def testTrainEvalWithReuse(self):
    train_batch_size = 5
    eval_batch_size = 2
    height, width = 224, 224
    num_classes = 1000

    train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
    inception.inception_v1(train_inputs, num_classes)
    eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
    logits, _ = inception.inception_v1(eval_inputs, num_classes, 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 testLogitsNotSqueezed(self):
    num_classes = 25
    images = tf.random_uniform([1, 224, 224, 3])
    logits, _ = inception.inception_v1(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 testBuildClassificationNetwork(self):
    batch_size = 5
    height, width = 224, 224
    num_classes = 1000

    inputs = tf.random_uniform((batch_size, height, width, 3))
    logits, end_points = inception.inception_v1(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
    self.assertListEqual(logits.get_shape().as_list(),
                         [batch_size, num_classes])
    self.assertTrue('Predictions' in end_points)
    self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
                         [batch_size, num_classes])
  def testEvaluation(self):
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000

    eval_inputs = tf.random_uniform((batch_size, height, width, 3))
    logits, _ = inception.inception_v1(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 = 224, 224
    num_classes = 1000

    inputs = tf.placeholder(tf.float32, (None, height, width, 3))
    logits, _ = inception.inception_v1(inputs, num_classes)
    self.assertTrue(logits.op.name.startswith('InceptionV1/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 = 224, 224
   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_v1(inputs, num_classes)
     self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
     self.assertListEqual(logits.get_shape().as_list(),
                          [batch_size, num_classes])
     pre_pool = end_points['Mixed_5c']
     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, 7, 7, 1024])