f7b4c6de2037ebedf6bc8ea5979e81666d54534f,research/object_detection/meta_architectures/center_net_meta_arch.py,,convert_strided_predictions_to_normalized_boxes,#,992

Before Change

                                        filter_nonoverlapping=False)
    return boxes

  box_lists = [box_list.BoxList(boxes) for boxes in tf.unstack(boxes, axis=0)]
  true_heights, true_widths, _ = tf.unstack(true_image_shapes, axis=1)

  true_heights_list = tf.unstack(true_heights, axis=0)
  true_widths_list = tf.unstack(true_widths, axis=0)

  box_lists = list(map(_normalize_boxlist,
                       zip(box_lists, true_heights_list, true_widths_list)))
  boxes = tf.stack([box_list_instance.get() for
                    box_list_instance in box_lists], axis=0)

  return boxes

After Change

  // Note: We use tf ops instead of functions in box_list_ops to make this
  // function compatible with dynamic batch size.
  boxes = boxes * stride
  true_image_shapes = tf.tile(true_image_shapes[:, tf.newaxis, :2], [1, 1, 2])
  boxes = boxes / tf.cast(true_image_shapes, tf.float32)
  boxes = tf.clip_by_value(boxes, 0.0, 1.0)
  return boxes

Instances