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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import logging | |
| import os | |
| import os.path as osp | |
| from mmengine.config import Config, DictAction | |
| from mmengine.logging import print_log | |
| from mmengine.registry import RUNNERS | |
| from mmengine.runner import Runner | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description='Train a model') | |
| 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( | |
| '--resume', action='store_true', help='Whether to resume checkpoint.') | |
| parser.add_argument( | |
| '--amp', | |
| action='store_true', | |
| default=False, | |
| help='Enable automatic-mixed-precision training') | |
| parser.add_argument( | |
| '--auto-scale-lr', | |
| action='store_true', | |
| help='Whether to scale the learning rate automatically. It requires ' | |
| '`auto_scale_lr` in config, and `base_batch_size` in `auto_scale_lr`') | |
| 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') | |
| # When using PyTorch version >= 2.0.0, the `torch.distributed.launch` | |
| # will pass the `--local-rank` parameter to `tools/train.py` instead | |
| # of `--local_rank`. | |
| parser.add_argument('--local_rank', '--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]) | |
| # enable automatic-mixed-precision training | |
| if args.amp: | |
| optim_wrapper = cfg.optim_wrapper.type | |
| if optim_wrapper == 'AmpOptimWrapper': | |
| print_log( | |
| 'AMP training is already enabled in your config.', | |
| logger='current', | |
| level=logging.WARNING) | |
| else: | |
| assert optim_wrapper == 'OptimWrapper', ( | |
| '`--amp` is only supported when the optimizer wrapper type is ' | |
| f'`OptimWrapper` but got {optim_wrapper}.') | |
| cfg.optim_wrapper.type = 'AmpOptimWrapper' | |
| cfg.optim_wrapper.loss_scale = 'dynamic' | |
| if args.resume: | |
| cfg.resume = True | |
| # enable automatically scaling LR | |
| if args.auto_scale_lr: | |
| if 'auto_scale_lr' in cfg and \ | |
| 'base_batch_size' in cfg.auto_scale_lr: | |
| cfg.auto_scale_lr.enable = True | |
| else: | |
| raise RuntimeError('Can not find "auto_scale_lr" or ' | |
| '"auto_scale_lr.base_batch_size" in your' | |
| ' configuration file.') | |
| # build the runner from config | |
| if 'runner_type' not in cfg: | |
| # build the default runner | |
| runner = Runner.from_cfg(cfg) | |
| else: | |
| # build customized runner from the registry | |
| # if 'runner_type' is set in the cfg | |
| runner = RUNNERS.build(cfg) | |
| # start training | |
| runner.train() | |
| if __name__ == '__main__': | |
| main() | |