timestep_limit = 5
else:
timestep_limit = 4
elif dataset == Dataset.INHOUSE:
timestep_limit = 2
if evaluate == Evaluate.TEST:
if patient == 40 or patient == 87 or patient == 100 or patient == 110:
timestep_limit = 3
elif patient == 122:
timestep_limit = 4
else:
timestep_limit = 2
elif evaluate == Evaluate.TRAINING:
if patient == 27:
timestep_limit = 3
else:
raise ValueError(f'Invalid dataset type given: {dataset}')
return timestep_limit
if __name__ == '__main__':
args = argparse.ArgumentParser(description='PyTorch Template')
args.add_argument('-c', '--config', default=None, type=str, help='config file path (default: None)')
args.add_argument('-r', '--resume', default=None, type=str, help='path to latest checkpoint (default: None)')
args.add_argument('-d', '--device', default=None, type=str, help='indices of GPUs to enable (default: all)')
args.add_argument('-e', '--evaluate', default=Evaluate.TEST, type=Evaluate, help='Either "training" or "test"; Determines the prefix of the folders to use')
args.add_argument('-m', '--dataset_type', default=Dataset.ISBI, type=Dataset, help='Dataset to use')
config = ConfigParser(*parse_cmd_args(args))
main(config)
if __name__ == '__main__':
args = argparse.ArgumentParser(description='PyTorch Template')
args.add_argument('-c',
'--config',
default=None,
type=str,
help='config file path (default: None)')
args.add_argument('-r',
'--resume',
default=None,
type=str,
help='path to latest checkpoint (default: None)')
args.add_argument('-d',
'--device',
default=None,
type=str,
help='indices of GPUs to enable (default: all)')
# custom cli options to modify configuration from default values given in json file.
CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
options = [
CustomArgs(['--lr', '--learning_rate'],
type=float,
target=('optimizer', 'args', 'lr')),
CustomArgs(['--bs', '--batch_size'],
type=int,
target=('data_loader', 'args', 'batch_size'))
]
config = ConfigParser(*parse_cmd_args(args, options))
main(config)