def add_data_specific_args()

in fastmri/pl_modules/data_module.py [0:0]

• 62 lines of code
• 1 McCabe index (conditional complexity)

    def add_data_specific_args(parent_parser):  # pragma: no-cover
        """
        Define parameters that only apply to this model
        """
        parser = ArgumentParser(parents=[parent_parser], add_help=False)

        # dataset arguments
        parser.add_argument(
            "--data_path",
            default=None,
            type=Path,
            help="Path to fastMRI data root",
        )
        parser.add_argument(
            "--test_path",
            default=None,
            type=Path,
            help="Path to data for test mode. This overwrites data_path and test_split",
        )
        parser.add_argument(
            "--challenge",
            choices=("singlecoil", "multicoil"),
            default="singlecoil",
            type=str,
            help="Which challenge to preprocess for",
        )
        parser.add_argument(
            "--test_split",
            choices=("test", "challenge"),
            default="test",
            type=str,
            help="Which data split to use as test split",
        )
        parser.add_argument(
            "--sample_rate",
            default=None,
            type=float,
            help="Fraction of slices in the dataset to use (train split only). If not given all will be used. Cannot set together with volume_sample_rate.",
        )
        parser.add_argument(
            "--volume_sample_rate",
            default=None,
            type=float,
            help="Fraction of volumes of the dataset to use (train split only). If not given all will be used. Cannot set together with sample_rate.",
        )
        parser.add_argument(
            "--use_dataset_cache_file",
            default=True,
            type=bool,
            help="Whether to cache dataset metadata in a pkl file",
        )
        parser.add_argument(
            "--combine_train_val",
            default=False,
            type=bool,
            help="Whether to combine train and val splits for training",
        )

        # data loader arguments
        parser.add_argument(
            "--batch_size", default=1, type=int, help="Data loader batch size"
        )
        parser.add_argument(
            "--num_workers",
            default=4,
            type=int,
            help="Number of workers to use in data loader",
        )

        return parser