https://github.com/zpye/SimpleInfer

A simple neural network inference framework
https://github.com/zpye/SimpleInfer

ai-framework cpp deep-learning inference-engine neural-network xmake

Last synced: 10 months ago
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A simple neural network inference framework

• Host: GitHub
• URL: https://github.com/zpye/SimpleInfer
• Owner: zpye
• License: mit
• Created: 2023-03-17T17:31:35.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-08-01T03:00:20.000Z (over 2 years ago)
• Last Synced: 2024-10-24T13:58:58.127Z (about 1 year ago)
• Topics: ai-framework, cpp, deep-learning, inference-engine, neural-network, xmake
• Language: C++
• Homepage: https://github.com/zpye/SimpleInfer
• Size: 2.15 MB
• Stars: 22
• Watchers: 2
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# SimpleInfer ![](https://github.com/zpye/SimpleInfer/workflows/xmake/badge.svg)

SimpleInfer is a neural network inference framework based on [KuiperInfer](https://github.com/zjhellofss/KuiperInfer).

## Build

SimpleInfer uses [xmake](https://xmake.io/#/) to build library and tests.

```shell

git clone --recursive https://github.com/zpye/SimpleInfer.git

cd SimpleInfer

xmake config -a x64 -m release

xmake -w --all

```

## Run

After building successfully, run test-yolo to check.

```shell

xmake run test-yolo

```

## YOLO Result

Here are visualized results of YOLO detection.

![result_31.jpg](imgs/result_31.jpg)

![result_bus.jpg](imgs/result_bus.jpg)

![result_car.jpg](imgs/result_car.jpg)

![result_zidane.jpg](imgs/result_zidane.jpg)

## Working With Python

1. Set environment `PYTHON_ROOT` where `python` binary exists, pybind11 needs `${PYTHON_ROOT}/include` and `${PYTHON_ROOT}/libs` for compiling and linking.

2. Set `--build_python=true` after `xmake config` and build: 

```shell

xmake config -a x64 -m release --build_python=true

xmake -w --all

```

3. install package by pip:

```shell

pip install build/python/

```

4. run python test:

```shell

python test/test_python/test_model.py

```

## (Experimental) Halide Programming

Note: Only upsample nearest layer has an implementation of Halide.

1. Set environment `HALIDE_ROOT` for Halide installation path, using release packages from [https://github.com/halide/Halide/releases](https://github.com/halide/Halide/releases) is a good choice.

2. Set `--halide=true` after `xmake config` and build: 

```shell

xmake config -a x64 -m release --halide=true

xmake build halide_layers

xmake -w --all

```

## Reference

[KuiperInfer](https://github.com/zjhellofss/KuiperInfer) -> basic framework

[ncnn](https://github.com/Tencent/ncnn), [simpleocv](https://github.com/zpye/simpleocv) -> pnnx ir, simpleocv and mat-pixel operations

[Eigen](https://gitlab.com/libeigen/eigen), [tensorflow](https://github.com/tensorflow/tensorflow) -> Eigen tensor

[abseil](https://github.com/abseil/abseil-cpp) -> logging, string format operations

[CGraph](https://github.com/ChunelFeng/CGraph) -> graph based pipeline

[highway](https://github.com/google/highway), [Simd](https://github.com/ermig1979/Simd) -> SIMD, GEMM, Winograd, parallel

[benchmark](https://github.com/google/benchmark), [Catch2](https://github.com/catchorg/Catch2) -> benchmark and unit tests

[pybind11](https://github.com/pybind/pybind11) -> python bindings of c++

[stb](https://github.com/nothings/stb) -> image loader and image writer

[Halide](https://github.com/halide/Halide) -> Halide programming

[tmp](https://github.com/zjhellofss/tmp) -> pnnx models