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)