text: import argparse
  import copy
  import os
  import os.path as osp
  import time
  
  import numpy as np
  
  import mmcv
  import torch
  from mmcv.runner import init_dist
  from mmcv.utils import Config, DictAction, get_git_hash
  
  import matplotlib.pyplot as plt
  
  from mmseg import __version__
  from mmseg.apis import set_random_seed
  from mmseg.apis import train_segmentor
  from mmseg.datasets import build_dataset
  from mmseg.models import build_segmentor
  from mmseg.utils import collect_env, get_root_logger
  
  from backbone import beit
  
  def parse_args():
      parser = argparse.ArgumentParser(description='Train a segmentor')
      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(
          '--load-from', help='the checkpoint file to load weights from')
      parser.add_argument(
          '--resume-from', help='the checkpoint file to resume from')
      parser.add_argument(
          '--no-validate',
          action='store_true',
          help='whether not 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(
          '--options', nargs='+', action=DictAction, help='custom options')
      parser.add_argument(
          '--launcher',
          choices=['none', 'pytorch', 'slurm', 'mpi'],
          default='none',
          help='job launcher')
      parser.add_argument('--local_rank', type=int, default=os.environ["LOCAL_RANK"])
      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)
      if args.options is not None:
          cfg.merge_from_dict(args.options)
      # set cudnn_benchmark
      if cfg.get('cudnn_benchmark', False):
          torch.backends.cudnn.benchmark = True
  
      # work_dir is determined in this priority: CLI > segment in file > 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.load_from is not None:
          cfg.load_from = args.load_from
      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)
  
      # 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 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 args.seed is not None:
          logger.info(f'Set random seed to {args.seed}, deterministic: '
                      f'{args.deterministic}')
          set_random_seed(args.seed, deterministic=args.deterministic)
      cfg.seed = args.seed
      meta['seed'] = args.seed
      meta['exp_name'] = osp.basename(args.config)
  
      model = build_segmentor(
          cfg.model,
          train_cfg=cfg.get('train_cfg'),
          test_cfg=cfg.get('test_cfg'))
  
      logger.info(model)
      
      print(cfg.data.train)
      
      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
      print(datasets[0].CLASSES)
      print(len(datasets[0].PALETTE))
      model.CLASSES = datasets[0].CLASSES
      print(datasets[0][0]["img"].shape)
      print(len(datasets[0]))
      print(datasets[0][0]["gt_semantic_seg"].shape)
      print(np.unique(datasets[0][0]["gt_semantic_seg"]))
      print(datasets[0].ignore_index)
      print(datasets[0].label_map)
      
      train_segmentor(
          model,
          datasets,
          cfg,
          distributed=distributed,
          validate=(not args.no_validate),
          timestamp=timestamp,
          meta=meta)
  
  
  if __name__ == '__main__':
      main()
  