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    解决Tensorflow sess.run导致的内存溢出问题

    栏目:代码类 时间:2020-02-05 12:06

    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")