mirror of https://github.com/tensorflow/models.git
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
# Copyright 2017 The TensorFlow Authors All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for data_provider."""
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import numpy as np
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import tensorflow as tf
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from tensorflow.contrib.slim import queues
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import datasets
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import data_provider
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class DataProviderTest(tf.test.TestCase):
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def setUp(self):
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tf.test.TestCase.setUp(self)
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def test_preprocessed_image_values_are_in_range(self):
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image_shape = (5, 4, 3)
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fake_image = np.random.randint(low=0, high=255, size=image_shape)
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image_tf = data_provider.preprocess_image(fake_image)
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with self.test_session() as sess:
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image_np = sess.run(image_tf)
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self.assertEqual(image_np.shape, image_shape)
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min_value, max_value = np.min(image_np), np.max(image_np)
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self.assertTrue((-1.28 < min_value) and (min_value < 1.27))
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self.assertTrue((-1.28 < max_value) and (max_value < 1.27))
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def test_provided_data_has_correct_shape(self):
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batch_size = 4
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data = data_provider.get_data(
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dataset=datasets.fsns_test.get_test_split(),
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batch_size=batch_size,
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augment=True,
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central_crop_size=None)
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with self.test_session() as sess, queues.QueueRunners(sess):
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images_np, labels_np = sess.run([data.images, data.labels_one_hot])
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self.assertEqual(images_np.shape, (batch_size, 150, 600, 3))
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self.assertEqual(labels_np.shape, (batch_size, 37, 134))
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def test_optionally_applies_central_crop(self):
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batch_size = 4
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data = data_provider.get_data(
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dataset=datasets.fsns_test.get_test_split(),
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batch_size=batch_size,
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augment=True,
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central_crop_size=(500, 100))
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with self.test_session() as sess, queues.QueueRunners(sess):
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images_np = sess.run(data.images)
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self.assertEqual(images_np.shape, (batch_size, 100, 500, 3))
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if __name__ == '__main__':
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tf.test.main()
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