code: # coding=utf-8
  # Copyright 2021 The Deeplab2 Authors.
  #
  # 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.
  
  """Tests for dataset_utils."""
  
  import numpy as np
  import tensorflow as tf
  
  from deeplab2.data import dataset_utils
  
  
  class DatasetUtilsTest(tf.test.TestCase):
  
    def _get_test_labels(self, num_classes, shape, label_divisor):
      num_ids_per_class = 35
      semantic_labels = np.random.randint(num_classes, size=shape)
      panoptic_labels = np.random.randint(
          num_ids_per_class, size=shape) + semantic_labels * label_divisor
  
      semantic_labels = tf.convert_to_tensor(semantic_labels, dtype=tf.int32)
      panoptic_labels = tf.convert_to_tensor(panoptic_labels, dtype=tf.int32)
  
      return panoptic_labels, semantic_labels
  
    def setUp(self):
      super().setUp()
      self._first_thing_class = 9
      self._num_classes = 19
      self._dataset_info = {
          'panoptic_label_divisor': 1000,
          'class_has_instances_list': tf.range(self._first_thing_class,
                                               self._num_classes)
      }
      self._num_ids = 37
      self._labels, self._semantic_classes = self._get_test_labels(
          self._num_classes, [2, 33, 33],
          self._dataset_info['panoptic_label_divisor'])
  
    def test_get_panoptic_and_semantic_label(self):
      # Note: self._labels contains one crowd instance per class.
      (returned_sem_labels, returned_pan_labels, returned_thing_mask,
       returned_crowd_region) = (
           dataset_utils.get_semantic_and_panoptic_label(
               self._dataset_info, self._labels, ignore_label=255))
  
      expected_semantic_labels = self._semantic_classes
      condition = self._labels % self._dataset_info['panoptic_label_divisor'] == 0
      condition = tf.logical_and(
          condition,
          tf.math.greater_equal(expected_semantic_labels,
                                self._first_thing_class))
      expected_crowd_labels = tf.where(condition, 1.0, 0.0)
      expected_pan_labels = tf.where(
          condition, 255 * self._dataset_info['panoptic_label_divisor'],
          self._labels)
      expected_thing_mask = tf.where(
          tf.math.greater_equal(expected_semantic_labels,
                                self._first_thing_class), 1.0, 0.0)
  
      self.assertListEqual(returned_sem_labels.shape.as_list(),
                           expected_semantic_labels.shape.as_list())
      self.assertListEqual(returned_pan_labels.shape.as_list(),
                           expected_pan_labels.shape.as_list())
      self.assertListEqual(returned_crowd_region.shape.as_list(),
                           expected_crowd_labels.shape.as_list())
      self.assertListEqual(returned_thing_mask.shape.as_list(),
                           expected_thing_mask.shape.as_list())
      np.testing.assert_equal(returned_sem_labels.numpy(),
                              expected_semantic_labels.numpy())
      np.testing.assert_equal(returned_pan_labels.numpy(),
                              expected_pan_labels.numpy())
      np.testing.assert_equal(returned_crowd_region.numpy(),
                              expected_crowd_labels.numpy())
      np.testing.assert_equal(returned_thing_mask.numpy(),
                              expected_thing_mask.numpy())
  
  if __name__ == '__main__':
    tf.test.main()
  
apis: tensorflow.math.greater_equal tensorflow.range tensorflow.where tensorflow.convert_to_tensor numpy.random.randint tensorflow.test.main