run.py 19.4 KB
Newer Older
1
2
3
4
"""Brain Mapper Run File

Description:

5
6
7
8
9
    This file contains all the relevant functions for running BrainMapper.
    The network can be ran in one of these modes:
        - train
        - evaluate path
        - evaluate whole
10

11
    TODO: Might be worth adding some information on uncertaintiy estimation, later down the line
12

13
14
15
16
17
18
19
20
21
22
23
24
25
Usage:

    In order to run the network, in the terminal, the user needs to pass it relevant arguments:

        $ ./setup.sh
        $ source env/bin/activate
        $ python run.py --mode ...

    The arguments for mode are the following:

        mode=train # For training the model
        mode=evaluate-score # For evaluating the model score
        mode=evaluate-mapping # For evaluating the model mapping
26
27
        # For clearning the experiments and logs directories of the last experiment
        mode=clear-experiment
28
        mode=clear-all # For clearing all the files from the experiments and logs directories/
29
30
31

"""

32
import os
33
import shutil
34
35
import argparse
import logging
36
37
38
39
from settings import Settings

import torch
import torch.utils.data as data
Andrei-Claudiu Roibu's avatar
Andrei-Claudiu Roibu committed
40
import numpy as np
41
42

from solver import Solver
43
from BrainMapperAE import BrainMapperAE3D
44
from utils.data_utils import get_datasets, data_preparation, update_shuffling_flag, create_folder
45
46
import utils.data_evaluation_utils as evaluations
from utils.data_logging_utils import LogWriter
47
48
49
50
51

# Set the default floating point tensor type to FloatTensor

torch.set_default_tensor_type(torch.FloatTensor)

52

53
54
55
def load_data(data_parameters):
    """Dataset Loader

56
    This function loads the training and validation datasets.
57
58
59
60
61
62

    Args:
        data_parameters (dict): Dictionary containing relevant information for the datafiles.

    Returns:
        train_data (dataset object): Pytorch map-style dataset object, mapping indices to training data samples.
63
        validation_data (dataset object): Pytorch map-style dataset object, mapping indices to testing data samples.
64
65
66

    """
    print("Data is loading...")
67
    train_data, validation_data = get_datasets(data_parameters)
68
69
    print("Data has loaded!")
    print("Training dataset size is {}".format(len(train_data)))
70
    print("Validation dataset size is {}".format(len(validation_data)))
71

72
    return train_data, validation_data
73

74

75
76
def train(data_parameters, training_parameters, network_parameters, misc_parameters):
    """Training Function
77

78
    This function trains a given model using the provided training data.
79
    Currently, the data loaded is set to have multiple sub-processes.
80
81
    A high enough number of workers assures that CPU computations are efficiently managed, i.e. that the bottleneck is indeed the neural network's forward and backward operations on the GPU (and not data generation)
    Loader memory is also pinned, to speed up data transfer from CPU to GPU  by using the page-locked memory.
82
    Train data is also re-shuffled at each training epoch.
83
84

    Args:
85
86
87
88
89
        data_parameters (dict): Dictionary containing relevant information for the datafiles.

        training_parameters(dict): Dictionary containing relevant hyperparameters for training the network.
        training_parameters = {
            'training_batch_size': 5
90
            'validation_batch_size: 5
91
92
93
94
95
96
97
98
99
100
101
102
103
            'use_pre_trained': False
            'pre_trained_path': 'pre_trained/path'
            'experiment_name': 'experiment_name'
            'learning_rate': 1e-4
            'optimizer_beta': (0.9, 0.999)
            'optimizer_epsilon': 1e-8
            'optimizer_weigth_decay': 1e-5
            'number_of_epochs': 10
            'loss_log_period': 50
            'learning_rate_scheduler_step_size': 3
            'learning_rate_scheduler_gamma': 1e-1
            'use_last_checkpoint': True
            'final_model_output_file': 'path/to/model'
104
        }
105

106
        network_parameters (dict): Contains information relevant parameters
107

108
109
110
111
112
113
114
115
        misc_parameters (dict): Dictionary of aditional hyperparameters
        misc_parameters = {
            'save_model_directory': 'directory_name'
            'model_name': 'BrainMapper'
            'logs_directory': 'log-directory'
            'device': 1
            'experiments_directory': 'experiments-directory'
        }
116
117
    """

118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
    def _train_runner(data_parameters, training_parameters, network_parameters, misc_parameters):
        """Wrapper for the training operation

        This function wraps the training operation for the network

        Args:
            data_parameters (dict): Dictionary containing relevant information for the datafiles.
            training_parameters(dict): Dictionary containing relevant hyperparameters for training the network.
            network_parameters (dict): Contains information relevant parameters
            misc_parameters (dict): Dictionary of aditional hyperparameters

        """
        train_data, validation_data = load_data(data_parameters)

        train_loader = data.DataLoader(
            dataset=train_data,
            batch_size=training_parameters['training_batch_size'],
            shuffle=True,
            num_workers=4,
            pin_memory=True
        )

        validation_loader = data.DataLoader(
            dataset=validation_data,
            batch_size=training_parameters['validation_batch_size'],
            shuffle=False,
            num_workers=4,
            pin_memory=True
        )

        if training_parameters['use_pre_trained']:
            BrainMapperModel = torch.load(
                training_parameters['pre_trained_path'])
Andrei Roibu's avatar
Andrei Roibu committed
151
        else:
152
            BrainMapperModel = BrainMapperAE3D(network_parameters)
153

154
155
        BrainMapperModel.reset_parameters()

156
        optimizer = torch.optim.Adam
Andrei Roibu's avatar
Andrei Roibu committed
157
        # optimizer = torch.optim.AdamW
158

159
160
161
162
        solver = Solver(model=BrainMapperModel,
                        device=misc_parameters['device'],
                        number_of_classes=network_parameters['number_of_classes'],
                        experiment_name=training_parameters['experiment_name'],
Andrei-Claudiu Roibu's avatar
Andrei-Claudiu Roibu committed
163
                        optimizer=optimizer,
164
                        optimizer_arguments={'lr': training_parameters['learning_rate'],
165
166
167
168
                                             'betas': training_parameters['optimizer_beta'],
                                             'eps': training_parameters['optimizer_epsilon'],
                                             'weight_decay': training_parameters['optimizer_weigth_decay']
                                             },
169
                        model_name=training_parameters['experiment_name'],
170
171
172
173
174
175
176
177
                        number_epochs=training_parameters['number_of_epochs'],
                        loss_log_period=training_parameters['loss_log_period'],
                        learning_rate_scheduler_step_size=training_parameters[
                            'learning_rate_scheduler_step_size'],
                        learning_rate_scheduler_gamma=training_parameters['learning_rate_scheduler_gamma'],
                        use_last_checkpoint=training_parameters['use_last_checkpoint'],
                        experiment_directory=misc_parameters['experiments_directory'],
                        logs_directory=misc_parameters['logs_directory'],
178
179
180
                        checkpoint_directory=misc_parameters['checkpoint_directory'],
                        save_model_directory=misc_parameters['save_model_directory'],
                        final_model_output_file=training_parameters['final_model_output_file']
181
182
183
184
                        )

        validation_loss = solver.train(train_loader, validation_loader)

185
        del train_data, validation_data, train_loader, validation_loader, BrainMapperModel, solver, optimizer
186
187
188
189
190
191
192
193
        torch.cuda.empty_cache()

        return validation_loss

    if data_parameters['k_fold'] is None:

        _ = _train_runner(data_parameters, training_parameters,
                          network_parameters, misc_parameters)
194

195
    else:
196
        print("Training initiated using K-fold Cross Validation!")
197
        k_fold_losses = []
198

199
        for k in range(data_parameters['k_fold']):
200

Andrei-Claudiu Roibu's avatar
Andrei-Claudiu Roibu committed
201
            print("K-fold Number: {}".format(k+1))
202

203
            data_parameters['train_list'] = os.path.join(
204
                data_parameters['data_folder_name'], 'train' + str(k+1)+'.txt')
205
            data_parameters['validation_list'] = os.path.join(
206
207
208
                data_parameters['data_folder_name'], 'validation' + str(k+1)+'.txt')
            training_parameters['final_model_output_file'] = training_parameters['final_model_output_file'].replace(
                ".pth.tar", str(k+1)+".pth.tar")
209

210
            validation_loss = _train_runner(
211
                data_parameters, training_parameters, network_parameters, misc_parameters)
212

213
            k_fold_losses.append(validation_loss)
214

215
216
217
        for k in range(data_parameters['k_fold']):
            print("K-fold Number: {} Loss: {}".format(k+1, k_fold_losses[k]))
        print("K-fold Cross Validation Avearge Loss: {}".format(np.mean(k_fold_losses)))
218

Andrei-Claudiu Roibu's avatar
Andrei-Claudiu Roibu committed
219

220
def evaluate_score(training_parameters, network_parameters, misc_parameters, evaluation_parameters):
221
222
223
224
225
226
227
228
229
230
231
    """Mapping Score Evaluator

    This function evaluates a given trained model by calculating the it's dice score prediction.

    Args:

        training_parameters(dict): Dictionary containing relevant hyperparameters for training the network.
        training_parameters = {
            'experiment_name': 'experiment_name'
        }

232
        network_parameters (dict): Contains information relevant parameters
233
234
235
        network_parameters= {
            'number_of_classes': 1
        }
236

237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
        misc_parameters (dict): Dictionary of aditional hyperparameters
        misc_parameters = {
            'logs_directory': 'log-directory'
            'device': 1
            'experiments_directory': 'experiments-directory'
        }

        evaluation_parameters (dict): Dictionary of parameters useful during evaluation.
        evaluation_parameters = {
            'trained_model_path': 'path/to/model'
            'data_directory': 'path/to/data'
            'targets_directory': 'path/to/targets'
            'data_list': 'path/to/datalist.txt/
            'orientation': 'coronal'
            'saved_predictions_directory': 'directory-of-saved-predictions'
        }
    """

255
    # TODO - NEED TO UPDATE THE DATA FUNCTIONS!
256

257
    prediction_output_path = os.path.join(misc_parameters['experiments_directory'],
258
259
260
261
                                          training_parameters['experiment_name'],
                                          evaluation_parameters['saved_predictions_directory']
                                          )

Andrei Roibu's avatar
Andrei Roibu committed
262
263
264
265
266
267
268
269
270
271
272
    evaluations.evaluate_correlation(trained_model_path=evaluation_parameters['trained_model_path'],
                                     data_directory=evaluation_parameters['data_directory'],
                                     mapping_data_file=mapping_evaluation_parameters['mapping_data_file'],
                                     target_data_file=evaluation_parameters['targets_directory'],
                                     data_list=evaluation_parameters['data_list'],
                                     prediction_output_path=prediction_output_path,
                                     brain_mask_path=mapping_evaluation_parameters['brain_mask_path'],
                                     rsfmri_mean_mask_path=mapping_evaluation_parameters[
                                         'rsfmri_mean_mask_path'],
                                     dmri_mean_mask_path=mapping_evaluation_parameters[
                                         'dmri_mean_mask_path'],
273
                                     mean_regression=mapping_evaluation_parameters['mean_regression'],
Andrei Roibu's avatar
Andrei Roibu committed
274
275
276
277
                                     scaling_factors=mapping_evaluation_parameters['scaling_factors'],
                                     regression_factors=mapping_evaluation_parameters['regression_factors'],
                                     device=misc_parameters['device'],
                                     )
278

279

280
281
def evaluate_mapping(mapping_evaluation_parameters):
    """Mapping Evaluator
282

283
284
285
286
287
    This function passes through the network an input and generates the rsfMRI outputs.

    Args:
        mapping_evaluation_parameters (dict): Dictionary of parameters useful during mapping evaluation.
        mapping_evaluation_parameters = {
288
            'trained_model_path': 'path/to/model'
289
290
291
292
293
294
295
296
297
            'data_directory': 'path/to/data'
            'data_list': 'path/to/datalist.txt/
            'prediction_output_path': 'directory-of-saved-predictions'
            'batch_size': 2
            'device': 0
            'exit_on_error': True
        }

    """
298
    trained_model_path = mapping_evaluation_parameters['trained_model_path']
299
    data_directory = mapping_evaluation_parameters['data_directory']
300
    mapping_data_file = mapping_evaluation_parameters['mapping_data_file']
301
302
    data_list = mapping_evaluation_parameters['data_list']
    prediction_output_path = mapping_evaluation_parameters['prediction_output_path']
Andrei Roibu's avatar
Andrei Roibu committed
303
304
    dmri_mean_mask_path = mapping_evaluation_parameters['dmri_mean_mask_path']
    rsfmri_mean_mask_path = mapping_evaluation_parameters['rsfmri_mean_mask_path']
305
    device = mapping_evaluation_parameters['device']
306
    exit_on_error = mapping_evaluation_parameters['exit_on_error']
307
    brain_mask_path = mapping_evaluation_parameters['brain_mask_path']
308
309
    mean_regression = mapping_evaluation_parameters['mean_regression']
    mean_subtraction = mapping_evaluation_parameters['mean_subtraction']
310
    scaling_factors = mapping_evaluation_parameters['scaling_factors']
Andrei Roibu's avatar
Andrei Roibu committed
311
    regression_factors = mapping_evaluation_parameters['regression_factors']
312

313
    evaluations.evaluate_mapping(trained_model_path,
Andrei Roibu's avatar
Andrei Roibu committed
314
315
316
317
318
319
320
                                 data_directory,
                                 mapping_data_file,
                                 data_list,
                                 prediction_output_path,
                                 brain_mask_path,
                                 dmri_mean_mask_path,
                                 rsfmri_mean_mask_path,
321
322
                                 mean_regression,
                                 mean_subtraction,
Andrei Roibu's avatar
Andrei Roibu committed
323
324
325
326
                                 scaling_factors,
                                 regression_factors,
                                 device=device,
                                 exit_on_error=exit_on_error)
Andrei-Claudiu Roibu's avatar
Andrei-Claudiu Roibu committed
327

328
329

def delete_files(folder):
330
    """ Clear Folder Contents
331

332
333
334
335
    Function which clears contents (like experiments or logs)

    Args:
        folder (str): Name of folders whose conents is to be deleted
336

337
    """
338

339
340
341
342
343
344
345
346
347
348
    for object_name in os.listdir(folder):
        file_path = os.path.join(folder, object_name)
        try:
            if os.path.isfile(file_path):
                os.unlink(file_path)
            elif os.path.isdir(file_path):
                shutil.rmtree(file_path)
        except Exception as exception:
            print(exception)

349
350

if __name__ == '__main__':
351
    parser = argparse.ArgumentParser()
352
353
354
355
    parser.add_argument('--mode', '-m', required=True,
                        help='run mode, valid values are train or evaluate')
    parser.add_argument('--settings_path', '-sp', required=False,
                        help='optional argument, set path to settings_evaluation.ini')
356
357
358
359
360
361
362
363
364
365

    arguments = parser.parse_args()

    settings = Settings('settings.ini')
    data_parameters = settings['DATA']
    training_parameters = settings['TRAINING']
    network_parameters = settings['NETWORK']
    misc_parameters = settings['MISC']
    evaluation_parameters = settings['EVALUATION']

366
367
    # Here we shuffle the data!

368
369
370
    if data_parameters['data_split_flag'] == True:
        print('Data is shuffling... This could take a few minutes!')

371
        if data_parameters['use_data_file'] == True:
372
            data_preparation(data_parameters['data_folder_name'],
Andrei Roibu's avatar
Andrei Roibu committed
373
374
375
376
377
378
379
380
381
382
                             data_parameters['test_percentage'],
                             data_parameters['subject_number'],
                             data_directory=data_parameters['data_directory'],
                             train_inputs=data_parameters['train_data_file'],
                             train_targets=data_parameters['train_output_targets'],
                             rsfMRI_mean_mask_path=data_parameters['rsfmri_mean_mask_path'],
                             dMRI_mean_mask_path=data_parameters['dmri_mean_mask_path'],
                             data_file=data_parameters['data_file'],
                             K_fold=data_parameters['k_fold']
                             )
383
        else:
384
            data_preparation(data_parameters['data_folder_name'],
Andrei Roibu's avatar
Andrei Roibu committed
385
386
387
388
389
390
391
392
393
                             data_parameters['test_percentage'],
                             data_parameters['subject_number'],
                             data_directory=data_parameters['data_directory'],
                             train_inputs=data_parameters['train_data_file'],
                             train_targets=data_parameters['train_output_targets'],
                             rsfMRI_mean_mask_path=data_parameters['rsfmri_mean_mask_path'],
                             dMRI_mean_mask_path=data_parameters['dmri_mean_mask_path'],
                             K_fold=data_parameters['k_fold']
                             )
394
        update_shuffling_flag('settings.ini')
395

396
397
        print('Data is shuffling... Complete!')

398
399
    if arguments.mode == 'train':
        train(data_parameters, training_parameters,
400
              network_parameters, misc_parameters)
401
402
403
404
405

    # NOTE: THE EVAL FUNCTIONS HAVE NOT YET BEEN DEBUGGED (16/04/20)

    elif arguments.mode == 'evaluate-score':
        evaluate_score(training_parameters,
406
                       network_parameters, misc_parameters, evaluation_parameters)
407
408
409
410
    elif arguments.mode == 'evaluate-mapping':
        logging.basicConfig(filename='evaluate-mapping-error.log')
        if arguments.settings_path is not None:
            settings_evaluation = Settings(arguments.settings_path)
411
        else:
412
413
414
415
416
417
418
419
420
421
422
423
424
            settings_evaluation = Settings('settings_evaluation.ini')
        mapping_evaluation_parameters = settings_evaluation['MAPPING']
        evaluate_mapping(mapping_evaluation_parameters)
    elif arguments.mode == 'clear-experiments':
        shutil.rmtree(os.path.join(
            misc_parameters['experiments_directory'], training_parameters['experiment_name']))
        shutil.rmtree(os.path.join(
            misc_parameters['logs_directory'], training_parameters['experiment_name']))
        print('Cleared the current experiments and logs directory successfully!')
    elif arguments.mode == 'clear-everything':
        delete_files(misc_parameters['experiments_directory'])
        delete_files(misc_parameters['logs_directory'])
        print('Cleared the current experiments and logs directory successfully!')
425
426
427
428
429
430
431
432
433
434
    elif arguments.mode == 'train-and-evaluate-mapping':
        if arguments.settings_path is not None:
            settings_evaluation = Settings(arguments.settings_path)
        else:
            settings_evaluation = Settings('settings_evaluation.ini')
        mapping_evaluation_parameters = settings_evaluation['MAPPING']
        train(data_parameters, training_parameters,
              network_parameters, misc_parameters)
        logging.basicConfig(filename='evaluate-mapping-error.log')
        evaluate_mapping(mapping_evaluation_parameters)
435
436
437
    elif arguments.mode == 'prepare-data':
        print('Ensure you have updated the settings.ini file accordingly! This call does nothing but pass after data was shuffled!')
        pass
438
439
    else:
        raise ValueError(
440
            'Invalid mode value! Only supports: train, evaluate-score, evaluate-mapping, train-and-evaluate-mapping, prepare-data, clear-experiments and clear-everything')