Internal change

PiperOrigin-RevId: 336132820

official/vision/beta/dataloaders/segmentation_input.py

+# Copyright 2020 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 segmentation datasets."""
+
+import tensorflow as tf
+from official.vision.beta.dataloaders import decoder
+from official.vision.beta.dataloaders import parser
+from official.vision.beta.ops import preprocess_ops
+
+
+class Decoder(decoder.Decoder):
+  """A tf.Example decoder for segmentation task."""
+
+  def __init__(self):
+    self._keys_to_features = {
+        'image/encoded': tf.io.FixedLenFeature((), tf.string, default_value=''),
+        'image/height': tf.io.FixedLenFeature((), tf.int64, default_value=0),
+        'image/width': tf.io.FixedLenFeature((), tf.int64, default_value=0),
+        'image/segmentation/class/encoded':
+            tf.io.FixedLenFeature((), tf.string, default_value='')
+    }
+
+  def decode(self, serialized_example):
+    return tf.io.parse_single_example(
+        serialized_example, self._keys_to_features)
+
+
+class Parser(parser.Parser):
+  """Parser to parse an image and its annotations into a dictionary of tensors."""
+
+  def __init__(self,
+               output_size,
+               resize_eval=False,
+               ignore_label=255,
+               aug_rand_hflip=False,
+               aug_scale_min=1.0,
+               aug_scale_max=1.0,
+               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: 'bool', if True, during evaluation, the max side of image and
+        label will be resized to output_size, otherwise image will be padded.
+      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.
+      dtype: `str`, data type. One of {`bfloat16`, `float32`, `float16`}.
+    """
+    self._output_size = output_size
+    self._resize_eval = resize_eval
+    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
+
+  def _prepare_image_and_label(self, data):
+    """Prepare normalized image and label."""
+    image = tf.io.decode_image(data['image/encoded'], channels=3)
+    label = tf.io.decode_image(data['image/segmentation/class/encoded'],
+                               channels=1)
+    height = data['image/height']
+    width = data['image/width']
+    image = tf.reshape(image, (height, width, 3))
+
+    label = tf.reshape(label, (1, height, width))
+    label = tf.cast(label, tf.float32)
+    # Normalizes image with mean and std pixel values.
+    image = preprocess_ops.normalize_image(image)
+    return image, label
+
+  def _parse_train_data(self, data):
+    """Parses data for training and evaluation."""
+    image, label = self._prepare_image_and_label(data)
+
+    # Flips image randomly during training.
+    if self._aug_rand_hflip:
+      image, label = preprocess_ops.random_horizontal_flip(image, masks=label)
+
+    # 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,
+        aug_scale_max=self._aug_scale_max)
+
+    # Resizes and crops boxes.
+    image_scale = image_info[2, :]
+    offset = image_info[3, :]
+
+    # Pad label and make sure the padded region assigned to the ignore label.
+    # The label is first offset by +1 and then padded with 0.
+    label += 1
+    label = tf.expand_dims(label, axis=3)
+    label = preprocess_ops.resize_and_crop_masks(
+        label, image_scale, self._output_size, offset)
+    label -= 1
+    label = tf.where(tf.equal(label, -1),
+                     self._ignore_label * tf.ones_like(label), label)
+    label = tf.squeeze(label, axis=0)
+    valid_mask = tf.not_equal(label, self._ignore_label)
+    labels = {
+        'masks': label,
+        'valid_masks': valid_mask
+    }
+
+    # Cast image as self._dtype
+    image = tf.cast(image, dtype=self._dtype)
+
+    return image, labels
+
+  def _parse_eval_data(self, data):
+    """Parses data for training and evaluation."""
+    image, label = self._prepare_image_and_label(data)
+    # The label is first offset by +1 and then padded with 0.
+    label += 1
+    label = tf.expand_dims(label, axis=3)
+
+    if self._resize_eval:
+      # Resizes and crops image.
+      image, image_info = preprocess_ops.resize_and_crop_image(
+          image, self._output_size, self._output_size)
+
+      # Resizes and crops mask.
+      image_scale = image_info[2, :]
+      offset = image_info[3, :]
+
+      label = preprocess_ops.resize_and_crop_masks(label, image_scale,
+                                                   self._output_size, offset)
+    else:
+      # Pads image and mask to output size.
+      image = tf.image.pad_to_bounding_box(image, 0, 0, self._output_size[0],
+                                           self._output_size[1])
+      label = tf.image.pad_to_bounding_box(label, 0, 0, self._output_size[0],
+                                           self._output_size[1])
+
+    label -= 1
+    label = tf.where(tf.equal(label, -1),
+                     self._ignore_label * tf.ones_like(label), label)
+    label = tf.squeeze(label, axis=0)
+
+    valid_mask = tf.not_equal(label, self._ignore_label)
+    labels = {
+        'masks': label,
+        'valid_masks': valid_mask
+    }
+
+    # Cast image as self._dtype
+    image = tf.cast(image, dtype=self._dtype)
+
+    return image, labels