content: 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 init_dist, set_random_seed
  from mmcv.utils import get_git_hash
  import numpy as np
  
  from mmaction import __version__
  from mmaction.apis import train_model
  from mmaction.datasets import build_dataset
  from mmaction.models import build_model
  from mmaction.utils import collect_env, get_root_logger
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='Train a recognizer')
      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(
          '--validate',
          action='store_true',
          help='whether to evaluate the checkpoint during training')
      group_gpus = parser.add_mutually_exclusive_group()
      group_gpus.add_argument(
          '--gpus',
          type=int,
          help='number of gpus to use '
          '(only applicable to non-distributed training)')
      group_gpus.add_argument(
          '--gpu-ids',
          type=int,
          nargs='+',
          help='ids 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(
          '--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('--weight_factor', nargs='+', type=float, default=[0.1, 0.2, 0.3, 0.4])
      parser.add_argument('--batch_size', type=int, default=8)
      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)
  
      cfg.merge_from_dict(args.cfg_options)
  
      # for hyperparameter searching
      cfg.log_config.hooks = [dict(type='TextLoggerHook')]
      cfg.checkpoint_config.interval = 10
      cfg.evaluation.interval = 10
      cfg.data.videos_per_gpu = args.batch_size
  
      # set cudnn_benchmark
      if cfg.get('cudnn_benchmark', False):
          torch.backends.cudnn.benchmark = True
  
      # work_dir is determined in this priority:
      # CLI > config file > default (base 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.gpu_ids is not None:
          cfg.gpu_ids = args.gpu_ids
      else:
          cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus)
  
      # 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)
  
      # The flag is used to determine whether it is omnisource training
      cfg.setdefault('omnisource', False)
  
      # 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 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: {cfg.text}')
  
      # set random seeds
      if args.seed is not None:
          logger.info(f'Set random seed to {args.seed}, '
                      f'deterministic: {args.deterministic}')
          set_random_seed(args.seed, deterministic=args.deterministic)
      cfg.seed = args.seed
      meta['seed'] = args.seed
      meta['config_name'] = osp.basename(args.config)
      meta['work_dir'] = osp.basename(cfg.work_dir.rstrip('/\\'))
  
      if cfg.omnisource:
          # If omnisource flag is set, cfg.data.train should be a list
          assert type(cfg.data.train) is list
          datasets = [build_dataset(dataset) for dataset in cfg.data.train]
      else:
          datasets = [build_dataset(cfg.data.train)]
  
      if len(cfg.workflow) == 2:
          # For simplicity, omnisource is not compatiable with val workflow,
          # we recommend you to use `--validate`
          assert not cfg.omnisource
          if args.validate:
              warnings.warn('val workflow is duplicated with `--validate`, '
                            'it is recommended to use `--validate`. see '
                            'https://github.com/open-mmlab/mmaction2/pull/123')
          val_dataset = copy.deepcopy(cfg.data.val)
          datasets.append(build_dataset(val_dataset))
      if cfg.checkpoint_config is not None:
          # save mmaction version, config file content and class names in
          # checkpoints as meta data
          cfg.checkpoint_config.meta = dict(
              mmaction_version=__version__ + get_git_hash(digits=7),
              config=cfg.text)
  
      # search for hyperprameters
      save_dir = cfg.work_dir
      for v in args.weight_factor:
          print("searching for v=%.1f"%(v))
          # change the hyperparameter
          cfg.model.debias_head.loss_factor = v
          cfg.work_dir = osp.join(save_dir, 'search_%.1f'%(v))
          # build the model
          model = build_model(
              cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
          # training and validation
          train_model(model, datasets, cfg, distributed=distributed, validate=args.validate, timestamp=timestamp, meta=meta)
  
  
  if __name__ == '__main__':
      main()
  