def eval_example_images(self,
                          ckpt_dir,
                          image_files,
                          labels_map_file,
                          enable_ema=True,
                          export_ckpt=None):
    """Eval a list of example images.

    Args:
      ckpt_dir: str. Checkpoint directory path.
      image_files: List[str]. A list of image file paths.
      labels_map_file: str. The labels map file path.
      enable_ema: enable expotential moving average.
      export_ckpt: export ckpt folder.

    Returns:
      A tuple (pred_idx, and pred_prob), where pred_idx is the top 5 prediction
      index and pred_prob is the top 5 prediction probability.
    """
    classes = json.loads(tf.gfile.Open(labels_map_file).read())
    pred_idx, pred_prob = self.run_inference(
        ckpt_dir, image_files, [0] * len(image_files), enable_ema, export_ckpt)
    for i in range(len(image_files)):
      print('predicted class for image {}: '.format(image_files[i]))
      for j, idx in enumerate(pred_idx[i]):
        print('  -> top_{} ({:4.2f}%): {}  '.format(j, pred_prob[i][j] * 100,
                                                    classes[str(idx)]))
    return pred_idx, pred_prob
Eval a list of example images.

    Args:
      ckpt_dir: str. Checkpoint directory path.
      image_files: List[str]. A list of image file paths.
      labels_map_file: str. The labels map file path.
      enable_ema: enable expotential moving average.
      export_ckpt: export ckpt folder.

    Returns:
      A tuple (pred_idx, and pred_prob), where pred_idx is the top 5 prediction
      index and pred_prob is the top 5 prediction probability.
    
tf_to_pytorch/convert_tf_to_pt/original_tf/utils.py