Function parse_args

methods/pt_map/io_utils.py:7–35

(script)

def parse_args(script):
    parser = argparse.ArgumentParser(description='few-shot script %s' % (script))
    parser.add_argument('--dataset', default='miniImagenet', help='CUB/miniImagenet')
    parser.add_argument('--model', default='WideResNet28_10', help='model:  WideResNet28_10/ResNet{18}')
    parser.add_argument('--method', default='S2M2_R', help='rotation/S2M2_R')
    parser.add_argument('--train_aug', action='store_true',
                        help='perform data augmentation or not during training ')  # still required for
    # save_features.py and test.py to find the model path correctly
    if script == 'train':
        parser.add_argument('--num_classes', default=200, type=int,
                            help='total number of classes')  # make it larger than the maximum label value in base class
        parser.add_argument('--save_freq', default=10, type=int, help='Save frequency')
        parser.add_argument('--start_epoch', default=0, type=int, help='Starting epoch')
        parser.add_argument('--stop_epoch', default=400, type=int,
                            help='Stopping epoch')  # for meta-learning methods, each epoch contains 100 episodes.
        # The default epoch number is dataset dependent. See train.py
        parser.add_argument('--resume', action='store_true',
                            help='continue from previous trained model with largest epoch')
        parser.add_argument('--lr', default=0.001, type=int, help='learning rate')
        parser.add_argument('--batch_size', default=16, type=int, help='batch size ')
        parser.add_argument('--test_batch_size', default=2, type=int, help='batch size ')
        parser.add_argument('--alpha', default=2.0, type=int, help='for S2M2 training ')
    elif script == 'test':
        parser.add_argument('--num_classes', default=200, type=int, help='total number of classes')
        parser.add_argument('--model_dir', type=str, help='the pretrained model path ')
        parser.add_argument('--file_name', type=str, help='where the features will be saved ')
        parser.add_argument('--json_dir', type=str, default='./', help='')

    return parser.parse_args()


def get_assigned_file(checkpoint_dir, num):