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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 data_provider.""" | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.contrib.slim import queues | |
| import datasets | |
| import data_provider | |
| class DataProviderTest(tf.test.TestCase): | |
| def setUp(self): | |
| tf.test.TestCase.setUp(self) | |
| def test_preprocessed_image_values_are_in_range(self): | |
| image_shape = (5, 4, 3) | |
| fake_image = np.random.randint(low=0, high=255, size=image_shape) | |
| image_tf = data_provider.preprocess_image(fake_image) | |
| with self.test_session() as sess: | |
| image_np = sess.run(image_tf) | |
| self.assertEqual(image_np.shape, image_shape) | |
| min_value, max_value = np.min(image_np), np.max(image_np) | |
| self.assertTrue((-1.28 < min_value) and (min_value < 1.27)) | |
| self.assertTrue((-1.28 < max_value) and (max_value < 1.27)) | |
| def test_provided_data_has_correct_shape(self): | |
| batch_size = 4 | |
| data = data_provider.get_data( | |
| dataset=datasets.fsns_test.get_test_split(), | |
| batch_size=batch_size, | |
| augment=True, | |
| central_crop_size=None) | |
| with self.test_session() as sess, queues.QueueRunners(sess): | |
| images_np, labels_np = sess.run([data.images, data.labels_one_hot]) | |
| self.assertEqual(images_np.shape, (batch_size, 150, 600, 3)) | |
| self.assertEqual(labels_np.shape, (batch_size, 37, 134)) | |
| def test_optionally_applies_central_crop(self): | |
| batch_size = 4 | |
| data = data_provider.get_data( | |
| dataset=datasets.fsns_test.get_test_split(), | |
| batch_size=batch_size, | |
| augment=True, | |
| central_crop_size=(500, 100)) | |
| with self.test_session() as sess, queues.QueueRunners(sess): | |
| images_np = sess.run(data.images) | |
| self.assertEqual(images_np.shape, (batch_size, 100, 500, 3)) | |
| if __name__ == '__main__': | |
| tf.test.main() | |