preprocess/detectron2/projects/TridentNet/train_net.py

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

"""
TridentNet Training Script.

This script is a simplified version of the training script in detectron2/tools.
"""

import os

from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import (
    default_argument_parser,
    default_setup,
    DefaultTrainer,
    launch,
)
from detectron2.evaluation import COCOEvaluator

from tridentnet import add_tridentnet_config


class Trainer(DefaultTrainer):
    @classmethod
    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        if output_folder is None:
            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
        return COCOEvaluator(dataset_name, output_dir=output_folder)


def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_cfg()
    add_tridentnet_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()
    default_setup(cfg, args)
    return cfg


def main(args):
    cfg = setup(args)

    if args.eval_only:
        model = Trainer.build_model(cfg)
        DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
            cfg.MODEL.WEIGHTS, resume=args.resume
        )
        res = Trainer.test(cfg, model)
        return res

    trainer = Trainer(cfg)
    trainer.resume_or_load(resume=args.resume)
    return trainer.train()


def invoke_main() -> None:
    args = default_argument_parser().parse_args()
    print("Command Line Args:", args)
    launch(
        main,
        args.num_gpus,
        num_machines=args.num_machines,
        machine_rank=args.machine_rank,
        dist_url=args.dist_url,
        args=(args,),
    )


if __name__ == "__main__":
    invoke_main()  # pragma: no cover