Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
@@ -0,0 +1,195 @@
|
||||
# Copyright (c) OpenMMLab. All rights reserved.
|
||||
import argparse
|
||||
import copy
|
||||
import os
|
||||
import os.path as osp
|
||||
import time
|
||||
import warnings
|
||||
|
||||
import mmcv
|
||||
import torch
|
||||
from mmcv import Config, DictAction
|
||||
from mmcv.runner import get_dist_info, init_dist, set_random_seed
|
||||
from mmcv.utils import get_git_hash
|
||||
|
||||
from mmpose import __version__
|
||||
from mmpose.apis import init_random_seed, train_model
|
||||
from mmpose.datasets import build_dataset
|
||||
from mmpose.models import build_posenet
|
||||
from mmpose.utils import collect_env, get_root_logger, setup_multi_processes
|
||||
import mmcv_custom
|
||||
|
||||
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-from', help='the checkpoint file to resume from')
|
||||
parser.add_argument(
|
||||
'--no-validate',
|
||||
action='store_true',
|
||||
help='whether not to evaluate the checkpoint during training')
|
||||
group_gpus = parser.add_mutually_exclusive_group()
|
||||
group_gpus.add_argument(
|
||||
'--gpus',
|
||||
type=int,
|
||||
help='(Deprecated, please use --gpu-id) number of gpus to use '
|
||||
'(only applicable to non-distributed training)')
|
||||
group_gpus.add_argument(
|
||||
'--gpu-ids',
|
||||
type=int,
|
||||
nargs='+',
|
||||
help='(Deprecated, please use --gpu-id) ids of gpus to use '
|
||||
'(only applicable to non-distributed training)')
|
||||
group_gpus.add_argument(
|
||||
'--gpu-id',
|
||||
type=int,
|
||||
default=0,
|
||||
help='id of gpu to use '
|
||||
'(only applicable to non-distributed training)')
|
||||
parser.add_argument('--seed', type=int, default=None, help='random seed')
|
||||
parser.add_argument(
|
||||
'--deterministic',
|
||||
action='store_true',
|
||||
help='whether to set deterministic options for CUDNN backend.')
|
||||
parser.add_argument(
|
||||
'--cfg-options',
|
||||
nargs='+',
|
||||
action=DictAction,
|
||||
default={},
|
||||
help='override some settings in the used config, the key-value pair '
|
||||
'in xxx=yyy format will be merged into config file. For example, '
|
||||
"'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
|
||||
parser.add_argument(
|
||||
'--launcher',
|
||||
choices=['none', 'pytorch', 'slurm', 'mpi'],
|
||||
default='none',
|
||||
help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
parser.add_argument(
|
||||
'--autoscale-lr',
|
||||
action='store_true',
|
||||
help='automatically scale lr with the number of gpus')
|
||||
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()
|
||||
|
||||
cfg = Config.fromfile(args.config)
|
||||
|
||||
if args.cfg_options is not None:
|
||||
cfg.merge_from_dict(args.cfg_options)
|
||||
|
||||
# set multi-process settings
|
||||
setup_multi_processes(cfg)
|
||||
|
||||
# set cudnn_benchmark
|
||||
if cfg.get('cudnn_benchmark', False):
|
||||
torch.backends.cudnn.benchmark = True
|
||||
|
||||
# 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])
|
||||
if args.resume_from is not None:
|
||||
cfg.resume_from = args.resume_from
|
||||
if args.gpus is not None:
|
||||
cfg.gpu_ids = range(1)
|
||||
warnings.warn('`--gpus` is deprecated because we only support '
|
||||
'single GPU mode in non-distributed training. '
|
||||
'Use `gpus=1` now.')
|
||||
if args.gpu_ids is not None:
|
||||
cfg.gpu_ids = args.gpu_ids[0:1]
|
||||
warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
|
||||
'Because we only support single GPU mode in '
|
||||
'non-distributed training. Use the first GPU '
|
||||
'in `gpu_ids` now.')
|
||||
if args.gpus is None and args.gpu_ids is None:
|
||||
cfg.gpu_ids = [args.gpu_id]
|
||||
|
||||
if args.autoscale_lr:
|
||||
# apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
|
||||
cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 8
|
||||
|
||||
# init distributed env first, since logger depends on the dist info.
|
||||
if args.launcher == 'none':
|
||||
distributed = False
|
||||
if len(cfg.gpu_ids) > 1:
|
||||
warnings.warn(
|
||||
f'We treat {cfg.gpu_ids} as gpu-ids, and reset to '
|
||||
f'{cfg.gpu_ids[0:1]} as gpu-ids to avoid potential error in '
|
||||
'non-distribute training time.')
|
||||
cfg.gpu_ids = cfg.gpu_ids[0:1]
|
||||
else:
|
||||
distributed = True
|
||||
init_dist(args.launcher, **cfg.dist_params)
|
||||
# re-set gpu_ids with distributed training mode
|
||||
_, world_size = get_dist_info()
|
||||
cfg.gpu_ids = range(world_size)
|
||||
|
||||
# create work_dir
|
||||
mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
|
||||
# init the logger before other steps
|
||||
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
|
||||
log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
|
||||
logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
|
||||
|
||||
# init the meta dict to record some important information such as
|
||||
# environment info and seed, which will be logged
|
||||
meta = dict()
|
||||
# log env info
|
||||
env_info_dict = collect_env()
|
||||
env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
|
||||
dash_line = '-' * 60 + '\n'
|
||||
logger.info('Environment info:\n' + dash_line + env_info + '\n' +
|
||||
dash_line)
|
||||
meta['env_info'] = env_info
|
||||
|
||||
# log some basic info
|
||||
logger.info(f'Distributed training: {distributed}')
|
||||
logger.info(f'Config:\n{cfg.pretty_text}')
|
||||
|
||||
# set random seeds
|
||||
seed = init_random_seed(args.seed)
|
||||
logger.info(f'Set random seed to {seed}, '
|
||||
f'deterministic: {args.deterministic}')
|
||||
set_random_seed(seed, deterministic=args.deterministic)
|
||||
cfg.seed = seed
|
||||
meta['seed'] = seed
|
||||
|
||||
model = build_posenet(cfg.model)
|
||||
datasets = [build_dataset(cfg.data.train)]
|
||||
|
||||
if len(cfg.workflow) == 2:
|
||||
val_dataset = copy.deepcopy(cfg.data.val)
|
||||
val_dataset.pipeline = cfg.data.train.pipeline
|
||||
datasets.append(build_dataset(val_dataset))
|
||||
|
||||
if cfg.checkpoint_config is not None:
|
||||
# save mmpose version, config file content
|
||||
# checkpoints as meta data
|
||||
cfg.checkpoint_config.meta = dict(
|
||||
mmpose_version=__version__ + get_git_hash(digits=7),
|
||||
config=cfg.pretty_text,
|
||||
)
|
||||
train_model(
|
||||
model,
|
||||
datasets,
|
||||
cfg,
|
||||
distributed=distributed,
|
||||
validate=(not args.no_validate),
|
||||
timestamp=timestamp,
|
||||
meta=meta)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user