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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.runner import Runner
from mmaction.registry import RUNNERS
def parse_args():
parser = argparse.ArgumentParser(description='Train a action recognizer')
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',
nargs='?',
type=str,
const='auto',
help='If specify checkpoint path, resume from it, while if not '
'specify, try to auto resume from the latest checkpoint '
'in the work directory.')
parser.add_argument(
'--amp',
action='store_true',
help='enable automatic-mixed-precision training')
parser.add_argument(
'--no-validate',
action='store_true',
help='whether not to evaluate the checkpoint during training')
parser.add_argument(
'--auto-scale-lr',
action='store_true',
help='whether to auto scale the learning rate according to the '
'actual batch size and the original batch size.')
parser.add_argument('--seed', type=int, default=None, help='random seed')
parser.add_argument(
'--diff-rank-seed',
action='store_true',
help='whether or not set different seeds for different ranks')
parser.add_argument(
'--deterministic',
action='store_true',
help='whether to set deterministic options for CUDNN backend.')
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', '--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 merge_args(cfg, args):
"""Merge CLI arguments to config."""
if args.no_validate:
cfg.val_cfg = None
cfg.val_dataloader = None
cfg.val_evaluator = None
cfg.launcher = args.launcher
# 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 is True:
optim_wrapper = cfg.optim_wrapper.get('type', 'OptimWrapper')
assert optim_wrapper in ['OptimWrapper', 'AmpOptimWrapper'], \
'`--amp` is not supported custom optimizer wrapper type ' \
f'`{optim_wrapper}.'
cfg.optim_wrapper.type = 'AmpOptimWrapper'
cfg.optim_wrapper.setdefault('loss_scale', 'dynamic')
# resume training
if args.resume == 'auto':
cfg.resume = True
cfg.load_from = None
elif args.resume is not None:
cfg.resume = True
cfg.load_from = args.resume
# enable auto scale learning rate
if args.auto_scale_lr:
cfg.auto_scale_lr.enable = True
# set random seeds
if cfg.get('randomness', None) is None:
cfg.randomness = dict(
seed=args.seed,
diff_rank_seed=args.diff_rank_seed,
deterministic=args.deterministic)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
return cfg
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# merge cli arguments to config
cfg = merge_args(cfg, args)
# 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()
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