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| # 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() | |