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single_recon_classif.py
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145 lines (86 loc) · 4.61 KB
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from util_functions.recon_classif_settings import get_recon_classif_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 probability of
detecting specific clinical labels from the reconstructed signals.
"""
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('-detectset', '--detectset', type=str, default=None)
parser.add_argument('-data_size', '--data_size', type=str, default=None)
parser.add_argument('-use_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('-alpha', '--alpha', type=float, default=None)
parser.add_argument('-parallel', '--parallel', 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('-plot_train', '--plot_train', action='store_const', const=True, default=False)
parser.add_argument('-test', '--test', action='store_const', const=True, default=False)
parser.add_argument('-plot_test', '--plot_test', action='store_const', const=True, default=False)
parser.add_argument('-eval', '--evaluate', 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', '--plot', 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)
args = vars(parser.parse_args())
manager, sub_classes = get_recon_classif_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()
manager.plot_test_stats()
manager.load_model_stats()
manager.plot_model_stats()
manager.load_model()
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)
manager.test()
manager.release_dataset()
manager.plot_test_stats(plot_sub_classes=sub_classes)
elif args['plot_test']:
print('Plot test...')
manager.load_test_stats()
manager.plot_test_stats(plot_sub_classes=sub_classes)
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()