Tensorflow DecodeError:运行Graph.ParseFromString（）时截断了消息

我从tensorflow/tensorflow/image_retaining/retain.py下载了代码，并根据需要对模型进行了一些修改（如查找训练图像文件夹的路径以及保存模型和标签的位置等）。在运行retain.py文件时，我在执行结束时收到以下消息

An exception has occurred, use %tb to see the full traceback.

SystemExit

使用%tb查看堆栈跟踪时，我得到

Traceback (most recent call last):

  File "<ipython-input-11-06ad74d82e7c>", line 1, in <module>
    runfile('C:/Users/Srikanth1.R/Desktop/Desktop/My_Folder/Inage analytics/hub-master/examples/image_retraining/retrain.py', wdir='C:/Users/Srikanth1.R/Desktop/Desktop/My_Folder/Inage analytics/hub-master/examples/image_retraining')

  File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/Srikanth1.R/Desktop/Desktop/My_Folder/Inage analytics/hub-master/examples/image_retraining/retrain.py", line 2424, in <module>
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

  File "C:\Users\Srikanth1.R\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\platform\app.py", line 134, in run

SystemExit

上述解码错误在某种程度上是否与我在运行retain.py时遇到的错误有关

或者这两个错误是独立的

谁能告诉我如何解决上述错误

---

提前感谢您？

您也可以使用下面提到的方法进行预测

with tf.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(
        sess, [tf.saved_model.tag_constants.SERVING], <path for .pb file>) 
    sess.run(...)

确保模型_文件应为冻结图形。 有关更多详细信息，请参阅链接

def load_graph(model_file):
  graph = tf.Graph()
  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)

  return graph


def read_tensor_from_image_file(file_name,
                                input_height=299,
                                input_width=299,
                                input_mean=0,
                                input_std=255):
  input_name = "file_reader"
  output_name = "normalized"
  file_reader = tf.read_file(file_name, input_name)
  if file_name.endswith(".png"):
    image_reader = tf.image.decode_png(
        file_reader, channels=3, name="png_reader")
  elif file_name.endswith(".gif"):
    image_reader = tf.squeeze(
        tf.image.decode_gif(file_reader, name="gif_reader"))
  elif file_name.endswith(".bmp"):
    image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")
  else:
    image_reader = tf.image.decode_jpeg(
        file_reader, channels=3, name="jpeg_reader")
  float_caster = tf.cast(image_reader, tf.float32)
  dims_expander = tf.expand_dims(float_caster, 0)
  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
  sess = tf.Session()
  result = sess.run(normalized)

  return result


def load_labels(label_file):
  label = []
  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
  for l in proto_as_ascii_lines:
    label.append(l.rstrip())
  return label


if __name__ == "__main__":
  file_name = "C:\\Users\\Srikanth1.R\\Desktop\\Car Images\\car.jpg"
  model_file = "C:\\Users\\Srikanth1.R\\Desktop\\Desktop\\My_Folder\\Inage analytics\\hub-master\\examples\\image_retraining\\tmp\\saved_model\\saved_model.pb"
  label_file = "C:\\Users\\Srikanth1.R\\Desktop\\Desktop\\My_Folder\\Inage analytics\\hub-master\\examples\\image_retraining\\tmp\\output_labels.txt"
  input_height = 299
  input_width = 299
  input_mean = 0
  input_std = 255
  input_layer = "input"
  output_layer = "InceptionV3/Predictions/Reshape_1"

  parser = argparse.ArgumentParser()
  parser.add_argument("--image", help="image to be processed")
  parser.add_argument("--graph", help="graph/model to be executed")
  parser.add_argument("--labels", help="name of file containing labels")
  parser.add_argument("--input_height", type=int, help="input height")
  parser.add_argument("--input_width", type=int, help="input width")
  parser.add_argument("--input_mean", type=int, help="input mean")
  parser.add_argument("--input_std", type=int, help="input std")
  parser.add_argument("--input_layer", help="name of input layer")
  parser.add_argument("--output_layer", help="name of output layer")
  args = parser.parse_args()

  if args.graph:
    model_file = args.graph
  if args.image:
    file_name = args.image
  if args.labels:
    label_file = args.labels
  if args.input_height:
    input_height = args.input_height
  if args.input_width:
    input_width = args.input_width
  if args.input_mean:
    input_mean = args.input_mean
  if args.input_std:
    input_std = args.input_std
  if args.input_layer:
    input_layer = args.input_layer
  if args.output_layer:
    output_layer = args.output_layer

  graph = load_graph(model_file)
  t = read_tensor_from_image_file(
      file_name,
      input_height=input_height,
      input_width=input_width,
      input_mean=input_mean,
      input_std=input_std)

  input_name = "import/" + input_layer
  output_name = "import/" + output_layer
  input_operation = graph.get_operation_by_name(input_name)
  output_operation = graph.get_operation_by_name(output_name)

  with tf.Session(graph=graph) as sess:
    results = sess.run(output_operation.outputs[0], {
        input_operation.outputs[0]: t
    })
  results = np.squeeze(results)

  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(label_file)
  for i in top_k:
    print(labels[i], results[i])

with tf.Session(graph=tf.Graph()) as sess:
    tf.saved_model.loader.load(
        sess, [tf.saved_model.tag_constants.SERVING], <path for .pb file>) 
    sess.run(...)

def load_graph(model_file):
  graph = tf.Graph()
  graph_def = tf.GraphDef()

  with open(model_file, "rb") as f:
    graph_def.ParseFromString(f.read())
  with graph.as_default():
    tf.import_graph_def(graph_def)

  return graph