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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.runner import Runner
def parse_args():
parser = argparse.ArgumentParser(description='Train a pose 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',
nargs='?',
type=str,
const='auto',
help='If specify checkpint 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',
default=False,
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(
'--show-dir',
help='directory where the visualization images will be saved.')
parser.add_argument(
'--show',
action='store_true',
help='whether to display the prediction results in a window.')
parser.add_argument(
'--interval',
type=int,
default=1,
help='visualize per interval samples.')
parser.add_argument(
'--wait-time',
type=float,
default=1,
help='display time of every window. (second)')
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('--seed', type=int, default=None, help='random seed')
# 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 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:
from mmengine.optim import AmpOptimWrapper, OptimWrapper
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
# visualization
if args.show or (args.show_dir is not None):
assert 'visualization' in cfg.default_hooks, \
'PoseVisualizationHook is not set in the ' \
'`default_hooks` field of config. Please set ' \
'`visualization=dict(type="PoseVisualizationHook")`'
cfg.default_hooks.visualization.enable = True
cfg.default_hooks.visualization.show = args.show
if args.show:
cfg.default_hooks.visualization.wait_time = args.wait_time
cfg.default_hooks.visualization.out_dir = args.show_dir
cfg.default_hooks.visualization.interval = args.interval
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
return cfg
def main():
args = parse_args()
# load config
cfg = Config.fromfile(args.config)
# merge CLI arguments to config
cfg = merge_args(cfg, args)
# set preprocess configs to model
if 'preprocess_cfg' in cfg:
cfg.model.setdefault('data_preprocessor',
cfg.get('preprocess_cfg', {}))
# build the runner from config
runner = Runner.from_cfg(cfg)
# If local_rank is 0 (main process), print the model architecture
if args.local_rank == 0:
print('Model Architecture')
print(runner.model)
# start training
runner.train()
if __name__ == '__main__':
main()
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