102 строки
4.4 KiB
Markdown
102 строки
4.4 KiB
Markdown
# ONNXRuntime-Extensions
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[](https://dev.azure.com/onnxruntime/onnxruntime/_build/latest?definitionId=213&branchName=main)
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## What's ONNXRuntime-Extensions
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Introduction: ONNXRuntime-Extensions is a library that extends the capability of the ONNX models and inference with ONNX Runtime, via ONNX Runtime Custom Operator ABIs. It includes a set of [ONNX Runtime Custom Operator](https://onnxruntime.ai/docs/reference/operators/add-custom-op.html) to support the common pre- and post-processing operators for vision, text, and nlp models. And it supports multiple languages and platforms, like Python on Windows/Linux/macOS, some mobile platforms like Android and iOS, and Web-Assembly etc. The basic workflow is to enhance a ONNX model firstly and then do the model inference with ONNX Runtime and ONNXRuntime-Extensions package.
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## Quickstart
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### **Python installation**
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```bash
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pip install onnxruntime-extensions
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````
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### **Nightly Build**
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#### <strong>on Windows</strong>
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```cmd
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pip install --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ onnxruntime-extensions
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```
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Please ensure that you have met the prerequisites of onnxruntime-extensions (e.g., onnx and onnxruntime) in your Python environment.
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#### <strong>on Linux/macOS</strong>
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Please make sure the compiler toolkit like gcc(later than g++ 8.0) or clang are installed before the following command
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```bash
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python -m pip install git+https://github.com/microsoft/onnxruntime-extensions.git
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```
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## Usage
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## 1. Generation of Pre-/Post-Processing ONNX Model
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The `onnxruntime-extensions` Python package provides a convenient way to generate the ONNX processing graph. This can be achieved by converting the Huggingface transformer data processing classes into the desired format. For more detailed information, please refer to the API below:
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```python
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help(onnxruntime_extensions.gen_processing_models)
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```
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### NOTE:
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The generation of model processing requires the **ONNX** package to be installed. The data processing models generated in this manner can be merged with other models using the [onnx.compose](https://onnx.ai/onnx/api/compose.html) if needed.
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## 2. Using Extensions for ONNX Runtime inference
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### Python
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There are individual packages for the following languages, please install it for the build.
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```python
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import onnxruntime as _ort
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from onnxruntime_extensions import get_library_path as _lib_path
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so = _ort.SessionOptions()
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so.register_custom_ops_library(_lib_path())
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# Run the ONNXRuntime Session, as ONNXRuntime docs suggested.
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# sess = _ort.InferenceSession(model, so)
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# sess.run (...)
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```
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### C++
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```c++
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// The line loads the customop library into ONNXRuntime engine to load the ONNX model with the custom op
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Ort::ThrowOnError(Ort::GetApi().RegisterCustomOpsLibrary((OrtSessionOptions*)session_options, custom_op_library_filename, &handle));
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// The regular ONNXRuntime invoking to run the model.
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Ort::Session session(env, model_uri, session_options);
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RunSession(session, inputs, outputs);
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```
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### Java
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```java
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var env = OrtEnvironment.getEnvironment();
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var sess_opt = new OrtSession.SessionOptions();
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/* Register the custom ops from onnxruntime-extensions */
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sess_opt.registerCustomOpLibrary(OrtxPackage.getLibraryPath());
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```
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### C#
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```C#
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SessionOptions options = new SessionOptions()
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options.RegisterOrtExtensions()
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session = new InferenceSession(model, options)
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```
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## Contributing
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This project welcomes contributions and suggestions. Most contributions require you to agree to a
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
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the rights to use your contribution. For details, visit https://cla.microsoft.com.
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When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
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a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
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provided by the bot. You will only need to do this once across all repos using our CLA.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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## License
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[MIT License](LICENSE)
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