fastreid.engine.defaults Namespace Reference

Classes
class	DefaultPredictor
class	DefaultTrainer

Functions
	default_argument_parser ()
	default_setup (cfg, args)

Detailed Description

This file contains components with some default boilerplate logic user may need
in training / testing. They will not work for everyone, but many users may find them useful.
The behavior of functions/classes in this file is subject to change,
since they are meant to represent the "common default behavior" people need in their projects.

Function Documentation

default_argument_parser()

fastreid.engine.defaults.default_argument_parser

(

)

Create a parser with some common arguments used by fastreid users.
Returns:
    argparse.ArgumentParser:

Definition at line 39 of file defaults.py.

def default_argument_parser():
    """
    Create a parser with some common arguments used by fastreid users.
    Returns:
        argparse.ArgumentParser:
    """
    parser = argparse.ArgumentParser(description="fastreid Training")
    parser.add_argument("--config-file", default="", metavar="FILE", help="path to config file")
    parser.add_argument(
        "--finetune",
        action="store_true",
        help="whether to attempt to finetune from the trained model",
    )
    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("--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
    port = 30000 + random.randint(1, 10000) + random.randint(1, 5000)
    parser.add_argument("--dist-url", default="tcp://127.0.0.1:{}".format(port))
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )
    return parser
 
 

default_setup()

fastreid.engine.defaults.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

Definition at line 79 of file defaults.py.

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