下面是调用模型进行批量测试的代码(出现溢出)，开始以为导致溢出的原因是数据读入方式问题引起的，用了tf , PIL和cv等方式读入图片数据，发现越来越慢，内存占用飙升，调试时发现是sess.run这里出了问题（随着for循环进行速度越来越慢）。

  # Creates graph from saved GraphDef
  create_graph(pb_path)
 
  # Init tf Session
  config = tf.ConfigProto()
  config.gpu_options.allow_growth = True
  sess = tf.Session(config=config)
  init = tf.global_variables_initializer()
  sess.run(init)
 
 
  input_image_tensor = sess.graph.get_tensor_by_name("create_inputs/batch:0") 
  output_tensor_name = sess.graph.get_tensor_by_name("conv6/out_1:0") 
 
 
  for filename in os.listdir(image_dir):
    image_path = os.path.join(image_dir, filename)
 
    start = time.time()
    image_data = cv2.imread(image_path)
    image_data = cv2.resize(image_data, (w, h))
    image_data_1 = image_data - IMG_MEAN
    input_image = np.expand_dims(image_data_1, 0)
 
    raw_output_up = tf.image.resize_bilinear(output_tensor_name, size=[h, w], align_corners=True) 
    raw_output_up = tf.argmax(raw_output_up, axis=3)
    
 
    predict_img = sess.run(raw_output_up, feed_dict={input_image_tensor: input_image})    # 1，height，width
    predict_img = np.squeeze(predict_img)   # height， width 
 
    voc_palette = visual.make_palette(3)
    masked_im = visual.vis_seg(image_data, predict_img, voc_palette)
    cv2.imwrite("%s_pred.png" % (save_dir + filename.split(".")[0]), masked_im)
 
 
    print(time.time() - start)
 
  print(">>>>>>Done")

下面是解决溢出问题的代码（将部分代码放在for循环外）

  # Creates graph from saved GraphDef
  create_graph(pb_path)
 
  # Init tf Session
  config = tf.ConfigProto()
  config.gpu_options.allow_growth = True
  sess = tf.Session(config=config)
  init = tf.global_variables_initializer()
  sess.run(init)
 
  input_image_tensor = sess.graph.get_tensor_by_name("create_inputs/batch:0") 
  output_tensor_name = sess.graph.get_tensor_by_name("conv6/out_1:0") 
  
##############################################################################################################
  raw_output_up = tf.image.resize_bilinear(output_tensor_name, size=[h, w], align_corners=True) 
  raw_output_up = tf.argmax(raw_output_up, axis=3)
##############################################################################################################
 
  for filename in os.listdir(image_dir):
    image_path = os.path.join(image_dir, filename)
 
    start = time.time()
    image_data = cv2.imread(image_path)
    image_data = cv2.resize(image_data, (w, h))
    image_data_1 = image_data - IMG_MEAN
    input_image = np.expand_dims(image_data_1, 0)
    
    predict_img = sess.run(raw_output_up, feed_dict={input_image_tensor: input_image})    # 1，height，width
    predict_img = np.squeeze(predict_img)   # height， width 
 
    voc_palette = visual.make_palette(3)
    masked_im = visual.vis_seg(image_data, predict_img, voc_palette)
    cv2.imwrite("%s_pred.png" % (save_dir + filename.split(".")[0]), masked_im)
    print(time.time() - start)
 
  print(">>>>>>Done")