def testNoBatchNormScaleByDefault(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with slim.arg_scope(inception.inception_v3_arg_scope()):
      inception.inception_v3(inputs, num_classes, is_training=False)

    self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])

  def testRaiseValueErrorWithInvalidDepthMultiplier(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    with self.assertRaises(ValueError):
      _ = inception.inception_v3(inputs, num_classes, depth_multiplier=-0.1)
    with self.assertRaises(ValueError):
      _ = inception.inception_v3(inputs, num_classes, depth_multiplier=0.0)

  def testBatchNormScale(self):
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.placeholder(tf.float32, (1, height, width, 3))
    with slim.arg_scope(
        inception.inception_v3_arg_scope(batch_norm_scale=True)):
      inception.inception_v3(inputs, num_classes, is_training=False)

    gamma_names = set(
        v.op.name for v in tf.global_variables('.*/BatchNorm/gamma:0$'))
    self.assertGreater(len(gamma_names), 0)
    for v in tf.global_variables('.*/BatchNorm/moving_mean:0$'):
      self.assertIn(v.op.name[:-len('moving_mean')] + 'gamma', gamma_names)

  def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 150, 150
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v3(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v3(eval_inputs, num_classes,
                                       is_training=False, reuse=True)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (eval_batch_size,))

  def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)

    endpoint_keys = [key for key in end_points.keys()
                     if key.startswith('Mixed') or key.startswith('Conv')]

    _, end_points_with_multiplier = inception.inception_v3(
        inputs, num_classes, scope='depth_multiplied_net',
        depth_multiplier=2.0)

    for key in endpoint_keys:
      original_depth = end_points[key].get_shape().as_list()[3]
      new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
      self.assertEqual(2.0 * original_depth, new_depth)

  def testBuildPreLogitsNetwork(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = None

    inputs = tf.random_uniform((batch_size, height, width, 3))
    net, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue(net.op.name.startswith('InceptionV3/Logits/AvgPool'))
    self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 2048])
    self.assertFalse('Logits' in end_points)
    self.assertFalse('Predictions' in end_points)

  def testLogitsNotSqueezed(self):
    num_classes = 25
    images = tf.random_uniform([1, 299, 299, 3])
    logits, _ = inception.inception_v3(images,
                                       num_classes=num_classes,
                                       spatial_squeeze=False)

    with self.test_session() as sess:
      tf.global_variables_initializer().run()
      logits_out = sess.run(logits)
      self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])

  def testHalfSizeImages(self):
    batch_size = 5
    height, width = 150, 150
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    logits, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    pre_pool = end_points['Mixed_7c']
    self.assertListEqual(pre_pool.get_shape().as_list(),
                         [batch_size, 3, 3, 2048])

  def testEvaluation(self):
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000

    eval_inputs = tf.random_uniform((batch_size, height, width, 3))
    logits, _ = inception.inception_v3(eval_inputs, num_classes,
                                       is_training=False)
    predictions = tf.argmax(logits, 1)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(predictions)
      self.assertEquals(output.shape, (batch_size,))

  def testUnknowBatchSize(self):
    batch_size = 1
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v3(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [None, num_classes])
    images = tf.random_uniform((batch_size, height, width, 3))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      output = sess.run(logits, {inputs: images.eval()})
      self.assertEquals(output.shape, (batch_size, num_classes))

 def testUnknownImageShape(self):
   tf.reset_default_graph()
   batch_size = 2
   height, width = 299, 299
   num_classes = 1000
   input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
   with self.test_session() as sess:
     inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
     logits, end_points = inception.inception_v3(inputs, num_classes)
     self.assertListEqual(logits.get_shape().as_list(),
                          [batch_size, num_classes])
     pre_pool = end_points['Mixed_7c']
     feed_dict = {inputs: input_np}
     tf.global_variables_initializer().run()
     pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
     self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])

def main():
    """
    You can also run these commands manually to generate the pb file
    1. git clone https://github.com/tensorflow/models.git
    2. export PYTHONPATH=Path_to_your_model_folder
    3. python alexnet.py
    """
    tf.set_random_seed(1)
    height, width = 299, 299
    num_classes = 1000
    inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name='input')
    inputs = tf.identity(inputs, "input_node")
    net, end_points  = inception.inception_v3(inputs, num_classes,is_training=False)
    print("nodes in the graph")
    for n in end_points:
        print(n + " => " + str(end_points[n]))
    net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
    run_model(net_outputs, argv[1], 'InceptionV3', argv[3] == 'True')

  def testBuildEndPoints(self):
    batch_size = 5
    height, width = 299, 299
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    _, end_points = inception.inception_v3(inputs, num_classes)
    self.assertTrue('Logits' in end_points)
    logits = end_points['Logits']
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('AuxLogits' in end_points)
    aux_logits = end_points['AuxLogits']
    self.assertListEqual(aux_logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('Mixed_7c' in end_points)
    pre_pool = end_points['Mixed_7c']
    self.assertListEqual(pre_pool.get_shape().as_list(),
                         [batch_size, 8, 8, 2048])
    self.assertTrue('PreLogits' in end_points)
    pre_logits = end_points['PreLogits']
    self.assertListEqual(pre_logits.get_shape().as_list(),
                         [batch_size, 1, 1, 2048])

 def create(self, images, num_classes, is_training):
   """See baseclass."""
   with slim.arg_scope(inception.inception_v3_arg_scope()):
     _, endpoints = inception.inception_v3(
         images, num_classes, create_aux_logits=False, is_training=is_training)
     return endpoints