def build_args()

in fastmri_examples/unet/unet_reproduce_20201111.py/unet_brain_leaderboard.py [0:0]

• 80 lines of code
• 11 McCabe index (conditional complexity)

def build_args():
    parser = ArgumentParser()

    # basic args
    path_config = pathlib.Path("../../fastmri_dirs.yaml")
    backend = "ddp"
    num_gpus = 32
    batch_size = 1

    # set defaults based on optional directory config
    data_path = fetch_dir("brain_path", path_config)
    default_root_dir = fetch_dir("log_path", path_config) / "unet" / "brain_leaderboard"

    # client arguments
    parser.add_argument(
        "--mode",
        default="train",
        choices=("train", "test"),
        type=str,
        help="Operation mode",
    )

    # data transform params
    parser.add_argument(
        "--mask_type",
        choices=("random", "equispaced"),
        default="equispaced",
        type=str,
        help="Type of k-space mask",
    )
    parser.add_argument(
        "--center_fractions",
        nargs="+",
        default=[0.08, 0.04],
        type=float,
        help="Number of center lines to use in mask",
    )
    parser.add_argument(
        "--accelerations",
        nargs="+",
        default=[4, 8],
        type=int,
        help="Acceleration rates to use for masks",
    )

    # data config with path to fastMRI data and batch size
    parser = FastMriDataModule.add_data_specific_args(parser)
    parser.set_defaults(
        data_path=data_path,  # path to fastMRI data
        mask_type="equispaced",  # equispaced for brain data
        challenge="multicoil",  # which challenge
        batch_size=batch_size,  # number of samples per batch
        test_path=None,  # path for test split, overwrites data_path
    )

    # module config
    parser = UnetModule.add_model_specific_args(parser)
    parser.set_defaults(
        in_chans=1,  # number of input channels to U-Net
        out_chans=1,  # number of output chanenls to U-Net
        chans=256,  # number of top-level U-Net channels
        num_pool_layers=4,  # number of U-Net pooling layers
        drop_prob=0.0,  # dropout probability
        lr=0.001,  # RMSProp learning rate
        lr_step_size=40,  # epoch at which to decrease learning rate
        lr_gamma=0.1,  # extent to which to decrease learning rate
        weight_decay=0.0,  # weight decay regularization strength
    )

    # trainer config
    parser = pl.Trainer.add_argparse_args(parser)
    parser.set_defaults(
        gpus=num_gpus,  # number of gpus to use
        replace_sampler_ddp=False,  # this is necessary for volume dispatch during val
        accelerator=backend,  # what distributed version to use
        seed=42,  # random seed
        deterministic=True,  # makes things slower, but deterministic
        default_root_dir=default_root_dir,  # directory for logs and checkpoints
        max_epochs=50,  # max number of epochs
    )

    args = parser.parse_args()

    # configure checkpointing in checkpoint_dir
    checkpoint_dir = args.default_root_dir / "checkpoints"
    if not checkpoint_dir.exists():
        checkpoint_dir.mkdir(parents=True)

    args.checkpoint_callback = pl.callbacks.ModelCheckpoint(
        filepath=args.default_root_dir / "checkpoints",
        verbose=True,
        prefix="",
    )

    # set default checkpoint if one exists in our checkpoint directory
    if args.resume_from_checkpoint is None:
        ckpt_list = sorted(checkpoint_dir.glob("*.ckpt"), key=os.path.getmtime)
        if ckpt_list:
            args.resume_from_checkpoint = str(ckpt_list[-1])

    return args