Export ONNX into Python#
Through OnnxInference#
The Python Runtime can be optimized by generating
custom python code and dynamically compile it.
OnnxInference
computes predictions based on an ONNX graph with a
python runtime or onnxruntime.
Method to_python
goes further by converting the ONNX graph into a standalone
python code. All operators may not be implemented.
External tools#
Another tool is implemented in onnx2py.py and converts an ONNX graph into a python code which produces this graph.
Export functions#
The following function converts an ONNX graph into Python code.
onnx API#
The python code creates the same exported onnx graph with onnx API.
mlprodict.onnx_tools.onnx_export.export2onnx (model_onnx, opset = None, verbose = True, name = None, rename = False, autopep_options = None)
Exports an ONNX model to the onnx syntax.
to numpy#
The python code creates a python function using numpy to produce the same results as the ONNX graph.
mlprodict.onnx_tools.onnx_export.export2numpy (model_onnx, opset = None, verbose = True, name = None, rename = False, autopep_options = None)
Exports an ONNX model to the numpy syntax. The exports does not work with all operators.
tf2onnx#
This function was used to write a converter for a function from tensorflow (RFFT). To speed up the development, the first step consisted into writing a numpy function equivalent to the tensorflow version. Then this function was converted into ONNX using the numpy API for ONNX. Finally, the ONNX graph was exported into a python code following tf2onnx API.
mlprodict.onnx_tools.onnx_export.export2tf2onnx (model_onnx, opset = None, verbose = True, name = None, rename = False, autopep_options = None)
Exports an ONNX model to the tensorflow-onnx syntax.