text: from __future__ import division
  import argparse
  import copy
  import os
  import os.path as osp
  import time
  
  import mmcv
  import torch
  from mmcv import Config
  from mmcv.runner import init_dist
  
  from mmdet import __version__
  from mmdet.apis import set_random_seed, train_detector
  from mmdet.datasets import build_dataset
  from mmdet.models import build_saliency_detector
  from mmdet.utils import collect_env, get_root_logger
  import yaml
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='Train a detector')
      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')
      # this is not allowed!
      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(
          '--launcher',
          choices=['none', 'pytorch', 'slurm', 'mpi'],
          default='none',
          help='job launcher')
      parser.add_argument('--local_rank', type=int, default=0)
      parser.add_argument(
          '--autoscale-lr',
          action='store_true',
          help='automatically scale lr with the number of gpus')
      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
  
      if args.gpu_ids is not None:
          cfg.gpu_ids = args.gpu_ids
      else:
          cfg.gpu_ids = list(range(1)) if args.gpus is None else list(range(args.gpus))
  
      if args.autoscale_lr:
          # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
          # For OD: bs=16 -- lr=0.001
          cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) * cfg.data.imgs_per_gpu / 16
  
      # 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)
  
      # 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, '{}.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
  
      model = build_saliency_detector(cfg.model)
  
      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))
      if cfg.checkpoint_config is not None:
          # save mmdet version, config file content and class names in
          # checkpoints as meta data
          cfg.checkpoint_config.meta = dict(
              mmdet_version=__version__,
              config=cfg.text)
  
      mmcv.dump(yaml.safe_load(cfg.dump()), osp.join(cfg.work_dir, 'cfg.yaml'))
  
      # you can reuse the detector training script.
      train_detector(
          model,
          datasets,
          cfg,
          distributed=distributed,
          validate=args.validate,
          timestamp=timestamp,
          meta=meta)
  
  
  if __name__ == '__main__':
      main()
  