sub-ADNI010S0422 ses-M00 unlabeled -sub-ADNI011S0002 ses-M00 unlabeled -sub-ADNI011S0008 ses-M00 unlabeled -sub-ADNI011S4845 ses-M06 unlabeled -sub-ADNI012S1009 ses-M12 unlabeled -sub-ADNI013S4268 ses-M00 unlabeled -sub-ADNI014S0520 ses-M00 unlabeled -sub-ADNI014S0548 ses-M36 unlabeled -sub-ADNI014S0558 ses-M00 unlabeled -sub-ADNI014S4577 ses-M06 unlabeled -sub-ADNI016S0702 ses-M00 unlabeled -sub-ADNI016S2007 ses-M00 unlabeled -sub-ADNI016S4121 ses-M00 unlabeled 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a/projects/Wen_MedIA_2020/data/ADNI/train/train_splits-5/split-1/unlabeled_baseline.tsv +++ /dev/null @@ -1,146 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S2073 ses-M00 unlabeled -sub-ADNI002S4262 ses-M24 unlabeled -sub-ADNI002S4270 ses-M00 unlabeled -sub-ADNI002S4746 ses-M00 unlabeled -sub-ADNI002S4799 ses-M00 unlabeled -sub-ADNI003S1074 ses-M00 unlabeled -sub-ADNI003S4288 ses-M24 unlabeled -sub-ADNI005S0223 ses-M06 unlabeled -sub-ADNI005S0602 ses-M84 unlabeled -sub-ADNI005S4168 ses-M00 unlabeled -sub-ADNI006S4713 ses-M00 unlabeled -sub-ADNI006S4960 ses-M00 unlabeled -sub-ADNI009S0842 ses-M72 unlabeled -sub-ADNI009S4359 ses-M00 unlabeled -sub-ADNI009S4388 ses-M06 unlabeled -sub-ADNI009S4543 ses-M00 unlabeled -sub-ADNI009S4741 ses-M00 unlabeled -sub-ADNI010S0420 ses-M48 unlabeled -sub-ADNI010S0422 ses-M00 unlabeled -sub-ADNI011S0002 ses-M00 unlabeled -sub-ADNI011S0008 ses-M00 unlabeled -sub-ADNI011S0022 ses-M24 unlabeled -sub-ADNI011S0168 ses-M00 unlabeled 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a/projects/Wen_MedIA_2020/data/ADNI/train/train_splits-5/split-2/unlabeled_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/train_splits-5/split-2/unlabeled_baseline.tsv deleted file mode 100644 index defb2f416..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/train_splits-5/split-2/unlabeled_baseline.tsv +++ /dev/null @@ -1,146 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S2010 ses-M00 unlabeled -sub-ADNI002S2073 ses-M00 unlabeled -sub-ADNI002S4262 ses-M24 unlabeled -sub-ADNI002S4270 ses-M00 unlabeled -sub-ADNI002S4746 ses-M00 unlabeled -sub-ADNI002S4799 ses-M00 unlabeled -sub-ADNI003S1074 ses-M00 unlabeled -sub-ADNI003S4288 ses-M24 unlabeled -sub-ADNI005S0223 ses-M06 unlabeled -sub-ADNI005S0602 ses-M84 unlabeled -sub-ADNI005S4168 ses-M00 unlabeled -sub-ADNI006S4713 ses-M00 unlabeled -sub-ADNI006S4960 ses-M00 unlabeled -sub-ADNI009S0842 ses-M72 unlabeled -sub-ADNI009S4337 ses-M00 unlabeled -sub-ADNI009S4359 ses-M00 unlabeled -sub-ADNI010S0420 ses-M48 unlabeled 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a/projects/Wen_MedIA_2020/data/ADNI/train/train_splits-5/split-4/unlabeled_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/train_splits-5/split-4/unlabeled_baseline.tsv deleted file mode 100644 index a91ca8078..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/train_splits-5/split-4/unlabeled_baseline.tsv +++ /dev/null @@ -1,146 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S2010 ses-M00 unlabeled -sub-ADNI002S2073 ses-M00 unlabeled -sub-ADNI002S4262 ses-M24 unlabeled -sub-ADNI002S4270 ses-M00 unlabeled -sub-ADNI002S4799 ses-M00 unlabeled -sub-ADNI003S1074 ses-M00 unlabeled -sub-ADNI005S0223 ses-M06 unlabeled -sub-ADNI005S0602 ses-M84 unlabeled -sub-ADNI005S4168 ses-M00 unlabeled -sub-ADNI006S4713 ses-M00 unlabeled -sub-ADNI006S4960 ses-M00 unlabeled -sub-ADNI009S0842 ses-M72 unlabeled -sub-ADNI009S4337 ses-M00 unlabeled -sub-ADNI009S4388 ses-M06 unlabeled -sub-ADNI009S4543 ses-M00 unlabeled -sub-ADNI009S4741 ses-M00 unlabeled -sub-ADNI010S0420 ses-M48 unlabeled 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unlabeled -sub-ADNI130S1200 ses-M36 unlabeled -sub-ADNI130S4641 ses-M00 unlabeled -sub-ADNI131S0123 ses-M72 unlabeled -sub-ADNI131S0384 ses-M00 unlabeled -sub-ADNI135S4566 ses-M00 unlabeled -sub-ADNI135S5273 ses-M24 unlabeled -sub-ADNI137S0443 ses-M00 unlabeled -sub-ADNI137S0669 ses-M00 unlabeled -sub-ADNI137S0722 ses-M00 unlabeled -sub-ADNI137S4351 ses-M00 unlabeled -sub-ADNI141S0717 ses-M48 unlabeled -sub-ADNI141S1245 ses-M00 unlabeled -sub-ADNI141S4426 ses-M00 unlabeled -sub-ADNI941S1194 ses-M36 unlabeled -sub-ADNI941S1202 ses-M24 unlabeled -sub-ADNI941S1203 ses-M36 unlabeled -sub-ADNI941S4100 ses-M00 unlabeled diff --git a/projects/Wen_MedIA_2020/data/ADNI/train/validation/AD_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/validation/AD_baseline.tsv deleted file mode 100755 index 367b11a68..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/validation/AD_baseline.tsv +++ /dev/null @@ -1,36 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI006S4546 ses-M00 AD 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a/projects/Wen_MedIA_2020/data/ADNI/train/validation/CN_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/validation/CN_baseline.tsv deleted file mode 100755 index 85c656720..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/validation/CN_baseline.tsv +++ /dev/null @@ -1,35 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S1280 ses-M00 CN -sub-ADNI002S4264 ses-M00 CN -sub-ADNI006S0681 ses-M00 CN -sub-ADNI006S4449 ses-M00 CN -sub-ADNI011S4105 ses-M00 CN -sub-ADNI011S4222 ses-M00 CN -sub-ADNI013S0502 ses-M00 CN -sub-ADNI019S4367 ses-M00 CN -sub-ADNI020S0097 ses-M00 CN -sub-ADNI021S0647 ses-M00 CN -sub-ADNI023S0963 ses-M00 CN -sub-ADNI023S4020 ses-M00 CN -sub-ADNI031S0618 ses-M00 CN -sub-ADNI033S0923 ses-M00 CN -sub-ADNI033S1086 ses-M00 CN -sub-ADNI062S0768 ses-M00 CN -sub-ADNI067S0019 ses-M00 CN -sub-ADNI072S0315 ses-M00 CN -sub-ADNI073S0312 ses-M00 CN -sub-ADNI094S1267 ses-M00 CN -sub-ADNI098S4018 ses-M00 CN -sub-ADNI099S4076 ses-M00 CN -sub-ADNI099S4086 ses-M00 CN -sub-ADNI100S1286 ses-M00 CN -sub-ADNI126S0506 ses-M00 CN -sub-ADNI128S0245 ses-M00 CN -sub-ADNI129S4369 ses-M00 CN -sub-ADNI131S0436 ses-M00 CN -sub-ADNI135S4598 ses-M00 CN -sub-ADNI136S0186 ses-M00 CN -sub-ADNI136S4269 ses-M00 CN -sub-ADNI137S4520 ses-M00 CN -sub-ADNI941S1197 ses-M00 CN -sub-ADNI941S4365 ses-M00 CN diff --git a/projects/Wen_MedIA_2020/data/ADNI/train/validation/MCI_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/validation/MCI_baseline.tsv deleted file mode 100755 index 04b4ec4a6..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/validation/MCI_baseline.tsv +++ /dev/null @@ -1,74 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S2043 ses-M00 MCI -sub-ADNI003S4524 ses-M00 MCI -sub-ADNI007S2058 ses-M00 MCI -sub-ADNI009S4814 ses-M00 MCI -sub-ADNI012S0917 ses-M00 MCI -sub-ADNI012S0932 ses-M00 MCI -sub-ADNI012S1165 ses-M00 MCI -sub-ADNI013S2324 ses-M00 MCI -sub-ADNI013S4917 ses-M00 MCI -sub-ADNI013S4985 ses-M00 MCI -sub-ADNI014S4328 ses-M00 MCI 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b/projects/Wen_MedIA_2020/data/ADNI/train/validation/Unlabeled_baseline.tsv deleted file mode 100644 index 7aac7b4c1..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/validation/Unlabeled_baseline.tsv +++ /dev/null @@ -1,28 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S4262 ses-M24 unlabeled -sub-ADNI002S4746 ses-M00 unlabeled -sub-ADNI006S4960 ses-M00 unlabeled -sub-ADNI009S0842 ses-M72 unlabeled -sub-ADNI009S4388 ses-M06 unlabeled -sub-ADNI011S0008 ses-M00 unlabeled -sub-ADNI012S1009 ses-M12 unlabeled -sub-ADNI014S0520 ses-M00 unlabeled -sub-ADNI016S4638 ses-M00 unlabeled -sub-ADNI018S0055 ses-M48 unlabeled -sub-ADNI019S4293 ses-M00 unlabeled -sub-ADNI023S0061 ses-M00 unlabeled -sub-ADNI031S2022 ses-M00 unlabeled -sub-ADNI031S4218 ses-M12 unlabeled -sub-ADNI032S4277 ses-M24 unlabeled -sub-ADNI037S4071 ses-M24 unlabeled -sub-ADNI037S4750 ses-M00 unlabeled -sub-ADNI053S4813 ses-M00 unlabeled -sub-ADNI067S4212 ses-M00 unlabeled -sub-ADNI073S5227 ses-M24 unlabeled 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/dev/null @@ -1,40 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S4171 ses-M00 pMCI -sub-ADNI002S4521 ses-M00 pMCI -sub-ADNI006S1130 ses-M00 pMCI -sub-ADNI007S0041 ses-M00 pMCI -sub-ADNI007S0128 ses-M00 pMCI -sub-ADNI007S0344 ses-M00 pMCI -sub-ADNI007S0698 ses-M60 pMCI -sub-ADNI009S2381 ses-M00 pMCI -sub-ADNI011S1282 ses-M00 pMCI -sub-ADNI011S4893 ses-M12 pMCI -sub-ADNI013S0240 ses-M00 pMCI -sub-ADNI014S4263 ses-M00 pMCI -sub-ADNI018S0155 ses-M00 pMCI -sub-ADNI018S0450 ses-M00 pMCI -sub-ADNI021S0424 ses-M12 pMCI -sub-ADNI022S1394 ses-M00 pMCI -sub-ADNI031S0294 ses-M00 pMCI -sub-ADNI031S0568 ses-M00 pMCI -sub-ADNI032S0187 ses-M00 pMCI -sub-ADNI041S1010 ses-M00 pMCI -sub-ADNI041S1412 ses-M00 pMCI -sub-ADNI051S4929 ses-M00 pMCI -sub-ADNI057S0839 ses-M00 pMCI -sub-ADNI057S1007 ses-M00 pMCI -sub-ADNI067S0098 ses-M00 pMCI -sub-ADNI067S4918 ses-M00 pMCI -sub-ADNI068S0872 ses-M24 pMCI -sub-ADNI082S0641 ses-M00 pMCI -sub-ADNI094S2216 ses-M00 pMCI -sub-ADNI099S0054 ses-M00 pMCI 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a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-0/unlabeled_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-0/unlabeled_baseline.tsv deleted file mode 100644 index 055772a6f..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-0/unlabeled_baseline.tsv +++ /dev/null @@ -1,38 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S4262 ses-M24 unlabeled -sub-ADNI002S4270 ses-M00 unlabeled -sub-ADNI002S4799 ses-M00 unlabeled -sub-ADNI003S1074 ses-M00 unlabeled -sub-ADNI006S4713 ses-M00 unlabeled -sub-ADNI006S4960 ses-M00 unlabeled -sub-ADNI009S0842 ses-M72 unlabeled -sub-ADNI011S0022 ses-M24 unlabeled -sub-ADNI011S0168 ses-M00 unlabeled -sub-ADNI011S2274 ses-M00 unlabeled -sub-ADNI013S4579 ses-M06 unlabeled -sub-ADNI016S1092 ses-M00 unlabeled -sub-ADNI020S0883 ses-M36 unlabeled -sub-ADNI021S2150 ses-M00 unlabeled -sub-ADNI023S0061 ses-M00 unlabeled -sub-ADNI029S4385 ses-M00 unlabeled -sub-ADNI032S1169 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a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-1/AD_baseline.tsv +++ /dev/null @@ -1,48 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S0955 ses-M00 AD -sub-ADNI002S1018 ses-M00 AD -sub-ADNI003S1059 ses-M00 AD -sub-ADNI003S4373 ses-M00 AD -sub-ADNI005S0221 ses-M00 AD -sub-ADNI005S5119 ses-M00 AD -sub-ADNI006S4867 ses-M00 AD -sub-ADNI014S1095 ses-M00 AD -sub-ADNI016S0991 ses-M00 AD -sub-ADNI016S4353 ses-M00 AD -sub-ADNI016S5032 ses-M00 AD -sub-ADNI018S0335 ses-M00 AD -sub-ADNI023S0084 ses-M00 AD -sub-ADNI023S5241 ses-M00 AD -sub-ADNI024S1171 ses-M00 AD -sub-ADNI031S0321 ses-M00 AD -sub-ADNI032S0147 ses-M00 AD -sub-ADNI036S0759 ses-M00 AD -sub-ADNI036S5210 ses-M00 AD -sub-ADNI037S4001 ses-M00 AD -sub-ADNI041S1435 ses-M00 AD -sub-ADNI051S1296 ses-M00 AD -sub-ADNI053S5208 ses-M00 AD -sub-ADNI057S1373 ses-M00 AD -sub-ADNI067S5205 ses-M00 AD -sub-ADNI068S4968 ses-M00 AD -sub-ADNI082S5029 ses-M00 AD -sub-ADNI094S1402 ses-M00 AD -sub-ADNI094S4089 ses-M00 AD 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a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-1/unlabeled_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-1/unlabeled_baseline.tsv deleted file mode 100644 index a66703425..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-1/unlabeled_baseline.tsv +++ /dev/null @@ -1,37 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S2010 ses-M00 unlabeled -sub-ADNI009S4337 ses-M00 unlabeled -sub-ADNI012S1009 ses-M12 unlabeled -sub-ADNI013S4268 ses-M00 unlabeled -sub-ADNI016S2007 ses-M00 unlabeled -sub-ADNI019S4293 ses-M00 unlabeled -sub-ADNI021S0178 ses-M00 unlabeled -sub-ADNI029S4652 ses-M12 unlabeled -sub-ADNI031S2018 ses-M00 unlabeled -sub-ADNI031S4474 ses-M00 unlabeled -sub-ADNI032S4277 ses-M24 unlabeled -sub-ADNI035S4114 ses-M00 unlabeled -sub-ADNI037S4214 ses-M00 unlabeled -sub-ADNI052S1352 ses-M00 unlabeled -sub-ADNI067S0056 ses-M00 unlabeled -sub-ADNI067S0059 ses-M00 unlabeled -sub-ADNI067S0077 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a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-2/AD_baseline.tsv +++ /dev/null @@ -1,48 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S0619 ses-M00 AD -sub-ADNI002S0938 ses-M00 AD -sub-ADNI003S4152 ses-M00 AD -sub-ADNI007S1304 ses-M00 AD -sub-ADNI010S0829 ses-M00 AD -sub-ADNI013S0699 ses-M00 AD -sub-ADNI019S4477 ses-M00 AD -sub-ADNI023S0093 ses-M00 AD -sub-ADNI023S0139 ses-M00 AD -sub-ADNI023S4501 ses-M00 AD -sub-ADNI027S1081 ses-M00 AD -sub-ADNI027S1082 ses-M00 AD -sub-ADNI027S4801 ses-M00 AD -sub-ADNI029S1184 ses-M00 AD -sub-ADNI031S1209 ses-M00 AD -sub-ADNI033S1281 ses-M00 AD -sub-ADNI036S5112 ses-M00 AD -sub-ADNI053S1044 ses-M00 AD -sub-ADNI062S0690 ses-M00 AD -sub-ADNI067S4728 ses-M00 AD -sub-ADNI068S5146 ses-M00 AD -sub-ADNI094S1090 ses-M00 AD -sub-ADNI114S0228 ses-M00 AD -sub-ADNI114S0374 ses-M00 AD -sub-ADNI116S0370 ses-M00 AD -sub-ADNI116S0392 ses-M00 AD -sub-ADNI116S4625 ses-M00 AD -sub-ADNI123S0091 ses-M00 AD -sub-ADNI126S0606 ses-M00 AD 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a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-2/unlabeled_baseline.tsv b/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-2/unlabeled_baseline.tsv deleted file mode 100644 index bd7a53f49..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-2/unlabeled_baseline.tsv +++ /dev/null @@ -1,37 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI009S4388 ses-M06 unlabeled -sub-ADNI009S4543 ses-M00 unlabeled -sub-ADNI009S4741 ses-M00 unlabeled -sub-ADNI010S0422 ses-M00 unlabeled -sub-ADNI011S0002 ses-M00 unlabeled -sub-ADNI014S0520 ses-M00 unlabeled -sub-ADNI029S5135 ses-M00 unlabeled -sub-ADNI031S2233 ses-M00 unlabeled -sub-ADNI031S4218 ses-M12 unlabeled -sub-ADNI036S4899 ses-M00 unlabeled -sub-ADNI037S0566 ses-M00 unlabeled -sub-ADNI037S4706 ses-M00 unlabeled -sub-ADNI041S4513 ses-M00 unlabeled -sub-ADNI051S5285 ses-M24 unlabeled -sub-ADNI053S4813 ses-M00 unlabeled -sub-ADNI057S0779 ses-M24 unlabeled -sub-ADNI067S4054 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a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-3/AD_baseline.tsv +++ /dev/null @@ -1,48 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI006S4192 ses-M00 AD -sub-ADNI006S4546 ses-M00 AD -sub-ADNI007S0316 ses-M00 AD -sub-ADNI010S0786 ses-M00 AD -sub-ADNI012S0712 ses-M00 AD -sub-ADNI012S0803 ses-M00 AD -sub-ADNI013S5071 ses-M00 AD -sub-ADNI014S0328 ses-M00 AD -sub-ADNI016S4583 ses-M00 AD -sub-ADNI016S5251 ses-M00 AD -sub-ADNI018S0286 ses-M00 AD -sub-ADNI018S4696 ses-M00 AD -sub-ADNI018S5240 ses-M00 AD -sub-ADNI019S4252 ses-M00 AD -sub-ADNI021S0753 ses-M00 AD -sub-ADNI021S4924 ses-M00 AD -sub-ADNI022S0219 ses-M00 AD -sub-ADNI023S0083 ses-M00 AD -sub-ADNI023S1262 ses-M00 AD -sub-ADNI023S5120 ses-M00 AD -sub-ADNI024S4280 ses-M00 AD -sub-ADNI024S4905 ses-M00 AD -sub-ADNI027S4938 ses-M00 AD -sub-ADNI029S0999 ses-M00 AD -sub-ADNI033S0724 ses-M00 AD -sub-ADNI033S1308 ses-M00 AD -sub-ADNI036S1001 ses-M00 AD -sub-ADNI037S4879 ses-M00 AD -sub-ADNI041S1368 ses-M00 AD 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b/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-3/unlabeled_baseline.tsv deleted file mode 100644 index ba814882e..000000000 --- a/projects/Wen_MedIA_2020/data/ADNI/train/validation_splits-5/split-3/unlabeled_baseline.tsv +++ /dev/null @@ -1,37 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S2073 ses-M00 unlabeled -sub-ADNI005S0223 ses-M06 unlabeled -sub-ADNI005S0602 ses-M84 unlabeled -sub-ADNI005S4168 ses-M00 unlabeled -sub-ADNI010S0420 ses-M48 unlabeled -sub-ADNI011S4845 ses-M06 unlabeled -sub-ADNI014S0558 ses-M00 unlabeled -sub-ADNI016S0702 ses-M00 unlabeled -sub-ADNI018S0055 ses-M48 unlabeled -sub-ADNI018S2180 ses-M00 unlabeled -sub-ADNI023S1190 ses-M84 unlabeled -sub-ADNI024S0985 ses-M60 unlabeled -sub-ADNI029S5166 ses-M24 unlabeled -sub-ADNI031S4005 ses-M00 unlabeled -sub-ADNI033S1098 ses-M96 unlabeled -sub-ADNI035S0555 ses-M48 unlabeled -sub-ADNI036S4430 ses-M00 unlabeled -sub-ADNI037S0467 ses-M48 unlabeled -sub-ADNI037S4750 ses-M00 unlabeled 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/dev/null @@ -1,37 +0,0 @@ -participant_id session_id diagnosis -sub-ADNI002S4746 ses-M00 unlabeled -sub-ADNI003S4288 ses-M24 unlabeled -sub-ADNI009S4359 ses-M00 unlabeled -sub-ADNI011S0008 ses-M00 unlabeled -sub-ADNI014S0548 ses-M36 unlabeled -sub-ADNI014S4577 ses-M06 unlabeled -sub-ADNI016S4121 ses-M00 unlabeled -sub-ADNI016S4638 ses-M00 unlabeled -sub-ADNI020S0899 ses-M36 unlabeled -sub-ADNI021S4419 ses-M00 unlabeled -sub-ADNI022S2263 ses-M00 unlabeled -sub-ADNI024S1063 ses-M24 unlabeled -sub-ADNI029S0843 ses-M36 unlabeled -sub-ADNI031S2022 ses-M00 unlabeled -sub-ADNI031S4947 ses-M00 unlabeled -sub-ADNI033S0920 ses-M84 unlabeled -sub-ADNI035S0156 ses-M84 unlabeled -sub-ADNI036S2378 ses-M00 unlabeled -sub-ADNI037S0454 ses-M72 unlabeled -sub-ADNI037S4381 ses-M00 unlabeled -sub-ADNI052S1168 ses-M00 unlabeled -sub-ADNI067S2301 ses-M00 unlabeled -sub-ADNI067S4767 ses-M00 unlabeled -sub-ADNI068S0127 ses-M60 unlabeled -sub-ADNI068S0210 ses-M36 unlabeled -sub-ADNI072S5207 ses-M06 unlabeled 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pMCI -sub-AIBL507 ses-M00 pMCI -sub-AIBL590 ses-M00 pMCI -sub-AIBL609 ses-M00 pMCI -sub-AIBL609 ses-M18 pMCI -sub-AIBL721 ses-M18 pMCI -sub-AIBL721 ses-M36 pMCI -sub-AIBL844 ses-M00 pMCI -sub-AIBL851 ses-M00 pMCI -sub-AIBL877 ses-M00 pMCI -sub-AIBL911 ses-M00 pMCI -sub-AIBL945 ses-M18 pMCI -sub-AIBL971 ses-M00 pMCI diff --git a/projects/Wen_MedIA_2020/data/AIBL/sMCI.tsv b/projects/Wen_MedIA_2020/data/AIBL/sMCI.tsv deleted file mode 100644 index fcf315078..000000000 --- a/projects/Wen_MedIA_2020/data/AIBL/sMCI.tsv +++ /dev/null @@ -1,19 +0,0 @@ -participant_id session_id diagnosis -sub-AIBL1067 ses-M00 sMCI -sub-AIBL1067 ses-M18 sMCI -sub-AIBL109 ses-M18 sMCI -sub-AIBL126 ses-M00 sMCI -sub-AIBL126 ses-M18 sMCI -sub-AIBL15 ses-M00 sMCI -sub-AIBL217 ses-M00 sMCI -sub-AIBL354 ses-M00 sMCI -sub-AIBL366 ses-M00 sMCI -sub-AIBL483 ses-M00 sMCI -sub-AIBL483 ses-M18 sMCI -sub-AIBL56 ses-M00 sMCI -sub-AIBL56 ses-M18 sMCI -sub-AIBL571 ses-M00 sMCI -sub-AIBL571 ses-M18 sMCI -sub-AIBL815 ses-M00 sMCI -sub-AIBL89 ses-M18 sMCI -sub-AIBL959 ses-M00 sMCI diff --git a/projects/Wen_MedIA_2020/data/AIBL/test/AD_baseline.tsv b/projects/Wen_MedIA_2020/data/AIBL/test/AD_baseline.tsv deleted file mode 100644 index 0693bf799..000000000 --- a/projects/Wen_MedIA_2020/data/AIBL/test/AD_baseline.tsv +++ /dev/null @@ -1,77 +0,0 @@ -participant_id session_id diagnosis -sub-AIBL10 ses-M18 AD -sub-AIBL1013 ses-M00 AD -sub-AIBL102 ses-M00 AD -sub-AIBL1032 ses-M00 AD -sub-AIBL104 ses-M18 AD -sub-AIBL1046 ses-M00 AD -sub-AIBL1049 ses-M00 AD -sub-AIBL1056 ses-M00 AD -sub-AIBL1066 ses-M00 AD -sub-AIBL107 ses-M00 AD -sub-AIBL1078 ses-M00 AD -sub-AIBL1089 ses-M00 AD -sub-AIBL1090 ses-M00 AD -sub-AIBL1100 ses-M00 AD -sub-AIBL1102 ses-M00 AD -sub-AIBL112 ses-M18 AD -sub-AIBL1122 ses-M00 AD -sub-AIBL1135 ses-M00 AD -sub-AIBL1139 ses-M00 AD -sub-AIBL1145 ses-M00 AD -sub-AIBL1209 ses-M00 AD -sub-AIBL1260 ses-M00 AD -sub-AIBL1264 ses-M00 AD -sub-AIBL1273 ses-M00 AD -sub-AIBL1298 ses-M00 AD -sub-AIBL1316 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a/projects/Wen_MedIA_2020/data/AIBL/test/MCI_baseline.tsv b/projects/Wen_MedIA_2020/data/AIBL/test/MCI_baseline.tsv deleted file mode 100644 index 462d90e24..000000000 --- a/projects/Wen_MedIA_2020/data/AIBL/test/MCI_baseline.tsv +++ /dev/null @@ -1,94 +0,0 @@ -participant_id session_id diagnosis -sub-AIBL100 ses-M00 MCI -sub-AIBL1005 ses-M00 MCI -sub-AIBL1020 ses-M00 MCI -sub-AIBL1067 ses-M00 MCI -sub-AIBL109 ses-M18 MCI -sub-AIBL1091 ses-M18 MCI -sub-AIBL1094 ses-M00 MCI -sub-AIBL1095 ses-M00 MCI -sub-AIBL111 ses-M00 MCI -sub-AIBL1125 ses-M00 MCI -sub-AIBL1157 ses-M00 MCI -sub-AIBL1183 ses-M00 MCI -sub-AIBL1196 ses-M00 MCI -sub-AIBL12 ses-M18 MCI -sub-AIBL126 ses-M00 MCI -sub-AIBL1261 ses-M00 MCI -sub-AIBL1262 ses-M00 MCI -sub-AIBL1272 ses-M00 MCI -sub-AIBL1277 ses-M00 MCI -sub-AIBL1279 ses-M00 MCI -sub-AIBL1296 ses-M00 MCI -sub-AIBL1308 ses-M00 MCI -sub-AIBL1321 ses-M00 MCI -sub-AIBL1329 ses-M00 MCI -sub-AIBL1345 ses-M00 MCI -sub-AIBL1349 ses-M00 MCI -sub-AIBL1352 ses-M00 MCI 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MCI -sub-AIBL366 ses-M00 MCI -sub-AIBL370 ses-M00 MCI -sub-AIBL380 ses-M00 MCI -sub-AIBL473 ses-M00 MCI -sub-AIBL483 ses-M00 MCI -sub-AIBL487 ses-M00 MCI -sub-AIBL507 ses-M00 MCI -sub-AIBL56 ses-M00 MCI -sub-AIBL566 ses-M00 MCI -sub-AIBL571 ses-M00 MCI -sub-AIBL590 ses-M00 MCI -sub-AIBL609 ses-M00 MCI -sub-AIBL611 ses-M00 MCI -sub-AIBL721 ses-M00 MCI -sub-AIBL815 ses-M00 MCI -sub-AIBL844 ses-M00 MCI -sub-AIBL851 ses-M00 MCI -sub-AIBL857 ses-M00 MCI -sub-AIBL877 ses-M00 MCI -sub-AIBL89 ses-M18 MCI -sub-AIBL902 ses-M00 MCI -sub-AIBL911 ses-M00 MCI -sub-AIBL945 ses-M18 MCI -sub-AIBL959 ses-M00 MCI -sub-AIBL965 ses-M00 MCI -sub-AIBL971 ses-M00 MCI -sub-AIBL990 ses-M18 MCI diff --git a/projects/Wen_MedIA_2020/data/AIBL/test/pMCI_baseline.tsv b/projects/Wen_MedIA_2020/data/AIBL/test/pMCI_baseline.tsv deleted file mode 100644 index 571de748a..000000000 --- a/projects/Wen_MedIA_2020/data/AIBL/test/pMCI_baseline.tsv +++ /dev/null @@ -1,21 +0,0 @@ -participant_id session_id diagnosis -sub-AIBL100 ses-M00 pMCI -sub-AIBL1091 ses-M18 pMCI -sub-AIBL1094 ses-M00 pMCI -sub-AIBL111 ses-M00 pMCI -sub-AIBL12 ses-M18 pMCI -sub-AIBL222 ses-M00 pMCI -sub-AIBL365 ses-M18 pMCI -sub-AIBL380 ses-M00 pMCI -sub-AIBL473 ses-M00 pMCI -sub-AIBL487 ses-M00 pMCI -sub-AIBL507 ses-M00 pMCI -sub-AIBL590 ses-M00 pMCI -sub-AIBL609 ses-M00 pMCI -sub-AIBL721 ses-M18 pMCI -sub-AIBL844 ses-M00 pMCI -sub-AIBL851 ses-M00 pMCI -sub-AIBL877 ses-M00 pMCI -sub-AIBL911 ses-M00 pMCI -sub-AIBL945 ses-M18 pMCI -sub-AIBL971 ses-M00 pMCI diff --git a/projects/Wen_MedIA_2020/data/AIBL/test/sMCI_baseline.tsv b/projects/Wen_MedIA_2020/data/AIBL/test/sMCI_baseline.tsv deleted file mode 100644 index 9d05a1c7e..000000000 --- a/projects/Wen_MedIA_2020/data/AIBL/test/sMCI_baseline.tsv +++ /dev/null @@ -1,14 +0,0 @@ -participant_id session_id diagnosis -sub-AIBL1067 ses-M00 sMCI -sub-AIBL109 ses-M18 sMCI -sub-AIBL126 ses-M00 sMCI -sub-AIBL15 ses-M00 sMCI -sub-AIBL217 ses-M00 sMCI -sub-AIBL354 ses-M00 sMCI -sub-AIBL366 ses-M00 sMCI -sub-AIBL483 ses-M00 sMCI -sub-AIBL56 ses-M00 sMCI -sub-AIBL571 ses-M00 sMCI -sub-AIBL815 ses-M00 sMCI -sub-AIBL89 ses-M18 sMCI -sub-AIBL959 ses-M00 sMCI diff --git a/projects/Wen_MedIA_2020/data/OASIS/AD.tsv b/projects/Wen_MedIA_2020/data/OASIS/AD.tsv deleted file mode 100644 index fa117cd44..000000000 --- a/projects/Wen_MedIA_2020/data/OASIS/AD.tsv +++ /dev/null @@ -1,79 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10003 ses-M00 AD -sub-OASIS10016 ses-M00 AD -sub-OASIS10022 ses-M00 AD -sub-OASIS10023 ses-M00 AD -sub-OASIS10028 ses-M00 AD -sub-OASIS10035 ses-M00 AD -sub-OASIS10039 ses-M00 AD -sub-OASIS10041 ses-M00 AD -sub-OASIS10042 ses-M00 AD -sub-OASIS10046 ses-M00 AD -sub-OASIS10052 ses-M00 AD -sub-OASIS10053 ses-M00 AD -sub-OASIS10056 ses-M00 AD -sub-OASIS10060 ses-M00 AD -sub-OASIS10066 ses-M00 AD -sub-OASIS10067 ses-M00 AD -sub-OASIS10073 ses-M00 AD -sub-OASIS10084 ses-M00 AD -sub-OASIS10094 ses-M00 AD -sub-OASIS10098 ses-M00 AD -sub-OASIS10120 ses-M00 AD -sub-OASIS10124 ses-M00 AD -sub-OASIS10134 ses-M00 AD -sub-OASIS10142 ses-M00 AD -sub-OASIS10143 ses-M00 AD -sub-OASIS10155 ses-M00 AD -sub-OASIS10158 ses-M00 AD -sub-OASIS10161 ses-M00 AD -sub-OASIS10164 ses-M00 AD -sub-OASIS10184 ses-M00 AD -sub-OASIS10185 ses-M00 AD -sub-OASIS10205 ses-M00 AD -sub-OASIS10210 ses-M00 AD -sub-OASIS10217 ses-M00 AD -sub-OASIS10226 ses-M00 AD -sub-OASIS10233 ses-M00 AD -sub-OASIS10238 ses-M00 AD -sub-OASIS10240 ses-M00 AD -sub-OASIS10243 ses-M00 AD -sub-OASIS10247 ses-M00 AD -sub-OASIS10263 ses-M00 AD -sub-OASIS10267 ses-M00 AD -sub-OASIS10268 ses-M00 AD -sub-OASIS10269 ses-M00 AD -sub-OASIS10272 ses-M00 AD -sub-OASIS10278 ses-M00 AD -sub-OASIS10286 ses-M00 AD -sub-OASIS10287 ses-M00 AD -sub-OASIS10288 ses-M00 AD -sub-OASIS10290 ses-M00 AD -sub-OASIS10298 ses-M00 AD -sub-OASIS10300 ses-M00 AD -sub-OASIS10307 ses-M00 AD -sub-OASIS10308 ses-M00 AD -sub-OASIS10315 ses-M00 AD -sub-OASIS10329 ses-M00 AD -sub-OASIS10335 ses-M00 AD -sub-OASIS10339 ses-M00 AD -sub-OASIS10351 ses-M00 AD -sub-OASIS10362 ses-M00 AD -sub-OASIS10374 ses-M00 AD -sub-OASIS10382 ses-M00 AD -sub-OASIS10388 ses-M00 AD -sub-OASIS10390 ses-M00 AD -sub-OASIS10405 ses-M00 AD -sub-OASIS10411 ses-M00 AD -sub-OASIS10418 ses-M00 AD -sub-OASIS10424 ses-M00 AD -sub-OASIS10425 ses-M00 AD -sub-OASIS10430 ses-M00 AD -sub-OASIS10432 ses-M00 AD -sub-OASIS10440 ses-M00 AD -sub-OASIS10441 ses-M00 AD -sub-OASIS10447 ses-M00 AD -sub-OASIS10451 ses-M00 AD -sub-OASIS10452 ses-M00 AD -sub-OASIS10453 ses-M00 AD -sub-OASIS10454 ses-M00 AD diff --git a/projects/Wen_MedIA_2020/data/OASIS/CN.tsv b/projects/Wen_MedIA_2020/data/OASIS/CN.tsv deleted file mode 100644 index fc1c5bd74..000000000 --- a/projects/Wen_MedIA_2020/data/OASIS/CN.tsv +++ /dev/null @@ -1,77 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10001 ses-M00 CN -sub-OASIS10013 ses-M00 CN -sub-OASIS10019 ses-M00 CN -sub-OASIS10030 ses-M00 CN -sub-OASIS10032 ses-M00 CN -sub-OASIS10033 ses-M00 CN -sub-OASIS10062 ses-M00 CN -sub-OASIS10064 ses-M00 CN -sub-OASIS10065 ses-M00 CN -sub-OASIS10068 ses-M00 CN -sub-OASIS10070 ses-M00 CN -sub-OASIS10075 ses-M00 CN -sub-OASIS10078 ses-M00 CN -sub-OASIS10083 ses-M00 CN -sub-OASIS10085 ses-M00 CN -sub-OASIS10106 ses-M00 CN -sub-OASIS10112 ses-M00 CN -sub-OASIS10113 ses-M00 CN -sub-OASIS10130 ses-M00 CN -sub-OASIS10133 ses-M00 CN -sub-OASIS10135 ses-M00 CN -sub-OASIS10139 ses-M00 CN -sub-OASIS10157 ses-M00 CN -sub-OASIS10165 ses-M00 CN -sub-OASIS10169 ses-M00 CN -sub-OASIS10170 ses-M00 CN -sub-OASIS10180 ses-M00 CN -sub-OASIS10195 ses-M00 CN -sub-OASIS10197 ses-M00 CN -sub-OASIS10201 ses-M00 CN -sub-OASIS10203 ses-M00 CN -sub-OASIS10206 ses-M00 CN -sub-OASIS10212 ses-M00 CN -sub-OASIS10216 ses-M00 CN -sub-OASIS10220 ses-M00 CN -sub-OASIS10221 ses-M00 CN -sub-OASIS10228 ses-M00 CN -sub-OASIS10234 ses-M00 CN -sub-OASIS10237 ses-M00 CN -sub-OASIS10241 ses-M00 CN -sub-OASIS10244 ses-M00 CN -sub-OASIS10254 ses-M00 CN -sub-OASIS10255 ses-M00 CN -sub-OASIS10256 ses-M00 CN -sub-OASIS10259 ses-M00 CN -sub-OASIS10260 ses-M00 CN -sub-OASIS10270 ses-M00 CN -sub-OASIS10271 ses-M00 CN -sub-OASIS10279 ses-M00 CN -sub-OASIS10280 ses-M00 CN -sub-OASIS10284 ses-M00 CN -sub-OASIS10292 ses-M00 CN -sub-OASIS10293 ses-M00 CN -sub-OASIS10299 ses-M00 CN -sub-OASIS10303 ses-M00 CN -sub-OASIS10322 ses-M00 CN -sub-OASIS10326 ses-M00 CN -sub-OASIS10330 ses-M00 CN -sub-OASIS10332 ses-M00 CN -sub-OASIS10341 ses-M00 CN -sub-OASIS10343 ses-M00 CN -sub-OASIS10354 ses-M00 CN -sub-OASIS10356 ses-M00 CN -sub-OASIS10363 ses-M00 CN -sub-OASIS10365 ses-M00 CN -sub-OASIS10369 ses-M00 CN -sub-OASIS10371 ses-M00 CN -sub-OASIS10404 ses-M00 CN -sub-OASIS10423 ses-M00 CN -sub-OASIS10426 ses-M00 CN -sub-OASIS10428 ses-M00 CN -sub-OASIS10438 ses-M00 CN -sub-OASIS10445 ses-M00 CN -sub-OASIS10446 ses-M00 CN -sub-OASIS10449 ses-M00 CN -sub-OASIS10457 ses-M00 CN diff --git a/projects/Wen_MedIA_2020/data/OASIS/test/AD_baseline.tsv b/projects/Wen_MedIA_2020/data/OASIS/test/AD_baseline.tsv deleted file mode 100644 index fa117cd44..000000000 --- a/projects/Wen_MedIA_2020/data/OASIS/test/AD_baseline.tsv +++ /dev/null @@ -1,79 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10003 ses-M00 AD -sub-OASIS10016 ses-M00 AD -sub-OASIS10022 ses-M00 AD -sub-OASIS10023 ses-M00 AD -sub-OASIS10028 ses-M00 AD -sub-OASIS10035 ses-M00 AD -sub-OASIS10039 ses-M00 AD -sub-OASIS10041 ses-M00 AD -sub-OASIS10042 ses-M00 AD -sub-OASIS10046 ses-M00 AD -sub-OASIS10052 ses-M00 AD -sub-OASIS10053 ses-M00 AD -sub-OASIS10056 ses-M00 AD -sub-OASIS10060 ses-M00 AD -sub-OASIS10066 ses-M00 AD -sub-OASIS10067 ses-M00 AD -sub-OASIS10073 ses-M00 AD -sub-OASIS10084 ses-M00 AD -sub-OASIS10094 ses-M00 AD -sub-OASIS10098 ses-M00 AD -sub-OASIS10120 ses-M00 AD -sub-OASIS10124 ses-M00 AD -sub-OASIS10134 ses-M00 AD -sub-OASIS10142 ses-M00 AD -sub-OASIS10143 ses-M00 AD -sub-OASIS10155 ses-M00 AD -sub-OASIS10158 ses-M00 AD -sub-OASIS10161 ses-M00 AD -sub-OASIS10164 ses-M00 AD -sub-OASIS10184 ses-M00 AD -sub-OASIS10185 ses-M00 AD -sub-OASIS10205 ses-M00 AD -sub-OASIS10210 ses-M00 AD -sub-OASIS10217 ses-M00 AD -sub-OASIS10226 ses-M00 AD -sub-OASIS10233 ses-M00 AD -sub-OASIS10238 ses-M00 AD -sub-OASIS10240 ses-M00 AD -sub-OASIS10243 ses-M00 AD -sub-OASIS10247 ses-M00 AD -sub-OASIS10263 ses-M00 AD -sub-OASIS10267 ses-M00 AD -sub-OASIS10268 ses-M00 AD -sub-OASIS10269 ses-M00 AD -sub-OASIS10272 ses-M00 AD -sub-OASIS10278 ses-M00 AD -sub-OASIS10286 ses-M00 AD -sub-OASIS10287 ses-M00 AD -sub-OASIS10288 ses-M00 AD -sub-OASIS10290 ses-M00 AD -sub-OASIS10298 ses-M00 AD -sub-OASIS10300 ses-M00 AD -sub-OASIS10307 ses-M00 AD -sub-OASIS10308 ses-M00 AD -sub-OASIS10315 ses-M00 AD -sub-OASIS10329 ses-M00 AD -sub-OASIS10335 ses-M00 AD -sub-OASIS10339 ses-M00 AD -sub-OASIS10351 ses-M00 AD -sub-OASIS10362 ses-M00 AD -sub-OASIS10374 ses-M00 AD -sub-OASIS10382 ses-M00 AD -sub-OASIS10388 ses-M00 AD -sub-OASIS10390 ses-M00 AD -sub-OASIS10405 ses-M00 AD -sub-OASIS10411 ses-M00 AD -sub-OASIS10418 ses-M00 AD -sub-OASIS10424 ses-M00 AD -sub-OASIS10425 ses-M00 AD -sub-OASIS10430 ses-M00 AD -sub-OASIS10432 ses-M00 AD -sub-OASIS10440 ses-M00 AD -sub-OASIS10441 ses-M00 AD -sub-OASIS10447 ses-M00 AD -sub-OASIS10451 ses-M00 AD -sub-OASIS10452 ses-M00 AD -sub-OASIS10453 ses-M00 AD -sub-OASIS10454 ses-M00 AD diff --git a/projects/Wen_MedIA_2020/data/OASIS/test/CN_baseline.tsv b/projects/Wen_MedIA_2020/data/OASIS/test/CN_baseline.tsv deleted file mode 100644 index fc1c5bd74..000000000 --- a/projects/Wen_MedIA_2020/data/OASIS/test/CN_baseline.tsv +++ /dev/null @@ -1,77 +0,0 @@ -participant_id session_id diagnosis -sub-OASIS10001 ses-M00 CN -sub-OASIS10013 ses-M00 CN -sub-OASIS10019 ses-M00 CN -sub-OASIS10030 ses-M00 CN -sub-OASIS10032 ses-M00 CN -sub-OASIS10033 ses-M00 CN -sub-OASIS10062 ses-M00 CN -sub-OASIS10064 ses-M00 CN -sub-OASIS10065 ses-M00 CN -sub-OASIS10068 ses-M00 CN -sub-OASIS10070 ses-M00 CN -sub-OASIS10075 ses-M00 CN -sub-OASIS10078 ses-M00 CN -sub-OASIS10083 ses-M00 CN -sub-OASIS10085 ses-M00 CN -sub-OASIS10106 ses-M00 CN -sub-OASIS10112 ses-M00 CN -sub-OASIS10113 ses-M00 CN -sub-OASIS10130 ses-M00 CN -sub-OASIS10133 ses-M00 CN -sub-OASIS10135 ses-M00 CN -sub-OASIS10139 ses-M00 CN -sub-OASIS10157 ses-M00 CN -sub-OASIS10165 ses-M00 CN -sub-OASIS10169 ses-M00 CN -sub-OASIS10170 ses-M00 CN -sub-OASIS10180 ses-M00 CN -sub-OASIS10195 ses-M00 CN -sub-OASIS10197 ses-M00 CN -sub-OASIS10201 ses-M00 CN -sub-OASIS10203 ses-M00 CN -sub-OASIS10206 ses-M00 CN -sub-OASIS10212 ses-M00 CN -sub-OASIS10216 ses-M00 CN -sub-OASIS10220 ses-M00 CN -sub-OASIS10221 ses-M00 CN -sub-OASIS10228 ses-M00 CN -sub-OASIS10234 ses-M00 CN -sub-OASIS10237 ses-M00 CN -sub-OASIS10241 ses-M00 CN -sub-OASIS10244 ses-M00 CN -sub-OASIS10254 ses-M00 CN -sub-OASIS10255 ses-M00 CN -sub-OASIS10256 ses-M00 CN -sub-OASIS10259 ses-M00 CN -sub-OASIS10260 ses-M00 CN -sub-OASIS10270 ses-M00 CN -sub-OASIS10271 ses-M00 CN -sub-OASIS10279 ses-M00 CN -sub-OASIS10280 ses-M00 CN -sub-OASIS10284 ses-M00 CN -sub-OASIS10292 ses-M00 CN -sub-OASIS10293 ses-M00 CN -sub-OASIS10299 ses-M00 CN -sub-OASIS10303 ses-M00 CN -sub-OASIS10322 ses-M00 CN -sub-OASIS10326 ses-M00 CN -sub-OASIS10330 ses-M00 CN -sub-OASIS10332 ses-M00 CN -sub-OASIS10341 ses-M00 CN -sub-OASIS10343 ses-M00 CN -sub-OASIS10354 ses-M00 CN -sub-OASIS10356 ses-M00 CN -sub-OASIS10363 ses-M00 CN -sub-OASIS10365 ses-M00 CN -sub-OASIS10369 ses-M00 CN -sub-OASIS10371 ses-M00 CN -sub-OASIS10404 ses-M00 CN -sub-OASIS10423 ses-M00 CN -sub-OASIS10426 ses-M00 CN -sub-OASIS10428 ses-M00 CN -sub-OASIS10438 ses-M00 CN -sub-OASIS10445 ses-M00 CN -sub-OASIS10446 ses-M00 CN -sub-OASIS10449 ses-M00 CN -sub-OASIS10457 ses-M00 CN diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-5.sh b/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-5.sh deleted file mode 100644 index 40d7d170f..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-5.sh +++ /dev/null @@ -1,113 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/patch_level/autoencoder -#SBATCH --output=./exp5/pytorch_job_%j.out -#SBATCH --error=./exp5/pytorch_job_%j.err -#SBATCH --job-name=exp5_AE -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=20 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-6.sh b/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-6.sh deleted file mode 100644 index 7aa73d5e0..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-6.sh +++ /dev/null @@ -1,112 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/patch_level/autoencoder -#SBATCH --output=./exp6/pytorch_job_%j.out -#SBATCH --error=./exp6/pytorch_job_%j.err -#SBATCH --job-name=exp6_AE -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=15 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-7.sh b/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-7.sh deleted file mode 100644 index 5e64c4ca4..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-7.sh +++ /dev/null @@ -1,109 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_gct3 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/autoencoder -#SBATCH --output=./exp7/pytorch_job_%j.out -#SBATCH --error=./exp7/pytorch_job_%j.err -#SBATCH --job-name=3DAE_patch -#SBATCH --gres=gpu:1 - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -COHORT="ADNI" -DATE="reproducibility_results" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=15 -BATCH=32 -BASELINE=1 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-8.sh b/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-8.sh deleted file mode 100644 index 5415a3ff9..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/autoencoder/experiment-8.sh +++ /dev/null @@ -1,109 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_gct3 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/autoencoder -#SBATCH --output=./exp8/pytorch_job_%j.out -#SBATCH --error=./exp8/pytorch_job_%j.err -#SBATCH --job-name=3DAE_patch -#SBATCH --gres=gpu:1 - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -COHORT="ADNI" -DATE="reproducibility_results" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=20 -BATCH=32 -BASELINE=1 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-12.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-12.sh deleted file mode 100644 index d11777148..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-12.sh +++ /dev/null @@ -1,113 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/single_cnn -#SBATCH --output=./exp12/pytorch_job_%j.out -#SBATCH --error=./exp12/pytorch_job_%j.err -#SBATCH --job-name=exp12_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="ADNI" -DATE="reproducibility_results" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_selectionThreshold-07" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-13.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-13.sh deleted file mode 100644 index 81bd9fadc..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-13.sh +++ /dev/null @@ -1,118 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/single_cnn -#SBATCH --output=./exp13/pytorch_job_%j.out -#SBATCH --error=./exp13/pytorch_job_%j.err -#SBATCH --job-name=exp13_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="ADNI" -DATE="reproducibility_results" -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_selectionThreshold-07" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-14.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-14.sh deleted file mode 100644 index 5fd60c16b..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-14.sh +++ /dev/null @@ -1,121 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp14/pytorch_job_%j.out -#SBATCH --error=./exp14/pytorch_job_%j.err -#SBATCH --job-name=exp14_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=15 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -TASK_NAME="${DIAGNOSES// /_}" - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_${NETWORK_TYPE}-cnn_selectionThreshold-0" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-15.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-15.sh deleted file mode 100644 index 0e3ea1efa..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-15.sh +++ /dev/null @@ -1,128 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp15/pytorch_job_%j.out -#SBATCH --error=./exp15/pytorch_job_%j.err -#SBATCH --job-name=exp15_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='sMCI pMCI' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_multi-cnn_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_path $T_PATH --transfer_learning_multicnn" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}_${NETWORK_TYPE}-cnn_selectionThreshold-0" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $TASK \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-16.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-16.sh deleted file mode 100644 index 296a24d91..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-16.sh +++ /dev/null @@ -1,121 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp16/pytorch_job_%j.out -#SBATCH --error=./exp16/pytorch_job_%j.err -#SBATCH --job-name=exp16_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=15 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -TASK_NAME="${DIAGNOSES// /_}" - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_${NETWORK_TYPE}-cnn_selectionThreshold-0" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-17.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-17.sh deleted file mode 100644 index 641139aec..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/alternative/experiment-17.sh +++ /dev/null @@ -1,128 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp17/pytorch_job_%j.out -#SBATCH --error=./exp17/pytorch_job_%j.err -#SBATCH --job-name=exp17_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='sMCI pMCI' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_multi-cnn_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_path $T_PATH --transfer_learning_multicnn" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}_${NETWORK_TYPE}-cnn_selectionThreshold-0" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $TASK \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-12.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-12.sh deleted file mode 100644 index f3c7bf5ea..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-12.sh +++ /dev/null @@ -1,113 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/patch_level/single_cnn -#SBATCH --output=./exp12/pytorch_job_%j.out -#SBATCH --error=./exp12/pytorch_job_%j.err -#SBATCH --job-name=exp12_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.f - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-13.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-13.sh deleted file mode 100644 index 61b0f4f55..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-13.sh +++ /dev/null @@ -1,118 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/single_cnn -#SBATCH --output=./exp13/pytorch_job_%j.out -#SBATCH --error=./exp13/pytorch_job_%j.err -#SBATCH --job-name=exp13_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="ADNI" -DATE="reproducibility_results_2" -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-14.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-14.sh deleted file mode 100644 index c4d059b84..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-14.sh +++ /dev/null @@ -1,121 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp14/pytorch_job_%j.out -#SBATCH --error=./exp14/pytorch_job_%j.err -#SBATCH --job-name=exp14_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results_2" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=15 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_${NETWORK_TYPE}-cnn" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-15-a.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-15-a.sh deleted file mode 100644 index 3e8ce7483..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-15-a.sh +++ /dev/null @@ -1,129 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_gct3 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp15/pytorch_job_%j.out -#SBATCH --error=./exp15/pytorch_job_%j.err -#SBATCH --job-name=exp15_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='sMCI pMCI' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}_${NETWORK_TYPE}-cnn" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $TASK \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-15.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-15.sh deleted file mode 100644 index 12bc67e0f..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-15.sh +++ /dev/null @@ -1,130 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp15/pytorch_job_%j.out -#SBATCH --error=./exp15/pytorch_job_%j.err -#SBATCH --job-name=exp15_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results_2" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='sMCI pMCI' -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_multi-cnn_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_path $T_PATH --transfer_learning_multicnn" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}_${NETWORK_TYPE}-cnn" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $TASK \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-16.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-16.sh deleted file mode 100644 index 6df8bcc94..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-16.sh +++ /dev/null @@ -1,121 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp16/pytorch_job_%j.out -#SBATCH --error=./exp16/pytorch_job_%j.err -#SBATCH --job-name=exp16_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results_2" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=15 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_${NETWORK_TYPE}-cnn" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-17.sh b/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-17.sh deleted file mode 100644 index b4e217c30..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/cnn/experiment-17.sh +++ /dev/null @@ -1,130 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/patch_level/multi_cnn -#SBATCH --output=./exp17/pytorch_job_%j.out -#SBATCH --error=./exp17/pytorch_job_%j.err -#SBATCH --job-name=exp17_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="ADNI" -DATE="reproducibility_results_2" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='sMCI pMCI' -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_multi-cnn_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_path $T_PATH --transfer_learning_multicnn" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}_${NETWORK_TYPE}-cnn" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $TASK \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test10.sh b/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test10.sh deleted file mode 100755 index 61be98c78..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test10.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/evaluation/patch -#SBATCH --output=./test10/pytorch_job_%j.out -#SBATCH --error=./test10/pytorch_job_%j.err -#SBATCH --job-name=test10_patch -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_multiCNN.py" -SELECTION="best_acc" - -# Data management -COHORT=$1 -DIAGNOSES="sMCI pMCI" - -BATCH=32 -GPU=1 -NUM_WORKERS=8 -#WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-sMCI_pMCI_baseline_baseline-1_norm-1_t-1_multi-cnn_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/3_test/test10/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --num_workers $NUM_WORKERS \ - --selection_threshold 0.7 \ - --batch_size $BATCH \ - --prepare_dl \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test11.sh b/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test11.sh deleted file mode 100755 index d28bf2191..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test11.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/evaluation/patch -#SBATCH --output=./test11/pytorch_job_%j.out -#SBATCH --error=./test11/pytorch_job_%j.err -#SBATCH --job-name=test11_patch -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_multiCNN.py" -SELECTION="best_acc" - -# Data management -COHORT=$1 -DIAGNOSES="AD CN" - -BATCH=16 -GPU=1 -NUM_WORKERS=8 -#WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_multi-cnn_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/3_test/test11/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --num_workers $NUM_WORKERS \ - --selection_threshold 0.7 \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test12.sh b/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test12.sh deleted file mode 100755 index 671fa10c6..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test12.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/evaluation/patch -#SBATCH --output=./test12/pytorch_job_%j.out -#SBATCH --error=./test12/pytorch_job_%j.err -#SBATCH --job-name=test12_patch -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_multiCNN.py" -SELECTION="best_acc" - -# Data management -COHORT=$1 -DIAGNOSES="sMCI pMCI" - -BATCH=32 -GPU=1 -NUM_WORKERS=8 -#WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-sMCI_pMCI_baseline-0_norm-1_t-1_multi-cnn_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/3_test/test12/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --num_workers $NUM_WORKERS \ - --selection_threshold 0.7 \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test9.sh b/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test9.sh deleted file mode 100755 index 3273c43c5..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/test9.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/evaluation/patch -#SBATCH --output=./test9/pytorch_job_%j.out -#SBATCH --error=./test9/pytorch_job_%j.err -#SBATCH --job-name=test9_patch -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_multiCNN.py" -SELECTION="best_acc" - -# Data management -COHORT=$1 -DIAGNOSES="AD CN" - -BATCH=16 -GPU=1 -NUM_WORKERS=8 -#WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_multi-cnn_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/3_test/test9/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --num_workers $NUM_WORKERS \ - --selection_threshold 0.7 \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/values.tmp b/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/values.tmp deleted file mode 100644 index b651886c3..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/evaluation/values.tmp +++ /dev/null @@ -1,20 +0,0 @@ -accuracy balanced_accuracy sensitivity specificity ppv npv -0.705 0.7050000000000001 0.79 0.62 0.6752136752136753 0.7469879518072289 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.64 0.64 0.63 0.65 0.6428571428571429 0.6372549019607843 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.635 0.635 0.62 0.65 0.6391752577319587 0.6310679611650486 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.71 0.71 0.72 0.7 0.7058823529411765 0.7142857142857143 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.69 0.69 0.7 0.68 0.6862745098039216 0.6938775510204082 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.705 0.7050000000000001 0.79 0.62 0.6752136752136753 0.7469879518072289 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.64 0.64 0.63 0.65 0.6428571428571429 0.6372549019607843 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.635 0.635 0.62 0.65 0.6391752577319587 0.6310679611650486 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.71 0.71 0.72 0.7 0.7058823529411765 0.7142857142857143 -accuracy balanced_accuracy sensitivity specificity ppv npv -0.69 0.69 0.7 0.68 0.6862745098039216 0.6938775510204082 diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/autoencoder/experiment-6-bis.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/autoencoder/experiment-6-bis.sh deleted file mode 100644 index 89eb22b28..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/autoencoder/experiment-6-bis.sh +++ /dev/null @@ -1,112 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/patch_level/autoencoder -#SBATCH --output=./exp6/pytorch_job_%j.out -#SBATCH --error=./exp6/pytorch_job_%j.err -#SBATCH --job-name=exp6_AE -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -COHORT="OASIS_atrophy" -DATE="trivial_tests_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=15 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/autoencoder/experiment-6.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/autoencoder/experiment-6.sh deleted file mode 100644 index 3c9f386b5..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/autoencoder/experiment-6.sh +++ /dev/null @@ -1,112 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/patch_level/autoencoder -#SBATCH --output=./exp6/pytorch_job_%j.out -#SBATCH --error=./exp6/pytorch_job_%j.err -#SBATCH --job-name=exp6_AE -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -COHORT="OASIS_random" -DATE="trivial_tests" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=15 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-13-bis.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-13-bis.sh deleted file mode 100644 index 710e56f13..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-13-bis.sh +++ /dev/null @@ -1,118 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/patch_level/single_cnn -#SBATCH --output=./exp13/pytorch_job_%j.out -#SBATCH --error=./exp13/pytorch_job_%j.err -#SBATCH --job-name=exp13_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="OASIS_atrophy" -DATE="trivial_tests_2" -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-13.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-13.sh deleted file mode 100644 index 7df3c0e7d..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-13.sh +++ /dev/null @@ -1,118 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/patch_level/single_cnn -#SBATCH --output=./exp13/pytorch_job_%j.out -#SBATCH --error=./exp13/pytorch_job_%j.err -#SBATCH --job-name=exp13_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="OASIS_random" -DATE="trivial_tests" -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-16-bis.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-16-bis.sh deleted file mode 100644 index 232013ab0..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-16-bis.sh +++ /dev/null @@ -1,122 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/patch_level/multi_cnn -#SBATCH --output=./exp16/pytorch_job_%j.out -#SBATCH --error=./exp16/pytorch_job_%j.err -#SBATCH --job-name=exp16_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="OASIS_atrophy" -DATE="trivial_tests_2" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=15 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_${NETWORK_TYPE}-cnn" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-16.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-16.sh deleted file mode 100644 index e9cdc6d9a..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/cnn/experiment-16.sh +++ /dev/null @@ -1,122 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/patch_level/multi_cnn -#SBATCH --output=./exp16/pytorch_job_%j.out -#SBATCH --error=./exp16/pytorch_job_%j.err -#SBATCH --job-name=exp16_cnn -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="multi" -COHORT="OASIS_random" -DATE="trivial_tests" -NUM_CNN=36 -USE_EXTRACTED_PATCHES=1 - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=15 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $USE_EXTRACTED_PATCHES = 1 ]; then - echo "Using extracted slices/patches" - OPTIONS="$OPTIONS --use_extracted_patches" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_${NETWORK_TYPE}-cnn" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --num_cnn $NUM_CNN \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --selection_threshold 0.7 \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11-bis.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11-bis.sh deleted file mode 100755 index 8ae93d32f..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11-bis.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=2:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation/patch_level -#SBATCH --output=./test11_job_%j.out -#SBATCH --error=./test11_job_%j.err -#SBATCH --job-name=test11_patch -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_multiCNN.py" -SELECTION="best_acc" - -# Data management -COHORT="OASIS_atrophy" -DIAGNOSES="AD CN" - -BATCH=16 -GPU=1 -NUM_WORKERS=8 -#WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_multi-cnn_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests_2/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --num_workers $NUM_WORKERS \ - --selection_threshold 0.7 \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11.sh deleted file mode 100755 index a00164836..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation/patch_level -#SBATCH --output=./test11_job_%j.out -#SBATCH --error=./test11_job_%j.err -#SBATCH --job-name=test11_patch -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_multiCNN.py" -SELECTION="best_acc" - -# Data management -COHORT="OASIS_random" -DIAGNOSES="AD CN" - -BATCH=16 -GPU=1 -NUM_WORKERS=8 -#WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_multi-cnn_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --num_workers $NUM_WORKERS \ - --selection_threshold 0.7 \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11_single-bis.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11_single-bis.sh deleted file mode 100755 index 890e340de..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11_single-bis.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation/patch_level -#SBATCH --output=./test11_singleCNN_job_%j.out -#SBATCH --error=./test11_singleCNN_job_%j.err -#SBATCH --job-name=test11_patch -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_singleCNN.py" -SELECTION="best_acc" - -# Data management -COHORT="OASIS_atrophy" -DIAGNOSES="AD CN" - -BATCH=16 -GPU=1 -NUM_WORKERS=8 -#WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests_2/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --num_workers $NUM_WORKERS \ - --selection_threshold 0.7 \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11_single.sh b/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11_single.sh deleted file mode 100755 index 90dbdf2ba..000000000 --- a/projects/Wen_MedIA_2020/scripts/patch_level/tests/evaluation/test11_single.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation/patch_level -#SBATCH --output=./test11_singleCNN_job_%j.out -#SBATCH --error=./test11_singleCNN_job_%j.err -#SBATCH --job-name=test11_patch -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_singleCNN.py" -SELECTION="best_acc" - -# Data management -COHORT="OASIS_random" -DIAGNOSES="AD CN" - -BATCH=16 -GPU=1 -NUM_WORKERS=8 -#WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --num_workers $NUM_WORKERS \ - --selection_threshold 0.7 \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/roi_based/autoencoder/experiment-3.sh b/projects/Wen_MedIA_2020/scripts/roi_based/autoencoder/experiment-3.sh deleted file mode 100644 index da4ba925a..000000000 --- a/projects/Wen_MedIA_2020/scripts/roi_based/autoencoder/experiment-3.sh +++ /dev/null @@ -1,117 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/patch_level/autoencoder -#SBATCH --output=./exp3/pytorch_job_%j.out -#SBATCH --error=./exp3/pytorch_job_%j.err -#SBATCH --job-name=exp3_AE -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -HIPPOCAMPUS_ROI=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=0 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $HIPPOCAMPUS_ROI = 1 ]; then - OPTIONS="$OPTIONS --hippocampus_roi" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_hippocampus" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --visualization \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/roi_based/autoencoder/experiment-4.sh b/projects/Wen_MedIA_2020/scripts/roi_based/autoencoder/experiment-4.sh deleted file mode 100644 index a624d293a..000000000 --- a/projects/Wen_MedIA_2020/scripts/roi_based/autoencoder/experiment-4.sh +++ /dev/null @@ -1,117 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/patch_level/autoencoder -#SBATCH --output=./exp4/pytorch_job_%j.out -#SBATCH --error=./exp4/pytorch_job_%j.err -#SBATCH --job-name=exp4_AE -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -HIPPOCAMPUS_ROI=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=100 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=0 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi - -if [ $HIPPOCAMPUS_ROI = 1 ]; then - OPTIONS="$OPTIONS --hippocampus_roi" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_hippocampus" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --visualization \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-10.sh b/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-10.sh deleted file mode 100644 index e574ce135..000000000 --- a/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-10.sh +++ /dev/null @@ -1,116 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/roi_based -#SBATCH --output=./exp10/pytorch_job_%j.out -#SBATCH --error=./exp10/pytorch_job_%j.err -#SBATCH --job-name=exp10_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -HIPPOCAMPUS_ROI=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=10 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_hippocampus_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi -if [ $HIPPOCAMPUS_ROI = 1 ]; then - OPTIONS="$OPTIONS --hippocampus_roi" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_hippocampus" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-11.sh b/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-11.sh deleted file mode 100644 index 2f61b3f57..000000000 --- a/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-11.sh +++ /dev/null @@ -1,125 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/roi_based -#SBATCH --output=./exp11/pytorch_job_%j.out -#SBATCH --error=./exp11/pytorch_job_%j.err -#SBATCH --job-name=exp11_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK="sMCI pMCI" -HIPPOCAMPUS_ROI=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=0 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_hippocampus_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi -if [ $HIPPOCAMPUS_ROI = 1 ]; then - OPTIONS="$OPTIONS --hippocampus_roi" -fi - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_baseline-${BASELINE}_norm-${NORMALIZATION}_hippocampus" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $TASK \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-8.sh b/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-8.sh deleted file mode 100644 index a9c57a1ec..000000000 --- a/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-8.sh +++ /dev/null @@ -1,116 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/roi_based -#SBATCH --output=./exp8/pytorch_job_%j.out -#SBATCH --error=./exp8/pytorch_job_%j.err -#SBATCH --job-name=exp8_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -HIPPOCAMPUS_ROI=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=10 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_hippocampus_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi -if [ $HIPPOCAMPUS_ROI = 1 ]; then - OPTIONS="$OPTIONS --hippocampus_roi" -fi - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_hippocampus" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-9.sh b/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-9.sh deleted file mode 100644 index 5ee0050cc..000000000 --- a/projects/Wen_MedIA_2020/scripts/roi_based/cnn/experiment-9.sh +++ /dev/null @@ -1,125 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/roi_based -#SBATCH --output=./exp9/pytorch_job_%j.out -#SBATCH --error=./exp9/pytorch_job_%j.err -#SBATCH --job-name=exp9_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv4_FC3" -NETWORK_TYPE="single" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK="sMCI pMCI" -HIPPOCAMPUS_ROI=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=200 -BATCH=32 -BASELINE=1 -ACCUMULATION=1 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=1e-3 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=20 - -# Pretraining -T_BOOL=1 -T_PATH="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_hippocampus_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then - OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then - OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="$OPTIONS --transfer_learning_path $T_PATH" -fi - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - OPTIONS="$OPTIONS --baseline" -fi -if [ $HIPPOCAMPUS_ROI = 1 ]; then - OPTIONS="$OPTIONS --hippocampus_roi" -fi - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="patch3D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_baseline-${BASELINE}_norm-${NORMALIZATION}_hippocampus" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - patch \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --network_type $NETWORK_TYPE \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $TASK \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/roi_based/evaluation/test5.sh b/projects/Wen_MedIA_2020/scripts/roi_based/evaluation/test5.sh deleted file mode 100755 index d6ef60cb5..000000000 --- a/projects/Wen_MedIA_2020/scripts/roi_based/evaluation/test5.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash - -set -x - -SCRIPT="evaluation_singleCNN" -SELECTION="best_acc" - -# Data management -COHORT=$1 -DIAGNOSES="AD CN" - -BATCH=16 -GPU=1 -NUM_WORKERS=8 -OPTIONS="--hippocampus_roi" -WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_hippocampus_splits-5" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$WORKDIR/test5/$OUTPUTDIR/" -IMGPATH="$WORKDIR/../../../${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -cd /network/lustre/iss01/home/elina.thibeausutre/Code/AD-DL/clinicadl -python -m clinicadl.patch_level.$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" - --diagnoses $DIAGNOSES - --num_workers $NUM_WORKERS \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/roi_based/evaluation/test6.sh b/projects/Wen_MedIA_2020/scripts/roi_based/evaluation/test6.sh deleted file mode 100755 index 8f396eca5..000000000 --- a/projects/Wen_MedIA_2020/scripts/roi_based/evaluation/test6.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash - -set -x -export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_singleCNN" -SELECTION="best_acc" - -# Data management -COHORT=$1 -DIAGNOSES="sMCI pMCI" - -BATCH=16 -GPU=1 -NUM_WORKERS=8 -OPTIONS="--hippocampus_roi" -WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/3_test" -OUTPUTDIR="patch3D_model-Conv4_FC3_preprocessing-linear_task-sMCI_pMCI_baseline-1_norm-1_hippocampus_splits-5" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$WORKDIR/test6/$OUTPUTDIR/" -IMGPATH="$WORKDIR/../../../${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -cd /network/lustre/iss01/home/elina.thibeausutre/Code/AD-DL/clinicadl -python -m clinicadl.patch_level.$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --selection $SELECTION \ - --dataset "test-$COHORT" - --diagnoses $DIAGNOSES - --num_workers $NUM_WORKERS \ - $OPTIONS \ - --batch_size $BATCH \ - --prepare_dl diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-18.sh b/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-18.sh deleted file mode 100644 index 50bfab925..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-18.sh +++ /dev/null @@ -1,95 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/slice_level -#SBATCH --output=./exp18/pytorch_job_%j.out -#SBATCH --error=./exp18/pytorch_job_%j.err -#SBATCH --job-name=exp18A_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="resnet18" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -MRI_PLANE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=32 -BASELINE=1 -LR=1e-6 -WEIGHT_DECAY=1e-4 -PATIENCE=15 -TOLERANCE=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="slice2D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-AD-CN_baseline-${BASELINE}_preparedl-1" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - slice \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --batch_size $BATCH \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --mri_plane $MRI_PLANE \ - --n_splits $SPLITS \ - --split $SPLIT \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --tolerance $TOLERANCE \ - --use_extracted_patches \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-19.sh b/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-19.sh deleted file mode 100644 index 82f9dbd9a..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-19.sh +++ /dev/null @@ -1,97 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --qos=qos_gpu-t4 -#SBATCH --time=60:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=32 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/slice_level -#SBATCH --output=./exp19/pytorch_job_%j.out -#SBATCH --error=./exp19/pytorch_job_%j.err -#SBATCH --job-name=exp19A_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="resnet18" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=48 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -MRI_PLANE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=100 -BATCH=32 -BASELINE=0 -LR=1e-6 -WEIGHT_DECAY=1e-4 -PATIENCE=15 -TOLERANCE=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="slice2D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-AD-CN_baseline-${BASELINE}_preparedl-1" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - slice \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --mri_plane $MRI_PLANE \ - --n_splits $SPLITS \ - --split $SPLIT \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --tolerance $TOLERANCE \ - --use_extracted_patches \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-20.sh b/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-20.sh deleted file mode 100644 index a7d1a6208..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/cnn/experiment-20.sh +++ /dev/null @@ -1,96 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=90:00:00 -#SBATCH --qos=qos_gpu-t4 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=32 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/train/slice_level -#SBATCH --output=./exp20/pytorch_job_%j.out -#SBATCH --error=./exp20/pytorch_job_%j.err -#SBATCH --job-name=exp20_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -SCRIPT="train_CNN_bad_data_split.py" - -# Network structure -NETWORK="resnet18" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=32 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -MRI_PLANE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=32 -BASELINE=1 -LR=1e-6 -WEIGHT_DECAY=1e-4 -PATIENCE=15 -TOLERANCE=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="slice2D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-AD-CN_baseline-${BASELINE}_preparedl-1_bad_split" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -python $HOME/code/AD-DL/clinicadl/clinicadl/slice_level/$SCRIPT \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - --network $NETWORK \ - --batch_size $BATCH \ - --diagnoses $DIAGNOSES \ - --mri_plane $MRI_PLANE \ - --n_splits $SPLITS \ - --split $SPLIT \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --tolerance $TOLERANCE \ - --prepare_dl \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/cnn/train_singleCNN_bad_data_split.py b/projects/Wen_MedIA_2020/scripts/slice_level/cnn/train_singleCNN_bad_data_split.py deleted file mode 100755 index bc1a5d7b5..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/cnn/train_singleCNN_bad_data_split.py +++ /dev/null @@ -1,301 +0,0 @@ -# coding: utf8 - -import argparse -from torch.utils.data import DataLoader -import torch -import os - -from clinicadl.tools.deep_learning.iotools import commandline_to_json, return_logger, check_and_clean, \ - write_requirements_version, translate_parameters -from clinicadl.tools.deep_learning.models import load_model, init_model -from clinicadl.tools.deep_learning.data import (load_data, - MRIDatasetSlice, - mix_slices, - generate_sampler, - get_transforms) -from clinicadl.tools.deep_learning.cnn_utils import train, test, mode_level_to_tsvs, get_criterion - - -def test_bad_cnn(model, output_dir, data_loader, subset_name, split, criterion, mode, logger, gpu=False): - - for selection in ["best_balanced_accuracy", "best_loss"]: - # load the best trained model during the training - model, best_epoch = load_model(model, os.path.join(output_dir, f'fold-{split}', 'models', selection), - gpu=gpu, filename='model_best.pth.tar') - results_df, metrics = test(model, data_loader, gpu, criterion, mode) - logger.info("%s level %s balanced accuracy is %f for model selected on %s" - % (mode, subset_name, metrics["balanced_accuracy"], selection)) - - mode_level_to_tsvs(output_dir, results_df, metrics, split, selection, mode, dataset=subset_name) - - -# Train 2D CNN - Slice level network -# The input MRI's dimension is 169*208*179 after cropping" -parser = argparse.ArgumentParser( - description="Argparser for Pytorch 2D CNN, The input MRI's dimension is 169*208*179 after cropping") - -# Mandatory argument -parser.add_argument( - "caps_directory", - type=str, - help="Path to the caps of image processing pipeline of DL") - -parser.add_argument( - "tsv_path", - type=str, - help="Path to tsv file of the population based on the diagnosis tsv files. " - "To note, the column name should be participant_id, session_id and diagnosis.") -parser.add_argument( - "output_dir", - type=str, - help="Path to store the classification outputs, and the tsv files containing the performances.") - -# Data argument -parser.add_argument( - "--mri_plane", - default=0, - type=int, - help='Which coordinate axis to take for slicing the MRI. ' - '0 is for saggital, 1 is for coronal and 2 is for axial direction, respectively ') - -parser.add_argument( - '--baseline', - default=False, - action="store_true", - help="Using baseline scans or all available longitudinal scans for training") - -# train argument -parser.add_argument( - "--model", - default="resnet18", - help="Deep network type. Only ResNet was designed for training from scratch.") - -parser.add_argument( - "--diagnoses", - default=["AD", "CN"], - type=str, - nargs="+", - help="Labels for any binary task") - -parser.add_argument( - "--learning_rate", "-lr", - default=1e-3, - type=float, - help="Learning rate of the optimization. (default=0.01)") - -parser.add_argument( - "--n_splits", - default=5, - type=int, - help="Define the cross validation, by default, we use 5-fold.") - -parser.add_argument( - "--split", - default=None, - type=int, - nargs="+", - help="Define a specific fold in the k-fold, this is very useful to find the optimal model, " - "when you do not want to run your k-fold validation") - -parser.add_argument( - "--epochs", - default=1, - type=int, - help="Epochs through the data. (default=20)") - -parser.add_argument( - "--batch_size", - default=32, - type=int, - help="Batch size for training. (default=1)") - -parser.add_argument( - "--optimizer", - default="Adam", - choices=["SGD", "Adadelta", "Adam"], - help="Optimizer of choice for training. (default=Adam)") - -parser.add_argument( - "--gpu", - default=False, - action="store_true", - help="If use gpu or cpu. Empty implies cpu usage.") - -parser.add_argument( - "--num_workers", - default=0, - type=int, - help='the number of batch being loaded in parallel') - -parser.add_argument( - "--weight_decay", - default=1e-2, - type=float, - help='weight decay (default: 1e-4)') - -parser.add_argument( - "--dropout", - default=0, - type=float, - help="Rate of dropout applied to dropout layers." -) - -parser.add_argument( - '--selection_threshold', - default=None, - type=float, - help='Threshold on the balanced accuracies to compute the subject-level ' - 'performance only based on patches with balanced accuracy > threshold.') - -parser.add_argument( - '--prepare_dl', - default=False, - action="store_true", - help="If True the outputs of preprocessing prepare_dl are used, else the whole MRI is loaded.") - -# early stopping arguments -parser.add_argument( - "--patience", - type=int, - default=10, - help="tolerated epochs without improving for early stopping.") - -parser.add_argument( - "--tolerance", - type=float, - default=0, - help="Tolerance of magnitude of performance after each epoch.") - -parser.add_argument( - '--verbose', - '-v', - action='count', - default=0) - - -def train_bad_cnn(params): - """ - Trains a single CNN and writes: - - logs obtained with Tensorboard during training, - - best models obtained according to two metrics on the validation set (loss and balanced accuracy), - - for patch and roi modes, the initialization state is saved as it is identical across all folds, - - final performances at the end of the training. - - If the training crashes it is possible to relaunch the training process from the checkpoint.pth.tar and - optimizer.pth.tar files which respectively contains the state of the model and the optimizer at the end - of the last epoch that was completed before the crash. - """ - main_logger = return_logger(params.verbose, "main process") - train_logger = return_logger(params.verbose, "train") - eval_logger = return_logger(params.verbose, "final evaluation") - check_and_clean(params.output_dir) - - params.mode = "slice" - params.network_type = "cnn" - commandline_to_json(params, logger=main_logger) - write_requirements_version(params.output_dir) - params = translate_parameters(params) - train_transforms, all_transforms = get_transforms(params.mode, - minmaxnormalization=True, - data_augmentation=False) - - if params.split is None: - if params.n_splits is None: - fold_iterator = range(1) - else: - fold_iterator = range(params.n_splits) - else: - fold_iterator = params.split - - for fi in fold_iterator: - main_logger.info("Fold %i" % fi) - - training_sub_df, valid_sub_df = load_data( - params.tsv_path, - params.diagnoses, - fi, - n_splits=params.n_splits, - baseline=params.baseline, - logger=main_logger, - multi_cohort=params.multi_cohort - ) - # split the training + validation by slice - training_df, valid_df = mix_slices( - training_sub_df, - valid_sub_df, - mri_plane=params.mri_plane - ) - - data_train = MRIDatasetSlice( - params.caps_directory, - training_df, - all_transformations=all_transforms, - train_transformations=train_transforms, - mri_plane=params.mri_plane, - prepare_dl=params.prepare_dl, - mixed=True - ) - - data_valid = MRIDatasetSlice( - params.caps_directory, - valid_df, - all_transformations=all_transforms, - train_transformations=train_transforms, - mri_plane=params.mri_plane, - prepare_dl=params.prepare_dl, - mixed=True - ) - - train_sampler = generate_sampler(data_train, params.sampler) - - train_loader = DataLoader( - data_train, - batch_size=params.batch_size, - sampler=train_sampler, - num_workers=params.num_workers, - pin_memory=True - ) - - valid_loader = DataLoader( - data_valid, - batch_size=params.batch_size, - shuffle=False, - num_workers=params.num_workers, - pin_memory=True - ) - - # Initialize the model - main_logger.info('Initialization of the model') - model = init_model(params, initial_shape=data_train.size) - - # Define criterion and optimizer - criterion = get_criterion("default") - optimizer = getattr(torch.optim, params.optimizer)(filter(lambda x: x.requires_grad, model.parameters()), - lr=params.learning_rate, - weight_decay=params.weight_decay) - - # Define output directories - log_dir = os.path.join( - params.output_dir, 'fold-%i' % fi, 'tensorboard_logs') - model_dir = os.path.join( - params.output_dir, 'fold-%i' % fi, 'models') - - main_logger.debug('Beginning the training task') - train(model, train_loader, valid_loader, criterion, - optimizer, False, log_dir, model_dir, params, train_logger) - - test_bad_cnn(model, params.output_dir, train_loader, "train", - fi, criterion, params.mode, eval_logger, gpu=params.gpu) - test_bad_cnn(model, params.output_dir, valid_loader, "validation", - fi, criterion, params.mode, eval_logger, gpu=params.gpu) - - -if __name__ == "__main__": - commandline = parser.parse_known_args() - commandline_to_json(commandline) - options = commandline[0] - if commandline[1]: - raise Exception( - "unknown arguments: %s" % - (parser.parse_known_args()[1])) - train_bad_cnn(options) diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/evaluation/test13.sh b/projects/Wen_MedIA_2020/scripts/slice_level/evaluation/test13.sh deleted file mode 100755 index e91796472..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/evaluation/test13.sh +++ /dev/null @@ -1,56 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/evaluation/slice -#SBATCH --output=./test13/pytorch_job_%j.out -#SBATCH --error=./test13/pytorch_job_%j.err -#SBATCH --job-name=test13_slice -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_test.py" -SELECTION="best_acc" - -# Data management -COHORT=$1 -DIAGNOSES="AD CN" - -BATCH=32 -GPU=1 -NUM_WORKERS=8 -OUTPUTDIR="slice2D_model-resnet18_preprocessing-linear_task-AD-CN_baseline-1_preparedl-1_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/3_test/test13/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/patch_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --selection $SELECTION - --num_workers $NUM_WORKERS \ - --batch_size $BATCH \ - --prepare_dl \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/tests/cnn/experiment-19-bis.sh b/projects/Wen_MedIA_2020/scripts/slice_level/tests/cnn/experiment-19-bis.sh deleted file mode 100644 index 0c29e3aae..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/tests/cnn/experiment-19-bis.sh +++ /dev/null @@ -1,96 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=80:00:00 -#SBATCH --qos=qos_gpu-t4 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=32 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/slice_level -#SBATCH --output=./exp19/pytorch_job_%j.out -#SBATCH --error=./exp19/pytorch_job_%j.err -#SBATCH --job-name=exp19A_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="resnet18" -COHORT="OASIS_atrophy" -DATE="trivial_tests_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=48 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -MRI_PLANE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=100 -BATCH=32 -BASELINE=0 -LR=1e-6 -WEIGHT_DECAY=1e-4 -PATIENCE=15 -TOLERANCE=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="slice2D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-AD-CN_baseline-${BASELINE}_preparedl-1" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - slice \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --mri_plane $MRI_PLANE \ - --n_splits $SPLITS \ - --split $SPLIT \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --tolerance $TOLERANCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/tests/cnn/experiment-19.sh b/projects/Wen_MedIA_2020/scripts/slice_level/tests/cnn/experiment-19.sh deleted file mode 100644 index bf30e04b7..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/tests/cnn/experiment-19.sh +++ /dev/null @@ -1,95 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=32 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/slice_level -#SBATCH --output=./exp19/pytorch_job_%j.out -#SBATCH --error=./exp19/pytorch_job_%j.err -#SBATCH --job-name=exp19A_cnn -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="resnet18" -COHORT="OASIS_random" -DATE="trivial_tests" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=48 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -MRI_PLANE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=100 -BATCH=32 -BASELINE=0 -LR=1e-6 -WEIGHT_DECAY=1e-4 -PATIENCE=15 -TOLERANCE=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="slice2D_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-AD-CN_baseline-${BASELINE}_preparedl-1" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - slice \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --mri_plane $MRI_PLANE \ - --n_splits $SPLITS \ - --split $SPLIT \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - --tolerance $TOLERANCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/tests/evaluation/test13-bis.sh b/projects/Wen_MedIA_2020/scripts/slice_level/tests/evaluation/test13-bis.sh deleted file mode 100755 index ed734ff14..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/tests/evaluation/test13-bis.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=5:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation/slice_level -#SBATCH --output=./pytorch_job_%j.out -#SBATCH --error=./pytorch_job_%j.err -#SBATCH --job-name=test13_slice -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_test.py" -SELECTION="best_acc" - -# Data management -COHORT="OASIS_atrophy" -DIAGNOSES="AD CN" - -BATCH=32 -GPU=1 -NUM_WORKERS=8 -OUTPUTDIR="slice2D_model-resnet18_preprocessing-linear_task-AD-CN_baseline-0_preparedl-1_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests_2/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/slice_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --selection $SELECTION \ - --num_workers $NUM_WORKERS \ - --batch_size $BATCH \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/slice_level/tests/evaluation/test13.sh b/projects/Wen_MedIA_2020/scripts/slice_level/tests/evaluation/test13.sh deleted file mode 100755 index b24677756..000000000 --- a/projects/Wen_MedIA_2020/scripts/slice_level/tests/evaluation/test13.sh +++ /dev/null @@ -1,55 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=5:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation/slice_level -#SBATCH --output=./pytorch_job_%j.out -#SBATCH --error=./pytorch_job_%j.err -#SBATCH --job-name=test13_slice -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -set -x -#export CUDA_VISIBLE_DEVICES=1 -SCRIPT="evaluation_test.py" -SELECTION="best_acc" - -# Data management -COHORT="OASIS_random" -DIAGNOSES="AD CN" - -BATCH=32 -GPU=1 -NUM_WORKERS=8 -OUTPUTDIR="slice2D_model-resnet18_preprocessing-linear_task-AD-CN_baseline-0_preparedl-1_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $COHORT = "ADNI" ]; then -IMGPATH="${IMGPATH}_rerun" -fi - -OPTIONS="" -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/slice_level/$SCRIPT \ - $IMGPATH \ - $TSVPATH \ - $RESULTSPATH \ - --dataset "test-$COHORT" \ - --diagnoses $DIAGNOSES \ - --selection $SELECTION \ - --num_workers $NUM_WORKERS \ - --batch_size $BATCH \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-1.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-1.sh deleted file mode 100644 index 9fcbd38a4..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-1.sh +++ /dev/null @@ -1,113 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/cnn -#SBATCH --output=./exp1/pytorch_job_%A_%a.out -#SBATCH --error=./exp1/pytorch_job_%A_%a.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='AD CN' -BASELINE=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=12 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=0 -PATIENCE=10 -TOLERANCE=0 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-2.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-2.sh deleted file mode 100644 index 2d85b652d..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-2.sh +++ /dev/null @@ -1,113 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/cnn -#SBATCH --output=./exp2/pytorch_job_%j.out -#SBATCH --error=./exp2/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='AD CN' -BASELINE=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=10 -TOLERANCE=0 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-3.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-3.sh deleted file mode 100644 index 6a52f754d..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-3.sh +++ /dev/null @@ -1,114 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/cnn -#SBATCH --output=./exp3/pytorch_job_%j.out -#SBATCH --error=./exp3/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='AD CN' -BASELINE=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=10 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="subject_model-Conv5_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-4.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-4.sh deleted file mode 100644 index 8ca3a9888..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-4.sh +++ /dev/null @@ -1,114 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/cnn -#SBATCH --output=./exp4/pytorch_job_%j.out -#SBATCH --error=./exp4/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='AD CN' -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="subject_model-Conv5_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-5.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-5.sh deleted file mode 100644 index a716f5250..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-5.sh +++ /dev/null @@ -1,115 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/cnn -#SBATCH --output=./exp5/pytorch_job_%j.out -#SBATCH --error=./exp5/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3_mni" -COHORT="ADNI" -CAPS_EXT="_skull_stripping" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}${CAPS_EXT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='mni' -TASK='AD CN' -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="subject_model-Conv5_FC3_preprocessing-mni_task-autoencoder_baseline-1_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-6.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-6.sh deleted file mode 100644 index a75413dcb..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-6.sh +++ /dev/null @@ -1,115 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/cnn -#SBATCH --output=./exp6/pytorch_job_%j.out -#SBATCH --error=./exp6/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='sMCI pMCI' -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=10 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="subject_model-Conv5_FC3_preprocessing-linear_task-AD_CN_norm-1_t-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_path $T_PATH" -fi - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-7.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-7.sh deleted file mode 100644 index df85f07f9..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/experiment-7.sh +++ /dev/null @@ -1,114 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/cnn -#SBATCH --output=./exp7/pytorch_job_%j.out -#SBATCH --error=./exp7/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='sMCI pMCI' -BASELINE=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-5 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=20 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="subject_model-Conv5_FC3_preprocessing-linear_task-AD_CN_baseline_norm-1_t-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_path $T_PATH" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-1.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-1.sh deleted file mode 100644 index 7fdc2377a..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-1.sh +++ /dev/null @@ -1,86 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=100:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=3DADDL -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="main.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="" -PREPROCESSING="linear" -BASELINE=1 -TASK='AD CN' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -SAMPLER="random" -F_LR=1e-4 -LR=1e-4 -NORMALIZATION=0 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=0 - -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT" - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -TASK_NAME="${TASK_NAME}_baseline" -OPTIONS="$OPTIONS --baseline" -PATIENCE=10 -fi -echo $TASK_NAME - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="${OPTIONS} -t $T_PATH -t_diagnoses $T_DIAGNOSES -t_e $T_EPOCHS -t_lr $T_LR" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-$SPLITS" -fi - -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT $TSVPATH $RESULTSPATH$NAME $IMGPATH $MODEL -d $TASK -w $NUM_WORKERS --batch_size $BATCH --epochs $EPOCHS -asteps $ACCUMULATION -esteps $EVALUATION -lr $LR --num_threads $NUM_THREADS --sampler $SAMPLER --patience $PATIENCE --tolerance $TOLERANCE --preprocessing $PREPROCESSING $OPTIONS --visualization diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-2.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-2.sh deleted file mode 100644 index f9fa89a8e..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-2.sh +++ /dev/null @@ -1,86 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=100:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=3DADDL -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="main.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="" -PREPROCESSING="linear" -BASELINE=1 -TASK='AD CN' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -SAMPLER="random" -F_LR=1e-4 -LR=1e-4 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=0 - -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT" - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -TASK_NAME="${TASK_NAME}_baseline" -OPTIONS="$OPTIONS --baseline" -PATIENCE=10 -fi -echo $TASK_NAME - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then - OPTIONS="${OPTIONS} -t $T_PATH -t_diagnoses $T_DIAGNOSES -t_e $T_EPOCHS -t_lr $T_LR" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-$SPLITS" -fi - -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT $TSVPATH $RESULTSPATH$NAME $IMGPATH $MODEL -d $TASK -w $NUM_WORKERS --batch_size $BATCH --epochs $EPOCHS -asteps $ACCUMULATION -esteps $EVALUATION -lr $LR --num_threads $NUM_THREADS --sampler $SAMPLER --patience $PATIENCE --tolerance $TOLERANCE --preprocessing $PREPROCESSING $OPTIONS --visualization diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-3.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-3.sh deleted file mode 100644 index 352e5dcdd..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-3.sh +++ /dev/null @@ -1,88 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=100:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=3DADDL -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="main.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="" -PREPROCESSING="linear" -BASELINE=1 -TASK='AD CN' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -SAMPLER="random" -F_LR=1e-4 -LR=1e-4 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="reproducibility_results/model-Conv5_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_t-0_splits-5" - -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT" -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/${T_PATH}" - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -TASK_NAME="${TASK_NAME}_baseline" -OPTIONS="$OPTIONS --baseline" -PATIENCE=10 -fi -echo $TASK_NAME - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="${OPTIONS} -t $T_PATH" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-$SPLITS" -fi - -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT $TSVPATH $RESULTSPATH$NAME $IMGPATH $MODEL -d $TASK -w $NUM_WORKERS --batch_size $BATCH --epochs $EPOCHS -asteps $ACCUMULATION -esteps $EVALUATION -lr $LR --num_threads $NUM_THREADS --sampler $SAMPLER --patience $PATIENCE --tolerance $TOLERANCE --preprocessing $PREPROCESSING $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-4.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-4.sh deleted file mode 100644 index 4f76e40ae..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-4.sh +++ /dev/null @@ -1,88 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=100:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=3DADDL -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="main.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="" -PREPROCESSING="linear" -BASELINE=0 -TASK='AD CN' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -SAMPLER="random" -F_LR=1e-4 -LR=1e-4 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="reproducibility_results/model-Conv5_FC3_preprocessing-linear_task-autoencoder_baseline-1_norm-1_t-0_splits-5" - -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT" -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/${T_PATH}" - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -TASK_NAME="${TASK_NAME}_baseline" -OPTIONS="$OPTIONS --baseline" -PATIENCE=10 -fi -echo $TASK_NAME - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="${OPTIONS} -t $T_PATH" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-$SPLITS" -fi - -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT $TSVPATH $RESULTSPATH$NAME $IMGPATH $MODEL -d $TASK -w $NUM_WORKERS --batch_size $BATCH --epochs $EPOCHS -asteps $ACCUMULATION -esteps $EVALUATION -lr $LR --num_threads $NUM_THREADS --sampler $SAMPLER --patience $PATIENCE --tolerance $TOLERANCE --preprocessing $PREPROCESSING $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-5.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-5.sh deleted file mode 100644 index bbfd29b32..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-5.sh +++ /dev/null @@ -1,88 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=100:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=2 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=3DADDL -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="main.py" -MODEL="Conv5_FC3_mni" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_skull_stripping" -PREPROCESSING="mniskullstrip" -BASELINE=0 -TASK='AD CN' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -SAMPLER="random" -F_LR=1e-4 -LR=1e-4 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="reproducibility_results/model-Conv5_FC3_preprocessing-mniskullstrip_task-autoencoder_baseline-1_norm-1_t-0_splits-5" - -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/${T_PATH}" - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -TASK_NAME="${TASK_NAME}_baseline" -OPTIONS="$OPTIONS --baseline" -PATIENCE=10 -fi -echo $TASK_NAME - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="${OPTIONS} -t $T_PATH" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-$SPLITS" -fi - -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT $TSVPATH $RESULTSPATH$NAME $IMGPATH $MODEL -d $TASK -w $NUM_WORKERS --batch_size $BATCH --epochs $EPOCHS -asteps $ACCUMULATION -esteps $EVALUATION -lr $LR --num_threads $NUM_THREADS --sampler $SAMPLER --patience $PATIENCE --tolerance $TOLERANCE --preprocessing $PREPROCESSING $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-6.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-6.sh deleted file mode 100644 index 00faa0f2b..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-6.sh +++ /dev/null @@ -1,88 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=100:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=3DADDL -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="main.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="" -PREPROCESSING="linear" -BASELINE=0 -TASK='sMCI pMCI' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -SAMPLER="random" -F_LR=1e-4 -LR=1e-4 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="reproducibility_results/model-Conv5_FC3_preprocessing-linear_task-AD_CN_norm-1_t-1_splits-5" - -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT" -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/${T_PATH}" - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -TASK_NAME="${TASK_NAME}_baseline" -OPTIONS="$OPTIONS --baseline" -PATIENCE=10 -fi -echo $TASK_NAME - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="${OPTIONS} -t $T_PATH" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-$SPLITS" -fi - -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT $TSVPATH $RESULTSPATH$NAME $IMGPATH $MODEL -d $TASK -w $NUM_WORKERS --batch_size $BATCH --epochs $EPOCHS -asteps $ACCUMULATION -esteps $EVALUATION -lr $LR --num_threads $NUM_THREADS --sampler $SAMPLER --patience $PATIENCE --tolerance $TOLERANCE --preprocessing $PREPROCESSING $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-7.sh b/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-7.sh deleted file mode 100644 index 32ff9f063..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/CNN/legacy/experiment-7.sh +++ /dev/null @@ -1,88 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=100:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=3DADDL -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="main.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="" -PREPROCESSING="linear" -BASELINE=1 -TASK='sMCI pMCI' -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -SAMPLER="random" -F_LR=1e-4 -LR=1e-4 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="reproducibility_results/model-Conv5_FC3_preprocessing-linear_task-AD_CN_baseline_norm-1_t-1_splits-5" - -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT" -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/${T_PATH}" - -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -TASK_NAME="${TASK_NAME}_baseline" -OPTIONS="$OPTIONS --baseline" -PATIENCE=10 -fi -echo $TASK_NAME - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="${OPTIONS} -t $T_PATH" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-$SPLITS" -fi - -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT $TSVPATH $RESULTSPATH$NAME $IMGPATH $MODEL -d $TASK -w $NUM_WORKERS --batch_size $BATCH --epochs $EPOCHS -asteps $ACCUMULATION -esteps $EVALUATION -lr $LR --num_threads $NUM_THREADS --sampler $SAMPLER --patience $PATIENCE --tolerance $TOLERANCE --preprocessing $PREPROCESSING $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/experiment-1.sh b/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/experiment-1.sh deleted file mode 100644 index f3f6b53bd..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/experiment-1.sh +++ /dev/null @@ -1,111 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/autoencoder -#SBATCH --output=./exp1/pytorch_job_%j.out -#SBATCH --error=./exp1/pytorch_job_%j.err -#SBATCH --job-name=3DAE_subj -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}_rerun" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -BASELINE=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=12 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/experiment-2.sh b/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/experiment-2.sh deleted file mode 100644 index a898320f9..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/experiment-2.sh +++ /dev/null @@ -1,112 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/train/subject_level/autoencoder -#SBATCH --output=./exp2/pytorch_job_%j.out -#SBATCH --error=./exp2/pytorch_job_%j.err -#SBATCH --job-name=3DAE_subj -#SBATCH --gres=gpu:1 -#SBATCH --array=1-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="ADNI" -DATE="reproducibility_results_2" -CAPS_EXT="_skull_stripping" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/$COHORT$CAPS_EXT" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='mni' -DIAGNOSES="AD CN MCI" -BASELINE=1 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=30 -BATCH=12 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/legacy/experiment-1.sh b/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/legacy/experiment-1.sh deleted file mode 100644 index d80cabba4..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/legacy/experiment-1.sh +++ /dev/null @@ -1,108 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=2-00:00:00 -#SBATCH --mem=17G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/pytorch_job_%j.out -#SBATCH --error=outputs/pytorch_job_%j.err -#SBATCH --job-name=3DAE -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="autoencoder_pretraining.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="" -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -BASELINE=1 -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/autoencoder_pretraining/model-Conv5_FC3_cohort-ADNI_task-autoencoder_gpu-1_workers-8_threads-0_epochs-100_lr-1e-4_norm-1_gl-0_sigmoid-0_batch-6_acc-2_eval-20_totalsplits-5/split-$SPLIT/model_best_loss.pth.tar" -T_DIFF=0 - -# Computation ressources -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 - -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $GREEDY_LEARNING = 1 ]; then -OPTIONS="$OPTIONS --greedy_learning" -fi - -if [ $SIGMOID = 1 ]; then -OPTIONS="${OPTIONS} --add_sigmoid" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME -echo $OPTIONS -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT \\ - $TSVPATH \ - $RESULTSPATH$NAME \ - $IMGPATH $MODEL \ - -w $NUM_WORKERS \ - --batch_size $BATCH \ - --epochs $EPOCHS \ - -asteps $ACCUMULATION \ - -esteps $EVALUATION \ - -lr $LR \ - --num_threads $NUM_THREADS \ - --diagnoses $DIAGNOSES \ - --preprocessing $PREPROCESSING \ - --patience $PATIENCE \ - $OPTIONS --visualization diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/legacy/experiment-2.sh b/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/legacy/experiment-2.sh deleted file mode 100644 index bcd8fdeb6..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/autoencoder/legacy/experiment-2.sh +++ /dev/null @@ -1,109 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=2-00:00:00 -#SBATCH --mem=17G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/pytorch_job_%j.out -#SBATCH --error=outputs/pytorch_job_%j.err -#SBATCH --job-name=3DAE -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="autoencoder_pretraining.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_skull_stripping" -PREPROCESSING='mniskullstrip' -DIAGNOSES="AD CN MCI" -BASELINE=1 -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -RUNS=1 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/autoencoder_pretraining/model-Conv5_FC3_cohort-ADNI_task-autoencoder_gpu-1_workers-8_threads-0_epochs-100_lr-1e-4_norm-1_gl-0_sigmoid-0_batch-6_acc-2_eval-20_totalsplits-5/split-$SPLIT/model_best_loss.pth.tar" -T_DIFF=0 - -# Computation ressources -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 - -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $GREEDY_LEARNING = 1 ]; then -OPTIONS="$OPTIONS --greedy_learning" -fi - -if [ $SIGMOID = 1 ]; then -OPTIONS="${OPTIONS} --add_sigmoid" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME -echo $OPTIONS -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT - $TSVPATH \ - $RESULTSPATH$NAME \ - $IMGPATH $MODEL \ - -w $NUM_WORKERS \ - --batch_size $BATCH \ - --epochs $EPOCHS \ - -asteps $ACCUMULATION \ - -esteps $EVALUATION \ - -lr $LR \ - --num_threads $NUM_THREADS \ - --diagnoses $DIAGNOSES \ - --preprocessing $PREPROCESSING \ - --patience $PATIENCE \ - $OPTIONS --visualization diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/evaluation/example_evaluation_ADNI.sh b/projects/Wen_MedIA_2020/scripts/subject_level/evaluation/example_evaluation_ADNI.sh deleted file mode 100644 index 5487b5792..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/evaluation/example_evaluation_ADNI.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs/AD-DL/test -#SBATCH --output=./subject/pytorch_job_%j.out -#SBATCH --error=./subject/pytorch_job_%j.err -#SBATCH --job-name=t_subject -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -SCRIPT="evaluation_test.py" - -# Data management -COHORT="ADNI" - -BATCH=2 -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -OUTPUTDIR="subject_model-Conv5_FC3_preprocessing-linear_task-AD_CN_baseline_norm-0_t-0_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATHC/results/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}_rerun" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT \ - $TSVPATH \ - $RESULTSPATH \ - $IMGPATH \ - $COHORT \ - -w $NUM_WORKERS \ - --num_threads $NUM_THREADS $OPTIONS \ - --batch_size $BATCH diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/evaluation/example_evaluation_ADNI_local.sh b/projects/Wen_MedIA_2020/scripts/subject_level/evaluation/example_evaluation_ADNI_local.sh deleted file mode 100755 index 1da8bf91e..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/evaluation/example_evaluation_ADNI_local.sh +++ /dev/null @@ -1,33 +0,0 @@ -#!/bin/bash - -set -x - -SCRIPT="evaluation_test.py" - -# Data management -COHORT="ADNI" - -BATCH=2 -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -WORKDIR="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/Experiments_results/Zenodo_rerun/test1" -OUTPUTDIR="subject_model-Conv5_FC3_preprocessing-linear_task-AD_CN_baseline_norm-0_t-0_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$WORKDIR/3_test/$OUTPUTDIR/" -IMGPATH="$WORKDIR/../../${COHORT}_rerun" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/subject_level/$SCRIPT \ - $RESULTSPATH \ - $IMGPATH \ - $TSVPATH \ - $COHORT \ - -w $NUM_WORKERS \ - --num_threads $NUM_THREADS $OPTIONS \ - --batch_size $BATCH diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-2-bis.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-2-bis.sh deleted file mode 100644 index 4da2e047c..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-2-bis.sh +++ /dev/null @@ -1,113 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/subject_level/cnn -#SBATCH --output=./exp2/pytorch_job_%j.out -#SBATCH --error=./exp2/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="OASIS_atrophy" -DATE="trivial_tests_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='AD CN' -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=10 -TOLERANCE=0 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-2.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-2.sh deleted file mode 100644 index 2a7f61404..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-2.sh +++ /dev/null @@ -1,113 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/subject_level/cnn -#SBATCH --output=./exp2/pytorch_job_%j.out -#SBATCH --error=./exp2/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="OASIS_random" -DATE="trivial_tests" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='AD CN' -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=10 -TOLERANCE=0 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-4-bis.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-4-bis.sh deleted file mode 100644 index 572674a80..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-4-bis.sh +++ /dev/null @@ -1,114 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/subject_level/cnn -#SBATCH --output=./exp4/pytorch_job_%j.out -#SBATCH --error=./exp4/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --array=0-4 -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="OASIS_atrophy" -DATE="trivial_tests_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='AD CN' -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="subject_model-Conv5_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-4.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-4.sh deleted file mode 100644 index c649cb19a..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/CNN/experiment-4.sh +++ /dev/null @@ -1,114 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/subject_level/cnn -#SBATCH --output=./exp4/pytorch_job_%j.out -#SBATCH --error=./exp4/pytorch_job_%j.err -#SBATCH --job-name=3CNN_subject -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --array=0-4 -#SBATCH --mail-user=mauricio.diaz-melo@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment taining CNN -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="OASIS_random" -DATE="trivial_tests" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -TASK='AD CN' -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -NORMALIZATION=1 -PATIENCE=5 -TOLERANCE=0 - -# Pretraining -T_BOOL=1 -T_PATH="subject_model-Conv5_FC3_preprocessing-linear_task-autoencoder_baseline-0_norm-1_splits-5" -T_PATH="$SCRATCH/results/$DATE/$T_PATH" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --transfer_learning_autoencoder --transfer_learning_path $T_PATH" -fi -TASK_NAME="${TASK// /_}" - -if [ $BASELINE = 1 ]; then - echo "using only baseline data" - TASK_NAME="${TASK_NAME}_baseline" - OPTIONS="$OPTIONS --baseline" -fi -echo $TASK_NAME - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-${TASK_NAME}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --diagnoses $TASK \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/experiment-1-bis.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/experiment-1-bis.sh deleted file mode 100644 index f3164c50b..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/experiment-1-bis.sh +++ /dev/null @@ -1,111 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/subject_level/autoencoder -#SBATCH --output=./exp1/pytorch_job_%j.out -#SBATCH --error=./exp1/pytorch_job_%j.err -#SBATCH --job-name=3DAE_subj -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="OASIS_atrophy" -DATE="trivial_tests_2" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=12 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/experiment-1.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/experiment-1.sh deleted file mode 100644 index bccde00c5..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/experiment-1.sh +++ /dev/null @@ -1,111 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/train/subject_level/autoencoder -#SBATCH --output=./exp1/pytorch_job_%j.out -#SBATCH --error=./exp1/pytorch_job_%j.err -#SBATCH --job-name=3DAE_subj -#SBATCH --gres=gpu:1 -#SBATCH --array=0-4 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -# Experiment training autoencoder -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -# Network structure -NETWORK="Conv5_FC3" -COHORT="OASIS_random" -DATE="trivial_tests" - -# Input arguments to clinicadl -CAPS_DIR="$SCRATCH/../commun/datasets/${COHORT}" -TSV_PATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/train" -OUTPUT_DIR="$SCRATCH/results/$DATE/" - -# Computation ressources -NUM_PROCESSORS=8 -GPU=1 - -# Dataset Management -PREPROCESSING='linear' -DIAGNOSES="AD CN" -BASELINE=0 -SPLITS=5 -SPLIT=$SLURM_ARRAY_TASK_ID - -# Training arguments -EPOCHS=50 -BATCH=12 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -WEIGHT_DECAY=0 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="" -T_DIFF=0 - -# Other options -OPTIONS="" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --use_gpu" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} --minmaxnormalization" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="subject_model-${NETWORK}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME - -# Run clinicadl -clinicadl train \ - subject \ - $CAPS_DIR \ - $TSV_PATH \ - $OUTPUT_DIR$NAME \ - $NETWORK \ - --train_autoencoder \ - --nproc $NUM_PROCESSORS \ - --batch_size $BATCH \ - --evaluation_steps $EVALUATION \ - --preprocessing $PREPROCESSING \ - --diagnoses $DIAGNOSES \ - --n_splits $SPLITS \ - --split $SPLIT \ - --accumulation_steps $ACCUMULATION \ - --epochs $EPOCHS \ - --learning_rate $LR \ - --weight_decay $WEIGHT_DECAY \ - --patience $PATIENCE \ - $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/legacy/experiment-1.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/legacy/experiment-1.sh deleted file mode 100644 index d80cabba4..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/legacy/experiment-1.sh +++ /dev/null @@ -1,108 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=2-00:00:00 -#SBATCH --mem=17G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/pytorch_job_%j.out -#SBATCH --error=outputs/pytorch_job_%j.err -#SBATCH --job-name=3DAE -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="autoencoder_pretraining.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="" -PREPROCESSING='linear' -DIAGNOSES="AD CN MCI" -BASELINE=1 -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/autoencoder_pretraining/model-Conv5_FC3_cohort-ADNI_task-autoencoder_gpu-1_workers-8_threads-0_epochs-100_lr-1e-4_norm-1_gl-0_sigmoid-0_batch-6_acc-2_eval-20_totalsplits-5/split-$SPLIT/model_best_loss.pth.tar" -T_DIFF=0 - -# Computation ressources -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 - -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $GREEDY_LEARNING = 1 ]; then -OPTIONS="$OPTIONS --greedy_learning" -fi - -if [ $SIGMOID = 1 ]; then -OPTIONS="${OPTIONS} --add_sigmoid" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME -echo $OPTIONS -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT \\ - $TSVPATH \ - $RESULTSPATH$NAME \ - $IMGPATH $MODEL \ - -w $NUM_WORKERS \ - --batch_size $BATCH \ - --epochs $EPOCHS \ - -asteps $ACCUMULATION \ - -esteps $EVALUATION \ - -lr $LR \ - --num_threads $NUM_THREADS \ - --diagnoses $DIAGNOSES \ - --preprocessing $PREPROCESSING \ - --patience $PATIENCE \ - $OPTIONS --visualization diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/legacy/experiment-2.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/legacy/experiment-2.sh deleted file mode 100644 index bcd8fdeb6..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/autoencoder/legacy/experiment-2.sh +++ /dev/null @@ -1,109 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu -#SBATCH --time=2-00:00:00 -#SBATCH --mem=17G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/pytorch_job_%j.out -#SBATCH --error=outputs/pytorch_job_%j.err -#SBATCH --job-name=3DAE -#SBATCH --gres=gpu:1 - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0_py27 -# Network structure -SCRIPT="autoencoder_pretraining.py" -MODEL="Conv5_FC3" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_skull_stripping" -PREPROCESSING='mniskullstrip' -DIAGNOSES="AD CN MCI" -BASELINE=1 -SPLITS=5 -SPLIT=$1 - -# Training arguments -EPOCHS=50 -RUNS=1 -BATCH=6 -ACCUMULATION=2 -EVALUATION=20 -LR=1e-4 -GREEDY_LEARNING=0 -SIGMOID=0 -NORMALIZATION=1 -PATIENCE=50 - -# Pretraining -T_BOOL=0 -T_PATH="/network/lustre/iss01/home/elina.thibeausutre/results/autoencoder_pretraining/model-Conv5_FC3_cohort-ADNI_task-autoencoder_gpu-1_workers-8_threads-0_epochs-100_lr-1e-4_norm-1_gl-0_sigmoid-0_batch-6_acc-2_eval-20_totalsplits-5/split-$SPLIT/model_best_loss.pth.tar" -T_DIFF=0 - -# Computation ressources -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 - -OPTIONS="" -DATE="reproducibility_results" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -if [ $GREEDY_LEARNING = 1 ]; then -OPTIONS="$OPTIONS --greedy_learning" -fi - -if [ $SIGMOID = 1 ]; then -OPTIONS="${OPTIONS} --add_sigmoid" -fi - -if [ $NORMALIZATION = 1 ]; then -OPTIONS="${OPTIONS} -n" -fi - -if [ $T_BOOL = 1 ]; then -OPTIONS="$OPTIONS --pretrained_path $T_PATH -d $T_DIFF" -fi - -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NAME="model-${MODEL}_preprocessing-${PREPROCESSING}_task-autoencoder_baseline-${BASELINE}_norm-${NORMALIZATION}_t-${T_BOOL}" - -if [ $SPLITS > 0 ]; then -echo "Use of $SPLITS-fold cross validation, split $SPLIT" -OPTIONS="$OPTIONS --n_splits $SPLITS --split $SPLIT" -NAME="${NAME}_splits-${SPLITS}" -fi - -echo $NAME -echo $OPTIONS -python /network/lustre/iss01/home/elina.thibeausutre/AD-DL/Code/clinicadl/clinicadl/classifiers/three_d_cnn/subject_level/$SCRIPT - $TSVPATH \ - $RESULTSPATH$NAME \ - $IMGPATH $MODEL \ - -w $NUM_WORKERS \ - --batch_size $BATCH \ - --epochs $EPOCHS \ - -asteps $ACCUMULATION \ - -esteps $EVALUATION \ - -lr $LR \ - --num_threads $NUM_THREADS \ - --diagnoses $DIAGNOSES \ - --preprocessing $PREPROCESSING \ - --patience $PATIENCE \ - $OPTIONS --visualization diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/evaluation_ADNI_exp4-bis.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/evaluation_ADNI_exp4-bis.sh deleted file mode 100644 index 9bd3bdf32..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/evaluation_ADNI_exp4-bis.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation -#SBATCH --output=./subject_level/pytorch_job_%j.out -#SBATCH --error=./subject_level/pytorch_job_%j.err -#SBATCH --job-name=t_subject -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -SCRIPT="evaluation_test.py" - -# Data management -COHORT="OASIS_atrophy" - -BATCH=2 -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -OUTPUTDIR="subject_model-Conv5_FC3_preprocessing-linear_task-AD_CN_norm-1_t-0_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests_2/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/subject_level/$SCRIPT \ - $RESULTSPATH \ - $IMGPATH \ - $TSVPATH \ - $COHORT \ - -w $NUM_WORKERS \ - --num_threads $NUM_THREADS $OPTIONS \ - --batch_size $BATCH diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/evaluation_ADNI_exp4.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/evaluation_ADNI_exp4.sh deleted file mode 100644 index 935009918..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/evaluation_ADNI_exp4.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation -#SBATCH --output=./subject_level/pytorch_job_%j.out -#SBATCH --error=./subject_level/pytorch_job_%j.err -#SBATCH --job-name=t_subject -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -SCRIPT="evaluation_test.py" - -# Data management -COHORT="OASIS_random" - -BATCH=2 -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -OUTPUTDIR="subject_model-Conv5_FC3_preprocessing-linear_task-AD_CN_norm-1_t-1_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/subject_level/$SCRIPT \ - $RESULTSPATH \ - $IMGPATH \ - $TSVPATH \ - $COHORT \ - -w $NUM_WORKERS \ - --num_threads $NUM_THREADS $OPTIONS \ - --batch_size $BATCH diff --git a/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/example_evaluation_ADNI.sh b/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/example_evaluation_ADNI.sh deleted file mode 100644 index f8d803fcf..000000000 --- a/projects/Wen_MedIA_2020/scripts/subject_level/tests/evaluation/example_evaluation_ADNI.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash -#SBATCH --partition=gpu_p1 -#SBATCH --time=20:00:00 -#SBATCH --mem=60G -#SBATCH --cpus-per-task=10 -#SBATCH --threads-per-core=1 # on réserve des coeurs physiques et non logiques -#SBATCH --ntasks=1 -#SBATCH --workdir=/gpfswork/rech/zft/upd53tc/jobs2/AD-DL/tests/evaluation -#SBATCH --output=./subject_level/pytorch_job_%j.out -#SBATCH --error=./subject_level/pytorch_job_%j.err -#SBATCH --job-name=t_subject -#SBATCH --gres=gpu:1 -#SBATCH --mail-type=END -#SBATCH --mail-user=mauricio.diaz@inria.fr - -#export http_proxy=http://10.10.2.1:8123 -#export https_proxy=http://10.10.2.1:8123 - -eval "$(conda shell.bash hook)" -conda activate clinicadl_env_py37 - -SCRIPT="evaluation_test.py" - -# Data management -COHORT="OASIS_random" - -BATCH=2 -GPU=1 -NUM_WORKERS=8 -NUM_THREADS=0 -OPTIONS="" -OUTPUTDIR="subject_model-Conv5_FC3_preprocessing-linear_task-AD_CN_norm-1_t-0_splits-5" - -TSVPATH="$HOME/code/AD-DL/data/$COHORT/lists_by_diagnosis/test/" -RESULTSPATH="$SCRATCH/results/trivial_tests/3_test/$OUTPUTDIR/" -IMGPATH="$SCRATCH/../commun/datasets/${COHORT}" - -if [ $GPU = 1 ]; then -OPTIONS="${OPTIONS} --gpu" -fi - -python $HOME/code/AD-DL/clinicadl/clinicadl/subject_level/$SCRIPT \ - $RESULTSPATH \ - $IMGPATH \ - $TSVPATH \ - $COHORT \ - -w $NUM_WORKERS \ - --num_threads $NUM_THREADS $OPTIONS \ - --batch_size $BATCH diff --git a/projects/Wen_MedIA_2020/scripts/svm/apply_validation/experiment-23.sh b/projects/Wen_MedIA_2020/scripts/svm/apply_validation/experiment-23.sh deleted file mode 100755 index dfeba30f4..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/apply_validation/experiment-23.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=1:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=SVM - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0 -# Network structure -SCRIPT="evaluation" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_clinica_spm" -BASELINE=1 -TASK='sMCI pMCI' -SPLITS=5 -SET="validation" - -NUM_WORKERS=16 -DATE="refactoring_results/svm4" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -TASK_NAME="train_AD_CN" -TASK_NAME="${TASK_NAME}_baseline-${BASELINE}_final" -echo $TASK_NAME - -NAME="model-svm_task-${TASK_NAME}" - -cd .. -python -m src.$SCRIPT $TSVPATH $IMGPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS --set $SET --train_mode diff --git a/projects/Wen_MedIA_2020/scripts/svm/apply_validation/experiment-26.sh b/projects/Wen_MedIA_2020/scripts/svm/apply_validation/experiment-26.sh deleted file mode 100755 index 8b2f68e52..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/apply_validation/experiment-26.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=1:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=SVM - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0 -# Network structure -SCRIPT="evaluation" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_clinica_spm" -BASELINE=0 -TASK='sMCI pMCI' -SPLITS=5 -SET="validation" - -NUM_WORKERS=16 -DATE="refactoring_results/svm4" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -TASK_NAME="train_AD_CN" -TASK_NAME="${TASK_NAME}_baseline-${BASELINE}_final" -echo $TASK_NAME - -NAME="model-svm_task-${TASK_NAME}" - -cd .. -python -m src.$SCRIPT $TSVPATH $IMGPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS --set $SET --train_mode diff --git a/projects/Wen_MedIA_2020/scripts/svm/src/__init__.py b/projects/Wen_MedIA_2020/scripts/svm/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/projects/Wen_MedIA_2020/scripts/svm/src/classification_utils.py b/projects/Wen_MedIA_2020/scripts/svm/src/classification_utils.py deleted file mode 100644 index 2946a2cf6..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/src/classification_utils.py +++ /dev/null @@ -1,550 +0,0 @@ -# coding: utf8 - -import abc -import os -import pandas as pd -from clinica.pipelines.machine_learning import base -import clinica.pipelines.machine_learning.voxel_based_io as vbio -import clinica.pipelines.machine_learning.ml_utils as utils -from multiprocessing.pool import ThreadPool -from os import path -import numpy as np -from sklearn.model_selection import StratifiedKFold -import nibabel as nib - -__author__ = "Junhao Wen" -__copyright__ = "Copyright 2018 The Aramis Lab Team" -__credits__ = ["Junhao Wen, Jorge Samper Gonzalez"] -__license__ = "See LICENSE.txt file" -__version__ = "0.1.0" -__maintainer__ = "Junhao Wen" -__email__ = "junhao.wen89@gmail.com" -__status__ = "Development" - - -class CAPSInput(base.MLInput): - - def __init__(self, caps_directory, diagnoses_tsv, group_id, image_type, precomputed_kernel=None): - """ - Args: - caps_directory: - subjects_visits_tsv: - diagnoses_tsv: - group_id: - image_type: 'T1', 'fdg', 'av45', 'pib' or 'flute' - precomputed_kernel: - """ - - self._caps_directory = caps_directory - self._group_id = group_id - self._image_type = image_type - self._images = None - self._x = None - self._y = None - self._kernel = None - - subjects_visits = pd.read_csv(diagnoses_tsv, sep='\t') - if list(subjects_visits.columns.values) != ['participant_id', 'session_id', 'diagnosis']: - raise Exception('Subjects and visits file is not in the correct format.') - self._subjects = list(subjects_visits.participant_id) - self._sessions = list(subjects_visits.session_id) - - diagnoses = pd.read_csv(diagnoses_tsv, sep='\t') - if 'diagnosis' not in list(diagnoses.columns.values): - raise Exception('Diagnoses file is not in the correct format.') - self._diagnoses = list(diagnoses.diagnosis) - - if image_type not in ['T1', 'fdg', 'av45', 'pib', 'flute', 'dwi']: - raise Exception("Incorrect image type. It must be one of the values 'T1', 'fdg', 'av45', 'pib', 'flute' or 'dwi'") - - if precomputed_kernel is not None: - if type(precomputed_kernel) == np.ndarray: - if precomputed_kernel.shape == (len(self._subjects), len(self._subjects)): - self._kernel = precomputed_kernel - else: - raise Exception("""Precomputed kernel provided is not in the correct format. - It must be a numpy.ndarray object with number of rows and columns equal to the number of subjects, - or a filename to a numpy txt file containing an object with the described format.""") - elif type(precomputed_kernel == str): - self._kernel = np.loadtxt(precomputed_kernel) - else: - raise Exception("""Precomputed kernel provided is not in the correct format. - It must be a numpy.ndarray object with number of rows and columns equal to the number of subjects, - or a filename to a numpy txt file containing an object with the described format.""") - - @abc.abstractmethod - def get_images(self): - """ - Returns: a list of filenames - """ - pass - - @abc.abstractmethod - def get_x(self): - """ - Returns: a numpy 2d-array. - """ - pass - - def get_y(self): - """ - Returns: a list of integers. Each integer represents a class. - """ - if self._y is not None: - return self._y - - unique = list(set(self._diagnoses)) - self._y = np.array([unique.index(x) for x in self._diagnoses]) - return self._y - - def get_kernel(self, kernel_function=utils.gram_matrix_linear, recompute_if_exists=False): - """ - Returns: a numpy 2d-array. - """ - if self._kernel is not None and not recompute_if_exists: - return self._kernel - - if self._x is None: - self.get_x() - - print("Computing kernel ...") - self._kernel = kernel_function(self._x) - print("Kernel computed") - return self._kernel - - def save_kernel(self, output_dir): - """ - Args: - output_dir: - Returns: - """ - if self._kernel is not None: - filename = path.join(output_dir, 'kernel.txt') - np.savetxt(filename, self._kernel) - return filename - raise Exception("Unable to save the kernel. Kernel must have been computed before.") - - @abc.abstractmethod - def save_weights_as_nifti(self, weights, output_dir): - pass - - -class CAPSVoxelBasedInput(CAPSInput): - - def __init__(self, caps_directory, diagnoses_tsv, group_id, image_type, fwhm=0, - modulated="on", pvc=None, mask_zeros=True, precomputed_kernel=None): - """ - Args: - caps_directory: - diagnoses_tsv: - group_id: - image_type: 'T1', 'fdg', 'av45', 'pib' or 'flute' - fwhm: - modulated: - mask_zeros: - precomputed_kernel: - """ - - super(CAPSVoxelBasedInput, self).__init__(caps_directory, diagnoses_tsv, group_id, - image_type, precomputed_kernel=precomputed_kernel) - - self._fwhm = fwhm - self._modulated = modulated - self._pvc = pvc - self._mask_zeros = mask_zeros - self._orig_shape = None - self._data_mask = None - - if modulated not in ['on', 'off']: - raise Exception("Incorrect modulation parameter. It must be one of the values 'on' or 'off'") - - def get_images(self): - """ - Returns: a list of filenames - """ - if self._images is not None: - return self._images - - if self._image_type == 'T1': - fwhm = '' if self._fwhm == 0 else '_fwhm-%dmm' % int(self._fwhm) - - self._images = [path.join(self._caps_directory, 'subjects', self._subjects[i], self._sessions[i], - 't1/spm/dartel/group-' + self._group_id, - '%s_%s_T1w_segm-graymatter_space-Ixi549Space_modulated-%s%s_probability.nii.gz' - % (self._subjects[i], self._sessions[i], self._modulated, fwhm)) - for i in range(len(self._subjects))] - else: - pvc = '' if self._pvc is None else '_pvc-%s' % self._pvc - fwhm = '' if self._fwhm == 0 else '_fwhm-%dmm' % int(self._fwhm) - suvr = 'pons' if self._image_type == 'fdg' else 'cerebellumPons' - - self._images = [path.join(self._caps_directory, 'subjects', self._subjects[i], self._sessions[i], - 'pet/preprocessing/group-' + self._group_id, - '%s_%s_task-rest_acq-%s_pet_space-Ixi549Space%s_suvr-%s_mask-brain%s_pet.nii.gz' - % (self._subjects[i], self._sessions[i], self._image_type, pvc, suvr, fwhm)) - for i in range(len(self._subjects))] - - for image in self._images: - if not path.exists(image): - raise Exception("File %s doesn't exists." % image) - - return self._images - - def get_x(self): - """ - Returns: a numpy 2d-array. - """ - if self._x is not None: - return self._x - - print('Loading ' + str(len(self.get_images())) + ' subjects') - self._x, self._orig_shape, self._data_mask = vbio.load_data(self._images, mask=self._mask_zeros) - print('Subjects loaded') - - return self._x - - def save_weights_as_nifti(self, weights, output_dir): - - if self._images is None: - self.get_images() - - output_filename = path.join(output_dir, 'weights.nii.gz') - data = vbio.revert_mask(weights, self._data_mask, self._orig_shape) - vbio.weights_to_nifti(data, self._images[0], output_filename) - - -class KFoldCV(base.MLValidation): - - def __init__(self, ml_algorithm): - self._ml_algorithm = ml_algorithm - self._fold_results = [] - self._classifier = None - self._best_params = None - self._cv = None - - def validate(self, y, n_folds=10, n_threads=15, splits_indices=None): - - if splits_indices is None: - skf = StratifiedKFold(n_splits=n_folds, shuffle=True, ) - self._cv = list(skf.split(np.zeros(len(y)), y)) - else: - self._cv = splits_indices - - async_pool = ThreadPool(n_threads) - async_result = {} - - for i in range(n_folds): - - train_index, test_index = self._cv[i] - async_result[i] = async_pool.apply_async(self._ml_algorithm.evaluate, (train_index, test_index)) - - async_pool.close() - async_pool.join() - - for i in range(n_folds): - self._fold_results.append(async_result[i].get()) - - # save the mean of the best models - self._classifier, self._best_params = self._ml_algorithm.apply_best_parameters(self._fold_results) - - return self._classifier, self._best_params, self._fold_results - - def save_results(self, output_dir): - if self._fold_results is None: - raise Exception("No results to save. Method validate() must be run before save_results().") - - subjects_folds = [] - results_folds = [] - container_dir = path.join(output_dir, 'folds') - - os.makedirs(container_dir, exist_ok=True) - - for i in range(len(self._fold_results)): - subjects_df = pd.DataFrame({'y': self._fold_results[i]['y'], - 'y_hat': self._fold_results[i]['y_hat'], - 'y_index': self._fold_results[i]['y_index']}) - subjects_df.to_csv(path.join(container_dir, 'subjects_fold-' + str(i) + '.tsv'), - index=False, sep='\t', encoding='utf-8') - subjects_folds.append(subjects_df) - - results_df = pd.DataFrame({'balanced_accuracy': self._fold_results[i]['evaluation']['balanced_accuracy'], - 'auc': self._fold_results[i]['auc'], - 'accuracy': self._fold_results[i]['evaluation']['accuracy'], - 'sensitivity': self._fold_results[i]['evaluation']['sensitivity'], - 'specificity': self._fold_results[i]['evaluation']['specificity'], - 'ppv': self._fold_results[i]['evaluation']['ppv'], - 'npv': self._fold_results[i]['evaluation']['npv']}, index=['i', ]) - results_df.to_csv(path.join(container_dir, 'results_fold-' + str(i) + '.tsv'), - index=False, sep='\t', encoding='utf-8') - results_folds.append(results_df) - - all_subjects = pd.concat(subjects_folds) - all_subjects.to_csv(path.join(output_dir, 'subjects.tsv'), - index=False, sep='\t', encoding='utf-8') - - all_results = pd.concat(results_folds) - all_results.to_csv(path.join(output_dir, 'results.tsv'), - index=False, sep='\t', encoding='utf-8') - - mean_results = pd.DataFrame(all_results.apply(np.nanmean).to_dict(), columns=all_results.columns, index=[0, ]) - mean_results.to_csv(path.join(output_dir, 'mean_results.tsv'), - index=False, sep='\t', encoding='utf-8') - print("Mean results of the classification:") - print("Balanced accuracy: %s" % (mean_results['balanced_accuracy'].to_string(index=False))) - print("specificity: %s" % (mean_results['specificity'].to_string(index=False))) - print("sensitivity: %s" % (mean_results['sensitivity'].to_string(index=False))) - print("auc: %s" % (mean_results['auc'].to_string(index=False))) - - -def extract_indices_from_5_fold(diagnosis_tsv_folder, n_splits, output_dir, baseline=True, diagnoses_list=['AD', 'CN']): - """ - This is a function to restore the indices of the 5 fold made for CNN based on all the diagnosis tsvs - :param diagnosis_tsv_folder: - :param n_splits: - :param baseline_or_longitudinal: - :return: the combined tsv file and the indices for 5 fold - """ - splits_indices = [] - - for i in range(n_splits): - train_df = pd.DataFrame() - valid_df = pd.DataFrame() - train_index = [] - valid_index = [] - - train_path = path.join(diagnosis_tsv_folder, 'train_splits-' + str(n_splits), - 'split-' + str(i)) - valid_path = path.join(diagnosis_tsv_folder, 'validation_splits-' + str(n_splits), - 'split-' + str(i)) - print("Train", train_path) - print("Valid", valid_path) - - for diagnosis in diagnoses_list: - - if baseline: - train_diagnosis_tsv = path.join(train_path, diagnosis + '_baseline.tsv') - else: - train_diagnosis_tsv = path.join(train_path, diagnosis + '.tsv') - - valid_diagnosis_tsv = path.join(valid_path, diagnosis + '_baseline.tsv') - - train_diagnosis_df = pd.read_csv(train_diagnosis_tsv, sep='\t') - valid_diagnosis_df = pd.read_csv(valid_diagnosis_tsv, sep='\t') - - train_df = pd.concat([train_df, train_diagnosis_df]) - valid_df = pd.concat([valid_df, valid_diagnosis_df]) - - train_df.reset_index(inplace=True, drop=True) - valid_df.reset_index(inplace=True, drop=True) - - if i == 0: - # only concatenate the train + valid for the first fold - all_df = pd.concat([train_df, valid_df]) - all_df.reset_index(inplace=True, drop=True) - - all_tsv = os.path.join(output_dir, 'all_subjects.tsv') - all_df.to_csv(all_tsv, index=False, sep='\t', encoding='utf-8') - - # find the index for the training and validation based on the concatenated tsv. - for j in range(len(train_df)): - row = train_df.iloc[j] - for index, row_all in all_df.iterrows(): - if row['participant_id'] == row_all['participant_id'] and row['session_id'] == row_all['session_id'] and row['diagnosis'] == row_all['diagnosis']: - train_index.append(index) - - for k in range(len(valid_df)): - row = valid_df.iloc[k] - for index, row_all in all_df.iterrows(): - if row['participant_id'] == row_all['participant_id'] and row['session_id'] == row_all['session_id'] and row['diagnosis'] == row_all['diagnosis']: - valid_index.append(index) - - # convert the list of index to be an array - train_index_array = np.asarray(train_index) - valid_index_array = np.asarray(valid_index) - - # convert the two arrays into a tuple - index_tuple = (train_index_array, valid_index_array) - splits_indices.append(index_tuple) - - return splits_indices, all_tsv - - -def load_data_svm(image_list, mask=True): - """ - Args: - image_list: - mask: - Returns: - """ - data = None - shape = None - data_mask = None - first = True - - for i in range(len(image_list)): - subj = nib.load(image_list[i]) - subj_data = np.nan_to_num(subj.get_data().flatten()) - - # Memory allocation for ndarray containing all data to avoid copying the array for each new subject - if first: - data = np.ndarray(shape=(len(image_list), subj_data.shape[0]), dtype=float, order='C') - shape = subj.get_data().shape - first = False - - data[i, :] = subj_data - - if mask: - data_mask = (data != 0).sum(axis=0) != 0 - data = data[:, data_mask] - - return data, shape, data_mask - - -def revert_mask(weights, mask, shape): - """ - Args: - weights: - mask: - shape: - Returns: - """ - - z = np.zeros(np.prod(shape)) - z[mask] = weights # ValueError: NumPy boolean array indexing assignment cannot assign 1636161 input values to the 1634188 output values where the mask is true - - new_weights = np.reshape(z, shape) - - return new_weights - - -def evaluate_prediction(y, y_hat): - - true_positive = 0.0 - true_negative = 0.0 - false_positive = 0.0 - false_negative = 0.0 - - tp = [] - tn = [] - fp = [] - fn = [] - - for i in range(len(y)): - if y[i] == 1: - if y_hat[i] == 1: - true_positive += 1 - tp.append(i) - else: - false_negative += 1 - fn.append(i) - else: # -1 - if y_hat[i] == 0: - true_negative += 1 - tn.append(i) - else: - false_positive += 1 - fp.append(i) - - accuracy = (true_positive + true_negative) / (true_positive + true_negative + false_positive + false_negative) - - if (true_positive + false_negative) != 0: - sensitivity = true_positive / (true_positive + false_negative) - else: - sensitivity = 0.0 - - if (false_positive + true_negative) != 0: - specificity = true_negative / (false_positive + true_negative) - else: - specificity = 0.0 - - if (true_positive + false_positive) != 0: - ppv = true_positive / (true_positive + false_positive) - else: - ppv = 0.0 - - if (true_negative + false_negative) != 0: - npv = true_negative / (true_negative + false_negative) - else: - npv = 0.0 - - balanced_accuracy = (sensitivity + specificity) / 2 - - results = {'accuracy': accuracy, - 'balanced_accuracy': balanced_accuracy, - 'sensitivity': sensitivity, - 'specificity': specificity, - 'ppv': ppv, - 'npv': npv - } - - return results - - -def save_data(df, output_dir, folder_name): - """ - Save data so it can be used by the workflow - :param df: - :param output_dir: - :param folder_name: - :return: path to the tsv files - """ - import os - from os import path - - results_dir = path.join(output_dir, 'data', folder_name) - os.makedirs(results_dir, exist_ok=True) - - df.to_csv(path.join(results_dir, 'all_subjects.tsv'), sep="\t", index=False) - - return path.join(results_dir, 'all_subjects.tsv') - - -def save_weights(classifier, x, output_dir): - - dual_coefficients = classifier.dual_coef_ - sv_indices = classifier.support_ - - weighted_sv = dual_coefficients.transpose() * x[sv_indices] - weights = np.sum(weighted_sv, 0) - - np.savetxt(path.join(output_dir, 'weights.txt'), weights) - - return weights - - -def apply_best_parameters_each_split(kernel, x, y, results_list, balanced, n_fold, diagnoses_tsv, output_dir): - """ - Save the best model for each fold - :param results_list: - :return: - """ - - from sklearn.svm import SVC - - best_c = results_list[n_fold]['best_parameter']['c'] - best_bal_acc = results_list[n_fold]['best_parameter']['balanced_accuracy'] - train_index = results_list[n_fold]['x_index'] - - if balanced: - svc = SVC(C=best_c, kernel='precomputed', probability=True, tol=1e-6, class_weight='balanced') - else: - svc = SVC(C=best_c, kernel='precomputed', probability=True, tol=1e-6) - - outer_kernel = kernel[train_index, :][:, train_index] - y_train = y[train_index] - - # save the training data for reconstruction use - df = pd.read_csv(diagnoses_tsv, sep='\t') - df_training = df.iloc[train_index] - - result_dir = path.join(output_dir, 'classifier', 'fold_' + str(n_fold)) - os.makedirs(result_dir, exist_ok=True) - - training_tsv = os.path.join(result_dir, 'training_subjects.tsv') - df_training.to_csv(training_tsv, index=False, sep='\t', encoding='utf-8') - - svc.fit(outer_kernel, y_train) - # save the weight - save_weights(svc, x[train_index], result_dir) - - return svc, {'c': best_c, 'balanced_accuracy': best_bal_acc}, train_index diff --git a/projects/Wen_MedIA_2020/scripts/svm/src/evaluation.py b/projects/Wen_MedIA_2020/scripts/svm/src/evaluation.py deleted file mode 100644 index 51d581c70..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/src/evaluation.py +++ /dev/null @@ -1,100 +0,0 @@ -# coding: utf8 - -import argparse -import os -from os import path -import pandas as pd -import numpy as np -from .classification_utils import load_data_svm, revert_mask, CAPSVoxelBasedInput, evaluate_prediction, save_data -from clinicadl.tools.deep_learning.data import load_data, load_data_test - -parser = argparse.ArgumentParser(description="Argparser for Pytorch 3D CNN") - -# Mandatory arguments -parser.add_argument("diagnosis_path", type=str, - help="Path to tsv files of the population." - " To note, the column name should be participant_id, session_id and diagnosis.") -parser.add_argument("caps_directory_source", type=str, - help="Path to input dir of the MRI (preprocessed CAPS_dir).") -parser.add_argument("caps_directory_target", type=str, - help="Path to input dir of the MRI (preprocessed CAPS_dir).") -parser.add_argument("output_dir", type=str, - help="Path to the output directory.") - -# Data Management -parser.add_argument("--diagnoses", "-d", default=['AD', 'CN'], nargs='+', type=str, - help="The diagnoses used for the classification") -parser.add_argument("--set", type=str, default="validation", - choices=["test-ADNI", "test-AIBL", "test-OASIS", "validation"], - help="Set to use for evaluation") -parser.add_argument("--n_splits", type=int, default=5, - help="Load the data of the corresponding k-fold CV") -parser.add_argument("--group_id_source", type=str, default='ADNIbl', - help="Select the group to load the corresponding data in CAPS.") -parser.add_argument("--group_id_target", type=str, default='ADNIbl', - help="Select the group to load the corresponding data in CAPS.") - -# Computational issues -parser.add_argument("--num_workers", '-w', default=1, type=int, - help='the number of batch being loaded in parallel') - - -def main(options): - # reconstruct the trained model from the source task - diagnosis_tsv_all = os.path.join(options.output_dir, 'all_subjects.tsv') - source_data = CAPSVoxelBasedInput(options.caps_directory_source, diagnosis_tsv_all, options.group_id_source, - "T1", fwhm=8, mask_zeros=False) - _, source_orig_shape, source_mask = load_data_svm(source_data.get_images(), mask=True) - - folder_name = options.set + '_' + '_'.join(options.diagnoses) - result_dir = path.join(options.output_dir, folder_name) - os.makedirs(result_dir, exist_ok=True) - - res_final = pd.DataFrame() - for fi in range(options.n_splits): - - # Load and generate tsv files - if options.set == "validation": - _, test_df = load_data(options.diagnosis_path, options.diagnoses, fi, options.n_splits, True) - else: - test_df = load_data_test(options.diagnosis_path, options.diagnoses) - - test_path = save_data(test_df, options.output_dir, path.join('fold_%i' % fi, folder_name)) - - target_data = CAPSVoxelBasedInput(options.caps_directory_target, test_path, - options.group_id_target, "T1", fwhm=8, mask_zeros=False) - - subjects = list(test_df.participant_id) - sessions = list(test_df.session_id) - subjects_sessions = [subjects[i] + '_' + sessions[i] for i in range(len(subjects))] - - weights = np.loadtxt(path.join(options.output_dir, 'classifier', 'fold_' + str(fi), 'weights.txt')) - - w = revert_mask(weights, source_mask, source_orig_shape).flatten() - b = np.loadtxt(path.join(options.output_dir, 'classifier', 'fold_' + str(fi), 'intersect.txt')) - - target_image = target_data.get_x() - target_label = target_data.get_y() - - y_hat = np.dot(w, target_image.transpose()) + b - y_binary = (y_hat < 0) * 1 - - evaluation = evaluate_prediction(list(target_label), list(y_binary)) - - del evaluation['confusion_matrix'] - res_df = pd.DataFrame(evaluation, index=[fi]) - - subjects_df = pd.DataFrame({'subject': np.array(subjects_sessions), - 'y': target_label, 'y_hat': y_binary, 'fold_index': fi}) - subjects_df.to_csv(path.join(result_dir, 'subjects_fold-' + str(fi) + '.tsv'), - index=False, sep='\t', encoding='utf-8') - res_final = res_final.append(res_df) - res_final.to_csv(path.join(result_dir, 'results.tsv'), sep='\t', index=False) - - -if __name__ == "__main__": - commandline = parser.parse_known_args() - options = commandline[0] - if commandline[1]: - raise Exception("unknown arguments: %s" % (parser.parse_known_args()[1])) - main(options) diff --git a/projects/Wen_MedIA_2020/scripts/svm/src/model.py b/projects/Wen_MedIA_2020/scripts/svm/src/model.py deleted file mode 100644 index 9bdd236c8..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/src/model.py +++ /dev/null @@ -1,95 +0,0 @@ -# coding: utf8 - -from os import path -import numpy as np -import os -from .classification_utils import KFoldCV, CAPSVoxelBasedInput, apply_best_parameters_each_split -from clinica.pipelines.machine_learning import base, algorithm - -__author__ = "Junhao Wen" -__copyright__ = "Copyright 2018 The Aramis Lab Team" -__credits__ = ["Junhao Wen, Jorge Samper Gonzalez"] -__license__ = "See LICENSE.txt file" -__version__ = "0.1.0" -__maintainer__ = "Junhao Wen" -__email__ = "junhao.wen89@gmail.com" -__status__ = "Development" - - -class VB_KFold_DualSVM(base.MLWorkflow): - - """ - This is a specific implementation of KFold with DualSVM, in which case the data split has been pre-defined. - """ - - def __init__(self, caps_directory, diagnoses_tsv, group_id, image_type, output_dir, fwhm=8, - modulated="on", pvc=None, precomputed_kernel=None, mask_zeros=True, n_threads=15, n_folds=5, - grid_search_folds=10, balanced=True, c_range=np.logspace(-6, 2, 17), splits_indices=None): - - # Here some parameters selected for this task - - self._output_dir = output_dir - self._n_threads = n_threads - self._n_folds = n_folds - self._grid_search_folds = grid_search_folds - self._balanced = balanced - self._c_range = c_range - self._splits_indices = splits_indices - self._diagnoses_tsv = diagnoses_tsv - self._caps_directory = caps_directory - self._group_id = group_id - - # In this case we are running a voxel based input approach - # - - self._input = CAPSVoxelBasedInput(caps_directory, diagnoses_tsv, group_id, - image_type, fwhm, modulated, pvc, mask_zeros, precomputed_kernel) - - self._validation = None - self._algorithm = None - - def run(self): - - # Call on parameters already computed - - x = self._input.get_x() - y = self._input.get_y() - kernel = self._input.get_kernel() - - # Now algorithm has been selected, in this case Dual SVM algorithm. - # Look at algorithm.py to understand the input necessary for each method - # input parameters were chosen previously - - self._algorithm = algorithm.DualSVMAlgorithm(kernel, - y, - balanced=self._balanced, - grid_search_folds=self._grid_search_folds, - c_range=self._c_range, - n_threads=self._n_threads) - # Here validation type is selected, it's the K fold cross-validation - - self._validation = KFoldCV(self._algorithm) - - classifier, best_params, results = self._validation.validate(y, n_threads=self._n_threads, splits_indices=self._splits_indices, n_folds=self._n_folds) - - # Creation of the path where all the results will be saved - - classifier_dir = path.join(self._output_dir, 'classifier') - os.makedirs(classifier_dir, exist_ok=True) - - # Here we have selected what we want save - self._algorithm.save_classifier(classifier, classifier_dir) - weights = self._algorithm.save_weights(classifier, x, classifier_dir) - self._algorithm.save_parameters(best_params, classifier_dir) - self._validation.save_results(self._output_dir) - self._input.save_weights_as_nifti(weights, classifier_dir) - # save the model for each split only fitting with the training data - for n_fold in range(self._n_folds): - svc, _, train_index = apply_best_parameters_each_split(kernel, x, y, results, self._balanced, n_fold, self._diagnoses_tsv, self._output_dir) - classifier_dir = path.join(self._output_dir, 'classifier', 'fold_' + str(n_fold)) - if not path.exists(classifier_dir): - os.makedirs(classifier_dir) - self._algorithm.save_classifier(svc, classifier_dir) - - # save the train index for recontruction purpose - np.savetxt(path.join(classifier_dir, 'train_index.txt'), train_index) diff --git a/projects/Wen_MedIA_2020/scripts/svm/src/train.py b/projects/Wen_MedIA_2020/scripts/svm/src/train.py deleted file mode 100644 index 6c3553a73..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/src/train.py +++ /dev/null @@ -1,56 +0,0 @@ -# coding: utf8 - -import argparse -import os -from .classification_utils import extract_indices_from_5_fold -from .model import VB_KFold_DualSVM -from clinicadl.tools.deep_learning import commandline_to_json - -parser = argparse.ArgumentParser(description="Argparser for Pytorch 3D CNN") - -# Mandatory arguments -parser.add_argument("diagnosis_path", type=str, - help="Path to tsv files of the population." - " To note, the column name should be participant_id, session_id and diagnosis.") -parser.add_argument("input_dir", type=str, - help="Path to input dir of the MRI (preprocessed CAPS_dir).") -parser.add_argument("output_dir", type=str, - help="Path to the outputs directory.") - -# Data Management -parser.add_argument("--diagnoses", "-d", default=['AD', 'CN'], nargs='+', type=str, - help="The diagnoses used for the classification") -parser.add_argument("--baseline", default=False, action="store_true", - help="Use only baseline data instead of all scans available") -parser.add_argument("--n_splits", type=int, default=5, - help="Load the data of the corresponding k-fold CV") -parser.add_argument("--group_id", type=str, default='ADNIbl', - help="Select the group to load the corresponding data in CAPS.") - -# Computational issues -parser.add_argument("--num_workers", '-w', default=1, type=int, - help='the number of batch being loaded in parallel') - - -def main(options): - - os.makedirs(options.output_dir, exist_ok=True) - - splits_indices, diagnosis_tsv = extract_indices_from_5_fold(options.diagnosis_path, options.n_splits, - options.output_dir, baseline=options.baseline, - diagnoses_list=options.diagnoses) - - wf = VB_KFold_DualSVM(options.input_dir, diagnosis_tsv, options.group_id, "T1", options.output_dir, - n_threads=options.num_workers, n_folds=options.n_splits, splits_indices=splits_indices) - - wf.run() - - -if __name__ == "__main__": - commandline = parser.parse_known_args() - commandline[0].split = None - commandline_to_json(commandline) - options = commandline[0] - if commandline[1]: - raise Exception("unknown arguments: %s" % (parser.parse_known_args()[1])) - main(options) diff --git a/projects/Wen_MedIA_2020/scripts/svm/train/experiment-21.sh b/projects/Wen_MedIA_2020/scripts/svm/train/experiment-21.sh deleted file mode 100755 index a6633686f..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/train/experiment-21.sh +++ /dev/null @@ -1,46 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=1:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=SVM - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0 -# Network structure -SCRIPT="train" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_clinica_spm" -BASELINE=1 -TASK='AD CN' -SPLITS=5 - -# Training arguments -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NUM_WORKERS=16 -DATE="refactoring_results/svm$1" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -TASK_NAME="train_AD_CN" -TASK_NAME="${TASK_NAME}_baseline-${BASELINE}_final" -echo $TASK_NAME - -NAME="model-svm_task-${TASK_NAME}" -cd .. -python -m src.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/svm/train/experiment-22.sh b/projects/Wen_MedIA_2020/scripts/svm/train/experiment-22.sh deleted file mode 100755 index 6acedc4cc..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/train/experiment-22.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=1:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=SVM - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0 -# Network structure -SCRIPT="train" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_clinica_spm" -BASELINE=1 -TASK='sMCI pMCI' -SPLITS=5 - -# Training arguments -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NUM_WORKERS=16 -DATE="refactoring_results/svm4" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -TASK_NAME="train_sMCI_pMCI" -TASK_NAME="${TASK_NAME}_baseline-${BASELINE}_final" -echo $TASK_NAME - -NAME="model-svm_task-${TASK_NAME}" - -cd .. -python -m src.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/svm/train/experiment-24.sh b/projects/Wen_MedIA_2020/scripts/svm/train/experiment-24.sh deleted file mode 100755 index dd8f26170..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/train/experiment-24.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=1:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=SVM - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0 -# Network structure -SCRIPT="train" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_clinica_spm" -BASELINE=0 -TASK='AD CN' -SPLITS=5 - -# Training arguments -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NUM_WORKERS=16 -DATE="refactoring_results/svm3" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -TASK_NAME="train_AD_CN" -TASK_NAME="${TASK_NAME}_baseline-${BASELINE}_final" -echo $TASK_NAME - -NAME="model-svm_task-${TASK_NAME}" - -cd .. -python -m src.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS diff --git a/projects/Wen_MedIA_2020/scripts/svm/train/experiment-25.sh b/projects/Wen_MedIA_2020/scripts/svm/train/experiment-25.sh deleted file mode 100755 index b7bfdf867..000000000 --- a/projects/Wen_MedIA_2020/scripts/svm/train/experiment-25.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash -#SBATCH --partition=normal -#SBATCH --time=1:00:00 -#SBATCH --mem=30G -#SBATCH --cpus-per-task=6 -#SBATCH --ntasks=1 -#SBATCH --nodes=1 -#SBATCH --workdir=. -#SBATCH --output=outputs/scratch_baseline_job_%j.out -#SBATCH --error=outputs/scratch_baseline_job_%j.err -#SBATCH --job-name=SVM - -export http_proxy=http://10.10.2.1:8123 -export https_proxy=http://10.10.2.1:8123 - -module load pytorch/1.0.0 -# Network structure -SCRIPT="train" - -# Dataset Management -COHORT='ADNI' -CAPS_EXT="_clinica_spm" -BASELINE=0 -TASK='sMCI pMCI' -SPLITS=5 - -# Training arguments -if [ $BASELINE = 1 ]; then -echo "using only baseline data" -OPTIONS="$OPTIONS --baseline" -fi - -NUM_WORKERS=16 -DATE="refactoring_results/svm3" - -TSVPATH="/network/lustre/iss01/home/elina.thibeausutre/data/Frontiers/$COHORT/lists_by_diagnosis/train/" -RESULTSPATH="/network/lustre/iss01/home/elina.thibeausutre/results/$DATE/" -IMGPATH="/network/lustre/dtlake01/aramis/users/clinica/CLINICA_datasets/CAPS/Frontiers_DL/$COHORT$CAPS_EXT" - -TASK_NAME="train_sMCI_pMCI" -TASK_NAME="${TASK_NAME}_baseline-${BASELINE}_final" -echo $TASK_NAME - -NAME="model-svm_task-${TASK_NAME}" - -cd .. -python -m src.$SCRIPT $TSVPATH $IMGPATH $RESULTSPATH$NAME -w $NUM_WORKERS -d $TASK $OPTIONS From 28f10297073eb5c9ca34c0b0cbcfc9414a96b431 Mon Sep 17 00:00:00 2001 From: Elina Thibeau-Sutre Date: Thu, 8 Apr 2021 16:04:23 +0200 Subject: [PATCH 11/17] Clarify README and docs --- README.md | 64 +++------------------------------------------------ docs/index.md | 2 +- 2 files changed, 4 insertions(+), 62 deletions(-) diff --git a/README.md b/README.md index 83074df25..01076d9ec 100755 --- a/README.md +++ b/README.md @@ -41,15 +41,8 @@ This repository hosts the source code of a **framework for the reproducible evaluation of deep learning classification experiments using anatomical MRI data for the computer-aided diagnosis of Alzheimer's disease (AD)**. -Automatic classification of AD using classical machine learning approaches can -be performed using the framework available here: -. - > **Disclaimer:** this software is **under development**. Some features can -change between different commits. A stable version is planned to be released -soon. The release v.0.0.1 corresponds to the date of submission of the -publication but in the meantime important changes are being done to facilitate -the use of the package. +change between different commits. The complete documentation of the project can be found on this [page](https://clinicadl.readthedocs.io/). @@ -75,63 +68,12 @@ pip install clinicadl site](https://aramislab.paris.inria.fr/clinicadl/tuto/intro.html) to start using **ClinicaDL** directly in a Google Colab instance! -## Overview - -### How to use ClinicaDL? - -`clinicadl` is an utility that is used through the command line. Several tasks -can be performed: - -- **Preparation of your imaging data** - * **T1w-weighted MR image preprocessing.** The `preprocessing` task - processes a dataset of T1 images stored in BIDS format and prepares to - extract the tensors (see paper for details on the preprocessing). Output - is stored using the [CAPS](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/) - hierarchy. - * **Quality check of preprocessed data.** The `quality_check` task uses a - pretrained network [(Fonov et al, - 2018)](https://www.biorxiv.org/content/10.1101/303487v1) to classify - adequately registered images. - * **Tensor extraction from preprocessed data.** The `extract` task allows - to create files in PyTorch format (`.pt`) with different options: the - complete MRI, 2D slices and/or 3D patches. This files are also stored in - the [CAPS](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/) hierarchy. - -- **Train & test your classifier** - * **Train neural networks.** The `train` task is designed to perform - training of CNN models using different kind of inputs, e.g., a full MRI - (3D-image), patches from a MRI (3D-patch), specific regions of a MRI - (ROI-based) or slices extracted from the MRI (2D-slices). Parameters used - during the training are configurable. This task allow also to train - autoencoders. - * **MRI classification.** The `classify` task uses previously trained models - to perform the inference of a particular or a set of MRI. - - -- **Utilitaries used for the preparation of imaging data and/or training your - classifier** - * **Process TSV files**. `tsvtool` includes many functions to get labels - from BIDS, perform k-fold or single splits, produce demographic analysis - of extracted labels and reproduce the restrictions made on AIBL and OASIS - in the original paper. - * **Generate a synthetic dataset.** The `generate` task is useful to obtain - synthetic datasets frequently used in functional tests. - -## Pretrained models - -Some of the pretrained models for the CNN networks described in -([Wen et al., 2020](https://doi.org/10.1016/j.media.2020.101694)) -are available on Zenodo: - - -Updated versions of the models will be published soon. - ## Related Repositories - [Clinica: Software platform for clinical neuroimaging studies](https://github.com/aramis-lab/clinica) - [AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning](https://github.com/aramis-lab/AD-ML) -## Scientific papers using this framework +## Reproducibility - Wen et al., 2020 [Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation](https://doi.org/10.1016/j.media.2020.101694) -([arXiv version](https://arxiv.org/abs/1904.07773)). Scripts and related data are available in `projects/Wen_MedIA_2020`. \ No newline at end of file +([arXiv version](https://arxiv.org/abs/1904.07773)). Corresponding version `v0.0.1`. \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 1a1657e1e..8fd1d6929 100644 --- a/docs/index.md +++ b/docs/index.md @@ -41,7 +41,7 @@ Pretrained models for the CNN networks implemented in ClinicaDL can be obtained These models were obtained during the experiments for publication. They correspond to a previous version of ClinicaDL, hence their file system is not compatible with the current version. -Updated versions of the models will be published soon. +Updated versions of most representative models are available [here](https://aramislab.paris.inria.fr/files/data/models/dl/models_v002/). ## Support - [Report an issue on GitHub](https://github.com/aramis-lab/AD-DL/issues) From 51305e58e1b309d2f532378be8ce25b3bfc3f32f Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Tue, 13 Apr 2021 10:56:20 +0200 Subject: [PATCH 12/17] Json generation (#145) Add json files for some tasks where the file wasn't generated. * Change name of JSON files * Add JSON to inference * Add JSON to tsvtool * Use f-strings * Add missing JSON file for comparison in test * Update CHANGELOG --- CHANGELOG | 6 ++++++ clinicadl/clinicadl/classify/inference.py | 12 ++++++++++-- .../clinicadl/tools/deep_learning/iotools.py | 9 +++++---- clinicadl/clinicadl/tools/tsv/data_formatting.py | 14 +++++++++++++- clinicadl/clinicadl/tools/tsv/data_split.py | 13 ++++++++++++- clinicadl/clinicadl/tools/tsv/kfold_split.py | 10 +++++++++- .../tsvtool/anonymous_reference/getlabels.json | 16 ++++++++++++++++ clinicadl/tests/test_tsvtool.py | 9 ++++----- 8 files changed, 75 insertions(+), 14 deletions(-) create mode 100644 clinicadl/tests/data/tsvtool/anonymous_reference/getlabels.json diff --git a/CHANGELOG b/CHANGELOG index 1e465959f..c3ed7612f 100644 --- a/CHANGELOG +++ b/CHANGELOG @@ -26,6 +26,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Security +## ClinicaDL 0.2.1 + +### Changed + +- JSON files are added for `clinicadl classify` and `clinicadl tsvtool getlabels|split|kfold` + ## ClinicaDL 0.2.0 ### Added diff --git a/clinicadl/clinicadl/classify/inference.py b/clinicadl/clinicadl/classify/inference.py index e6585e6c0..1fa2b183a 100644 --- a/clinicadl/clinicadl/classify/inference.py +++ b/clinicadl/clinicadl/classify/inference.py @@ -1,10 +1,10 @@ # coding: utf8 -from os.path import isdir, join, abspath, exists +from os.path import join, exists from os import strerror, makedirs, listdir import errno import pathlib -from clinicadl.tools.deep_learning import create_model, load_model, read_json +from clinicadl.tools.deep_learning import create_model, load_model, read_json, commandline_to_json from clinicadl.tools.deep_learning.iotools import return_logger, translate_parameters from clinicadl.tools.deep_learning.data import return_dataset, get_transforms, compute_num_cnn, load_data_test from clinicadl.tools.deep_learning.cnn_utils import test, soft_voting_to_tsvs, mode_level_to_tsvs, get_criterion @@ -191,6 +191,14 @@ def inference_from_model(caps_dir, makedirs(performance_dir, exist_ok=True) + commandline_to_json({ + "output_dir": model_path, + "caps_dir": caps_dir, + "tsv_path": tsv_path, + "prefix": prefix, + "labels": labels + }, filename=f"commandline_classify-{prefix}") + # It launch the corresponding function, depending on the mode. inference_from_model_generic( caps_dir, diff --git a/clinicadl/clinicadl/tools/deep_learning/iotools.py b/clinicadl/clinicadl/tools/deep_learning/iotools.py index 2e0b54305..3648acbaa 100644 --- a/clinicadl/clinicadl/tools/deep_learning/iotools.py +++ b/clinicadl/clinicadl/tools/deep_learning/iotools.py @@ -99,13 +99,14 @@ def check_and_clean(d): os.makedirs(d) -def commandline_to_json(commandline, logger=None): +def commandline_to_json(commandline, logger=None, filename="commandline.json"): """ This is a function to write the python argparse object into a json file. This helps for DL when searching for hyperparameters Args: - commandline: (tuple) the output of `parser.parse_known_args()` - logger: (logging object) writer to stdout and stderr + commandline: (Namespace or dict) the output of `parser.parse_known_args()` + logger: (logging object) writer to stdout and stderr + filename: (str) name of the JSON file. :return: """ @@ -142,7 +143,7 @@ def commandline_to_json(commandline, logger=None): # save to json file json = json.dumps(commandline_arg_dict, skipkeys=True, indent=4) logger.info("Path of json file: %s" % os.path.join(output_dir, "commandline.json")) - f = open(os.path.join(output_dir, "commandline.json"), "w") + f = open(os.path.join(output_dir, filename), "w") f.write(json) f.close() diff --git a/clinicadl/clinicadl/tools/tsv/data_formatting.py b/clinicadl/clinicadl/tools/tsv/data_formatting.py index 2a9bbe5dd..944f42300 100644 --- a/clinicadl/clinicadl/tools/tsv/data_formatting.py +++ b/clinicadl/clinicadl/tools/tsv/data_formatting.py @@ -10,7 +10,7 @@ in the OASIS dataset is not done in this script. Moreover a quality check may be needed at the end of preprocessing pipelines, leading to the removal of some subjects. """ -from ..deep_learning.iotools import return_logger +from ..deep_learning.iotools import return_logger, commandline_to_json from .tsv_utils import neighbour_session, last_session, after_end_screening, find_label, first_session import pandas as pd from os import path @@ -362,6 +362,18 @@ def get_labels(merged_tsv, missing_mods, results_path, """ logger = return_logger(verbose, "getlabels") + commandline_to_json({ + "output_dir": results_path, + "merged_tsv": merged_tsv, + "missing_mods": missing_mods, + "diagnoses": diagnoses, + "modality": modality, + "restriction_path": restriction_path, + "time_horizon": time_horizon, + "variables_of_interest": variables_of_interest, + "remove_smc": remove_smc + }, filename="getlabels.json") + # Reading files bids_df = pd.read_csv(merged_tsv, sep='\t') bids_df.set_index(['participant_id', 'session_id'], inplace=True) diff --git a/clinicadl/clinicadl/tools/tsv/data_split.py b/clinicadl/clinicadl/tools/tsv/data_split.py index 06066b107..47baf812d 100644 --- a/clinicadl/clinicadl/tools/tsv/data_split.py +++ b/clinicadl/clinicadl/tools/tsv/data_split.py @@ -2,7 +2,7 @@ from .tsv_utils import complementary_list, extract_baseline, chi2, category_conversion, remove_unicity, find_label, \ retrieve_longitudinal, remove_sub_labels -from ..deep_learning.iotools import return_logger +from ..deep_learning.iotools import return_logger, commandline_to_json from scipy.stats import ttest_ind import shutil import pandas as pd @@ -159,6 +159,17 @@ def split_diagnoses(formatted_data_path, n_test=100, subset_name="test", MCI_sub """ logger = return_logger(verbose, "split") + commandline_to_json({ + "output_dir": formatted_data_path, + "n_test": n_test, + "subset_name": subset_name, + "MCI_sub_categories": MCI_sub_categories, + "p_age_threshold": p_age_threshold, + "p_sex_threshold": p_sex_threshold, + "categorical_split_variable": categorical_split_variable, + "ignore_demographics": ignore_demographics + }, filename="split.json") + # Read files results_path = formatted_data_path diff --git a/clinicadl/clinicadl/tools/tsv/kfold_split.py b/clinicadl/clinicadl/tools/tsv/kfold_split.py index aee183727..28229b381 100644 --- a/clinicadl/clinicadl/tools/tsv/kfold_split.py +++ b/clinicadl/clinicadl/tools/tsv/kfold_split.py @@ -1,7 +1,7 @@ # coding: utf8 from .tsv_utils import extract_baseline, retrieve_longitudinal, remove_sub_labels -from ..deep_learning.iotools import return_logger +from ..deep_learning.iotools import return_logger, commandline_to_json import shutil from sklearn.model_selection import StratifiedKFold from os import path @@ -93,6 +93,14 @@ def split_diagnoses(formatted_data_path, """ logger = return_logger(verbose, 'k-fold split') + commandline_to_json({ + "output_dir": formatted_data_path, + "n_splits": n_splits, + "subset_name": subset_name, + "MCI_sub_categories": MCI_sub_categories, + "stratification": stratification + }, filename="kfold.json") + # Read files results_path = formatted_data_path diff --git a/clinicadl/tests/data/tsvtool/anonymous_reference/getlabels.json b/clinicadl/tests/data/tsvtool/anonymous_reference/getlabels.json new file mode 100644 index 000000000..fb54ae570 --- /dev/null +++ b/clinicadl/tests/data/tsvtool/anonymous_reference/getlabels.json @@ -0,0 +1,16 @@ +{ + "merged_tsv": "data/tsvtool/anonymous_BIDS.tsv", + "missing_mods": "data/tsvtool/anonymous_missing_mods", + "diagnoses": [ + "AD", + "CN", + "MCI", + "pMCI", + "sMCI" + ], + "modality": "t1w", + "restriction_path": null, + "time_horizon": 36, + "variables_of_interest": null, + "remove_smc": true +} \ No newline at end of file diff --git a/clinicadl/tests/test_tsvtool.py b/clinicadl/tests/test_tsvtool.py index 57c515f02..54eeaf43f 100644 --- a/clinicadl/tests/test_tsvtool.py +++ b/clinicadl/tests/test_tsvtool.py @@ -14,8 +14,8 @@ def test_getlabels(): """Checks that getlabels is working and that it is coherent with previous version in reference_path""" output_path = "data/tsvtool_test" - flag_getlabels = not os.system("clinicadl tsvtool getlabels %s %s %s --diagnoses %s -vvv" - % (merged_tsv, missing_mods, output_path, diagnoses)) + flag_getlabels = not os.system(f"clinicadl tsvtool getlabels {merged_tsv} {missing_mods} {output_path} " + f"--diagnoses {diagnoses} -vvv") assert flag_getlabels for file in os.listdir(output_path): out_df = pd.read_csv(path.join(output_path, file), sep="\t") @@ -57,9 +57,8 @@ def test_analysis(): """Checks that analysis can be performed""" results_path = path.join("data", "tsvtool", "analysis.tsv") ref_analysis_path = path.join("data", "tsvtool", "anonymous_analysis.tsv") - flag_analysis = not os.system("clinicadl tsvtool analysis %s %s %s " - "--diagnoses AD CN MCI sMCI pMCI" - % (merged_tsv, reference_path, results_path)) + flag_analysis = not os.system(f"clinicadl tsvtool analysis {merged_tsv} {reference_path} {results_path} " + "--diagnoses AD CN MCI sMCI pMCI") assert flag_analysis ref_df = pd.read_csv(ref_analysis_path, sep="\t") out_df = pd.read_csv(results_path, sep="\t") From 9557414ed8187e54deacdf651f76d78e1783d3dc Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Tue, 13 Apr 2021 14:11:46 +0200 Subject: [PATCH 13/17] Update README.md Co-authored-by: nburgos --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 01076d9ec..2c5b15a45 100755 --- a/README.md +++ b/README.md @@ -73,7 +73,13 @@ using **ClinicaDL** directly in a Google Colab instance! - [Clinica: Software platform for clinical neuroimaging studies](https://github.com/aramis-lab/clinica) - [AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning](https://github.com/aramis-lab/AD-ML) +## Citing us + +- Wen, J., Thibeau-Sutre, E., Samper-González, J., Routier, A., Bottani, S., Durrleman, S., Burgos, N., and Colliot, O.: ‘Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation’, *Medical Image Analysis*, 63: 101694, 2020. [doi:10.1016/j.media.2020.101694](https://doi.org/10.1016/j.media.2020.101694) +- Routier, A., Burgos, N., Díaz, M., Bacci, M., Bottani, S., El-Rifai O., Fontanella, S., Gori, P., Guillon, J., Guyot, A., Hassanaly, R., Jacquemont, T., Lu, P., Marcoux, A., Moreau, T., Samper-González, J., Teichmann, M., Thibeau-Sutre, E., Vaillant G., Wen, J., Wild, A., Habert, M.-O., Durrleman, S., and Colliot, O.: ‘Clinica: An Open Source Software Platform for Reproducible Clinical Neuroscience Studies’, 2021. [hal-02308126](https://hal.inria.fr/hal-02308126) + + ## Reproducibility - Wen et al., 2020 [Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation](https://doi.org/10.1016/j.media.2020.101694) -([arXiv version](https://arxiv.org/abs/1904.07773)). Corresponding version `v0.0.1`. \ No newline at end of file +([arXiv version](https://arxiv.org/abs/1904.07773)). Corresponding version `v0.0.1`. From fae1458d1788707e6d881c5274d46683a19f8222 Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Tue, 13 Apr 2021 14:12:52 +0200 Subject: [PATCH 14/17] Apply suggestions from code review Co-authored-by: mdiazmel --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2c5b15a45..394f4baf1 100755 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ evaluation of deep learning classification experiments using anatomical MRI data for the computer-aided diagnosis of Alzheimer's disease (AD)**. > **Disclaimer:** this software is **under development**. Some features can -change between different commits. +change between different releases and/or commits. The complete documentation of the project can be found on this [page](https://clinicadl.readthedocs.io/). From af85d93c45dff143fc5d734c4e4f71156fde3f96 Mon Sep 17 00:00:00 2001 From: Elina Thibeau-Sutre Date: Tue, 13 Apr 2021 14:15:52 +0200 Subject: [PATCH 15/17] Apply review suggestions --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 394f4baf1..1b270705b 100755 --- a/README.md +++ b/README.md @@ -44,8 +44,8 @@ data for the computer-aided diagnosis of Alzheimer's disease (AD)**. > **Disclaimer:** this software is **under development**. Some features can change between different releases and/or commits. -The complete documentation of the project can be found on -this [page](https://clinicadl.readthedocs.io/). +To access the full documentation of the project, follow the link +[https://clinicadl.readthedocs.io/](https://clinicadl.readthedocs.io/). If you find a problem when using it or if you want to provide us feedback, please [open an issue](https://github.com/aramis-lab/ad-dl/issues) or write on the [forum](https://groups.google.com/forum/#!forum/clinica-user). @@ -81,5 +81,5 @@ using **ClinicaDL** directly in a Google Colab instance! ## Reproducibility -- Wen et al., 2020 [Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation](https://doi.org/10.1016/j.media.2020.101694) -([arXiv version](https://arxiv.org/abs/1904.07773)). Corresponding version `v0.0.1`. +To reproduce the results published in [Wen et al., MedIA, 2020](https://doi.org/10.1016/j.media.2020.101694) ([arXiv version](https://arxiv.org/abs/1904.07773)) +please use the version of ClinicaDL tagged `[v0.0.1](https://github.com/aramis-lab/AD-DL/tree/v.0.0.1)`. From 9d1551951a4c899de70d44f5b8ba93735739235d Mon Sep 17 00:00:00 2001 From: Elina Thibeau Sutre Date: Tue, 13 Apr 2021 16:00:01 +0200 Subject: [PATCH 16/17] Fix single roi (#148) * Prevent training multi-CNN if only one element is extracted per image * Write image level results if one element per image exist * Update CHANGELOG * Fix style --- CHANGELOG | 5 ++++ .../tools/deep_learning/cnn_utils.py | 23 +++++++++++++++++++ clinicadl/clinicadl/train/train_multiCNN.py | 3 +++ clinicadl/clinicadl/train/train_singleCNN.py | 7 ++++-- 4 files changed, 36 insertions(+), 2 deletions(-) diff --git a/CHANGELOG b/CHANGELOG index 0d2da8ea8..ff675ea33 100644 --- a/CHANGELOG +++ b/CHANGELOG @@ -28,8 +28,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## ClinicaDL 0.2.1 +### Added + +- the `multi_cohort` flag in train allows to train on several CAPS at the same time. + ### Changed +- `clinicadl train roi` now allows any ROI defined by a mask. - Update README.md to avoid duplicates. - JSON files are added for `clinicadl classify` and `clinicadl tsvtool getlabels|split|kfold` diff --git a/clinicadl/clinicadl/tools/deep_learning/cnn_utils.py b/clinicadl/clinicadl/tools/deep_learning/cnn_utils.py index c24d0dfed..b6f3aed6d 100644 --- a/clinicadl/clinicadl/tools/deep_learning/cnn_utils.py +++ b/clinicadl/clinicadl/tools/deep_learning/cnn_utils.py @@ -584,6 +584,29 @@ def soft_voting(performance_df, validation_df, mode, selection_threshold=None, u return df_final, results +def mode_to_image_tsvs(output_dir, fold, selection, mode, dataset="test"): + """ + Copy mode-level tsvs to name them as image-level tsvs + Args: + output_dir: (str) path to the output directory. + fold: (int) Fold number of the cross-validation. + selection: (str) criterion on which the model is selected (either best_loss or best_acc) + mode: (str) input used by the network. Chosen from ['patch', 'roi', 'slice']. + dataset: (str) name of the dataset for which the soft-voting is performed. If different from training or + validation, the weights of soft voting will be computed on validation accuracies. + """ + sub_df = retrieve_sub_level_results(output_dir, fold, selection, mode, dataset, 1) + sub_df.drop([f'{mode}_id', 'proba0', 'proba1'], axis=1, inplace=True) + + performance_path = os.path.join(output_dir, f'fold-{fold}', 'cnn_classification', selection) + sub_df.to_csv(os.path.join(performance_path, f'{dataset}_image_level_prediction.tsv'), + index=False, sep='\t') + metrics_df = pd.read_csv(os.path.join(performance_path, f'{dataset}_{mode}_level_metrics.tsv'), sep="\t") + metrics_df.drop([f'{mode}_id'], axis=1, inplace=True) + metrics_df.to_csv(os.path.join(performance_path, f'{dataset}_image_level_metrics.tsv'), + index=False, sep='\t') + + def check_prediction(row): if row["true_label"] == row["predicted_label"]: return 1 diff --git a/clinicadl/clinicadl/train/train_multiCNN.py b/clinicadl/clinicadl/train/train_multiCNN.py index e8a5b8c63..142104b4d 100755 --- a/clinicadl/clinicadl/train/train_multiCNN.py +++ b/clinicadl/clinicadl/train/train_multiCNN.py @@ -43,6 +43,9 @@ def train_multi_cnn(params): num_cnn = compute_num_cnn(params.input_dir, params.tsv_path, params, data="train") + if num_cnn == 1: + raise ValueError("Multi-CNN framework cannot be performed on a dataset computing one element per image.") + if params.split is None: if params.n_splits is None: fold_iterator = range(1) diff --git a/clinicadl/clinicadl/train/train_singleCNN.py b/clinicadl/clinicadl/train/train_singleCNN.py index 229083ea6..327f09a97 100755 --- a/clinicadl/clinicadl/train/train_singleCNN.py +++ b/clinicadl/clinicadl/train/train_singleCNN.py @@ -9,7 +9,8 @@ load_data, return_dataset, generate_sampler) -from ..tools.deep_learning.cnn_utils import train, get_criterion, test, mode_level_to_tsvs, soft_voting_to_tsvs +from ..tools.deep_learning.cnn_utils import train, get_criterion, test, mode_level_to_tsvs, soft_voting_to_tsvs, \ + mode_to_image_tsvs from ..tools.deep_learning.iotools import return_logger, check_and_clean from ..tools.deep_learning.iotools import commandline_to_json, write_requirements_version, translate_parameters @@ -130,6 +131,8 @@ def test_single_cnn(model, output_dir, data_loader, subset_name, split, criterio mode_level_to_tsvs(output_dir, results_df, metrics, split, selection, mode, dataset=subset_name) # Soft voting - if mode in ["patch", "roi", "slice"]: + if data_loader.dataset.elem_per_image > 1: soft_voting_to_tsvs(output_dir, split, logger=logger, selection=selection, mode=mode, dataset=subset_name, selection_threshold=selection_threshold) + elif mode != "image": + mode_to_image_tsvs(output_dir, split, selection=selection, mode=mode, dataset=subset_name) From fad17359ca34490978a38d8c41d5c3d0c3338267 Mon Sep 17 00:00:00 2001 From: Mauricio DIAZ Date: Tue, 13 Apr 2021 17:52:56 +0200 Subject: [PATCH 17/17] Prepare release 0.2.1 --- clinicadl/clinicadl/VERSION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/clinicadl/clinicadl/VERSION b/clinicadl/clinicadl/VERSION index 0ea3a944b..0c62199f1 100644 --- a/clinicadl/clinicadl/VERSION +++ b/clinicadl/clinicadl/VERSION @@ -1 +1 @@ -0.2.0 +0.2.1