text: # Copyright 2016 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.
  # ==============================================================================
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  
  import xception
  
  slim = tf.contrib.slim
  
  
  class XceptionTest(tf.test.TestCase):
  
    def testBuild(self):
      batch_size = 5
      height, width = 299, 299
      num_classes = 1001
      with self.test_session():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, end_points = xception.xception(inputs, num_classes)
  
        #Entry Flow
        self.assertEquals(end_points['Xception/block1_res_conv'].get_shape().as_list(), [5, 74, 74 ,128])
        self.assertEquals(end_points['Xception/block2_res_conv'].get_shape().as_list(), [5, 37, 37, 256])
        self.assertEquals(end_points['Xception/block3_res_conv'].get_shape().as_list(), [5, 19, 19, 728])
  
        #Mid Flow
        self.assertEquals(end_points['Xception/block5_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
        self.assertEquals(end_points['Xception/block6_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
        self.assertEquals(end_points['Xception/block7_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
        self.assertEquals(end_points['Xception/block8_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
        self.assertEquals(end_points['Xception/block9_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
        self.assertEquals(end_points['Xception/block10_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
        self.assertEquals(end_points['Xception/block11_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
        self.assertEquals(end_points['Xception/block12_dws_conv3'].get_shape().as_list(), [5, 19, 19, 728])
        self.assertEquals(end_points['Xception/block12_res_conv'].get_shape().as_list(), [5, 10, 10, 1024])
  
        #Exit Flow
        self.assertEquals(end_points['Xception/block14_dws_conv1'].get_shape().as_list(), [5, 10, 10, 1536])
        self.assertEquals(end_points['Xception/block14_dws_conv2'].get_shape().as_list(), [5, 10, 10, 2048])
        self.assertEquals(end_points['Xception/block15_avg_pool'].get_shape().as_list(), [5, 1, 1, 2048])
        self.assertEquals(end_points['Xception/block15_conv1'].get_shape().as_list(), [5, 1, 1, 2048])
        self.assertEquals(end_points['Xception/block15_conv2'].get_shape().as_list(), [5, 1, 1, 1001])
  
        #Check outputs
        self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
        self.assertListEqual(end_points['Predictions'].get_shape().as_list(), [batch_size, num_classes])
  
  
    def testEvaluation(self):
      batch_size = 1
      height, width = 299, 299
      num_classes = 1001
      with self.test_session():
        eval_inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, _ = xception.xception(eval_inputs, is_training=False)
        self.assertListEqual(logits.get_shape().as_list(),
                             [batch_size, num_classes])
        predictions = tf.argmax(logits, 1)
        self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
  
  
    def testForward(self):
      batch_size = 1
      height, width = 299, 299
      with self.test_session() as sess:
        inputs = tf.random_uniform((batch_size, height, width, 3))
        logits, _ = xception.xception(inputs)
        sess.run(tf.global_variables_initializer())
        output = sess.run(logits)
        self.assertTrue(output.any())
  
  if __name__ == '__main__':
    tf.test.main()