Libonnx

A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.

Getting Started

The library's .c and .h files can be dropped into a project and compiled along with it. Before use, should be allocated struct onnx_context_t * and you can pass an array of struct resolver_t * for hardware acceleration.

The filename is path to the format of onnx model.

struct onnx_context_t * ctx = onnx_context_alloc_from_file(filename, NULL, 0);

Then, you can get input and output tensor using onnx_tensor_search function.

struct onnx_tensor_t * input = onnx_tensor_search(ctx, "input-tensor-name");
struct onnx_tensor_t * output = onnx_tensor_search(ctx, "output-tensor-name");

When the input tensor has been setting, you can run inference engine using onnx_run function and the result will putting into the output tensor.

onnx_run(ctx);

Finally, you must free struct onnx_context_t * using onnx_context_free function.

onnx_context_free(ctx);

Examples

Just type make at the root directory, you will see a static library and some binary of examples and tests for usage.

cd libonnx
make

Screenshots

• Mnist handwritten digit prediction

Notes

This library based on the onnx version 1.8.0 with the newest opset 13 support. The supported operator table in the documents directory.

Links

• The chinese discussion posts
• The onnx operators documentation
• The tutorials for creating ONNX models
• The pre-trained onnx models

License

This library is free software; you can redistribute it and or modify it under the terms of the MIT license. See MIT License for details.