text: from __future__ import division
  import argparse
  import os
  import os.path as osp
  import sys
  sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))
  import torch
  from mmcv import Config
  
  from mmdet import __version__
  from mmdet.apis import (get_root_logger, init_dist, set_random_seed,
                          train_detector)
  from mmdet.datasets import build_dataset
  from mmdet.models import build_detector
  
  
  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')
      parser.add_argument(
          '--validate',
          action='store_true',
          help='whether to evaluate the checkpoint during training')
      parser.add_argument(
          '--gpus',
          type=int,
          default=1,
          help='number of gpus to use '
          '(only applicable to non-distributed training)')
      parser.add_argument('--seed', type=int, default=None, help='random seed')
      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
      cfg.gpus = args.gpus
  
      if args.autoscale_lr:
          # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)
          cfg.optimizer['lr'] = cfg.optimizer['lr'] * cfg.gpus / 8
  
      # 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)
  
      # init logger before other steps
      logger = get_root_logger(cfg.log_level)
      logger.info('Distributed training: {}'.format(distributed))
  
      # set random seeds
      if args.seed is not None:
          logger.info('Set random seed to {}'.format(args.seed))
          set_random_seed(args.seed)
  
      model = build_detector(
          cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
  
      datasets = [build_dataset(cfg.data.train)]
      if len(cfg.workflow) == 2:
          datasets.append(build_dataset(cfg.data.val))
      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,
              CLASSES=datasets[0].CLASSES)
      # add an attribute for visualization convenience
      model.CLASSES = datasets[0].CLASSES
      train_detector(
          model,
          datasets,
          cfg,
          distributed=distributed,
          validate=args.validate,
          logger=logger)
  
  
  if __name__ == '__main__':
      main()
  