2feab9beb8c59f05ca2e1938f484b6bce638af47,models/experimental/mask_rcnn/dataloader.py,InputReader,__call__,#InputReader#Any#,293

Before Change

    // Parse the fetched records to input tensors for model function.
    dataset = dataset.map(_dataset_parser, num_parallel_calls=64)
    dataset = dataset.prefetch(batch_size)
    dataset = dataset.batch(batch_size, drop_remainder=True)

    def _process_example(images, score_targets, box_targets, source_ids,
                         image_info, boxes, is_crowds, areas, classes,
                         cropped_gt_masks):
      Processes one batch of data.
      // Transposes images for TPU performance.
      // Given the batch size, the batch dimesion (N) goes to either the minor
      // ((H, W, C, N) when N > C) or the second-minor ((H, W, N, C) when N < C)
      // dimension. Here, we assume N is 4 or 8 and C is 3, so we use
      // (H, W, C, N).
      if (params["transpose_input"] and
          self._mode == tf.estimator.ModeKeys.TRAIN):
        images = tf.transpose(images, [1, 2, 3, 0])

      labels = {}
      for level in range(params["min_level"], params["max_level"] + 1):
        labels["score_targets_%d" % level] = score_targets[level]
        labels["box_targets_%d" % level] = box_targets[level]
      // Concatenate groundtruth annotations to a tensor.
      groundtruth_data = tf.concat([boxes, is_crowds, areas, classes], axis=2)
      labels["source_ids"] = source_ids
      labels["groundtruth_data"] = groundtruth_data
      labels["image_info"] = image_info
      labels["cropped_gt_masks"] = cropped_gt_masks
      if self._mode == tf.estimator.ModeKeys.PREDICT:
        features = dict(
            images=images,
            image_info=image_info,
            groundtruth_data=groundtruth_data,
            source_ids=source_ids)
        return features
      else:
        return images, labels

    dataset = dataset.map(_process_example)
    dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE)

    if self._num_examples > 0:
      dataset = dataset.take(self._num_examples)

After Change

      dataset = dataset.shuffle(64)

    // Parse the fetched records to input tensors for model function.
    dataset = dataset.apply(
        tf.contrib.data.map_and_batch(
            _dataset_parser, batch_size=batch_size,
            num_parallel_batches=64, drop_remainder=True))

    // Transposes images for TPU performance.
    // Given the batch size, the batch dimesion (N) goes to either the minor
    // ((H, W, C, N) when N > C) or the second-minor ((H, W, N, C) when N < C)

Instances