7f95637dd388e6c9d253847d634bfc7b0af05dda,eval_ch.py,,parse_args,#,21

Before Change

        type=str,
        required=True,
        help="type of model to use. see model_provider for options.")
    parser.add_argument(
        "--use-pretrained",
        action="store_true",
        help="enable using pretrained model from gluon.")
    parser.add_argument(
        "--resume",
        type=str,
        default="",
        help="resume from previously saved parameters if not None")

    parser.add_argument(
        "--num-gpus",
        type=int,
        default=0,
        help="number of gpus to use.")
    parser.add_argument(
        "-j",
        "--num-data-workers",
        dest="num_workers",
        default=4,
        type=int,
        help="number of preprocessing workers")

    parser.add_argument(
        "--batch-size",
        type=int,
        default=32,
        help="training batch size per device (CPU/GPU).")

    parser.add_argument(
        "--save-dir",
        type=str,
        default="",
        help="directory of saved models and log-files")
    parser.add_argument(
        "--logging-file-name",
        type=str,
        default="train.log",

After Change

def parse_args():
    parser = argparse.ArgumentParser(
        description="Evaluate a model for image classification/segmentation (Chainer)",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        "--dataset",
        type=str,
        default="ImageNet1K",
        help="dataset name. options are ImageNet1K, CUB200_2011, CIFAR10, CIFAR100, SVHN, VOC2012, ADE20K, Cityscapes, "
             "COCO")
    parser.add_argument(
        "--work-dir",
        type=str,
        default=os.path.join("..", "imgclsmob_data"),
        help="path to working directory only for dataset root path preset")

    args, _ = parser.parse_known_args()
    dataset_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
    dataset_metainfo.add_dataset_parser_arguments(

Instances