text: import argparse
  import copy
  import os
  import os.path as osp
  import time
  
  import mmcv
  import torch
  from mmcv.runner import init_dist
  from mmcv.utils import Config, DictAction, get_git_hash
  
  from mmseg import __version__
  from mmseg.apis import set_random_seed, train_segmentor
  from mmseg.datasets import build_dataset
  from mmseg.models import build_segmentor
  from mmseg.utils import collect_env, get_root_logger
  import ssl
  ssl._create_default_https_context = ssl._create_unverified_context
  
  import urllib.request
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  torch.cuda.set_device(0)
  
  
  def train(config, model, output_folder):
  
      cfg = Config.fromfile(config)
      if cfg.get('cudnn_benchmark', False):
          torch.backends.cudnn.benchmark = True
  
      if 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(config))[0])
      # if args.load_from is not None:
      #     cfg.load_from = None
      # if args.resume_from is not None:
      #     cfg.resume_from = model     
      if model is not None:
          cfg.load_from = model
      cfg.gpu_ids = range(1)
      distributed = False
  
  
      # create work_dir
      mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
      # dump config
      cfg.dump(osp.join(cfg.work_dir, osp.basename(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
      # if seed is not None:
      #     logger.info(f'Set random seed to {seed}, deterministic: '
      #                 f'{args.deterministic}')
      #     set_random_seed(args.seed, deterministic=args.deterministic)
      seed = None
      cfg.seed = seed
      meta['seed'] = seed
      meta['exp_name'] = osp.basename(config)
  
      model = build_segmentor(
          cfg.model,
          train_cfg=cfg.get('train_cfg'),
          test_cfg=cfg.get('test_cfg'))
  
      logger.info(model)
      cfg.data.train['data_root'] = output_folder
      cfg.data.test['data_root'] = output_folder
      cfg.data.val['data_root'] = output_folder
      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 mmseg version, config file content and class names in
          # checkpoints as meta data
          cfg.checkpoint_config.meta = dict(
              mmseg_version=f'{__version__}+{get_git_hash()[:7]}',
              config=cfg.pretty_text,
              CLASSES=datasets[0].CLASSES,
              PALETTE=datasets[0].PALETTE)
      # add an attribute for visualization convenience
      model.CLASSES = datasets[0].CLASSES
  
      # cfg.data.test['img_dir'] = img_path
      train_segmentor(
          model,
          datasets,
          cfg,
          distributed=distributed,
          validate=True,
          timestamp=timestamp,
          meta=meta)
  
  
  if __name__ == '__main__':
      main()
  