text: # coding=utf-8
  # Copyright 2018 The Tensor2Tensor 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.
  """image_utils mnist."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy as np
  from tensor2tensor.data_generators import image_utils
  
  import tensorflow as tf
  
  
  class ImageTest(tf.test.TestCase):
  
    def testImageAugmentation(self):
      x = np.random.rand(500, 500, 3)
      with self.test_session() as session:
        y = image_utils.image_augmentation(tf.constant(x))
        res = session.run(y)
      self.assertEqual(res.shape, (299, 299, 3))
  
    def testImageGenerator(self):
      # 2 random images
      np.random.seed(1111)  # To avoid any flakiness.
      image1 = np.random.randint(0, 255, size=(10, 12, 3))
      image2 = np.random.randint(0, 255, size=(10, 12, 3))
      # Call image generator on the 2 images with labels [1, 2].
      encoded_imgs, labels = [], []
      for dictionary in image_utils.image_generator([image1, image2], [1, 2]):
        self.assertEqual(
            sorted(list(dictionary)), [
                "image/class/label", "image/encoded", "image/format",
                "image/height", "image/width"
            ])
        self.assertEqual(dictionary["image/format"], ["png"])
        self.assertEqual(dictionary["image/height"], [12])
        self.assertEqual(dictionary["image/width"], [10])
        encoded_imgs.append(dictionary["image/encoded"])
        labels.append(dictionary["image/class/label"])
  
      # Check that the result labels match the inputs.
      self.assertEqual(len(labels), 2)
      self.assertEqual(labels[0], [1])
      self.assertEqual(labels[1], [2])
  
      # Decode images and check that they match the inputs.
      self.assertEqual(len(encoded_imgs), 2)
      image_t = tf.placeholder(dtype=tf.string)
      decoded_png_t = tf.image.decode_png(image_t)
      with self.test_session() as sess:
        encoded_img1 = encoded_imgs[0]
        self.assertEqual(len(encoded_img1), 1)
        decoded1 = sess.run(decoded_png_t, feed_dict={image_t: encoded_img1[0]})
        self.assertAllClose(decoded1, image1)
        encoded_img2 = encoded_imgs[1]
        self.assertEqual(len(encoded_img2), 1)
        decoded2 = sess.run(decoded_png_t, feed_dict={image_t: encoded_img2[0]})
        self.assertAllClose(decoded2, image2)
  
  
  if __name__ == "__main__":
    tf.test.main()
  