e12bd6a5e5195e158384059da3d5d47638ba12a1,official/vision/detection/dataloader/maskrcnn_parser.py,Parser,_parse_train_data,#Parser#Any#,145

Before Change

      num_masks = tf.shape(masks)[0]
      masks = tf.image.crop_and_resize(
          tf.expand_dims(masks, axis=-1),
          box_utils.normalize_boxes(boxes, tf.shape(image)[0:2]),
          box_indices=tf.range(num_masks, dtype=tf.int32),
          crop_size=[self._mask_crop_size, self._mask_crop_size],
          method="bilinear")
      masks = tf.squeeze(masks, axis=-1)

    // Assigns anchor targets.
    // Note that after the target assignment, box targets are absolute pixel
    // offsets w.r.t. the scaled image.
    input_anchor = anchor.Anchor(
        self._min_level,
        self._max_level,
        self._num_scales,
        self._aspect_ratios,
        self._anchor_size,
        (image_height, image_width))
    anchor_labeler = anchor.RpnAnchorLabeler(
        input_anchor,
        self._rpn_match_threshold,
        self._rpn_unmatched_threshold,
        self._rpn_batch_size_per_im,
        self._rpn_fg_fraction)
    rpn_score_targets, rpn_box_targets = anchor_labeler.label_anchors(
        boxes, tf.cast(tf.expand_dims(classes, axis=-1), dtype=tf.float32))

    // If bfloat16 is used, casts input image to tf.bfloat16.
    if self._use_bfloat16:
      image = tf.cast(image, dtype=tf.bfloat16)

    // Packs labels for model_fn outputs.
    labels = {
        "anchor_boxes": input_anchor.multilevel_boxes,
        "image_info": image_info,
        "rpn_score_targets": rpn_score_targets,
        "rpn_box_targets": rpn_box_targets,
    }
    labels["gt_boxes"] = input_utils.pad_to_fixed_size(
        boxes, self._max_num_instances, -1)
    labels["gt_classes"] = input_utils.pad_to_fixed_size(
        classes, self._max_num_instances, -1)
    if self._include_mask:
      labels["gt_masks"] = input_utils.pad_to_fixed_size(
          masks, self._max_num_instances, -1)

    return image, labels

After Change

    classes = tf.gather(classes, indices)
    if self._include_mask:
      masks = tf.gather(masks, indices)
      cropped_boxes = boxes + tf.cast(
          tf.tile(tf.expand_dims(offset, axis=0), [1, 2]), dtype=tf.float32)
      cropped_boxes = box_utils.normalize_boxes(
          cropped_boxes, image_info[1, :])
      num_masks = tf.shape(masks)[0]
      masks = tf.image.crop_and_resize(
          tf.expand_dims(masks, axis=-1),
          cropped_boxes,
          box_indices=tf.range(num_masks, dtype=tf.int32),
          crop_size=[self._mask_crop_size, self._mask_crop_size],
          method="bilinear")
      masks = tf.squeeze(masks, axis=-1)

    // Assigns anchor targets.
    // Note that after the target assignment, box targets are absolute pixel
    // offsets w.r.t. the scaled image.
    input_anchor = anchor.Anchor(
        self._min_level,
        self._max_level,
        self._num_scales,
        self._aspect_ratios,
        self._anchor_size,
        (image_height, image_width))
    anchor_labeler = anchor.RpnAnchorLabeler(
        input_anchor,
        self._rpn_match_threshold,
        self._rpn_unmatched_threshold,
        self._rpn_batch_size_per_im,
        self._rpn_fg_fraction)
    rpn_score_targets, rpn_box_targets = anchor_labeler.label_anchors(
        boxes, tf.cast(tf.expand_dims(classes, axis=-1), dtype=tf.float32))

    // If bfloat16 is used, casts input image to tf.bfloat16.
    if self._use_bfloat16:
      image = tf.cast(image, dtype=tf.bfloat16)

    // Packs labels for model_fn outputs.
    labels = {
        "anchor_boxes": input_anchor.multilevel_boxes,
        "image_info": image_info,
        "rpn_score_targets": rpn_score_targets,
        "rpn_box_targets": rpn_box_targets,
    }
    labels["gt_boxes"] = input_utils.pad_to_fixed_size(
        boxes, self._max_num_instances, -1)
    labels["gt_classes"] = input_utils.pad_to_fixed_size(
        classes, self._max_num_instances, -1)
    if self._include_mask:
      labels["gt_masks"] = input_utils.pad_to_fixed_size(
          masks, self._max_num_instances, -1)

    return image, labels

Instances