An Open Neural Network Exchange (ONNX) Optimization and Transformation Tool.
Project description
ONNXifier
A simple tool to convert any IR format to ONNX file.
| Framework | Status |
|---|---|
| OpenVINO | ✅ |
| ONNXRuntime | ✅ |
- ✅: well supported
- 🪛: partially supported
- 🚧: developing
Usage
- Install from PyPI
pip install onnxifier
- Convert IR using CLI
onnxify model.xml
usage: onnxify input_model.xml [output_model.onnx]
onnxify command-line api
options:
-h, --help show this help message and exit
-a [ACTIVATE ...], --activate [ACTIVATE ...]
select passes to be activated, activate L1, L2 and L3 passes if not set.
-r [REMOVE ...], --remove [REMOVE ...]
specify passes to be removed from activated passes.
-n, --no-passes do not run any optimizing passes, just convert the model
--print [PRINT] print the name of all optimizing passes
--format {protobuf,textproto,json,onnxtxt}
onnx file format
-s, --infer-shapes infer model shapes
-c CONFIG_FILE, --config-file CONFIG_FILE
specify a json-format config file for passes
-u, --uncheck no checking output model
--check check optimized model with random inputs
--checker-backend {onnx,openvino,onnxruntime}
backend for accuracy checking, defaults to openvino
-v OPSET_VERSION, --opset-version OPSET_VERSION
target opset version, defaults to 19
-vv [{DEBUG,INFO,WARNING,ERROR,CRITICAL}], --log-level [{DEBUG,INFO,WARNING,ERROR,CRITICAL}]
specify the level of log messages to be printed, defaults to INFO
-R, --recursive recursively optimize nested functions
--nodes [NODES ...] specify a set of node names to apply passes only on these nodes
To print pass information:
onnxify --print all
onnxify --print fuse_swish
onnxify --print l1
TODO
Contribute
- pyright type checking
pip install -U pyright
pyright onnxifier
- mypy type checking
pip install -U mypy
mypy onnxifier --disable-error-code=import-untyped --disable-error=override --disable-error=call-overload
- pre-commit checking
pip install -U pre-commit
pre-commit run --all-files
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