def get_estimator_eval_metric_ops(self, eval_dict):
    """Returns metric ops for use in tf.estimator.EstimatorSpec.

    Args:
      eval_dict: A dictionary that holds an image, groundtruth, and detections
        for a batched example. Note that, we use only the first example for
        visualization. See eval_util.result_dict_for_batched_example() for a
        convenient method for constructing such a dictionary. The dictionary
        contains
        fields.InputDataFields.original_image: [batch_size, H, W, 3] image.
        fields.InputDataFields.original_image_spatial_shape: [batch_size, 2]
          tensor containing the size of the original image.
        fields.InputDataFields.true_image_shape: [batch_size, 3]
          tensor containing the spatial size of the upadded original image.
        fields.InputDataFields.groundtruth_boxes - [batch_size, num_boxes, 4]
          float32 tensor with groundtruth boxes in range [0.0, 1.0].
        fields.InputDataFields.groundtruth_classes - [batch_size, num_boxes]
          int64 tensor with 1-indexed groundtruth classes.
        fields.InputDataFields.groundtruth_instance_masks - (optional)
          [batch_size, num_boxes, H, W] int64 tensor with instance masks.
        fields.InputDataFields.groundtruth_keypoints - (optional)
          [batch_size, num_boxes, num_keypoints, 2] float32 tensor with
          keypoint coordinates in format [y, x].
        fields.InputDataFields.groundtruth_keypoint_visibilities - (optional)
          [batch_size, num_boxes, num_keypoints] bool tensor with
          keypoint visibilities.
        fields.DetectionResultFields.detection_boxes - [batch_size,
          max_num_boxes, 4] float32 tensor with detection boxes in range [0.0,
          1.0].
        fields.DetectionResultFields.detection_classes - [batch_size,
          max_num_boxes] int64 tensor with 1-indexed detection classes.
        fields.DetectionResultFields.detection_scores - [batch_size,
          max_num_boxes] float32 tensor with detection scores.
        fields.DetectionResultFields.detection_masks - (optional) [batch_size,
          max_num_boxes, H, W] float32 tensor of binarized masks.
        fields.DetectionResultFields.detection_keypoints - (optional)
          [batch_size, max_num_boxes, num_keypoints, 2] float32 tensor with
          keypoints.
        fields.DetectionResultFields.detection_keypoint_scores - (optional)
          [batch_size, max_num_boxes, num_keypoints] float32 tensor with
          keypoints scores.

    Returns:
      A dictionary of image summary names to tuple of (value_op, update_op). The
      `update_op` is the same for all items in the dictionary, and is
      responsible for saving a single side-by-side image with detections and
      groundtruth. Each `value_op` holds the tf.summary.image string for a given
      image.
    """
    if self._max_examples_to_draw == 0:
      return {}
    images = self.images_from_evaluation_dict(eval_dict)

    def get_images():
      """Returns a list of images, padded to self._max_images_to_draw."""
      images = self._images
      while len(images) < self._max_examples_to_draw:
        images.append(np.array(0, dtype=np.uint8))
      self.clear()
      return images

    def image_summary_or_default_string(summary_name, image):
      """Returns image summaries for non-padded elements."""
      return tf.cond(
          tf.equal(tf.size(tf.shape(image)), 4),
          lambda: tf.summary.image(summary_name, image),
          lambda: tf.constant(''))

    if tf.executing_eagerly():
      update_op = self.add_images([[images[0]]])
      image_tensors = get_images()
    else:
      update_op = tf.py_func(self.add_images, [[images[0]]], [])
      image_tensors = tf.py_func(
          get_images, [], [tf.uint8] * self._max_examples_to_draw)
    eval_metric_ops = {}
    for i, image in enumerate(image_tensors):
      summary_name = self._summary_name_prefix + '/' + str(i)
      value_op = image_summary_or_default_string(summary_name, image)
      eval_metric_ops[summary_name] = (value_op, update_op)
    return eval_metric_ops
Returns metric ops for use in tf.estimator.EstimatorSpec.

    Args:
      eval_dict: A dictionary that holds an image, groundtruth, and detections
        for a batched example. Note that, we use only the first example for
        visualization. See eval_util.result_dict_for_batched_example() for a
        convenient method for constructing such a dictionary. The dictionary
        contains
        fields.InputDataFields.original_image: [batch_size, H, W, 3] image.
        fields.InputDataFields.original_image_spatial_shape: [batch_size, 2]
          tensor containing the size of the original image.
        fields.InputDataFields.true_image_shape: [batch_size, 3]
          tensor containing the spatial size of the upadded original image.
        fields.InputDataFields.groundtruth_boxes - [batch_size, num_boxes, 4]
          float32 tensor with groundtruth boxes in range [0.0, 1.0].
        fields.InputDataFields.groundtruth_classes - [batch_size, num_boxes]
          int64 tensor with 1-indexed groundtruth classes.
        fields.InputDataFields.groundtruth_instance_masks - (optional)
          [batch_size, num_boxes, H, W] int64 tensor with instance masks.
        fields.InputDataFields.groundtruth_keypoints - (optional)
          [batch_size, num_boxes, num_keypoints, 2] float32 tensor with
          keypoint coordinates in format [y, x].
        fields.InputDataFields.groundtruth_keypoint_visibilities - (optional)
          [batch_size, num_boxes, num_keypoints] bool tensor with
          keypoint visibilities.
        fields.DetectionResultFields.detection_boxes - [batch_size,
          max_num_boxes, 4] float32 tensor with detection boxes in range [0.0,
          1.0].
        fields.DetectionResultFields.detection_classes - [batch_size,
          max_num_boxes] int64 tensor with 1-indexed detection classes.
        fields.DetectionResultFields.detection_scores - [batch_size,
          max_num_boxes] float32 tensor with detection scores.
        fields.DetectionResultFields.detection_masks - (optional) [batch_size,
          max_num_boxes, H, W] float32 tensor of binarized masks.
        fields.DetectionResultFields.detection_keypoints - (optional)
          [batch_size, max_num_boxes, num_keypoints, 2] float32 tensor with
          keypoints.
        fields.DetectionResultFields.detection_keypoint_scores - (optional)
          [batch_size, max_num_boxes, num_keypoints] float32 tensor with
          keypoints scores.

    Returns:
      A dictionary of image summary names to tuple of (value_op, update_op). The
      `update_op` is the same for all items in the dictionary, and is
      responsible for saving a single side-by-side image with detections and
      groundtruth. Each `value_op` holds the tf.summary.image string for a given
      image.
    
inference_api/src/main/object_detection/utils/visualization_utils.py