mirror of https://github.com/open-mmlab/mmocr.git
211 lines
7.5 KiB
Python
211 lines
7.5 KiB
Python
#!/usr/bin/env python
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import argparse
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import copy
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import os
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import os.path as osp
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import time
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import warnings
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import mmcv
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import torch
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from mmcv import Config, DictAction
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from mmcv.runner import get_dist_info, init_dist, set_random_seed
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from mmcv.utils import get_git_hash
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from mmocr import __version__
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from mmocr.apis import train_detector
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from mmocr.datasets import build_dataset
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from mmocr.models import build_detector
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from mmocr.utils import collect_env, get_root_logger
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def parse_args():
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parser = argparse.ArgumentParser(description='Train a detector.')
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parser.add_argument('config', help='Train config file path.')
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parser.add_argument('--work-dir', help='The dir to save logs and models.')
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parser.add_argument(
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'--load-from', help='The checkpoint file to load from.')
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parser.add_argument(
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'--resume-from', help='The checkpoint file to resume from.')
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parser.add_argument(
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'--no-validate',
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action='store_true',
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help='Whether not to evaluate the checkpoint during training.')
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group_gpus = parser.add_mutually_exclusive_group()
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group_gpus.add_argument(
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'--gpus',
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type=int,
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help='Number of gpus to use '
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'(only applicable to non-distributed training).')
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group_gpus.add_argument(
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'--gpu-ids',
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type=int,
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nargs='+',
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help='ids of gpus to use '
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'(only applicable to non-distributed training).')
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parser.add_argument('--seed', type=int, default=None, help='Random seed.')
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parser.add_argument(
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'--deterministic',
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action='store_true',
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help='Whether to set deterministic options for CUDNN backend.')
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parser.add_argument(
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'--options',
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nargs='+',
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action=DictAction,
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help='Override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file (deprecate), '
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'change to --cfg-options instead.')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='Override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be of the form of either '
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'key="[a,b]" or key=a,b .The argument also allows nested list/tuple '
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'values, e.g. key="[(a,b),(c,d)]". Note that the quotation marks '
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'are necessary and that no white space is allowed.')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='Options for job launcher.')
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parser.add_argument('--local_rank', type=int, default=0)
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parser.add_argument(
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'--mc-config',
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type=str,
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default='',
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help='Memory cache config for image loading speed-up during training.')
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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if args.options and args.cfg_options:
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raise ValueError(
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'--options and --cfg-options cannot be both '
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'specified, --options is deprecated in favor of --cfg-options')
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if args.options:
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warnings.warn('--options is deprecated in favor of --cfg-options')
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args.cfg_options = args.options
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return args
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def main():
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args = parse_args()
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cfg = Config.fromfile(args.config)
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# update mc config
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if args.mc_config:
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mc = Config.fromfile(args.mc_config)
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if isinstance(cfg.data.train, list):
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for i in range(len(cfg.data.train)):
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cfg.data.train[i].pipeline[0].update(
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file_client_args=mc['mc_file_client_args'])
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else:
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cfg.data.train.pipeline[0].update(
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file_client_args=mc['mc_file_client_args'])
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# import modules from string list.
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if cfg.get('custom_imports', None):
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from mmcv.utils import import_modules_from_strings
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import_modules_from_strings(**cfg['custom_imports'])
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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# work_dir is determined in this priority: CLI > segment in file > filename
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if args.work_dir is not None:
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# update configs according to CLI args if args.work_dir is not None
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cfg.work_dir = args.work_dir
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elif cfg.get('work_dir', None) is None:
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# use config filename as default work_dir if cfg.work_dir is None
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cfg.work_dir = osp.join('./work_dirs',
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osp.splitext(osp.basename(args.config))[0])
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if args.load_from is not None:
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cfg.load_from = args.load_from
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if args.resume_from is not None:
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cfg.resume_from = args.resume_from
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if args.gpu_ids is not None:
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cfg.gpu_ids = args.gpu_ids
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else:
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cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
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# init distributed env first, since logger depends on the dist info.
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if args.launcher == 'none':
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distributed = False
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else:
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distributed = True
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init_dist(args.launcher, **cfg.dist_params)
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# re-set gpu_ids with distributed training mode
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_, world_size = get_dist_info()
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cfg.gpu_ids = range(world_size)
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# create work_dir
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mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
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# dump config
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cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
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# init the logger before other steps
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timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
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log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
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logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
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# init the meta dict to record some important information such as
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# environment info and seed, which will be logged
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meta = dict()
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# log env info
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env_info_dict = collect_env()
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env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
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dash_line = '-' * 60 + '\n'
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logger.info('Environment info:\n' + dash_line + env_info + '\n' +
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dash_line)
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meta['env_info'] = env_info
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meta['config'] = cfg.pretty_text
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# log some basic info
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logger.info(f'Distributed training: {distributed}')
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logger.info(f'Config:\n{cfg.pretty_text}')
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# set random seeds
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if args.seed is not None:
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logger.info(f'Set random seed to {args.seed}, '
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f'deterministic: {args.deterministic}')
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set_random_seed(args.seed, deterministic=args.deterministic)
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cfg.seed = args.seed
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meta['seed'] = args.seed
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meta['exp_name'] = osp.basename(args.config)
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model = build_detector(
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cfg.model,
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train_cfg=cfg.get('train_cfg'),
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test_cfg=cfg.get('test_cfg'))
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datasets = [build_dataset(cfg.data.train)]
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if len(cfg.workflow) == 2:
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val_dataset = copy.deepcopy(cfg.data.val)
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val_dataset.pipeline = cfg.data.train.pipeline
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datasets.append(build_dataset(val_dataset))
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if cfg.checkpoint_config is not None:
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# save mmdet version, config file content and class names in
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# checkpoints as meta data
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cfg.checkpoint_config.meta = dict(
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mmocr_version=__version__ + get_git_hash()[:7],
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CLASSES=datasets[0].CLASSES)
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# add an attribute for visualization convenience
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model.CLASSES = datasets[0].CLASSES
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train_detector(
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model,
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datasets,
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cfg,
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distributed=distributed,
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validate=(not args.no_validate),
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timestamp=timestamp,
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meta=meta)
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if __name__ == '__main__':
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main()
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