text: """Inference a pretrained model."""
  
  import argparse
  import os
  
  import datasets  # pylint: disable=unused-import
  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
  
  MODEL_SERVER = "https://dl.cv.ethz.ch/bdd100k/ins_seg/models/"
  
  
  def parse_args() -> argparse.Namespace:
      """Arguements definitions."""
      parser = argparse.ArgumentParser(
          description="MMDet test (and eval) a model"
      )
      parser.add_argument("config", help="test config file path")
      parser.add_argument(
          "--work-dir",
          help="the directory to save the file containing evaluation metrics",
      )
      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(
          "--format-dir", help="directory where the outputs are saved."
      )
      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. If the value to "
          'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
          'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
          "Note that the quotation marks are necessary and that no white space "
          "is allowed.",
      )
      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)
      return args
  
  
  def main() -> None:
      """Main function for model inference."""
      args = parse_args()
  
      assert 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 '--format-only', "
          "'--show' or '--show-dir'"
      )
  
      cfg = Config.fromfile(args.config)
      if cfg.load_from is None:
          cfg_name = os.path.split(args.config)[-1].replace(
              "_bdd100k.py", ".pth"
          )
          cfg.load_from = MODEL_SERVER + cfg_name
      if args.cfg_options is not None:
          cfg.merge_from_dict(args.cfg_options)
      # 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
      samples_per_gpu = 1
      if isinstance(cfg.data.test, dict):
          cfg.data.test.test_mode = True  # type: ignore
          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(  # type: ignore
                  cfg.data.test.pipeline  # type: ignore
              )
      elif isinstance(cfg.data.test, list):
          for ds_cfg in cfg.data.test:
              ds_cfg.test_mode = True
          samples_per_gpu = max(
              [ds_cfg.pop("samples_per_gpu", 1) for ds_cfg in cfg.data.test]
          )
          if samples_per_gpu > 1:
              for ds_cfg in cfg.data.test:
                  ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
  
      # 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)
  
      rank, _ = get_dist_info()
  
      # build the dataloader
      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
      cfg.model.train_cfg = None
      model = build_detector(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, cfg.load_from, 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.get("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.format_only:
              dataset.convert_format(outputs, args.format_dir)
  
  
  if __name__ == "__main__":
      main()
  