Function default_argument_parser

train_tools/visdrone_train.py:166–220

Create a parser with some common arguments used by detectron2 users. Args: epilog (str): epilog passed to ArgumentParser describing the usage. Returns: argparse.ArgumentParser:

(epilog=None)

def default_argument_parser(epilog=None):
    """
    Create a parser with some common arguments used by detectron2 users.

    Args:
        epilog (str): epilog passed to ArgumentParser describing the usage.

    Returns:
        argparse.ArgumentParser:
    """
    parser = argparse.ArgumentParser(
        epilog=epilog
        or f"""
        Examples:

        Run on single machine:
            $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth

        Run on multiple machines:
            (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url <URL> [--other-flags]
            (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url <URL> [--other-flags]
        """,
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )
    parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
    parser.add_argument(
        "--resume",
        action="store_true",
        help="whether to attempt to resume from the checkpoint directory",
    )
    parser.add_argument("--eval-only", action="store_true", help="perform evaluation only")
    parser.add_argument("--no-pretrain", action="store_true", help="whether to load pretrained model")
    parser.add_argument("--num-gpus", type=int, default=1, help="number of gpus *per machine*")
    parser.add_argument("--num-machines", type=int, default=1, help="total number of machines")
    parser.add_argument(
        "--machine-rank", type=int, default=0, help="the rank of this machine (unique per machine)"
    )

    # PyTorch still may leave orphan processes in multi-gpu training.
    # Therefore we use a deterministic way to obtain port,
    # so that users are aware of orphan processes by seeing the port occupied.
    port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14
    parser.add_argument(
        "--dist-url",
        default="tcp://127.0.0.1:{}".format(port),
        help="initialization URL for pytorch distributed backend. See "
        "https://pytorch.org/docs/stable/distributed.html for details.",
    )
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )
    return parser


def setup(args):