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Running
on
Zero
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import setup_cache_size_limit_of_dynamo
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
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(
'--amp',
action='store_true',
default=False,
help='enable automatic-mixed-precision training')
parser.add_argument(
'--auto-scale-lr',
action='store_true',
help='enable automatically scaling LR.')
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(
'--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()
# Reduce the number of repeated compilations and improve
# training speed.
setup_cache_size_limit_of_dynamo()
# 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 is True:
cfg.optim_wrapper.type = 'AmpOptimWrapper'
cfg.optim_wrapper.loss_scale = 'dynamic'
# enable automatically scaling LR
if args.auto_scale_lr:
if 'auto_scale_lr' in cfg and \
'enable' in cfg.auto_scale_lr 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.enable" or '
'"auto_scale_lr.base_batch_size" in your'
' configuration file.')
# resume is determined in this priority: resume from > auto_resume
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
# 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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