# Copyright (c) OpenMMLab. All rights reserved. from __future__ import division import argparse 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 from mmselfsup import __version__ from mmselfsup.apis import init_random_seed, set_random_seed, train_model from mmselfsup.datasets import build_dataset from mmselfsup.models import build_algorithm from mmselfsup.utils import collect_env, get_root_logger, setup_multi_processes def parse_args(): parser = argparse.ArgumentParser(description='Train a 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') group_gpus = parser.add_mutually_exclusive_group() group_gpus.add_argument( '--gpus', type=int, default=1, 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, 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', 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() 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 work_type = args.config.split('/')[1] cfg.work_dir = osp.join('./work_dirs', work_type, 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] # 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', 'DenseCL' ], f'{cfg.model.type} does not support non-dist training.' 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'train_{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 meta['config'] = cfg.pretty_text # 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 meta['exp_name'] = osp.basename(args.config) model = build_algorithm(cfg.model) model.init_weights() datasets = [build_dataset(cfg.data.train)] assert len(cfg.workflow) == 1, 'Validation is called by hook.' if cfg.checkpoint_config is not None: # save mmselfsup version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict( mmselfsup_version=__version__, config=cfg.pretty_text) train_model( model, datasets, cfg, distributed=distributed, timestamp=timestamp, meta=meta) if __name__ == '__main__': main()