1

我知道没有多少专家可以帮助解决这个问题，但我们在尝试将 XGBoost ML 模型转换为 ONNX ML 模型时遇到了一些麻烦。

当使用单一输入类型进行转换时，一切似乎都很好，但是当使用多种类型时。

我收到一个错误，提示只有一个输入类型。

您是否有一个示例（python 语句），其中使用 onnxmltools（具有多个 TensorTypes）转换 xgboost/another 模型。

例如：

onnxmltools.convert_xgboost(xgb_reg, initial_types=[
('input', FloatTensorType([1, 2])),
('another_input', Int64TensorType([1, 1]))
])

上面的语句产生错误 有没有人有一个关于如何处理多种输入类型的例子？

RuntimeError                              Traceback (most recent call last)
<ipython-input-196-4ad3856a5ad3> in <module>()
      8 xgb_reg.predict(X_heter)
      9 
---> 10onnxmltools.convert_xgboost(xgb_reg, initial_types=[('input', FloatTensorType([1, 2])),('another_input', Int64TensorType([1, 1]))])
/opt/anaconda3/lib/python3.6/site-packages/onnxmltools/convert/main.py in convert_xgboost(*args, **kwargs)
     83     if not utils.keras2onnx_installed():
     84         raise RuntimeError('keras2onnx is not installed. Please install it to use this feature.')
---> 85 
     86     from keras2onnx import convert_tensorflow as convert
     87     return convert(frozen_graph_def, name, input_names, output_names, doc_string,
/opt/anaconda3/lib/python3.6/site-packages/onnxmltools/convert/xgboost/convert.py in convert(model, name, initial_types, doc_string, target_opset, targeted_onnx, custom_conversion_functions, custom_shape_calculators)
     44     return onnx_model
/opt/anaconda3/lib/python3.6/site-packages/onnxconverter_common/topology.py in compile(self)
    676         self._resolve_duplicates()
    677         self._fix_shapes()
--> 678self._infer_all_types()
    679         self._check_structure()
    680 
/opt/anaconda3/lib/python3.6/site-packages/onnxconverter_common/topology.py in _infer_all_types(self)
    551                 pass  # in Keras converter, the shape calculator can be optional.
    552             else:
--> 553operator.infer_types()
    554 
    555     def _resolve_duplicates(self):
/opt/anaconda3/lib/python3.6/site-packages/onnxconverter_common/topology.py in infer_types(self)
    105     def infer_types(self):
    106         # Invoke a core inference function
--> 107get_shape_calculator(self.type)(self)
    108 
    109 
/opt/anaconda3/lib/python3.6/site-packages/onnxconverter_common/shape_calculator.py in calculate_linear_regressor_output_shapes(operator)
     68     shape may be [N, 1].
     69     '''
---> 70check_input_and_output_numbers(operator, input_count_range=1, output_count_range=1)
     71 
     72     N = operator.inputs[0].type.shape[0]
/opt/anaconda3/lib/python3.6/site-packages/onnxconverter_common/utils.py in check_input_and_output_numbers(operator, input_count_range, output_count_range)
    283         raise RuntimeError(
    284             'For operator %s (type: %s), at most %s input(s) is(are) supported but we got %s output(s) which are %s'
--> 285             % (operator.full_name, operator.type, max_input_count, len(operator.inputs), operator.input_full_names))
    286 
    287     if min_output_count is not None and len(operator.outputs) < min_output_count:
RuntimeError: For operator XGBRegressor (type: XGBRegressor), at most 1 input(s) is(are) supported but we got 2 output(s) which are ['input', 'another_input']
4

1 回答 1

0

是的，如果所有输入都是数字类型，那么这就是一个问题，浮点数或整数都没有关系。一种解决方法是将所有都视为浮点数并创建单个输入节点。它将是 mxn 大小的矩阵输入。

于 2020-09-03T21:14:17.343 回答