下面是調用模型進行批量測試的代碼(出現溢出)，開始以為導致溢出的原因是數據讀入方式問題引起的，用了tf , PIL和cv等方式讀入圖片數據，發現越來越慢，內存占用飆升，調試時發現是sess.run這里出了問題（隨著for循環進行速度越來越慢）。

  # 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")

下面是解決溢出問題的代碼（將部分代碼放在for循環外）

  # 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")