# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp # TODO import mmcls and mmseg from mmdet.core import * # noqa: F401,F403 from mmdet.datasets import * # noqa: F401,F403 from mmdet.metrics import * # noqa: F401,F403 from mmdet.models import * # noqa: F401,F403 from mmengine.config import Config, DictAction from mmengine.runner import Runner from mmrazor.core import * # noqa: F401,F403 from mmrazor.models import * # noqa: F401,F403 def parse_args(): parser = argparse.ArgumentParser(description='Train an algorithm') parser.add_argument('config', help='train config file path') parser.add_argument('--work-dir', help='the dir to save logs and models') parser.add_argument( '--cfg-options', nargs='+', action=DictAction, help='override some settings in the used config, the key-value pair ' 'in xxx=yyy format will be merged into config file. If the value to ' 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' 'Note that the quotation marks are necessary and that no white space ' 'is allowed.') parser.add_argument( '--launcher', choices=['none', 'pytorch', 'slurm', 'mpi'], default='none', help='job launcher') parser.add_argument('--local_rank', type=int, default=0) args = parser.parse_args() if 'LOCAL_RANK' not in os.environ: os.environ['LOCAL_RANK'] = str(args.local_rank) return args def main(): args = parse_args() # load config cfg = Config.fromfile(args.config) cfg.launcher = args.launcher if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # work_dir is determined in this priority: CLI > segment in file > filename if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get('work_dir', None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) # build the runner from config runner = Runner.from_cfg(cfg) # start training runner.train() if __name__ == '__main__': main()