亲宝软件园·资讯

展开

Tensorflow sess.run内存溢出 解决Tensorflow sess.run导致的内存溢出问题

hustwayne 人气:0

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")

总结:

在迭代过程中, 在sess.run的for循环中不要加入tensorflow一些op操作,会增加图节点,否则随着迭代的进行,tf的图会越来越大,最终导致溢出;

建议不要使用tf.gfile.FastGFile(image_path, 'rb').read()读入数据(有可能会造成溢出),用opencv之类读取。

以上这篇解决Tensoflow sess.run导致的内存溢出问题就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。

您可能感兴趣的文章:

加载全部内容

相关教程
猜你喜欢
用户评论