text: import os
  import argparse
  import config as cfg
  from torch.utils.data import DataLoader
  from models.models import YOLOv3
  from yolov3.utils.utils import load_classes
  from yolov3.utils.datasets import ImageFolder
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--read_table', action='store_true')
      args = parser.parse_args()
      cfg.merge_config(args)
      cfg.show_config(args)
  
      model = YOLOv3(args.config_path, args.weight_path)
  
      image_folder = 'yolov3/data/samples'
      test_image_path = os.listdir(image_folder)
  
      classes = load_classes(args.class_path)
  
      for image_path in test_image_path:
          output = model.predict(os.path.join(image_folder, image_path))
          for out in output:
              for o in out:
                  cls = int(o[-1])
                  prob = o[-2].item()
                  print(classes[cls], prob)
  
  """
      dataloader = DataLoader(
          ImageFolder(image_folder),
          batch_size=9,
          num_workers=4,
          pin_memory=True
      )
  
      with torch.no_grad():
          for path, image in dataloader:
              output = model.forward(image)
              for out in output:
                  for o in out:
                      cls = int(o[-1].item())
                      prob = o[-2].item()
                      print(classes[cls], prob)
  """
  