text: import argparse
  import os
  import warnings
  import json
  import mmcv
  import torch
  from mmcv import Config, DictAction
  from mmcv.cnn import fuse_conv_bn
  from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
  from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                           wrap_fp16_model)
  
  from mmdet.apis import multi_gpu_test, single_gpu_test
  from mmdet.datasets import (build_dataloader, build_dataset,
                              replace_ImageToTensor)
  from mmdet.models import build_detector
  
  
  def parse_args():
      parser = argparse.ArgumentParser(
          description='MMDet test (and eval) a model')
      parser.add_argument('config', help='test config file path')
      parser.add_argument('checkpoint', help='checkpoint file')
      parser.add_argument('--out', help='output result file in pickle format')
      parser.add_argument(
          '--fuse-conv-bn',
          action='store_true',
          help='Whether to fuse conv and bn, this will slightly increase'
          'the inference speed')
      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., "bbox",'
          ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
      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(
          '--show-score-thr',
          type=float,
          default=0.3,
          help='score threshold (default: 0.3)')
      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(
          '--cfg-options',
          nargs='+',
          action=DictAction,
          help='override some settings in the used config, the key-value pair '
          'in xxx=yyy format will be merged into config file.')
      parser.add_argument(
          '--options',
          nargs='+',
          action=DictAction,
          help='custom options for evaluation, the key-value pair in xxx=yyy '
          'format will be kwargs for dataset.evaluate() function (deprecate), '
          'change to --eval-options instead.')
      parser.add_argument(
          '--eval-options',
          nargs='+',
          action=DictAction,
          help='custom options for evaluation, the key-value pair in xxx=yyy '
          'format will be kwargs for dataset.evaluate() function')
      parser.add_argument(
          '--launcher',
          choices=['none', 'pytorch', 'slurm', 'mpi'],
          default='none',
          help='job launcher')
      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)
  
      if args.options and args.eval_options:
          raise ValueError(
              '--options and --eval-options cannot be both '
              'specified, --options is deprecated in favor of --eval-options')
      if args.options:
          warnings.warn('--options is deprecated in favor of --eval-options')
          args.eval_options = args.options
      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 = Config.fromfile(args.config)
      if args.cfg_options is not None:
          cfg.merge_from_dict(args.cfg_options)
      # import modules from string list.
      if cfg.get('custom_imports', None):
          from mmcv.utils import import_modules_from_strings
          import_modules_from_strings(**cfg['custom_imports'])
      # set cudnn_benchmark
      if cfg.get('cudnn_benchmark', False):
          torch.backends.cudnn.benchmark = True
      cfg.model.pretrained = None
      if cfg.model.get('neck'):
          if isinstance(cfg.model.neck, list):
              for neck_cfg in cfg.model.neck:
                  if neck_cfg.get('rfp_backbone'):
                      if neck_cfg.rfp_backbone.get('pretrained'):
                          neck_cfg.rfp_backbone.pretrained = None
          elif cfg.model.neck.get('rfp_backbone'):
              if cfg.model.neck.rfp_backbone.get('pretrained'):
                  cfg.model.neck.rfp_backbone.pretrained = None
  
      # in case the test dataset is concatenated
      if isinstance(cfg.data.test, dict):
          cfg.data.test.test_mode = True
      elif isinstance(cfg.data.test, list):
          for ds_cfg in cfg.data.test:
              ds_cfg.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)
  
      # build the dataloader
      samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
      if samples_per_gpu > 1:
          # Replace 'ImageToTensor' to 'DefaultFormatBundle'
          cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
      dataset = build_dataset(cfg.data.test)
      data_loader = build_dataloader(
          dataset,
          samples_per_gpu=samples_per_gpu,
          workers_per_gpu=cfg.data.workers_per_gpu,
          dist=distributed,
          shuffle=False)
  
      # build the model and load checkpoint
      model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.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 args.fuse_conv_bn:
          model = fuse_conv_bn(model)
      # old versions did not save class info in checkpoints, this walkaround is
      # for backward compatibility
      if 'CLASSES' in checkpoint['meta']:
          model.CLASSES = checkpoint['meta']['CLASSES']
      else:
          model.CLASSES = dataset.CLASSES
  
      if not distributed:
          model = MMDataParallel(model, device_ids=[0])
          outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
                                    args.show_score_thr)
      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)
  
      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:
              eval_kwargs = cfg.get('evaluation', {}).copy()
              # hard-code way to remove EvalHook args
              for key in ['interval', 'tmpdir', 'start', 'gpu_collect']:
                  eval_kwargs.pop(key, None)
              eval_kwargs.update(dict(metric=args.eval, **kwargs))
              res=dataset.evaluate(outputs, **eval_kwargs)
              print(res)
              with open('res.json', 'w') as outfile:
                  json.dump(res,outfile,indent=4,ensure_ascii = False)
  
  if __name__ == '__main__':
      main()
  