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| # Lint as: python2, python3 | |
| # Copyright 2018 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. | |
| # ============================================================================== | |
| """Tests for deeplab.datasets.data_generator.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import collections | |
| from six.moves import range | |
| import tensorflow as tf | |
| from deeplab import common | |
| from deeplab.datasets import data_generator | |
| ImageAttributes = collections.namedtuple( | |
| 'ImageAttributes', ['image', 'label', 'height', 'width', 'image_name']) | |
| class DatasetTest(tf.test.TestCase): | |
| # Note: training dataset cannot be tested since there is shuffle operation. | |
| # When disabling the shuffle, training dataset is operated same as validation | |
| # dataset. Therefore it is not tested again. | |
| def testPascalVocSegTestData(self): | |
| dataset = data_generator.Dataset( | |
| dataset_name='pascal_voc_seg', | |
| split_name='val', | |
| dataset_dir= | |
| 'deeplab/testing/pascal_voc_seg', | |
| batch_size=1, | |
| crop_size=[3, 3], # Use small size for testing. | |
| min_resize_value=3, | |
| max_resize_value=3, | |
| resize_factor=None, | |
| min_scale_factor=0.01, | |
| max_scale_factor=2.0, | |
| scale_factor_step_size=0.25, | |
| is_training=False, | |
| model_variant='mobilenet_v2') | |
| self.assertAllEqual(dataset.num_of_classes, 21) | |
| self.assertAllEqual(dataset.ignore_label, 255) | |
| num_of_images = 3 | |
| with self.test_session() as sess: | |
| iterator = dataset.get_one_shot_iterator() | |
| for i in range(num_of_images): | |
| batch = iterator.get_next() | |
| batch, = sess.run([batch]) | |
| image_attributes = _get_attributes_of_image(i) | |
| self.assertEqual(batch[common.HEIGHT][0], image_attributes.height) | |
| self.assertEqual(batch[common.WIDTH][0], image_attributes.width) | |
| self.assertEqual(batch[common.IMAGE_NAME][0], | |
| image_attributes.image_name.encode()) | |
| # All data have been read. | |
| with self.assertRaisesRegexp(tf.errors.OutOfRangeError, ''): | |
| sess.run([iterator.get_next()]) | |
| def _get_attributes_of_image(index): | |
| """Gets the attributes of the image. | |
| Args: | |
| index: Index of image in all images. | |
| Returns: | |
| Attributes of the image in the format of ImageAttributes. | |
| Raises: | |
| ValueError: If index is of wrong value. | |
| """ | |
| if index == 0: | |
| return ImageAttributes( | |
| image=None, | |
| label=None, | |
| height=366, | |
| width=500, | |
| image_name='2007_000033') | |
| elif index == 1: | |
| return ImageAttributes( | |
| image=None, | |
| label=None, | |
| height=335, | |
| width=500, | |
| image_name='2007_000042') | |
| elif index == 2: | |
| return ImageAttributes( | |
| image=None, | |
| label=None, | |
| height=333, | |
| width=500, | |
| image_name='2007_000061') | |
| else: | |
| raise ValueError('Index can only be 0, 1 or 2.') | |
| if __name__ == '__main__': | |
| tf.test.main() | |