models/official/vision/detection/dataloader/shapemask_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.

Parse image and ground truths in a dataset to training targets and package them
into (image, labels) tuple for ShapeMask.

Weicheng Kuo, Anelia Angelova, Jitendra Malik, Tsung-Yi Lin
ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors.
arXiv:1904.03239.
"""

import tensorflow 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 class_utils
from official.vision.detection.utils import dataloader_utils
from official.vision.detection.utils import input_utils


def pad_to_size(input_tensor, size):
  """Pads data with zeros to a given length at the first dimension if needed.

  Args:
    input_tensor: `Tensor` with any dimension.
    size: `int` number for the first dimension of output Tensor.

  Returns:
    `Tensor` with the first dimension padded to `size` if the first diemsion
    is less than `size`, otherwise no padding.
  """
  input_shape = tf.shape(input_tensor)
  padding_shape = []

  # Computes the padding length on the first dimension.
  padding_length = tf.maximum(0, size - tf.shape(input_tensor)[0])
  assert_length = tf.Assert(
      tf.greater_equal(padding_length, 0), [padding_length])
  with tf.control_dependencies([assert_length]):
    padding_shape.append(padding_length)

  # Copies shapes of the rest of input shape dimensions.
  for i in range(1, len(input_shape)):
    padding_shape.append(tf.shape(input=input_tensor)[i])

  # Pads input tensor to the fixed first dimension.
  paddings = tf.cast(tf.zeros(padding_shape), input_tensor.dtype)
  padded_tensor = tf.concat([input_tensor, paddings], axis=0)
  return padded_tensor


class Parser(object):
  """ShapeMask 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,
               use_category=True,
               outer_box_scale=1.0,
               box_jitter_scale=0.025,
               num_sampled_masks=8,
               mask_crop_size=32,
               mask_min_level=3,
               mask_max_level=5,
               upsample_factor=4,
               match_threshold=0.5,
               unmatched_threshold=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,
               use_bfloat16=True,
               mask_train_class='all',
               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.
      use_category: if `False`, treat all object in all classes in one
        foreground category.
      outer_box_scale: `float` number in a range of [1.0, inf) representing
        the scale from object box to outer box. The mask branch predicts
        instance mask enclosed in outer box.
      box_jitter_scale: `float` number representing the noise magnitude to
        jitter the training groundtruth boxes for mask branch.
      num_sampled_masks: `int` number of sampled masks for training.
      mask_crop_size: `list` for [height, width] of output training masks.
      mask_min_level: `int` number indicating the minimum feature level to
        obtain instance features.
      mask_max_level: `int` number indicating the maximum feature level to
        obtain instance features.
      upsample_factor: `int` factor of upsampling the fine mask predictions.
      match_threshold: `float` number between 0 and 1 representing the
        lower-bound threshold to assign positive labels for anchors. An anchor
        with a score over the threshold is labeled positive.
      unmatched_threshold: `float` number between 0 and 1 representing the
        upper-bound threshold to assign negative labels for anchors. An anchor
        with a score below the threshold is labeled negative.
      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`.
      use_bfloat16: `bool`, if True, cast output image to tf.bfloat16.
      mask_train_class: a string of experiment mode: `all`, `voc` or `nonvoc`.
      mode: a ModeKeys. Specifies if this is training, evaluation, prediction
        or prediction with groundtruths in the outputs.
    """
    self._mode = mode
    self._mask_train_class = mask_train_class
    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=True)

    # 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
    self._match_threshold = match_threshold
    self._unmatched_threshold = unmatched_threshold

    # Data augmentation.
    self._aug_rand_hflip = aug_rand_hflip
    self._aug_scale_min = aug_scale_min
    self._aug_scale_max = aug_scale_max

    # Device.
    self._use_bfloat16 = use_bfloat16

    # ShapeMask specific.
    # Control of which category to use.
    self._use_category = use_category
    self._num_sampled_masks = num_sampled_masks
    self._mask_crop_size = mask_crop_size
    self._mask_min_level = mask_min_level
    self._mask_max_level = mask_max_level
    self._outer_box_scale = outer_box_scale
    self._box_jitter_scale = box_jitter_scale
    self._up_sample_factor = upsample_factor

    # 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:
      inputs:
        image: image tensor that is preproessed to have normalized value and
          dimension [output_size[0], output_size[1], 3]
        mask_boxes: sampled boxes that tightly enclose the training masks. The
          box is represented in [y1, x1, y2, x2] format. The tensor is sampled
          to the fixed dimension [self._num_sampled_masks, 4].
        mask_outer_boxes: loose box that enclose sampled tight box. The
          box is represented in [y1, x1, y2, x2] format. The tensor is sampled
          to the fixed dimension [self._num_sampled_masks, 4].
        mask_classes: the class ids of sampled training masks. The tensor has
          shape [self._num_sampled_masks].
      labels:
        cls_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.
        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.
        num_positives: number of positive anchors in the image.
        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.
        image_scale: 2D float `Tensor` representing scale factors that apply
          to [height, width] of input image.
        mask_targets: training binary mask targets. The tensor has shape
          [self._num_sampled_masks, self._mask_crop_size, self._mask_crop_size].
        mask_is_valid: the binary tensor to indicate if the sampled masks are
          valide. The sampled masks are invalid when no mask annotations are
          included in the image. The tensor has shape [1].
        groundtruths:
          source_id: source image id. Default value -1 if the source id is empty
            in the groundtruth annotation.
          boxes: groundtruth bounding box annotations. The box is represented in
            [y1, x1, y2, x2] format. The tensor is padded with -1 to the fixed
            dimension [self._max_num_instances, 4].
          classes: groundtruth classes annotations. The tensor is padded with
            -1 to the fixed dimension [self._max_num_instances].
          areas: groundtruth areas annotations. The tensor is padded with -1
            to the fixed dimension [self._max_num_instances].
          is_crowds: groundtruth annotations to indicate if an annotation
            represents a group of instances by value {0, 1}. The tensor is
            padded with 0 to the fixed dimension [self._max_num_instances].
    """
    with tf.name_scope('parser'):
      data = self._example_decoder.decode(value)
      return self._parse_fn(data)

  def _parse_train_data(self, data):
    """Parse data for ShapeMask training."""
    classes = data['groundtruth_classes']
    boxes = data['groundtruth_boxes']
    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)
      masks = tf.gather(masks, indices)

    # Gets original image and its size.
    image = data['image']
    image_shape = tf.shape(image)[0:2]

    # If not using category, makes all categories with id = 0.
    if not self._use_category:
      classes = tf.cast(tf.greater(classes, 0), dtype=tf.float32)

    # Normalizes image with mean and std pixel values.
    image = input_utils.normalize_image(image)

    # Flips image randomly during training.
    if self._aug_rand_hflip:
      image, boxes, masks = input_utils.random_horizontal_flip(
          image, boxes, masks)

    # Converts boxes from normalized coordinates to pixel coordinates.
    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,
        self._output_size,
        aug_scale_min=self._aug_scale_min,
        aug_scale_max=self._aug_scale_max)
    image_scale = image_info[2, :]
    offset = image_info[3, :]

    # Resizes and crops boxes and masks.
    boxes = input_utils.resize_and_crop_boxes(
        boxes, image_scale, image_info[1, :], offset)

    # Filters out ground truth boxes that are all zeros.
    indices = box_utils.get_non_empty_box_indices(boxes)
    boxes = tf.gather(boxes, indices)
    classes = tf.gather(classes, indices)
    masks = tf.gather(masks, indices)

    # Assigns anchors.
    input_anchor = anchor.Anchor(
        self._min_level, self._max_level, self._num_scales,
        self._aspect_ratios, self._anchor_size, self._output_size)
    anchor_labeler = anchor.AnchorLabeler(
        input_anchor, self._match_threshold, self._unmatched_threshold)
    (cls_targets,
     box_targets,
     num_positives) = anchor_labeler.label_anchors(
         boxes,
         tf.cast(tf.expand_dims(classes, axis=1), tf.float32))

    # Sample groundtruth masks/boxes/classes for mask branch.
    num_masks = tf.shape(masks)[0]
    mask_shape = tf.shape(masks)[1:3]

    # Pad sampled boxes/masks/classes to a constant batch size.
    padded_boxes = pad_to_size(boxes, self._num_sampled_masks)
    padded_classes = pad_to_size(classes, self._num_sampled_masks)
    padded_masks = pad_to_size(masks, self._num_sampled_masks)

    # Randomly sample groundtruth masks for mask branch training. For the image
    # without groundtruth masks, it will sample the dummy padded tensors.
    rand_indices = tf.random.shuffle(
        tf.range(tf.maximum(num_masks, self._num_sampled_masks)))
    rand_indices = tf.math.mod(rand_indices, tf.maximum(num_masks, 1))
    rand_indices = rand_indices[0:self._num_sampled_masks]
    rand_indices = tf.reshape(rand_indices, [self._num_sampled_masks])

    sampled_boxes = tf.gather(padded_boxes, rand_indices)
    sampled_classes = tf.gather(padded_classes, rand_indices)
    sampled_masks = tf.gather(padded_masks, rand_indices)
    # Jitter the sampled boxes to mimic the noisy detections.
    sampled_boxes = box_utils.jitter_boxes(
        sampled_boxes, noise_scale=self._box_jitter_scale)
    sampled_boxes = box_utils.clip_boxes(sampled_boxes, self._output_size)
    # Compute mask targets in feature crop. A feature crop fully contains a
    # sampled box.
    mask_outer_boxes = box_utils.compute_outer_boxes(
        sampled_boxes, tf.shape(image)[0:2], scale=self._outer_box_scale)
    mask_outer_boxes = box_utils.clip_boxes(mask_outer_boxes, self._output_size)
    # Compensate the offset of mask_outer_boxes to map it back to original image
    # scale.
    mask_outer_boxes_ori = mask_outer_boxes
    mask_outer_boxes_ori += tf.tile(tf.expand_dims(offset, axis=0), [1, 2])
    mask_outer_boxes_ori /= tf.tile(tf.expand_dims(image_scale, axis=0), [1, 2])
    norm_mask_outer_boxes_ori = box_utils.normalize_boxes(
        mask_outer_boxes_ori, mask_shape)

    # Set sampled_masks shape to [batch_size, height, width, 1].
    sampled_masks = tf.cast(tf.expand_dims(sampled_masks, axis=-1), tf.float32)
    mask_targets = tf.image.crop_and_resize(
        sampled_masks,
        norm_mask_outer_boxes_ori,
        box_indices=tf.range(self._num_sampled_masks),
        crop_size=[self._mask_crop_size, self._mask_crop_size],
        method='bilinear',
        extrapolation_value=0,
        name='train_mask_targets')
    mask_targets = tf.where(tf.greater_equal(mask_targets, 0.5),
                            tf.ones_like(mask_targets),
                            tf.zeros_like(mask_targets))
    mask_targets = tf.squeeze(mask_targets, axis=-1)
    if self._up_sample_factor > 1:
      fine_mask_targets = tf.image.crop_and_resize(
          sampled_masks,
          norm_mask_outer_boxes_ori,
          box_indices=tf.range(self._num_sampled_masks),
          crop_size=[
              self._mask_crop_size * self._up_sample_factor,
              self._mask_crop_size * self._up_sample_factor
          ],
          method='bilinear',
          extrapolation_value=0,
          name='train_mask_targets')
      fine_mask_targets = tf.where(
          tf.greater_equal(fine_mask_targets, 0.5),
          tf.ones_like(fine_mask_targets), tf.zeros_like(fine_mask_targets))
      fine_mask_targets = tf.squeeze(fine_mask_targets, axis=-1)
    else:
      fine_mask_targets = mask_targets

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

    valid_image = tf.cast(tf.not_equal(num_masks, 0), tf.int32)
    if self._mask_train_class == 'all':
      mask_is_valid = valid_image * tf.ones_like(sampled_classes, tf.int32)
    else:
      # Get the intersection of sampled classes with training splits.
      mask_valid_classes = tf.cast(
          tf.expand_dims(
              class_utils.coco_split_class_ids(self._mask_train_class), 1),
          sampled_classes.dtype)
      match = tf.reduce_any(
          tf.equal(tf.expand_dims(sampled_classes, 0), mask_valid_classes), 0)
      mask_is_valid = valid_image * tf.cast(match, tf.int32)

    # Packs labels for model_fn outputs.
    labels = {
        'cls_targets': cls_targets,
        'box_targets': box_targets,
        'anchor_boxes': input_anchor.multilevel_boxes,
        'num_positives': num_positives,
        'image_info': image_info,
        # For ShapeMask.
        'mask_targets': mask_targets,
        'fine_mask_targets': fine_mask_targets,
        'mask_is_valid': mask_is_valid,
    }

    inputs = {
        'image': image,
        'image_info': image_info,
        'mask_boxes': sampled_boxes,
        'mask_outer_boxes': mask_outer_boxes,
        'mask_classes': sampled_classes,
    }
    return inputs, labels

  def _parse_predict_data(self, data):
    """Parse data for ShapeMask training."""
    classes = data['groundtruth_classes']
    boxes = data['groundtruth_boxes']
    masks = data['groundtruth_instance_masks']

    # Gets original image and its size.
    image = data['image']
    image_shape = tf.shape(image)[0:2]

    # If not using category, makes all categories with id = 0.
    if not self._use_category:
      classes = tf.cast(tf.greater(classes, 0), dtype=tf.float32)

    # Normalizes image with mean and std pixel values.
    image = input_utils.normalize_image(image)

    # Converts boxes from normalized coordinates to pixel coordinates.
    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,
        self._output_size,
        aug_scale_min=1.0,
        aug_scale_max=1.0)
    image_scale = image_info[2, :]
    offset = image_info[3, :]

    # Resizes and crops boxes and masks.
    boxes = input_utils.resize_and_crop_boxes(
        boxes, image_scale, image_info[1, :], offset)
    masks = input_utils.resize_and_crop_masks(
        tf.expand_dims(masks, axis=-1), image_scale, self._output_size, offset)

    # Filters out ground truth boxes that are all zeros.
    indices = box_utils.get_non_empty_box_indices(boxes)
    boxes = tf.gather(boxes, indices)
    classes = tf.gather(classes, indices)

    # Assigns anchors.
    input_anchor = anchor.Anchor(
        self._min_level, self._max_level, self._num_scales,
        self._aspect_ratios, self._anchor_size, self._output_size)
    anchor_labeler = anchor.AnchorLabeler(
        input_anchor, self._match_threshold, self._unmatched_threshold)

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

    labels = {
        '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.
      groundtruths = {
          'source_id': data['source_id'],
          'height': data['height'],
          'width': data['width'],
          'num_detections': tf.shape(data['groundtruth_classes']),
          'boxes': box_utils.denormalize_boxes(
              data['groundtruth_boxes'], image_shape),
          'classes': data['groundtruth_classes'],
          # 'masks': tf.squeeze(masks, axis=-1),
          '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)
      # Computes training labels.
      (cls_targets,
       box_targets,
       num_positives) = anchor_labeler.label_anchors(
           boxes,
           tf.cast(tf.expand_dims(classes, axis=1), tf.float32))
      # Packs labels for model_fn outputs.
      labels.update({
          'cls_targets': cls_targets,
          'box_targets': box_targets,
          'num_positives': num_positives,
          'groundtruths': groundtruths,
      })

    inputs = {
        'image': image,
        'image_info': image_info,
    }

    return inputs, labels