train_predictor.py

import os
from fluidestimator.config import get_cfg
from fluidestimator.engine import DefaultTrainer, default_argument_parser, default_setup, launch
from fluidestimator.evaluation import DatasetEvaluators, PIV2DEvaluator, PIVFoamEvaluator


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")
        evaluator_list = []
        if dataset_name == 'PIV2D':
            evaluator_list.append(PIV2DEvaluator(dataset_name, output_dir=output_folder))
        if dataset_name == 'PIVFoam':
            evaluator_list.append(PIVFoamEvaluator(dataset_name, output_dir=output_folder))
        if len(evaluator_list) == 0:
            raise NotImplementedError(
                "no Evaluator for the dataset {} ".format(
                    dataset_name
                )
            )
        elif len(evaluator_list) == 1:
            return evaluator_list[0]
        return DatasetEvaluators(evaluator_list)


def setup(args):
    """
    Create configs and perform basic setups.
    """
    cfg = get_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)
    os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
    trainer = Trainer(cfg)
    trainer.resume_or_load(resume=args.resume)
    return trainer.train()

if __name__ == "__main__":
    args = default_argument_parser().parse_args()
    args.resume = True
    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,),
    )