train_net.py

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

"""
Detection Training Script.
"""

import logging
import sys
from typing import List

import detectron2.utils.comm as comm
from d2go.distributed import launch
from d2go.setup import (
    basic_argument_parser,
    build_basic_cli_args,
    post_mortem_if_fail_for_main,
    prepare_for_launch,
    setup_after_launch,
)
from d2go.utils.misc import (
    dump_trained_model_configs,
    print_metrics_table,
    save_binary_outputs,
)
from detectron2.engine.defaults import create_ddp_model


logger = logging.getLogger("d2go.tools.train_net")


def main(
    cfg,
    output_dir,
    runner=None,
    eval_only=False,
    # NOTE: always enable resume when running on cluster
    resume=True,
):
    setup_after_launch(cfg, output_dir, runner)

    model = runner.build_model(cfg)
    logger.info("Model:\n{}".format(model))

    if eval_only:
        checkpointer = runner.build_checkpointer(cfg, model, save_dir=output_dir)
        # checkpointer.resume_or_load() will skip all additional checkpointable
        # which may not be desired like ema states
        if resume and checkpointer.has_checkpoint():
            checkpoint = checkpointer.resume_or_load(cfg.MODEL.WEIGHTS, resume=resume)
        else:
            checkpoint = checkpointer.load(cfg.MODEL.WEIGHTS)
        train_iter = checkpoint.get("iteration", None)
        model.eval()
        metrics = runner.do_test(cfg, model, train_iter=train_iter)
        print_metrics_table(metrics)
        return {
            "accuracy": metrics,
            "model_configs": {},
            "metrics": metrics,
        }

    model = create_ddp_model(
        model,
        fp16_compression=cfg.MODEL.DDP_FP16_GRAD_COMPRESS,
        device_ids=None if cfg.MODEL.DEVICE == "cpu" else [comm.get_local_rank()],
        broadcast_buffers=False,
        find_unused_parameters=cfg.MODEL.DDP_FIND_UNUSED_PARAMETERS,
    )

    trained_cfgs = runner.do_train(cfg, model, resume=resume)
    metrics = runner.do_test(cfg, model)
    print_metrics_table(metrics)

    # dump config files for trained models
    trained_model_configs = dump_trained_model_configs(cfg.OUTPUT_DIR, trained_cfgs)
    return {
        # for e2e_workflow
        "accuracy": metrics,
        # for unit_workflow
        "model_configs": trained_model_configs,
        "metrics": metrics,
    }


def run_with_cmdline_args(args):
    cfg, output_dir, runner = prepare_for_launch(args)

    outputs = launch(
        post_mortem_if_fail_for_main(main),
        num_processes_per_machine=args.num_processes,
        num_machines=args.num_machines,
        machine_rank=args.machine_rank,
        dist_url=args.dist_url,
        backend=args.dist_backend,
        args=(cfg, output_dir, runner, args.eval_only, args.resume),
    )

    if args.save_return_file is not None:
        save_binary_outputs(args.save_return_file, outputs)

    return outputs


def cli(args=None):
    parser = basic_argument_parser(requires_output_dir=False)
    parser.add_argument(
        "--eval-only", action="store_true", help="perform evaluation only"
    )
    parser.add_argument(
        "--resume",
        action="store_true",
        help="whether to attempt to resume from the checkpoint directory",
    )
    args = sys.argv[1:] if args is None else args
    run_with_cmdline_args(parser.parse_args(args))


def build_cli_args(
    eval_only: bool = False,
    resume: bool = False,
    **kwargs,
) -> List[str]:
    """Returns parameters in the form of CLI arguments for train_net binary.

    For the list of non-train_net-specific parameters, see build_basic_cli_args."""
    args = build_basic_cli_args(**kwargs)
    if eval_only:
        args += ["--eval-only"]
    if resume:
        args += ["--resume"]
    return args


if __name__ == "__main__":
    cli()