# 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 from mmcls import __version__ from mmcls.apis import init_random_seed, set_random_seed, train_model from mmcls.datasets import build_dataset from mmcls.models import build_classifier from mmcls.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') 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( '--device', help='device used for training. (Deprecated)') 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, 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 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] # init distributed env first, since logger depends on the dist info. if args.launcher == 'none': distributed = False else: distributed = True init_dist(args.launcher, **cfg.dist_params) _, world_size = get_dist_info() cfg.gpu_ids = range(world_size) # create work_dir mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) # dump config cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) # 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_classifier(cfg.model) model.init_weights() 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)) # save mmcls version, config file content and class names in # runner as meta data meta.update( dict( mmcls_version=__version__, config=cfg.pretty_text, CLASSES=datasets[0].CLASSES)) # add an attribute for visualization convenience train_model( model, datasets, cfg, distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, device='cpu' if args.device == 'cpu' else 'cuda', meta=meta) if __name__ == '__main__': main()