Simple ONNX constant encoder/decoder.
Project description
sed4onnx
Simple ONNX constant encoder/decoder.
https://github.com/PINTO0309/simple-onnx-processing-tools
Key concept
- Since the constant values in the JSON files generated by onnx2json are Base64-encoded values, ASCII <-> Base64 conversion is required when rewriting JSON constant values.
- After writing the converted Base64 strings to JSON using this tool, json2onnx can be used to regenerate the constant-modified ONNX file.
1. Setup
1-1. HostPC
### option
$ echo export PATH="~/.local/bin:$PATH" >> ~/.bashrc \
&& source ~/.bashrc
### run
$ pip install -U sed4onnx
1-2. Docker
https://github.com/PINTO0309/simple-onnx-processing-tools#docker
2. CLI Usage
$ sed4onnx -h
usage:
sed4onnx [-h]
--constant_string CONSTANT_STRING
[--dtype {float32,float64,uint8,int8,int32,int64}]
[--mode {encode,decode}]
optional arguments:
-h, --help
show this help message and exit.
--constant_string CONSTANT_STRING
Strings to be encoded and decoded for ONNX constants.
--dtype {float32,float64,uint8,int8,int32,int64}
Data type.
--mode {encode,decode}
encode: Converts the string specified in constant_string to a Base64 format string
that can be embedded in ONNX constants.
decode: Converts a Base64 string specified in constant_string to ASCII like Numpy string.
3. In-script Usage
>>> from sed4onnx import encode
>>> from sed4onnx import decode
>>> help(encode)
Help on function encode in module sed4onnx.onnx_constant_encoder_decoder:
encode(constant_string: str) -> str
Parameters
----------
constant_string: str
ASCII string to be encoded.
Returns
-------
encoded_string: str
Base64-encoded ASCII string.
>>> help(decode)
Help on function decode in module sed4onnx.onnx_constant_encoder_decoder:
decode(constant_string: str, dtype: str) -> numpy.ndarray
decode
Parameters
----------
constant_string: str
Base64 string to be decoded.
dtype: str
'float32' or 'float64' or 'uint8' or 'int8' or 'int32' or 'int64'
Returns
-------
decoded_ndarray: np.ndarray
Base64-decoded numpy.ndarray.
4. CLI Execution
$ sed4onnx \
--constant_string [-1,3,224,224] \
--mode encode
$ sed4onnx \
--constant_string '//////////8DAAAAAAAAAOAAAAAAAAAA4AAAAAAAAAA=' \
--dtype int64 \
--mode decode
5. In-script Execution
from sed4onnx import encode
from sed4onnx import decode
base64_string = encode(
constant_string='[-1,3,224,224]',
)
numpy_ndarray = decode(
constant_string='//////////8DAAAAAAAAAOAAAAAAAAAA4AAAAAAAAAA=',
dtype='int64',
)
6. Sample
$ sed4onnx \
--constant_string [-1,3,224,224] \
--mode encode
//////////8DAAAAAAAAAOAAAAAAAAAA4AAAAAAAAAA=
$ sed4onnx \
--constant_string '//////////8DAAAAAAAAAOAAAAAAAAAA4AAAAAAAAAA=' \
--dtype int64 \
--mode decode
[-1,3,224,224]
7. Reference
- https://github.com/onnx/onnx/blob/main/docs/Operators.md
- https://docs.nvidia.com/deeplearning/tensorrt/onnx-graphsurgeon/docs/index.html
- https://github.com/NVIDIA/TensorRT/tree/main/tools/onnx-graphsurgeon
- https://github.com/PINTO0309/simple-onnx-processing-tools
- https://github.com/PINTO0309/PINTO_model_zoo
8. Issues
https://github.com/PINTO0309/simple-onnx-processing-tools/issues
Project details
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