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我有一个在 tensorflow 1.15 中训练过的预训练 tensorflow 模型，现在我正试图用预训练的 ms-coco 对象检测模型替换它，ms-coco 运行没有任何问题，但只要我尝试替换所需的模型（我确实更新了标签映射字典）但我仍然面临以下错误。

 File "Object_Detector.py", line 43, in __init__
    od_graph_def.ParseFromString(serialized_graph)
  File "C:\Python\lib\site-packages\google\protobuf\message.py", line 185, in ParseFromString
    self.MergeFromString(serialized)
  File "C:\Python\lib\site-packages\google\protobuf\internal\python_message.py", line 1083, in MergeFromString
    if self._InternalParse(serialized, 0, length) != length:
  File "C:\Python\lib\site-packages\google\protobuf\internal\python_message.py", line 1120, in InternalParse
    pos = field_decoder(buffer, new_pos, end, self, field_dict)
  File "C:\Python\lib\site-packages\google\protobuf\internal\decoder.py", line 633, in DecodeField
    if value._InternalParse(buffer, pos, new_pos) != new_pos:
  File "C:\Python\lib\site-packages\google\protobuf\internal\python_message.py", line 1120, in InternalParse
    pos = field_decoder(buffer, new_pos, end, self, field_dict)
  File "C:\Python\lib\site-packages\google\protobuf\internal\decoder.py", line 612, in DecodeRepeatedField
    if value.add()._InternalParse(buffer, pos, new_pos) != new_pos:
  File "C:\Python\lib\site-packages\google\protobuf\internal\python_message.py", line 1120, in InternalParse
    pos = field_decoder(buffer, new_pos, end, self, field_dict)
  File "C:\Python\lib\site-packages\google\protobuf\internal\decoder.py", line 743, in DecodeMap
    if submsg._InternalParse(buffer, pos, new_pos) != new_pos:
  File "C:\Python\lib\site-packages\google\protobuf\internal\python_message.py", line 1109, in InternalParse
    new_pos = local_SkipField(buffer, new_pos, end, tag_bytes)
  File "C:\Python\lib\site-packages\google\protobuf\internal\decoder.py", line 850, in SkipField
    return WIRETYPE_TO_SKIPPER[wire_type](buffer, pos, end)
  File "C:\Python\lib\site-packages\google\protobuf\internal\decoder.py", line 799, in _SkipGroup
    new_pos = SkipField(buffer, pos, end, tag_bytes)
  File "C:\Python\lib\site-packages\google\protobuf\internal\decoder.py", line 850, in SkipField
    return WIRETYPE_TO_SKIPPER[wire_type](buffer, pos, end)
  File "C:\Python\lib\site-packages\google\protobuf\internal\decoder.py", line 814, in _SkipFixed32
    raise _DecodeError('Truncated message.')
google.protobuf.message.DecodeError: Truncated message.

这是代码

def __init__(self):
    self.Objkt_boxes = []
    os.chdir(cwd)
    detect_model_name = 'Pretrained_Model_SSD'
    PATH_TO_CKPT = detect_model_name + '/saved_model.pb' # saved_model.pb is to be replaced with frozen_inference_graph.pb       
    self.detection_graph = tf.Graph()
    with self.detection_graph.as_default():
        od_graph_def = tf.compat.v1.GraphDef()
        with tf.compat.v2.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')
        
        self.sess = tf.Session(graph=self.detection_graph)
        self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
        self.boxes = self.detection_graph.get_tensor_by_name('detection_boxes:0')
        self.scores =self.detection_graph.get_tensor_by_name('detection_scores:0')
        self.classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
        self.num_detections =self.detection_graph.get_tensor_by_name('num_detections:0')

saved_model.pb 的 label_map 文件

item {
  id: 1
  name: 'car'
}

item {
  id: 2
  name: 'pedestrian'
}

item {
  id: 3
  name: 'trafficLight-GreenLeft'
}

item {
  id: 4
  name: 'trafficLight-Green'
}

item {
  id: 5
  name: 'trafficLight-Red'
}

item {
  id: 6
  name: 'trafficLight-RedLeft'
}

item {
  id: 7
  name: 'trafficLight'
}

item {
  id: 8
  name: 'truck'
}

item {
  id: 9
  name: 'biker'
}

item {
  id: 10
  name: 'trafficLight-Yellow'
}
item {
  id: 11
  name: 'trafficLight-YellowLeft'
}

我怎样才能让这个模型与 tf2.7 一起工作？

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