示例#1
0
def default_setup(cfg, args):
    """
    Perform some basic common setups at the beginning of a job, including:
    1. Set up the detectron2 logger
    2. Log basic information about environment, cmdline arguments, and config
    3. Backup the config to the output directory
    Args:
        cfg (CfgNode): the full config to be used
        args (argparse.NameSpace): the command line arguments to be logged
    """
    output_dir = cfg.OUTPUT_DIR
    if comm.is_main_process() and output_dir:
        PathManager.mkdirs(output_dir)

    rank = comm.get_rank()
    setup_logger(output_dir, distributed_rank=rank, name="fvcore")
    logger = setup_logger(output_dir, distributed_rank=rank)

    logger.info("Rank of current process: {}. World size: {}".format(rank, comm.get_world_size()))
    logger.info("Environment info:\n" + collect_env_info())

    logger.info("Command line arguments: " + str(args))
    if hasattr(args, "config_file") and args.config_file != "":
        logger.info(
            "Contents of args.config_file={}:\n{}".format(
                args.config_file, PathManager.open(args.config_file, "r").read()
            )
        )

    logger.info("Running with full config:\n{}".format(cfg))
    if comm.is_main_process() and output_dir:
        # Note: some of our scripts may expect the existence of
        # config.yaml in output directory
        path = os.path.join(output_dir, "config.yaml")
        with PathManager.open(path, "w") as f:
            f.write(cfg.dump())
        logger.info("Full config saved to {}".format(os.path.abspath(path)))

    # make sure each worker has a different, yet deterministic seed if specified
    seed_all_rng()

    # cudnn benchmark has large overhead. It shouldn't be used considering the small size of
    # typical validation set.
    if not (hasattr(args, "eval_only") and args.eval_only):
        torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
示例#2
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    except AttributeError:
        data.append(("torchvision", "unknown"))

    try:
        import fvcore

        data.append(("fvcore", fvcore.__version__))
    except ImportError:
        pass

    try:
        import cv2

        data.append(("cv2", cv2.__version__))
    except ImportError:
        pass
    env_str = tabulate(data) + "\n"
    env_str += collect_torch_env()
    return env_str


if __name__ == "__main__":
    try:
        import detectron2  # noqa
    except ImportError:
        print(collect_env_info())
    else:
        from fastreid.utils.collect_env import collect_env_info

        print(collect_env_info())