4d641f7f56d4da78303c5a55846baa97d6505c86,object_detection/core/post_processing.py,,batch_multiclass_non_max_suppression,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,167

Before Change

         dtype=[tf.float32, tf.float32, tf.float32, tf.float32, tf.float32],
         parallel_iterations=parallel_iterations)

    nms_dict = {
        "detection_boxes": batch_detection_boxes,
        "detection_scores": batch_detection_scores,
        "detection_classes": batch_detection_classes,
        "num_detections": batch_num_detections
    }
    if original_masks is not None:
      nms_dict["detection_masks"] = batch_detection_masks
    return nms_dict

After Change

    if batch_size is None:
      batch_size = tf.shape(boxes)[0]
    if num_anchors is None:
      num_anchors = tf.shape(boxes)[1]

    // If num valid boxes aren"t provided, create one and mark all boxes as
    // valid.
    if num_valid_boxes is None:
      num_valid_boxes = tf.ones([batch_size], dtype=tf.int32) * num_anchors

    // If masks aren"t provided, create dummy masks so we can only have one copy
    // of single_image_nms_fn and discard the dummy masks after map_fn.
    if masks is None:

Instances