text: import argparse
  import logging
  import os
  
  import torch
  import torch.utils.data
  
  from model.config import cfg
  from model.engine.inference import do_evaluation
  from model.modeling.detector import build_detection_model
  from model.utils import dist_util
  from model.utils.checkpoint import CheckPointer
  from model.utils.dist_util import synchronize
  from model.utils.logger import setup_logger
  
  
  def evaluation(cfg, ckpt, distributed):
      logger = logging.getLogger("SSD.inference")
  
      model = build_detection_model(cfg)
      checkpointer = CheckPointer(model, save_dir=cfg.OUTPUT_DIR, logger=logger)
      device = torch.device(cfg.MODEL.DEVICE)
      model.to(device)
      checkpointer.load(ckpt, use_latest=ckpt is None)
      do_evaluation(cfg, model, distributed)
  
  
  def main():
      parser = argparse.ArgumentParser(description='SSD Evaluation on VOC and COCO dataset.')
      parser.add_argument(
          "--config-file",
          default="",
          metavar="FILE",
          help="path to config file",
          type=str,
      )
      parser.add_argument("--local_rank", type=int, default=0)
      parser.add_argument(
          "--ckpt",
          help="The path to the checkpoint for test, default is the latest checkpoint.",
          default=None,
          type=str,
      )
  
      parser.add_argument("--output_dir", default="eval_results", type=str, help="The directory to store evaluation results.")
  
      parser.add_argument(
          "opts",
          help="Modify config options using the command-line",
          default=None,
          nargs=argparse.REMAINDER,
      )
      args = parser.parse_args()
  
      num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
      distributed = num_gpus > 1
  
      if torch.cuda.is_available():
          # This flag allows you to enable the inbuilt cudnn auto-tuner to
          # find the best algorithm to use for your hardware.
          torch.backends.cudnn.benchmark = True
      if distributed:
          torch.cuda.set_device(args.local_rank)
          torch.distributed.init_process_group(backend="nccl", init_method="env://")
          synchronize()
  
      cfg.merge_from_file(args.config_file)
      cfg.merge_from_list(args.opts)
      cfg.freeze()
      
      # evaluation(cfg, ckpt=args.ckpt, distributed=distributed)
  
      logger = setup_logger("SSD", dist_util.get_rank(), cfg.OUTPUT_DIR)
      logger.info("Using {} GPUs".format(num_gpus))
      logger.info(args)
  
      logger.info("Loaded configuration file {}".format(args.config_file))
      with open(args.config_file, "r") as cf:
          config_str = "\n" + cf.read()
          logger.info(config_str)
      logger.info("Running with config:\n{}".format(cfg))
      evaluation(cfg, ckpt=args.ckpt, distributed=distributed)
  
  
  if __name__ == '__main__':
      main()
  