Internal change.

PiperOrigin-RevId: 277584854

official/vision/detection/dataloader/maskrcnn_parser.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Data parser and processing for Mask R-CNN."""
+
+import tensorflow.compat.v2 as tf
+
+from official.vision.detection.dataloader import anchor
+from official.vision.detection.dataloader import mode_keys as ModeKeys
+from official.vision.detection.dataloader import tf_example_decoder
+from official.vision.detection.utils import box_utils
+from official.vision.detection.utils import dataloader_utils
+from official.vision.detection.utils import input_utils
+
+
+class Parser(object):
+  """Parser to parse an image and its annotations into a dictionary of tensors."""
+
+  def __init__(self,
+               output_size,
+               min_level,
+               max_level,
+               num_scales,
+               aspect_ratios,
+               anchor_size,
+               rpn_match_threshold=0.7,
+               rpn_unmatched_threshold=0.3,
+               rpn_batch_size_per_im=256,
+               rpn_fg_fraction=0.5,
+               aug_rand_hflip=False,
+               aug_scale_min=1.0,
+               aug_scale_max=1.0,
+               skip_crowd_during_training=True,
+               max_num_instances=100,
+               include_mask=False,
+               mask_crop_size=112,
+               use_bfloat16=True,
+               mode=None):
+    """Initializes parameters for parsing annotations in the dataset.
+
+    Args:
+      output_size: `Tensor` or `list` for [height, width] of output image. The
+        output_size should be divided by the largest feature stride 2^max_level.
+      min_level: `int` number of minimum level of the output feature pyramid.
+      max_level: `int` number of maximum level of the output feature pyramid.
+      num_scales: `int` number representing intermediate scales added
+        on each level. For instances, num_scales=2 adds one additional
+        intermediate anchor scales [2^0, 2^0.5] on each level.
+      aspect_ratios: `list` of float numbers representing the aspect raito
+        anchors added on each level. The number indicates the ratio of width to
+        height. For instances, aspect_ratios=[1.0, 2.0, 0.5] adds three anchors
+        on each scale level.
+      anchor_size: `float` number representing the scale of size of the base
+        anchor to the feature stride 2^level.
+      rpn_match_threshold:
+      rpn_unmatched_threshold:
+      rpn_batch_size_per_im:
+      rpn_fg_fraction:
+      aug_rand_hflip: `bool`, if True, augment training with random
+        horizontal flip.
+      aug_scale_min: `float`, the minimum scale applied to `output_size` for
+        data augmentation during training.
+      aug_scale_max: `float`, the maximum scale applied to `output_size` for
+        data augmentation during training.
+      skip_crowd_during_training: `bool`, if True, skip annotations labeled with
+        `is_crowd` equals to 1.
+      max_num_instances: `int` number of maximum number of instances in an
+        image. The groundtruth data will be padded to `max_num_instances`.
+      include_mask: a bool to indicate whether parse mask groundtruth.
+      mask_crop_size: the size which groundtruth mask is cropped to.
+      use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
+      mode: a ModeKeys. Specifies if this is training, evaluation, prediction
+        or prediction with groundtruths in the outputs.
+    """
+    self._mode = mode
+    self._max_num_instances = max_num_instances
+    self._skip_crowd_during_training = skip_crowd_during_training
+    self._is_training = (mode == ModeKeys.TRAIN)
+
+    self._example_decoder = tf_example_decoder.TfExampleDecoder(
+        include_mask=include_mask)
+
+    # Anchor.
+    self._output_size = output_size
+    self._min_level = min_level
+    self._max_level = max_level
+    self._num_scales = num_scales
+    self._aspect_ratios = aspect_ratios
+    self._anchor_size = anchor_size
+
+    # Target assigning.
+    self._rpn_match_threshold = rpn_match_threshold
+    self._rpn_unmatched_threshold = rpn_unmatched_threshold
+    self._rpn_batch_size_per_im = rpn_batch_size_per_im
+    self._rpn_fg_fraction = rpn_fg_fraction
+
+    # Data augmentation.
+    self._aug_rand_hflip = aug_rand_hflip
+    self._aug_scale_min = aug_scale_min
+    self._aug_scale_max = aug_scale_max
+
+    # Mask.
+    self._include_mask = include_mask
+    self._mask_crop_size = mask_crop_size
+
+    # Device.
+    self._use_bfloat16 = use_bfloat16
+
+    # Data is parsed depending on the model Modekey.
+    if mode == ModeKeys.TRAIN:
+      self._parse_fn = self._parse_train_data
+    elif mode == ModeKeys.EVAL:
+      self._parse_fn = self._parse_eval_data
+    elif mode == ModeKeys.PREDICT or mode == ModeKeys.PREDICT_WITH_GT:
+      self._parse_fn = self._parse_predict_data
+    else:
+      raise ValueError('mode is not defined.')
+
+  def __call__(self, value):
+    """Parses data to an image and associated training labels.
+
+    Args:
+      value: a string tensor holding a serialized tf.Example proto.
+
+    Returns:
+      image, labels: if mode == ModeKeys.TRAIN. see _parse_train_data.
+      {'images': image, 'labels': labels}: if mode == ModeKeys.PREDICT
+        or ModeKeys.PREDICT_WITH_GT.
+    """
+    with tf.name_scope('parser'):
+      data = self._example_decoder.decode(value)
+      return self._parse_fn(data)
+
+  def _parse_train_data(self, data):
+    """Parses data for training.
+
+    Args:
+      data: the decoded tensor dictionary from TfExampleDecoder.
+
+    Returns:
+      image: image tensor that is preproessed to have normalized value and
+        dimension [output_size[0], output_size[1], 3]
+      labels: a dictionary of tensors used for training. The following describes
+        {key: value} pairs in the dictionary.
+        image_info: a 2D `Tensor` that encodes the information of the image and
+          the applied preprocessing. It is in the format of
+          [[original_height, original_width], [scaled_height, scaled_width],
+        anchor_boxes: ordered dictionary with keys
+          [min_level, min_level+1, ..., max_level]. The values are tensor with
+          shape [height_l, width_l, 4] representing anchor boxes at each level.
+        rpn_score_targets: ordered dictionary with keys
+          [min_level, min_level+1, ..., max_level]. The values are tensor with
+          shape [height_l, width_l, anchors_per_location]. The height_l and
+          width_l represent the dimension of class logits at l-th level.
+        rpn_box_targets: ordered dictionary with keys
+          [min_level, min_level+1, ..., max_level]. The values are tensor with
+          shape [height_l, width_l, anchors_per_location * 4]. The height_l and
+          width_l represent the dimension of bounding box regression output at
+          l-th level.
+        gt_boxes: Groundtruth bounding box annotations. The box is represented
+           in [y1, x1, y2, x2] format. The coordinates are w.r.t the scaled
+           image that is fed to the network. The tennsor is padded with -1 to
+           the fixed dimension [self._max_num_instances, 4].
+        gt_classes: Groundtruth classes annotations. The tennsor is padded
+          with -1 to the fixed dimension [self._max_num_instances].
+        gt_masks: groundtrugh masks cropped by the bounding box and
+          resized to a fixed size determined by mask_crop_size.
+    """
+    classes = data['groundtruth_classes']
+    boxes = data['groundtruth_boxes']
+    if self._include_mask:
+      masks = data['groundtruth_instance_masks']
+
+    is_crowds = data['groundtruth_is_crowd']
+    # Skips annotations with `is_crowd` = True.
+    if self._skip_crowd_during_training and self._is_training:
+      num_groundtrtuhs = tf.shape(classes)[0]
+      with tf.control_dependencies([num_groundtrtuhs, is_crowds]):
+        indices = tf.cond(
+            tf.greater(tf.size(is_crowds), 0),
+            lambda: tf.where(tf.logical_not(is_crowds))[:, 0],
+            lambda: tf.cast(tf.range(num_groundtrtuhs), tf.int64))
+      classes = tf.gather(classes, indices)
+      boxes = tf.gather(boxes, indices)
+      if self._include_mask:
+        masks = tf.gather(masks, indices)
+
+    # Gets original image and its size.
+    image = data['image']
+    image_shape = tf.shape(image)[0:2]
+
+    # Normalizes image with mean and std pixel values.
+    image = input_utils.normalize_image(image)
+
+    # Flips image randomly during training.
+    if self._aug_rand_hflip:
+      if self._include_mask:
+        image, boxes, masks = input_utils.random_horizontal_flip(
+            image, boxes, masks)
+      else:
+        image, boxes = input_utils.random_horizontal_flip(
+            image, boxes)
+
+    # Converts boxes from normalized coordinates to pixel coordinates.
+    # Now the coordinates of boxes are w.r.t. the original image.
+    boxes = box_utils.denormalize_boxes(boxes, image_shape)
+
+    # Resizes and crops image.
+    image, image_info = input_utils.resize_and_crop_image(
+        image,
+        self._output_size,
+        padded_size=input_utils.compute_padded_size(
+            self._output_size, 2 ** self._max_level),
+        aug_scale_min=self._aug_scale_min,
+        aug_scale_max=self._aug_scale_max)
+    image_height, image_width, _ = image.get_shape().as_list()
+
+    # Resizes and crops boxes.
+    # Now the coordinates of boxes are w.r.t the scaled image.
+    image_scale = image_info[2, :]
+    offset = image_info[3, :]
+    boxes = input_utils.resize_and_crop_boxes(
+        boxes, image_scale, (image_height, image_width), offset)
+    if self._include_mask:
+      masks = input_utils.resize_and_crop_masks(
+          tf.expand_dims(masks, axis=-1),
+          image_scale, (image_height, image_width), offset)
+      masks = tf.squeeze(masks, axis=-1)
+
+    # Filters out ground truth boxes that are all zeros.
+    indices = input_utils.get_non_empty_box_indices(boxes)
+    boxes = tf.gather(boxes, indices)
+    classes = tf.gather(classes, indices)
+    if self._include_mask:
+      masks = tf.gather(masks, indices)
+      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
+
+  def _parse_eval_data(self, data):
+    """Parses data for evaluation."""
+    raise NotImplementedError('Not implemented!')
+
+  def _parse_predict_data(self, data):
+    """Parses data for prediction.
+
+    Args:
+      data: the decoded tensor dictionary from TfExampleDecoder.
+
+    Returns:
+      A dictionary of {'images': image, 'labels': labels} where
+        image: image tensor that is preproessed to have normalized value and
+          dimension [output_size[0], output_size[1], 3]
+        labels: a dictionary of tensors used for training. The following
+          describes {key: value} pairs in the dictionary.
+          source_ids: Source image id. Default value -1 if the source id is
+            empty in the groundtruth annotation.
+          image_info: a 2D `Tensor` that encodes the information of the image
+            and the applied preprocessing. It is in the format of
+            [[original_height, original_width], [scaled_height, scaled_width],
+          anchor_boxes: ordered dictionary with keys
+            [min_level, min_level+1, ..., max_level]. The values are tensor with
+            shape [height_l, width_l, 4] representing anchor boxes at each
+            level.
+    """
+    # Gets original image and its size.
+    image = data['image']
+    image_shape = tf.shape(image)[0:2]
+
+    # Normalizes image with mean and std pixel values.
+    image = input_utils.normalize_image(image)
+
+    # Resizes and crops image.
+    image, image_info = input_utils.resize_and_crop_image(
+        image,
+        self._output_size,
+        padded_size=input_utils.compute_padded_size(
+            self._output_size, 2 ** self._max_level),
+        aug_scale_min=1.0,
+        aug_scale_max=1.0)
+    image_height, image_width, _ = image.get_shape().as_list()
+
+    # If bfloat16 is used, casts input image to tf.bfloat16.
+    if self._use_bfloat16:
+      image = tf.cast(image, dtype=tf.bfloat16)
+
+    # Compute Anchor boxes.
+    input_anchor = anchor.Anchor(
+        self._min_level,
+        self._max_level,
+        self._num_scales,
+        self._aspect_ratios,
+        self._anchor_size,
+        (image_height, image_width))
+
+    labels = {
+        'source_id': dataloader_utils.process_source_id(data['source_id']),
+        'anchor_boxes': input_anchor.multilevel_boxes,
+        'image_info': image_info,
+    }
+
+    if self._mode == ModeKeys.PREDICT_WITH_GT:
+      # Converts boxes from normalized coordinates to pixel coordinates.
+      boxes = box_utils.denormalize_boxes(
+          data['groundtruth_boxes'], image_shape)
+      groundtruths = {
+          'source_id': data['source_id'],
+          'height': data['height'],
+          'width': data['width'],
+          'num_detections': tf.shape(data['groundtruth_classes']),
+          'boxes': boxes,
+          'classes': data['groundtruth_classes'],
+          'areas': data['groundtruth_area'],
+          'is_crowds': tf.cast(data['groundtruth_is_crowd'], tf.int32),
+      }
+      groundtruths['source_id'] = dataloader_utils.process_source_id(
+          groundtruths['source_id'])
+      groundtruths = dataloader_utils.pad_groundtruths_to_fixed_size(
+          groundtruths, self._max_num_instances)
+      labels['groundtruths'] = groundtruths
+
+    return image, labels