train_net_da.py

import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import default_argument_parser, default_setup, launch
from detectron2.evaluation import (
    verify_results,
)

from da_faster import add_da_config
from da_faster.modeling.meta_arch.da_rcnn import DaGeneralizedRCNN # noqa
from da_faster.modeling.meta_arch.vgg import build_vgg_backbone # noqa
from da_faster.modeling.meta_arch.roi_heads import DaStandardROIHeads # noqa
from da_faster.engine.trainer import DATrainer
from da_faster.data.evaluator import build_evaluator
from da_faster.data.register import register_my_cityscapes


class My_Trainer(DATrainer):
    """
    We use the "DATrainer" which contains pre-defined default logic for
    standard training workflow. They may not work for you, especially if you
    are working on a new research project. In that case you can write your
    own training loop. You can use "tools/plain_train_net.py" as an example.
    """

    @classmethod
    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        return build_evaluator(cfg, dataset_name, output_folder)

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


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

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

    """
    If you'd like to do anything fancier than the standard training logic,
    consider writing your own training loop (see plain_train_net.py) or
    subclassing the trainer.
    """
    trainer = My_Trainer(cfg)
    trainer.resume_or_load(resume=args.resume)
    return trainer.train()


if __name__ == "__main__":
    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,),
    )