6.4. TopsIDEAS onnx fold_constant

描述

onnx模型常量折叠，来源于onnxsim中的常量折叠方法。

命令行

使用方法

usage: topsideas onnx fold_constant [-h] --input_onnx INPUT_ONNX [--output_onnx OUTPUT_ONNX] [--check_n CHECK_N] [--input_shape INPUT_SHAPE [INPUT_SHAPE ...]]
                                    [--input-data-path INPUT_DATA_PATH [INPUT_DATA_PATH ...]] [--skip-shape-inference] [--dynamic-input-shape]

参数

:::{table} topsideas onnx fold_constant 参数列表
:widths: 8 32 20 45

short	long	default	help
-h	--help		show this help message and exit
	--input_onnx	None	Provide the original onnx file.
	--output_onnx	fold.onnx	Export the modified onnx file.
	--check_n	3	Check whether the output is correct with n random inputs.
	--input_shape	[]	Overwrite input shapes if not set –dynamic-input-shape, otherwise used for generating random inputs in checking. Format: –input_shape name:shape. For example: –input_shape input1:[1,3,224,224] input2:[4] input3:[]. If omitted, uses the current model inputs.
	--input-data-path	None	input data, The value should be ‘input_name1:xxx1.bin’ ‘input_name2:xxx2.bin …’, input data should be a binary data file.
	--skip-shape-inference		Skip shape inference. Shape inference causes segfault on some large models.
	--dynamic-input-shape		This option enables dynamic input shape support.’Shape’ ops will not be eliminated in this case.Note that ‘–input_shape’ is also needed for generating random inputs and checking equality. If ‘dynamic_input_shape’ is False, the input shape in simplified model will be overwritten by the value of ‘input_shapes’ param.

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示例

topsideas onnx fold_constant --input_onnx=demo.onnx --output_onnx=demo_fold.onnx  

:::{figure-md}

onnx常量折叠示例 :::

API

使用方法

from topsideas.onnx import FoldConstant
result = FoldConstant.run(input_mp)

参数

:::{table} topsideas.onnx.FoldConstant 参数列表
:widths: 25 40 35

argument	type	default
input_mp	onnx.onnx_ml_pb2.ModelProto
check_n	int	3
dynamic_input_shape	bool	None
input_shape	str	None
skip_shape_inference	bool	False
custom_lib	str	None
input_data_path	str	None
RETURN	onnx.onnx_ml_pb2.ModelProto

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