def eval_example_images(model_name, ckpt_dir, image_files, labels_map_file):
  """Eval a list of example images.

  Args:
    model_name: str. The name of model to eval.
    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.

  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.
  """
  eval_ckpt_driver = EvalCkptDriver(model_name)
  classes = json.loads(tf.gfile.Open(labels_map_file).read())
  pred_idx, pred_prob = eval_ckpt_driver.run_inference(
      ckpt_dir, image_files, [0] * len(image_files))
  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:
    model_name: str. The name of model to eval.
    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.

  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/eval_ckpt_main.py