run.py 16.6 KB
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"""Brain Mapper Run File

Description:

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    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
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    TODO: Might be worth adding some information on uncertaintiy estimation, later down the line
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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
        mode=clear-experiment # For clearning the experiments and logs directories of the last experiment
        mode=clear-all # For clearing all the files from the experiments and logs directories/
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"""

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import os
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import shutil
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import argparse
import logging
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from settings import Settings

import torch
import torch.utils.data as data

from solver import Solver
from BrainMapperUNet import BrainMapperUNet
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from utils.data_utils import get_datasets, data_test_train_validation_split, update_shuffling_flag
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import utils.data_evaluation_utils as evaluations
from utils.data_logging_utils import LogWriter
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# Set the default floating point tensor type to FloatTensor

torch.set_default_tensor_type(torch.FloatTensor)

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def load_data(data_parameters):
    """Dataset Loader

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    This function loads the training and validation datasets.
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    TODO: Will need to define if all the training data is loaded as bulk or individually!

    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.
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        validation_data (dataset object): Pytorch map-style dataset object, mapping indices to testing data samples.
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    """
    print("Data is loading...")
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    train_data, validation_data = get_datasets(data_parameters)
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    print("Data has loaded!")
    print("Training dataset size is {}".format(len(train_data)))
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    print("Validation dataset size is {}".format(len(validation_data)))
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    return train_data, validation_data
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def train(data_parameters, training_parameters, network_parameters, misc_parameters):
    """Training Function
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    This function trains a given model using the provided training data.
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    Currently, the data loaded is set to have multiple sub-processes. 
    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.
    Train data is also re-shuffled at each training epoch. 

    Args:
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        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
            'test_batch_size: 5
            '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'
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        }
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        network_parameters (dict): Contains information relevant parameters 
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        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'
        }
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    """

    train_data, test_data = load_data(data_parameters)

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

    test_loader = data.DataLoader(
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        dataset=test_data,
        batch_size=training_parameters['test_batch_size'],
        shuffle=False,
        num_workers=4,
        pin_memory=True
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    )

    if training_parameters['use_pre_trained']:
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        BrainMapperModel = torch.load(training_parameters['pre_trained_path'])
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    else:
        BrainMapperModel = BrainMapperUNet(network_parameters)

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    solver = Solver(model=BrainMapperModel,
                    device=misc_parameters['device'],
                    number_of_classes=network_parameters['number_of_classes'],
                    experiment_name=training_parameters['experiment_name'],
                    optimizer_arguments={'lr': training_parameters['learning_rate'],
                                         'betas': training_parameters['optimizer_beta'],
                                         'eps': training_parameters['optimizer_epsilon'],
                                         'weight_decay': training_parameters['optimizer_weigth_decay']
                                         },
                    model_name=misc_parameters['model_name'],
                    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']
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                    )

    solver.train(train_loader, test_loader)

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    model_output_path = os.path.join(
        misc_parameters['save_model_directory'], training_parameters['final_model_output_file'])
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    BrainMapperModel.save(model_output_path)

    print("Final Model Saved in: {}".format(model_output_path))
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def evaluate_score(training_parameters, network_parameters, misc_parameters, evaluation_parameters):
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    """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'
        }

        network_parameters (dict): Contains information relevant parameters 
        network_parameters= {
            'number_of_classes': 1
        }
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        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'
        }
    """

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    # TODO - NEED TO UPDATE THE DATA FUNCTIONS!
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    logWriter = LogWriter(number_of_classes=network_parameters['number_of_classes'],
                          logs_directory=misc_parameters['logs_directory'],
                          experiment_name=training_parameters['experiment_name']
                          )
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    prediction_output_path = os.path.join(misc_parameters['experiments_directory'],
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                                          training_parameters['experiment_name'],
                                          evaluation_parameters['saved_predictions_directory']
                                          )

    average_dice_score = evaluations.evaluate_dice_score(trained_model_path=evaluation_parameters['trained_model_path'],
                                                         number_of_classes=network_parameters['number_of_classes'],
                                                         data_directory=evaluation_parameters['data_directory'],
                                                         targets_directory=evaluation_parameters[
                                                             'targets_directory'],
                                                         data_list=evaluation_parameters['data_list'],
                                                         orientation=evaluation_parameters['orientation'],
                                                         prediction_output_path=prediction_output_path,
                                                         device=misc_parameters['device'],
                                                         LogWriter=logWriter
                                                         )
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    logWriter.close()

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def evaluate_mapping(mapping_evaluation_parameters):
    """Mapping Evaluator
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    This function passes through the network an input and generates the rsfMRI outputs.
    This function allows the user to either use one or two or three paths.
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    The convention for the different model paths is as follows:
    - model1: coronal
    - model2: axial
    - model3: saggital
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    However, this convention can be changed either bellow or the settings file.

    Args:
        mapping_evaluation_parameters (dict): Dictionary of parameters useful during mapping evaluation.
        mapping_evaluation_parameters = {
            'trained_model1_path': 'path/to/model1'
            'trained_model2_path': 'path/to/model2'
            'trained_model3_path': 'path/to/model3'
            'data_directory': 'path/to/data'
            'data_list': 'path/to/datalist.txt/
            'orientation1': 'coronal'
            'orientation2': 'axial'
            'orientation3': 'sagittal'
            'prediction_output_path': 'directory-of-saved-predictions'
            'batch_size': 2
            'device': 0
            'exit_on_error': True
            'number_of_paths': 3
        }

    """
    trained_model1_path = mapping_evaluation_parameters['trained_model1_path']
    trained_model2_path = mapping_evaluation_parameters['trained_model2_path']
    trained_model3_path = mapping_evaluation_parameters['trained_model3_path']
    data_directory = mapping_evaluation_parameters['data_directory']
    data_list = mapping_evaluation_parameters['data_list']
    orientation1 = mapping_evaluation_parameters['orientation1']
    orientation2 = mapping_evaluation_parameters['orientation2']
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    orientation3 = mapping_evaluation_parameters['orientation3']
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    prediction_output_path = mapping_evaluation_parameters['prediction_output_path']
    batch_size = mapping_evaluation_parameters['batch_size']
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    device = mapping_evaluation_parameters['device']
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    exit_on_error = mapping_evaluation_parameters['exit_on_error']

    if mapping_evaluation_parameters['number_of_paths'] == 1:
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        evaluations.evaluate_single_path(trained_model1_path,
                                         data_directory,
                                         data_list,
                                         orientation1,
                                         prediction_output_path,
                                         batch_size,
                                         device=device,
                                         exit_on_error=exit_on_error)
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    elif mapping_evaluation_parameters['number_of_paths'] == 2:
        evaluations.evaluate_two_paths(trained_model1_path,
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                                       trained_model2_path,
                                       data_directory,
                                       data_list,
                                       orientation1,
                                       orientation2,
                                       prediction_output_path,
                                       batch_size,
                                       device=device,
                                       exit_on_error=exit_on_error)
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    elif mapping_evaluation_parameters['number_of_paths'] == 3:
        evaluations.evaluate_all_paths(trained_model1_path,
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                                       trained_model2_path,
                                       trained_model3_path,
                                       data_directory,
                                       data_list,
                                       orientation1,
                                       orientation2,
                                       orientation3,
                                       prediction_output_path,
                                       batch_size,
                                       device=device,
                                       exit_on_error=exit_on_error)


def delete_files(folder):
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    """ Clear Folder Contents
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    Function which clears contents (like experiments or logs)

    Args:
        folder (str): Name of folders whose conents is to be deleted
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    Returns:
        None
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    Raises:
        Exception: Any error
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    """
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    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)

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if __name__ == '__main__':
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    parser = argparse.ArgumentParser()
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    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')
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    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']

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    data_shuffling_flag = data_parameters['data_split_flag']

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    load_data(data_parameters)

    # if data_shuffling_flag == True:
    #     # Here we shuffle the data!
    #     data_test_train_validation_split(data_parameters['data_directory'], data_parameters['train_percentage'], data_parameters['validation_percentage'])
    #     update_shuffling_flag('settings.ini')
    #     # TODO: This might also be a very good point to add cross-validation later
    # else:

    #     if arguments.mode == 'train':
    #         train(data_parameters, training_parameters,
    #             network_parameters, misc_parameters)
    #     elif arguments.mode == 'evaluate-score':
    #         evaluate_score(training_parameters,
    #                     network_parameters, misc_parameters, evaluation_parameters)
    #     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)
    #         else:
    #             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!')
    #     else:
    #         raise ValueError(
    #             'Invalid mode value! Only supports: train, evaluate-score, evaluate-mapping, clear-experiments and clear-everything')