143 lines
4.7 KiB
Python
143 lines
4.7 KiB
Python
from __future__ import division
|
|
import argparse
|
|
import importlib
|
|
import os
|
|
import os.path as osp
|
|
import time
|
|
|
|
import mmcv
|
|
import torch
|
|
from mmcv import Config
|
|
from mmcv.runner import init_dist
|
|
|
|
from openselfsup import __version__
|
|
from openselfsup.apis import set_random_seed, train_model
|
|
from openselfsup.datasets import build_dataset
|
|
from openselfsup.models import build_model
|
|
from openselfsup.utils import collect_env, get_root_logger, traverse_replace
|
|
|
|
|
|
def parse_args():
|
|
parser = argparse.ArgumentParser(description='Train a model')
|
|
parser.add_argument('config', help='train config file path')
|
|
parser.add_argument(
|
|
'--work_dir',
|
|
type=str,
|
|
default=None,
|
|
help='the dir to save logs and models')
|
|
parser.add_argument(
|
|
'--resume_from', help='the checkpoint file to resume from')
|
|
parser.add_argument(
|
|
'--pretrained', default=None, help='pretrained model file')
|
|
parser.add_argument(
|
|
'--gpus',
|
|
type=int,
|
|
default=1,
|
|
help='number of gpus 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(
|
|
'--launcher',
|
|
choices=['none', 'pytorch', 'slurm', 'mpi'],
|
|
default='none',
|
|
help='job launcher')
|
|
parser.add_argument('--local_rank', type=int, default=0)
|
|
parser.add_argument('--port', type=int, default=29500,
|
|
help='port only works when launcher=="slurm"')
|
|
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)
|
|
# set cudnn_benchmark
|
|
if cfg.get('cudnn_benchmark', False):
|
|
torch.backends.cudnn.benchmark = True
|
|
# update configs according to CLI args
|
|
if args.work_dir is not None:
|
|
cfg.work_dir = args.work_dir
|
|
if args.resume_from is not None:
|
|
cfg.resume_from = args.resume_from
|
|
cfg.gpus = args.gpus
|
|
|
|
# check memcached package exists
|
|
if importlib.util.find_spec('mc') is None:
|
|
traverse_replace(cfg, 'memcached', False)
|
|
|
|
# init distributed env first, since logger depends on the dist info.
|
|
if args.launcher == 'none':
|
|
distributed = False
|
|
assert cfg.model.type not in \
|
|
['DeepCluster', 'MOCO', 'SimCLR', 'ODC', 'NPID'], \
|
|
"{} does not support non-dist training.".format(cfg.model.type)
|
|
else:
|
|
distributed = True
|
|
if args.launcher == 'slurm':
|
|
cfg.dist_params['port'] = args.port
|
|
init_dist(args.launcher, **cfg.dist_params)
|
|
|
|
# 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, 'train_{}.log'.format(timestamp))
|
|
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([('{}: {}'.format(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('Distributed training: {}'.format(distributed))
|
|
logger.info('Config:\n{}'.format(cfg.text))
|
|
|
|
# set random seeds
|
|
if args.seed is not None:
|
|
logger.info('Set random seed to {}, deterministic: {}'.format(
|
|
args.seed, args.deterministic))
|
|
set_random_seed(args.seed, deterministic=args.deterministic)
|
|
cfg.seed = args.seed
|
|
meta['seed'] = args.seed
|
|
|
|
if args.pretrained is not None:
|
|
assert isinstance(args.pretrained, str)
|
|
cfg.model.pretrained = args.pretrained
|
|
model = build_model(cfg.model)
|
|
|
|
datasets = [build_dataset(cfg.data.train)]
|
|
assert len(cfg.workflow) == 1, "Validation is called by hook."
|
|
if cfg.checkpoint_config is not None:
|
|
# save openselfsup version, config file content and class names in
|
|
# checkpoints as meta data
|
|
cfg.checkpoint_config.meta = dict(
|
|
openselfsup_version=__version__, config=cfg.text)
|
|
# add an attribute for visualization convenience
|
|
train_model(
|
|
model,
|
|
datasets,
|
|
cfg,
|
|
distributed=distributed,
|
|
timestamp=timestamp,
|
|
meta=meta)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|