import tensorflow as tf

            nrof_preprocess_threads = 4
            image_size = (args.image_size, args.image_size)
            eval_input_queue = data_flow_ops.FIFOQueue(capacity=2000000,
                                        dtypes=[tf.string, tf.int32, tf.int32],
                                        shapes=[(1,), (1,), (1,)],
                                        shared_name=None, name=None)
            eval_enqueue_op = eval_input_queue.enqueue_many([image_paths_placeholder, labels_placeholder, control_placeholder], name='eval_enqueue_op')
            image_batch, label_batch = facenet.create_input_pipeline(eval_input_queue, image_size, nrof_preprocess_threads, batch_size_placeholder)

            # Load the model
            input_map = {'image_batch': image_batch, 'label_batch': label_batch, 'phase_train': phase_train_placeholder}
            facenet.load_model(args.model, input_map=input_map)

            # Get output tensor
            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
#
            coord = tf.train.Coordinator()
            tf.train.start_queue_runners(coord=coord, sess=sess)

            evaluate(sess, eval_enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder,
                embeddings, label_batch, paths, actual_issame, args.lfw_batch_size, args.lfw_nrof_folds, args.distance_metric, args.subtract_mean,
                args.use_flipped_images, args.use_fixed_image_standardization, args.warmup_steps, args.max_steps)


def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder, control_placeholder,
        embeddings, labels, image_paths, actual_issame, batch_size, nrof_folds, distance_metric, subtract_mean, use_flipped_images, use_fixed_image_standardization,
        warmup_steps, max_steps):
    # Run forward pass to calculate embeddings
