text: Title: why the time for encoding face embedding is so long?
  Body: I rewrite the compare.py to check the time for  facenet's face embedding encoding, but to my surprise,the time is above 60ms on my Geforce RTX2070 card,I also check the time for ArcFace,it only use 10 ms; I also found when my check program was running, the GPU load report by GPU-Z is only about 25%,it was clear the GPU's power is not fully  utilized, so why the time for facenet's embedding encoding is so long? why GPU's power can not be fully  utilized?
  
  below is my code to check the time rewrite in compare.py:
     
  def main(args):
  
      images = load_and_align_data(args.image_files, args.image_size, args.margin, args.gpu_memory_fraction)
      with tf.Graph().as_default():
  
          with tf.Session() as sess:
        
              # Load the model
              facenet.load_model(args.model)
      
              # Get input and output tensors
              images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
              embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
              phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
  
              # Run forward pass to calculate embeddings
              feed_dict = { images_placeholder: images, phase_train_placeholder:False }
              emb = sess.run(embeddings, feed_dict=feed_dict)
              
              # Check embedding encode time
              testTimes = 100
              tCount = 0
              for t in range(1,testTimes+1):
                  t0 = time.time()
                  sess.run(embeddings,feed_dict=feed_dict)
                  t1 = time.time()
                  print("Test",t," time=",(t1-t0)*1000.0,"ms")
                  tCount += t1-t0
              avgTime = tCount/testTimes * 1000.0
              print("AvgRefTime=",avgTime, "ms")
  