text: import argparse
  import os
  
  import mmcv
  import torch
  from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
  import os
  from mmcv.runner import get_dist_info, init_dist, load_checkpoint
  from tools.fuse_conv_bn import fuse_module
  from mmdet.apis import multi_gpu_test, single_gpu_test
  from mmdet.core import wrap_fp16_model
  from mmdet.datasets import build_dataloader, build_dataset
  from mmdet.models import build_detector
  print('CUDA available: {}'.format(torch.cuda.is_available()))
  
  print('the available CUDA number is : {}'.format(torch.cuda.device_count()))
  # python -m torch.distributed.launch --nproc_per_node=4 --master_port=29500 tools/test.py
  class MultipleKVAction(argparse.Action):
      """
      argparse action to split an argument into KEY=VALUE form
      on the first = and append to a dictionary.
      """
  
      def _is_int(self, val):
          try:
              _ = int(val)
              return True
          except Exception:
              return False
  
      def _is_float(self, val):
          try:
              _ = float(val)
              return True
          except Exception:
              return False
  
      def _is_bool(self, val):
          return val.lower() in ['true', 'false']
  
      def __call__(self, parser, namespace, values, option_string=None):
          options = {}
          for val in values:
              parts = val.split('=')
              key = parts[0].strip()
              if len(parts) > 2:
                  val = '='.join(parts[1:])
              else:
                  val = parts[1].strip()
              # try parsing val to bool/int/float first
              if self._is_bool(val):
                  import json
                  val = json.loads(val.lower())
              elif self._is_int(val):
                  val = int(val)
              elif self._is_float(val):
                  val = float(val)
              options[key] = val
          setattr(namespace, self.dest, options)
  
  
  def parse_args():
      parser = argparse.ArgumentParser(
          description='MMDet test (and eval) a model')
      parser.add_argument('--config', default='../configs/fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py', type=str, help='test config file path')
      # parser.add_argument('--config', default='../configs/faster_rcnn_r50_fpn_1x_gcn.py', type=str, help='test config file path')
      parser.add_argument('--checkpoint', default='../work_dirs/fcos_r50_caffe_fpn_gn_1x_4gpu/epoch_12.pth', type=str, help='checkpoint file')
      # parser.add_argument('--checkpoint', default='../checkpoints/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth', type=str, help='checkpoint file')
      parser.add_argument('--out', help='output result file in pickle format')
      parser.add_argument(
          '--fuse_conv_bn',
          default=True, type=bool,
          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,
          default= ("bbox"),
          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', default=False, type=bool, help='show results')
      parser.add_argument(
          '--gpu_collect',
          default=True, type=bool,
          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=MultipleKVAction, 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=0)
      parser.add_argument('--bs', type=int, default=2)
      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, \
          ('Please specify at least one operation (save/eval/format/show the '
           'results) with the argument "--out", "--eval", "--format_only" '
           'or "--show"')
  
      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)
      # set cudnn_benchmark
      if cfg.get('cudnn_benchmark', False):
          torch.backends.cudnn.benchmark = 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)
  
      # 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,
          imgs_per_gpu=1,
          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_module(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)
      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('\nwriting results to {}'.format(args.out))
              mmcv.dump(outputs, args.out)
          kwargs = {} if args.options is None else args.options
          if args.format_only:
              dataset.format_results(outputs, **kwargs)
          if args.eval:
              dataset.evaluate(outputs, args.eval, **kwargs)
  
  
  if __name__ == '__main__':
      main()
  