TensorFlow加载含custom_objects模型报unknown opcode错误如何解决

复现手写文本识别方向相关论文的Easter2模型时遇到模型加载问题：完成模型训练后，加载模型执行数据预测任务时触发SystemError: unknown opcode报错，调用模型加载接口时已传入custom_objects参数。

• Python 3.6.0
• TensorFlow 1.15.0

checkpoint = tensorflow.keras.models.load_model(
    checkpoint_path,
    custom_objects={'<lambda>': lambda x, y: y,
                    'tf': tf}
)

File "E:/OCR_model/2022-5_Improving convolutional models for handwritten text recognition/Easter2-main/src/my_test.py", line 15, in <module>
    test_on_iam(show=False, partition="validation", checkpoint=checkpoint_path, uncased=True)
  File "E:\OCR_model\2022-5_Improving convolutional models for handwritten text recognition\Easter2-main\src\predict.py", line 72, in test_on_iam
    model = load_easter_model(checkpoint)
  File "E:\OCR_model\2022-5_Improving convolutional models for handwritten text recognition\Easter2-main\src\predict.py", line 33, in load_easter_model
    'tf': tf}
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\saving\save.py", line 143, in load_model
    return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\saving\hdf5_format.py", line 162, in load_model_from_hdf5
    custom_objects=custom_objects)
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\saving\model_config.py", line 55, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\layers\serialization.py", line 105, in deserialize
    printable_module_name='layer')
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\utils\generic_utils.py", line 191, in deserialize_keras_object
    list(custom_objects.items())))
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 1081, in from_config
    process_node(layer, node_data)
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\engine\network.py", line 1039, in process_node
    layer(input_tensors, **kwargs)
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\engine\base_layer.py", line 854, in __call__
    outputs = call_fn(cast_inputs, *args, **kwargs)
  File "F:\Anaconda3_4_2_0\install\envs\py36_tf1_15\lib\site-packages\tensorflow_core\python\keras\layers\core.py", line 789, in call
    return self.function(inputs, **arguments)
  File "E:/OCR_model/2022-5_Improving convolutional models for handwritten text recognition/Easter2-main/src/easter_model.py", line 31, in ctc_custom
    y_pred, labels, input_length, label_length = args
SystemError: unknown opcode

这个报错本质是Python字节码解析失败，结合现有环境和代码，按优先级排查解决：

• 首先修复环境版本问题：把当前Python 3.6.0升级到3.6系列最终维护版3.6.13。3.6.0是Python3.6的初始发布版，opcode定义和TensorFlow1.15编译依赖的字节码版本存在兼容偏差，是触发这类报错的常见环境诱因。同时必须保证模型训练、加载两个环节的Python、TensorFlow版本完全一致，跨版本（哪怕是同大版本下的小版本差）存储加载Keras H5模型，都可能因为字节码规则不匹配触发这个错误。
• 补全custom_objects里的自定义对象注册：当前注册列表只写了匿名lambda和tf，完全没有注册模型中实际用到的自定义CTC损失函数ctc_custom，模型反序列化到对应Lambda层时找不到正确的函数引用，就会触发字节码解析错误。修改加载代码，显式导入并注册所有自定义对象，不要用匿名lambda占位：

# 先从模型定义文件导入自己实现的ctc_custom损失
from easter_model import ctc_custom
import tensorflow as tf

checkpoint = tf.keras.models.load_model(
    checkpoint_path,
    custom_objects={
        'ctc_custom': ctc_custom,
        'tf': tf
    }
)

• 如果上述两步操作后仍报错，直接绕开整模型加载的逻辑：在预测脚本中先手动复现和训练阶段完全一致的Easter2模型结构，再调用model.load_weights(checkpoint_path)仅加载模型权重参数。这种方式完全跳过H5文件存储的模型结构、自定义函数字节码的反序列化过程，从根源上避免opcode解析错误，是TensorFlow1.x阶段加载自定义Keras模型兼容性最高的方案。

内容的提问来源于stack exchange，提问作者xiaobaixuejishu