Merge pull request #15 from srihari-humbarwadi/panoptic-deeplab-dataloader

panoptic deeplab dataloader

official/vision/beta/projects/panoptic_maskrcnn/dataloaders/panoptic_deeplab_input.py

+# Copyright 2022 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 Panoptic Deeplab."""
+
+import numpy as np
+import tensorflow as tf
+
+from official.vision.beta.dataloaders import parser
+from official.vision.beta.dataloaders import tf_example_decoder
+from official.vision.beta.ops import preprocess_ops
+
+
+def _compute_gaussian_from_std(sigma):
+  """Computes the Gaussian and its size from a given standard deviation."""
+  size = int(6 * sigma + 3)
+  x = np.arange(size, dtype=np.float)
+  y = x[:, np.newaxis]
+  x0, y0 = 3 * sigma + 1, 3 * sigma + 1
+  gaussian = tf.constant(
+      np.exp(-((x - x0)**2 + (y - y0)**2) / (2 * sigma**2)),
+      dtype=tf.float32)
+  return gaussian, size
+
+
+class TfExampleDecoder(tf_example_decoder.TfExampleDecoder):
+  """Tensorflow Example proto decoder."""
+
+  def __init__(self, regenerate_source_id):
+    super(TfExampleDecoder,
+          self).__init__(
+              include_mask=True,
+              regenerate_source_id=regenerate_source_id)
+
+    self._panoptic_keys_to_features = {
+        'image/panoptic/category_mask':
+            tf.io.FixedLenFeature((), tf.string, default_value=''),
+        'image/panoptic/instance_mask':
+            tf.io.FixedLenFeature((), tf.string, default_value='')
+    }
+
+  def decode(self, serialized_example):
+    decoded_tensors = super(TfExampleDecoder,
+                            self).decode(serialized_example)
+    parsed_tensors = tf.io.parse_single_example(
+        serialized_example, self._panoptic_keys_to_features)
+
+    category_mask = tf.io.decode_image(
+        parsed_tensors['image/panoptic/category_mask'], channels=1)
+    instance_mask = tf.io.decode_image(
+        parsed_tensors['image/panoptic/instance_mask'], channels=1)
+    category_mask.set_shape([None, None, 1])
+    instance_mask.set_shape([None, None, 1])
+
+    decoded_tensors.update({
+        'groundtruth_panoptic_category_mask': category_mask,
+        'groundtruth_panoptic_instance_mask': instance_mask
+    })
+    return decoded_tensors
+
+
+class Parser(parser.Parser):
+  """Parser to parse an image and its annotations into a dictionary of tensors."""
+
+  def __init__(
+      self,
+      output_size,
+      resize_eval_groundtruth=True,
+      groundtruth_padded_size=None,
+      ignore_label=0,
+      aug_rand_hflip=False,
+      aug_scale_min=1.0,
+      aug_scale_max=1.0,
+      sigma=8.0,
+      dtype='float32'):
+    """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.
+      resize_eval_groundtruth: `bool`, if True, eval groundtruth masks are
+        resized to output_size.
+      groundtruth_padded_size: `Tensor` or `list` for [height, width]. When
+        resize_eval_groundtruth is set to False, the groundtruth masks are
+        padded to this size.
+      ignore_label: `int` the pixel with ignore label will not used for training
+        and evaluation.
+      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.
+      sigma: `float`, standard deviation for generating 2D Gaussian to encode
+        centers.
+      dtype: `str`, data type. One of {`bfloat16`, `float32`, `float16`}.
+    """
+    self._output_size = output_size
+    self._resize_eval_groundtruth = resize_eval_groundtruth
+    if (not resize_eval_groundtruth) and (groundtruth_padded_size is None):
+      raise ValueError(
+          'groundtruth_padded_size ([height, width]) needs to be'
+          'specified when resize_eval_groundtruth is False.')
+    self._groundtruth_padded_size = groundtruth_padded_size
+    self._ignore_label = ignore_label
+
+    # Data augmentation.
+    self._aug_rand_hflip = aug_rand_hflip
+    self._aug_scale_min = aug_scale_min
+    self._aug_scale_max = aug_scale_max
+
+    # dtype.
+    self._dtype = dtype
+
+    self._sigma = sigma
+    self._gaussian, self._gaussian_size = _compute_gaussian_from_std(
+        self._sigma)
+    self._gaussian = tf.reshape(self._gaussian, shape=[-1])
+
+  def _resize_and_crop_mask(self, mask, image_info, is_training):
+    """Resizes and crops mask using `image_info` dict"""
+    height = image_info[0][0]
+    width = image_info[0][1]
+    mask = tf.reshape(mask, shape=[1, height, width, 1])
+    mask += 1
+
+    if is_training or self._resize_eval_groundtruth:
+      image_scale = image_info[2, :]
+      offset = image_info[3, :]
+      mask = preprocess_ops.resize_and_crop_masks(
+          mask,
+          image_scale,
+          self._output_size,
+          offset)
+    else:
+      mask = tf.image.pad_to_bounding_box(
+          mask, 0, 0,
+          self._groundtruth_padded_size[0],
+          self._groundtruth_padded_size[1])
+    mask -= 1
+    
+    # Assign ignore label to the padded region.
+    mask = tf.where(
+        tf.equal(mask, -1),
+        self._ignore_label * tf.ones_like(mask),
+        mask)
+    mask = tf.squeeze(mask, axis=0)
+    return mask
+
+  def _parse_data(self, data, is_training):
+    image = data['image']
+    image = preprocess_ops.normalize_image(image)
+
+    category_mask = tf.cast(
+        data['groundtruth_panoptic_category_mask'][:, :, 0],
+        dtype=tf.float32)
+    instance_mask = tf.cast(
+        data['groundtruth_panoptic_instance_mask'][:, :, 0],
+        dtype=tf.float32)
+
+    # Flips image randomly during training.
+    if self._aug_rand_hflip and is_training:
+      masks = tf.stack([category_mask, instance_mask], axis=0)
+      image, _, masks = preprocess_ops.random_horizontal_flip(
+          image=image, masks=masks)
+      category_mask = masks[0]
+      instance_mask = masks[1]
+
+    # Resizes and crops image.
+    image, image_info = preprocess_ops.resize_and_crop_image(
+        image,
+        self._output_size,
+        self._output_size,
+        aug_scale_min=self._aug_scale_min if is_training else 1.0,
+        aug_scale_max=self._aug_scale_max if is_training else 1.0)
+
+    category_mask = self._resize_and_crop_mask(
+        category_mask,
+        image_info,
+        is_training=is_training)
+    instance_mask = self._resize_and_crop_mask(
+        instance_mask,
+        image_info,
+        is_training=is_training)
+
+    centers_heatmap, centers_offset = self._encode_centers_and_offets(
+        instance_mask=instance_mask[:, :, 0])
+
+    # Cast image and labels as self._dtype
+    image = tf.cast(image, dtype=self._dtype)
+    category_mask = tf.cast(category_mask, dtype=self._dtype)
+    instance_mask = tf.cast(instance_mask, dtype=self._dtype)
+    centers_heatmap = tf.cast(centers_heatmap, dtype=self._dtype)
+    centers_offset = tf.cast(centers_offset, dtype=self._dtype)
+    things_mask = tf.cast(
+        tf.not_equal(instance_mask, self._ignore_label),
+        dtype=self._dtype)
+
+    labels = {
+        'category_mask': category_mask,
+        'instance_mask': instance_mask,
+        'centers_heatmap': centers_heatmap,
+        'centers_offset': centers_offset,
+        'things_mask': things_mask
+    }
+    return image, labels
+
+  def _parse_train_data(self, data):
+    """Parses data for training."""
+    return self._parse_data(data=data, is_training=True)
+
+  def _parse_eval_data(self, data):
+    """Parses data for evaluation."""
+    return self._parse_data(data=data, is_training=False)
+
+  def _encode_centers_and_offets(self, instance_mask):
+    """Generates center heatmaps and offets from instance id mask
+
+    Args:
+      instance_mask: `tf.Tensor` of shape [height, width] representing
+        groundtruth instance id mask.
+    Returns:
+      centers_heatmap: `tf.Tensor` of shape [height, width, 1]
+      centers_offset: `tf.Tensor` of shape [height, width, 2]
+    """
+    shape = tf.shape(instance_mask)
+    height, width = shape[0], shape[1]
+
+    padding_start = int(3 * self._sigma + 1)
+    padding_end = int(3 * self._sigma + 2)
+    
+    # padding should be equal to self._gaussian_size which is calculated
+    # as size = int(6 * sigma + 3)
+    padding = padding_start + padding_end
+
+    centers_heatmap = tf.zeros(
+        shape=[height + padding, width + padding],
+        dtype=tf.float32)
+    centers_offset_y = tf.zeros(
+        shape=[height, width],
+        dtype=tf.float32)
+    centers_offset_x = tf.zeros(
+        shape=[height, width],
+        dtype=tf.float32)
+
+    unique_instance_ids, _ = tf.unique(tf.reshape(instance_mask, [-1]))
+
+    # The following method for encoding center heatmaps and offets is inspired
+    # by the reference implementation available at 
+    # https://github.com/google-research/deeplab2/blob/main/data/sample_generator.py  # pylint: disable=line-too-long
+    for instance_id in unique_instance_ids:
+      if instance_id == self._ignore_label:
+        continue
+
+      mask = tf.equal(instance_mask, instance_id)
+      mask_indices = tf.cast(tf.where(mask), dtype=tf.float32)
+      mask_center = tf.reduce_mean(mask_indices, axis=0)
+      mask_center_y = tf.cast(tf.round(mask_center[0]), dtype=tf.int32)
+      mask_center_x = tf.cast(tf.round(mask_center[1]), dtype=tf.int32)
+
+      gaussian_size = self._gaussian_size
+      indices_y = tf.range(mask_center_y, mask_center_y + gaussian_size)
+      indices_x = tf.range(mask_center_x, mask_center_x + gaussian_size)
+
+      indices = tf.stack(tf.meshgrid(indices_y, indices_x))
+      indices = tf.reshape(
+          indices, shape=[2, gaussian_size * gaussian_size])
+      indices = tf.transpose(indices)
+
+      centers_heatmap = tf.tensor_scatter_nd_max(
+          tensor=centers_heatmap,
+          indices=indices,
+          updates=self._gaussian)
+
+      centers_offset_y = tf.tensor_scatter_nd_update(
+          tensor=centers_offset_y,
+          indices=tf.cast(mask_indices, dtype=tf.int32),
+          updates=tf.cast(mask_center_y, dtype=tf.float32) - mask_indices[:, 0])
+
+      centers_offset_x = tf.tensor_scatter_nd_update(
+          tensor=centers_offset_x,
+          indices=tf.cast(mask_indices, dtype=tf.int32),
+          updates=tf.cast(mask_center_x, dtype=tf.float32) - mask_indices[:, 1])
+
+    centers_heatmap = centers_heatmap[
+        padding_start:padding_start + height,
+        padding_start:padding_start + width]
+    centers_heatmap = tf.expand_dims(centers_heatmap, axis=-1)
+    
+    centers_offset = tf.stack(
+        [centers_offset_y, centers_offset_x], 
+        axis=-1)
+    
+    return centers_heatmap, centers_offset