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| # Copyright 2017 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 FSNS datasets module.""" | |
| import collections | |
| import os | |
| import tensorflow as tf | |
| from tensorflow.contrib import slim | |
| from datasets import fsns | |
| from datasets import unittest_utils | |
| FLAGS = tf.flags.FLAGS | |
| def get_test_split(): | |
| config = fsns.DEFAULT_CONFIG.copy() | |
| config['splits'] = {'test': {'size': 5, 'pattern': 'fsns-00000-of-00001'}} | |
| return fsns.get_split('test', dataset_dir(), config) | |
| def dataset_dir(): | |
| return os.path.join(os.path.dirname(__file__), 'testdata/fsns') | |
| class FsnsTest(tf.test.TestCase): | |
| def test_decodes_example_proto(self): | |
| expected_label = range(37) | |
| expected_image, encoded = unittest_utils.create_random_image( | |
| 'PNG', shape=(150, 600, 3)) | |
| serialized = unittest_utils.create_serialized_example({ | |
| 'image/encoded': [encoded], | |
| 'image/format': [b'PNG'], | |
| 'image/class': | |
| expected_label, | |
| 'image/unpadded_class': | |
| range(10), | |
| 'image/text': [b'Raw text'], | |
| 'image/orig_width': [150], | |
| 'image/width': [600] | |
| }) | |
| decoder = fsns.get_split('train', dataset_dir()).decoder | |
| with self.test_session() as sess: | |
| data_tuple = collections.namedtuple('DecodedData', decoder.list_items()) | |
| data = sess.run(data_tuple(*decoder.decode(serialized))) | |
| self.assertAllEqual(expected_image, data.image) | |
| self.assertAllEqual(expected_label, data.label) | |
| self.assertEqual([b'Raw text'], data.text) | |
| self.assertEqual([1], data.num_of_views) | |
| def test_label_has_shape_defined(self): | |
| serialized = 'fake' | |
| decoder = fsns.get_split('train', dataset_dir()).decoder | |
| [label_tf] = decoder.decode(serialized, ['label']) | |
| self.assertEqual(label_tf.get_shape().dims[0], 37) | |
| def test_dataset_tuple_has_all_extra_attributes(self): | |
| dataset = fsns.get_split('train', dataset_dir()) | |
| self.assertTrue(dataset.charset) | |
| self.assertTrue(dataset.num_char_classes) | |
| self.assertTrue(dataset.num_of_views) | |
| self.assertTrue(dataset.max_sequence_length) | |
| self.assertTrue(dataset.null_code) | |
| def test_can_use_the_test_data(self): | |
| batch_size = 1 | |
| dataset = get_test_split() | |
| provider = slim.dataset_data_provider.DatasetDataProvider( | |
| dataset, | |
| shuffle=True, | |
| common_queue_capacity=2 * batch_size, | |
| common_queue_min=batch_size) | |
| image_tf, label_tf = provider.get(['image', 'label']) | |
| with self.test_session() as sess: | |
| sess.run(tf.global_variables_initializer()) | |
| with slim.queues.QueueRunners(sess): | |
| image_np, label_np = sess.run([image_tf, label_tf]) | |
| self.assertEqual((150, 600, 3), image_np.shape) | |
| self.assertEqual((37, ), label_np.shape) | |
| if __name__ == '__main__': | |
| tf.test.main() | |