def testNoBatchNormScaleByDefault(self):
        height, width = 299, 299
        num_classes = 1000
        inputs = tf.placeholder(tf.float32, (1, height, width, 3))
        with tf.contrib.slim.arg_scope(
                inception.inception_resnet_v2_arg_scope()):
            inception.inception_resnet_v2(inputs,
                                          num_classes,
                                          is_training=False)

        self.assertEqual(tf.global_variables('.*/BatchNorm/gamma:0$'), [])
    def testBatchNormScale(self):
        height, width = 299, 299
        num_classes = 1000
        inputs = tf.placeholder(tf.float32, (1, height, width, 3))
        with tf.contrib.slim.arg_scope(
                inception.inception_resnet_v2_arg_scope(
                    batch_norm_scale=True)):
            inception.inception_resnet_v2(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 testVariablesSetDevice(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = 1000
     with self.test_session():
         inputs = tf.random_uniform((batch_size, height, width, 3))
         # Force all Variables to reside on the device.
         with tf.variable_scope('on_cpu'), tf.device('/cpu:0'):
             inception.inception_resnet_v2(inputs, num_classes)
         with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
             inception.inception_resnet_v2(inputs, num_classes)
         for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
                                    scope='on_cpu'):
             self.assertDeviceEqual(v.device, '/cpu:0')
         for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
                                    scope='on_gpu'):
             self.assertDeviceEqual(v.device, '/gpu:0')
 def testTrainEvalWithReuse(self):
     train_batch_size = 5
     eval_batch_size = 2
     height, width = 150, 150
     num_classes = 1000
     with self.test_session() as sess:
         train_inputs = tf.random_uniform(
             (train_batch_size, height, width, 3))
         inception.inception_resnet_v2(train_inputs, num_classes)
         eval_inputs = tf.random_uniform(
             (eval_batch_size, height, width, 3))
         logits, _ = inception.inception_resnet_v2(eval_inputs,
                                                   num_classes,
                                                   is_training=False,
                                                   reuse=True)
         predictions = tf.argmax(logits, 1)
         sess.run(tf.global_variables_initializer())
         output = sess.run(predictions)
         self.assertEqual(output.shape, (eval_batch_size, ))
 def testBuildNoClasses(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = None
     with self.test_session():
         inputs = tf.random_uniform((batch_size, height, width, 3))
         net, endpoints = inception.inception_resnet_v2(inputs, num_classes)
         self.assertTrue('AuxLogits' not in endpoints)
         self.assertTrue('Logits' not in endpoints)
         self.assertTrue(
             net.op.name.startswith('InceptionResnetV2/Logits/AvgPool'))
         self.assertListEqual(net.get_shape().as_list(),
                              [batch_size, 1, 1, 1536])
 def testBuildWithoutAuxLogits(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = 1000
     with self.test_session():
         inputs = tf.random_uniform((batch_size, height, width, 3))
         logits, endpoints = inception.inception_resnet_v2(
             inputs, num_classes, create_aux_logits=False)
         self.assertTrue('AuxLogits' not in endpoints)
         self.assertTrue(
             logits.op.name.startswith('InceptionResnetV2/Logits'))
         self.assertListEqual(logits.get_shape().as_list(),
                              [batch_size, num_classes])
 def testEvaluation(self):
     batch_size = 2
     height, width = 299, 299
     num_classes = 1000
     with self.test_session() as sess:
         eval_inputs = tf.random_uniform((batch_size, height, width, 3))
         logits, _ = inception.inception_resnet_v2(eval_inputs,
                                                   num_classes,
                                                   is_training=False)
         predictions = tf.argmax(logits, 1)
         sess.run(tf.global_variables_initializer())
         output = sess.run(predictions)
         self.assertEqual(output.shape, (batch_size, ))
 def testUnknownBatchSize(self):
     batch_size = 1
     height, width = 299, 299
     num_classes = 1000
     with self.test_session() as sess:
         inputs = tf.placeholder(tf.float32, (None, height, width, 3))
         logits, _ = inception.inception_resnet_v2(inputs, num_classes)
         self.assertTrue(
             logits.op.name.startswith('InceptionResnetV2/Logits'))
         self.assertListEqual(logits.get_shape().as_list(),
                              [None, num_classes])
         images = tf.random_uniform((batch_size, height, width, 3))
         sess.run(tf.global_variables_initializer())
         output = sess.run(logits, {inputs: images.eval()})
         self.assertEqual(output.shape, (batch_size, num_classes))
 def testGlobalPool(self):
     batch_size = 1
     height, width = 330, 400
     num_classes = 1000
     with self.test_session():
         inputs = tf.random_uniform((batch_size, height, width, 3))
         logits, end_points = inception.inception_resnet_v2(
             inputs, num_classes)
         self.assertTrue(
             logits.op.name.startswith('InceptionResnetV2/Logits'))
         self.assertListEqual(logits.get_shape().as_list(),
                              [batch_size, num_classes])
         pre_pool = end_points['Conv2d_7b_1x1']
         self.assertListEqual(pre_pool.get_shape().as_list(),
                              [batch_size, 8, 11, 1536])
 def testBuildEndPoints(self):
     batch_size = 5
     height, width = 299, 299
     num_classes = 1000
     with self.test_session():
         inputs = tf.random_uniform((batch_size, height, width, 3))
         _, end_points = inception.inception_resnet_v2(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])
         pre_pool = end_points['Conv2d_7b_1x1']
         self.assertListEqual(pre_pool.get_shape().as_list(),
                              [batch_size, 8, 8, 1536])
 def testGlobalPoolUnknownImageShape(self):
     batch_size = 1
     height, width = 330, 400
     num_classes = 1000
     with self.test_session() as sess:
         inputs = tf.placeholder(tf.float32, (batch_size, None, None, 3))
         logits, end_points = inception.inception_resnet_v2(
             inputs, num_classes, create_aux_logits=False)
         self.assertTrue(
             logits.op.name.startswith('InceptionResnetV2/Logits'))
         self.assertListEqual(logits.get_shape().as_list(),
                              [batch_size, num_classes])
         pre_pool = end_points['Conv2d_7b_1x1']
         images = tf.random_uniform((batch_size, height, width, 3))
         sess.run(tf.global_variables_initializer())
         logits_out, pre_pool_out = sess.run([logits, pre_pool],
                                             {inputs: images.eval()})
         self.assertTupleEqual(logits_out.shape, (batch_size, num_classes))
         self.assertTupleEqual(pre_pool_out.shape,
                               (batch_size, 8, 11, 1536))