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- """
- From original at https://github.com/facebookresearch/detectron2/blob/master/detectron2/modeling/__init__.py
- Original copyright of Facebook code below, modifications by Yehao Li, Copyright 2021.
- """
-
- # Copyright (c) Facebook, Inc. and its affiliates.
- import logging
- import os
- from collections import OrderedDict
- import torch
- import xmodaler.utils.comm as comm
- from xmodaler.checkpoint import XmodalerCheckpointer
- from xmodaler.config import get_cfg
- from xmodaler.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch, build_engine
- from xmodaler.modeling import add_config
-
- def setup(args):
- """
- Create configs and perform basic setups.
- """
- cfg = get_cfg()
- tmp_cfg = cfg.load_from_file_tmp(args.config_file)
- add_config(cfg, tmp_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 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 = build_engine(cfg)
- trainer.resume_or_load(resume=args.resume)
-
- if args.eval_only:
- res = None
- if trainer.val_data_loader is not None:
- res = trainer.test(trainer.cfg, trainer.model, trainer.val_data_loader, trainer.val_evaluator, epoch=-1)
- if comm.is_main_process():
- print(res)
-
- if trainer.test_data_loader is not None:
- res = trainer.test(trainer.cfg, trainer.model, trainer.test_data_loader, trainer.test_evaluator, epoch=-1)
- if comm.is_main_process():
- print(res)
- return res
-
- 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,),
- )
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