xpi-nn-infer 0.0.1

A lightweight toolbox for NN model convert and infer on ARM SoC devices like RaspberryPi, OrangePi, LubanCat, etc.

xpi-nn-infer

A lightweight toolbox for NN model convert and infer on ARM SoC devices like RaspberryPi, OrangePi, LubanCat, etc.

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Installation

You can either install from PyPI:

# create venv (optional but recommended!!)
conda create -n xpi python==3.13    # py3.10 or later
conda activate xpi
# install framework
pip install xpi-nn-infer
# install default backends
python -m xpi_nn_infer.tools.install_default_backends -y

or install locally:

# create venv (optional but recommended!!)
conda create -n xpi python==3.13    # py3.10 or later
conda activate xpi
# clone this repo
git clone https://gitee.com/kahsolt/xpi-nn-infer.git
cd xpi-nn-infer
# install locally
pip install -e .
# install default backends
python -m xpi_nn_infer.tools.install_default_backends -y

Usage

⚪ Use via command line

# benchmark with random inputs
python -m xpi_nn_infer.tools.benchmark_torchvision -K torch
python -m xpi_nn_infer.tools.benchmark_torchvision -K onnx     -M mobilenet_v3_small
python -m xpi_nn_infer.tools.benchmark_torchvision -K openvino -M mobilenet_v3_large              # cls
python -m xpi_nn_infer.tools.benchmark_torchvision -K openvino -M lraspp_mobilenet_v3_large       # seg
python -m xpi_nn_infer.tools.benchmark_torchvision -K openvino -M ssdlite320_mobilenet_v3_large   # det (but not supported yet :(

# infer from random inputs, image file or folder
python -m xpi_nn_infer.tools.infer_torchvision -K openvino -M resnet18 -I random
python -m xpi_nn_infer.tools.infer_torchvision -K openvino -M resnet18 -I path/to/your/image.jpg
python -m xpi_nn_infer.tools.infer_torchvision -K openvino -M resnet18 -I path/to/your/image_folder

⚪ Use via API

ℹ TODO! TODO!! TODO!!!

Configurations

ℹ xpi-nn-infer is a bare framework, you need configure backends to make it work.

Envvars

• MODEL_PATH: folder path for auto-downloaded or converted model checkpoints, defaults to <site-packages>/xpi_nn_infer/MODEL_PATH (PyPI install) or <xpi-nn-infer>/MODEL_PATH (local install)

Model providers

Thanks to all the open-source model providers 🎉

name	supported	comment
torchvision	√
ultralytics
paddleocr
transformers
diffusers
modelscope

NN backends

name	supported	comment
torch	√
tensorflow
tflite
paddle
paddlelite
onnx	√
openvino	√
ncnn
mnn
mace

IO backends (⚠ Work In Progress!!)

| name | supported | type | comment | | :-: | :-: | :-: | | pillow | √ | img | | | skimage | | img | | | imageio | | img | | | torchvision | | img | | | cv2 | | img/cam | | | rpicam | | cam | | | ffmpy | | vid | | | moviepy | | vid | | | wave | | aud | | | soundfile | | aud | | | sounddevice | | mic | | | pyaudio | | aud/mic | | | pydub | | aud | | | librosa | | aud | | | scipy | | aud | |

Supported Devices

• BCM2837: RaspberryPi 3B
• H618: OrangePi Zero 3
• RK3576: LubanCat3
• RK3399: FMX1 Pro, MRK3399