old_contents: # 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.
  
  """Xception tests."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  # Dependency imports
  
  import numpy as np
  
  from tensor2tensor.data_generators import problem_hparams
  from tensor2tensor.models import xception
  from tensor2tensor.utils import registry
  
  import tensorflow as tf
  
  
  class XceptionTest(tf.test.TestCase):
  
    def _testXception(self, img_size, output_size):
      vocab_size = 9
      batch_size = 3
      x = np.random.random_integers(
          0, high=255, size=(batch_size, img_size, img_size, 3))
      y = np.random.random_integers(
          1, high=vocab_size - 1, size=(batch_size, 1, 1, 1))
      hparams = xception.xception_tiny()
      p_hparams = problem_hparams.test_problem_hparams(vocab_size, vocab_size)
      p_hparams.input_modality["inputs"] = (registry.Modalities.IMAGE, None)
      with self.test_session() as session:
        features = {
            "inputs": tf.constant(x, dtype=tf.int32),
            "targets": tf.constant(y, dtype=tf.int32),
        }
        model = xception.Xception(hparams, tf.estimator.ModeKeys.TRAIN, p_hparams)
        logits, _ = model(features)
        session.run(tf.global_variables_initializer())
        res = session.run(logits)
      self.assertEqual(res.shape, output_size + (1, vocab_size))
  
    def testXceptionSmall(self):
      self._testXception(img_size=9, output_size=(3, 5, 5))
  
    def testXceptionLarge(self):
      self._testXception(img_size=256, output_size=(3, 8, 8))
  
  
  if __name__ == "__main__":
    tf.test.main()
  
new_contents: # 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.
  
  """Xception tests."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  # Dependency imports
  
  import numpy as np
  
  from tensor2tensor.data_generators import problem_hparams
  from tensor2tensor.models import xception
  from tensor2tensor.utils import registry
  
  import tensorflow as tf
  
  
  class XceptionTest(tf.test.TestCase):
  
    def _testXception(self, img_size):
      vocab_size = 9
      batch_size = 3
      x = np.random.random_integers(
          0, high=255, size=(batch_size, img_size, img_size, 3))
      y = np.random.random_integers(
          1, high=vocab_size - 1, size=(batch_size, 1, 1, 1))
      hparams = xception.xception_tiny()
      p_hparams = problem_hparams.test_problem_hparams(vocab_size, vocab_size)
      p_hparams.input_modality["inputs"] = (registry.Modalities.IMAGE, None)
      p_hparams.target_modality = (registry.Modalities.CLASS_LABEL, vocab_size)
      with self.test_session() as session:
        features = {
            "inputs": tf.constant(x, dtype=tf.int32),
            "targets": tf.constant(y, dtype=tf.int32),
        }
        model = xception.Xception(hparams, tf.estimator.ModeKeys.TRAIN, p_hparams)
        logits, _ = model(features)
        session.run(tf.global_variables_initializer())
        res = session.run(logits)
      self.assertEqual(res.shape, (batch_size, 1, 1, 1, vocab_size))
  
    def testXceptionSmallImage(self):
      self._testXception(img_size=9)
  
    def testXceptionLargeImage(self):
      self._testXception(img_size=256)
  
  
  if __name__ == "__main__":
    tf.test.main()
  
subject: Update xception_test with correct target modality