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single_reconstruction.py
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181 lines (110 loc) · 6.7 KB
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from util_functions.reconstruction_settings import get_reconstruction_manager
import argparse
"""
This script makes it possible to design and train a Deep Learning
model taking a subset of ECG leads as input and generating a full 12-lead
ECG as output. The reconstruction loss is given by the mathematical difference
between the recostructed signal and the original 12-lead ECG.
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-device', '--device', type=str, default=None)
parser.add_argument('-input', '--input_leads', type=str, default=None)
parser.add_argument('-output', '--output_leads', type=str, default=None)
parser.add_argument('-dataset', '--dataset', type=str, default=None)
parser.add_argument('-data_size', '--data_size', type=str, default=None)
parser.add_argument('-input_channel', '--input_channel', type=int, default=None)
parser.add_argument('-middle_channel', '--middle_channel', type=int, default=None)
parser.add_argument('-output_channel', '--output_channel', type=int, default=None)
parser.add_argument('-input_depth', '--input_depth', type=int, default=None)
parser.add_argument('-middle_depth', '--middle_depth', type=int, default=None)
parser.add_argument('-output_depth', '--output_depth', type=int, default=None)
parser.add_argument('-input_kernel', '--input_kernel', type=int, default=None)
parser.add_argument('-middle_kernel', '--middle_kernel', type=int, default=None)
parser.add_argument('-output_kernel', '--output_kernel', type=int, default=None)
parser.add_argument('-residual', '--use_residual', type=str, default=None)
parser.add_argument('-epoch', '--epoch_num', type=int, default=None)
parser.add_argument('-batch', '--batch_size', type=int, default=None)
parser.add_argument('-ppercent', '--prioritize_percent', type=float, default=None)
parser.add_argument('-psize', '--prioritize_size', type=int, default=None)
parser.add_argument('-optim', '--optimizer', type=str, default=None)
parser.add_argument('-lr', '--learning_rate', type=float, default=None)
parser.add_argument('-mom', '--momentum', type=float, default=None)
parser.add_argument('-decay', '--weight_decay', type=float, default=None)
parser.add_argument('-nest', '--nesterov', type=str, default=None)
parser.add_argument('-format', '--plot_format', type=str, default='png')
parser.add_argument('-train', '--train', action='store_const', const=True, default=False)
parser.add_argument('-test', '--test', action='store_const', const=True, default=False)
parser.add_argument('-eval', '--evaluate', action='store_const', const=True, default=False)
parser.add_argument('-plot', '--plot', action='store_const', const=True, default=False)
parser.add_argument('-plot_train', '--plot_train', action='store_const', const=True, default=False)
parser.add_argument('-plot_test', '--plot_test', action='store_const', const=True, default=False)
parser.add_argument('-plot_model', '--plot_model', action='store_const', const=True, default=False)
parser.add_argument('-plot_random', '--plot_random', action='store_const', const=True, default=False)
parser.add_argument('-plot_error', '--plot_error', action='store_const', const=True, default=False)
parser.add_argument('-plot_sub_class', '--plot_sub_class', action='store_const', const=True, default=False)
parser.add_argument('-loss_per_sample', '--compute_loss_per_sample', action='store_const', const=True, default=False)
args = vars(parser.parse_args())
manager, sub_classes = get_reconstruction_manager(args)
if args['plot']:
print('Plot all...')
manager.load_train_stats()
manager.plot_train_stats()
manager.load_valid_stats()
manager.plot_valid_stats()
manager.load_test_stats(compute_loss_per_sample=args['compute_loss_per_sample'])
manager.plot_test_stats(plot_sub_classes = sub_classes, compute_loss_per_sample=args['compute_loss_per_sample'])
manager.load_model_stats()
manager.plot_model_stats()
manager.load_model()
manager.plot_random_example(plot_format=args['plot_format'])
manager.plot_error_example(plot_format=args['plot_format'])
else:
if args['train']:
print('Train...')
manager.reset_model()
manager.load_dataset(train=True, valid=True)
manager.train()
manager.release_dataset()
manager.plot_train_stats()
manager.plot_valid_stats()
elif args['plot_train']:
print('Plot train...')
manager.load_train_stats()
manager.plot_train_stats()
manager.load_valid_stats()
manager.plot_valid_stats()
if args['test']:
print('Test...')
manager.load_model()
manager.load_dataset(test=True, extract_qrs=args['compute_loss_per_sample'])
manager.test(compute_loss_per_sample=args['compute_loss_per_sample'])
manager.release_dataset()
manager.plot_test_stats(plot_sub_classes = sub_classes, compute_loss_per_sample=args['compute_loss_per_sample'])
elif args['plot_test']:
print('Plot test...')
manager.load_test_stats(compute_loss_per_sample=args['compute_loss_per_sample'])
manager.plot_test_stats(plot_sub_classes = sub_classes, compute_loss_per_sample=args['compute_loss_per_sample'])
if args['evaluate']:
print('Evaluate...')
manager.load_model()
manager.compute_model_stats()
manager.plot_model_stats()
elif args['plot_model']:
print('Plot model...')
manager.load_model_stats()
manager.plot_model_stats()
if args['plot_random']:
print('Plot random examples...')
manager.load_model()
manager.plot_random_example(plot_format=args['plot_format'])
if args['plot_error']:
print('Plot error examples...')
manager.load_test_stats(compute_loss_per_sample=args['compute_loss_per_sample'])
manager.load_model()
manager.plot_error_example(plot_format=args['plot_format'])
if args['plot_sub_class']:
print('Plot sub class examples...')
manager.load_test_stats(compute_loss_per_sample=args['compute_loss_per_sample'])
manager.load_model()
manager.plot_sub_class_example(plot_sub_classes = sub_classes, plot_format=args['plot_format'])