code: import argparse
  import os
  from os.path import join
  
  import mmcv
  import torch
  from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
  from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                           wrap_fp16_model)
  from mmcv.utils import DictAction
  
  from mmseg.apis import multi_gpu_test, single_gpu_test
  from mmseg.datasets import build_dataloader, build_dataset
  from mmseg.models import build_segmentor
  
  
  def parse_args():
      parser = argparse.ArgumentParser(
          description='mmseg test (and eval) a model')
      parser.add_argument('config', help='test config file path')
      parser.add_argument('checkpoint', help='checkpoint file')
      parser.add_argument('--image-dir', help='Another image directory for evaluation')
      parser.add_argument('--mask', action='store_true', default=False, help='Save results as masks')
      parser.add_argument(
          '--aug-test', action='store_true', help='Use Flip and Multi scale aug')
      parser.add_argument('--out', help='output result file in pickle format')
      parser.add_argument(
          '--format-only',
          action='store_true',
          help='Format the output results without perform evaluation. It is'
          'useful when you want to format the result to a specific format and '
          'submit it to the test server')
      parser.add_argument(
          '--eval',
          type=str,
          nargs='+',
          help='evaluation metrics, which depends on the dataset, e.g., "mIoU"'
          ' for generic datasets, and "cityscapes" for Cityscapes')
      parser.add_argument('--show', action='store_true', help='show results')
      parser.add_argument(
          '--show-dir', help='directory where painted images will be saved')
      parser.add_argument(
          '--gpu-collect',
          action='store_true',
          help='whether to use gpu to collect results.')
      parser.add_argument(
          '--tmpdir',
          help='tmp directory used for collecting results from multiple '
          'workers, available when gpu_collect is not specified')
      parser.add_argument(
          '--options', nargs='+', action=DictAction, help='custom options')
      parser.add_argument(
          '--eval-options',
          nargs='+',
          action=DictAction,
          help='custom options for evaluation')
      parser.add_argument(
          '--launcher',
          choices=['none', 'pytorch', 'slurm', 'mpi'],
          default='none',
          help='job launcher')
      parser.add_argument(
          '--opacity',
          type=float,
          default=0.5,
          help='Opacity of painted segmentation map. In (0, 1] range.')
      parser.add_argument('--local_rank', type=int, default=0)
      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()
  
      assert args.out or args.eval or args.format_only or args.show \
          or args.show_dir, \
          ('Please specify at least one operation (save/eval/format/show the '
           'results / save the results) with the argument "--out", "--eval"'
           ', "--format-only", "--show" or "--show-dir"')
  
      if args.eval and args.format_only:
          raise ValueError('--eval and --format_only cannot be both specified')
  
      if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
          raise ValueError('The output file must be a pkl file.')
  
      cfg = mmcv.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
      if args.aug_test:
          # hard code index
          cfg.data.test.pipeline[1].img_ratios = [
              0.5, 0.75, 1.0, 1.25, 1.5, 1.75
          ]
          cfg.data.test.pipeline[1].flip = True
      cfg.model.pretrained = None
      cfg.data.test.test_mode = True
  
      # 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)
  
      # Modify config
      if not args.image_dir:
          cfg.data.test.data_root = '.'
          cfg.data.test.img_dir = 'test_images'
      else:
          cfg.data.test.data_root = '.'
          cfg.data.test.img_dir = args.image_dir
  
      cfg.data.test.split = 'test_list.txt'
  
      image_names = os.listdir(join(cfg.data.test.data_root, cfg.data.test.img_dir))
      image_ids = [img_name.split('.')[0] for img_name in image_names]
  
      with open(cfg.data.test.split, 'w') as f:
          f.write('\n'.join(image_ids))
      
      # build the dataloader
      # TODO: support multiple images per gpu (only minor changes are needed)
      dataset = build_dataset(cfg.data.test)
      data_loader = build_dataloader(
          dataset,
          samples_per_gpu=1,
          workers_per_gpu=cfg.data.workers_per_gpu,
          dist=distributed,
          shuffle=False)
  
      # build the model and load checkpoint
      cfg.model.train_cfg = None
      model = build_segmentor(cfg.model, test_cfg=cfg.get('test_cfg'))
      fp16_cfg = cfg.get('fp16', None)
      if fp16_cfg is not None:
          wrap_fp16_model(model)
      checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
      if 'CLASSES' in checkpoint.get('meta', {}):
          model.CLASSES = checkpoint['meta']['CLASSES']
      else:
          print('"CLASSES" not found in meta, use dataset.CLASSES instead')
          model.CLASSES = dataset.CLASSES
      if 'PALETTE' in checkpoint.get('meta', {}):
          model.PALETTE = checkpoint['meta']['PALETTE']
      else:
          print('"PALETTE" not found in meta, use dataset.PALETTE instead')
          model.PALETTE = dataset.PALETTE
  
      efficient_test = False
      if args.eval_options is not None:
          efficient_test = args.eval_options.get('efficient_test', False)
  
      if not distributed:
          model = MMDataParallel(model, device_ids=[0])
          outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
                                    efficient_test, args.opacity)
          print(outputs[0].shape, set(outputs[0].flatten().tolist()))
      else:
          model = MMDistributedDataParallel(
              model.cuda(),
              device_ids=[torch.cuda.current_device()],
              broadcast_buffers=False)
          outputs = multi_gpu_test(model, data_loader, args.tmpdir,
                                   args.gpu_collect, efficient_test)
  
      rank, _ = get_dist_info()
      if rank == 0:
          if args.out:
              print(f'\nwriting results to {args.out}')
              mmcv.dump(outputs, args.out)
          kwargs = {} if args.eval_options is None else args.eval_options
          if args.format_only:
              dataset.format_results(outputs, **kwargs)
          if args.eval:
              dataset.evaluate(outputs, args.eval, **kwargs)
  
  
  if __name__ == '__main__':
      main()
  
apis: torch.cuda.current_device