mirror of https://github.com/open-mmlab/mmyolo.git
124 lines
4.2 KiB
Python
124 lines
4.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import logging
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import os
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import os.path as osp
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from mmdet.utils import setup_cache_size_limit_of_dynamo
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from mmengine.config import Config, DictAction
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from mmengine.logging import print_log
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from mmengine.runner import Runner
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from mmyolo.registry import RUNNERS
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from mmyolo.utils import is_metainfo_lower
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def parse_args():
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parser = argparse.ArgumentParser(description='Train a detector')
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parser.add_argument('config', help='train config file path')
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parser.add_argument('--work-dir', help='the dir to save logs and models')
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parser.add_argument(
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'--amp',
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action='store_true',
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default=False,
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help='enable automatic-mixed-precision training')
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parser.add_argument(
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'--resume',
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nargs='?',
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type=str,
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const='auto',
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help='If specify checkpoint path, resume from it, while if not '
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'specify, try to auto resume from the latest checkpoint '
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'in the work directory.')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='job launcher')
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# When using PyTorch version >= 2.0.0, the `torch.distributed.launch`
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# will pass the `--local-rank` parameter to `tools/train.py` instead
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# of `--local_rank`.
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parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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return args
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def main():
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args = parse_args()
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# Reduce the number of repeated compilations and improve
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# training speed.
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setup_cache_size_limit_of_dynamo()
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# load config
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cfg = Config.fromfile(args.config)
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# replace the ${key} with the value of cfg.key
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# cfg = replace_cfg_vals(cfg)
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cfg.launcher = args.launcher
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# work_dir is determined in this priority: CLI > segment in file > filename
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if args.work_dir is not None:
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# update configs according to CLI args if args.work_dir is not None
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cfg.work_dir = args.work_dir
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elif cfg.get('work_dir', None) is None:
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# use config filename as default work_dir if cfg.work_dir is None
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cfg.work_dir = osp.join('./work_dirs',
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osp.splitext(osp.basename(args.config))[0])
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# enable automatic-mixed-precision training
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if args.amp is True:
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optim_wrapper = cfg.optim_wrapper.type
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if optim_wrapper == 'AmpOptimWrapper':
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print_log(
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'AMP training is already enabled in your config.',
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logger='current',
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level=logging.WARNING)
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else:
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assert optim_wrapper == 'OptimWrapper', (
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'`--amp` is only supported when the optimizer wrapper type is '
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f'`OptimWrapper` but got {optim_wrapper}.')
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cfg.optim_wrapper.type = 'AmpOptimWrapper'
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cfg.optim_wrapper.loss_scale = 'dynamic'
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# resume is determined in this priority: resume from > auto_resume
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if args.resume == 'auto':
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cfg.resume = True
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cfg.load_from = None
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elif args.resume is not None:
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cfg.resume = True
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cfg.load_from = args.resume
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# Determine whether the custom metainfo fields are all lowercase
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is_metainfo_lower(cfg)
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# build the runner from config
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if 'runner_type' not in cfg:
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# build the default runner
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runner = Runner.from_cfg(cfg)
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else:
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# build customized runner from the registry
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# if 'runner_type' is set in the cfg
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runner = RUNNERS.build(cfg)
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# start training
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runner.train()
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if __name__ == '__main__':
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main()
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