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deeplab2 / data /dataset_utils_test.py
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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()