My platform

• raspberry pi 3b
• 2022-04-04-raspios-bullseye-armhf-lite.img
• cpu: 4 core armv8, memory: 1G

Install ncnn

1. dependencies

$ python -m pip install onnx-simplifier

2. build ncnn

Just follow the ncnn official tutoral of build-for-linux to install ncnn. Following steps are all carried out on my raspberry pi:

step 1: install dependencies

$ sudo apt install build-essential git cmake libprotobuf-dev protobuf-compiler libopencv-dev

step 2: (optional) install vulkan

step 3: build
I am using commit 5725c028c0980efd, and I have not tested over other commits.

$ git clone https://github.com/Tencent/ncnn.git
$ cd ncnn
$ git reset --hard 5725c028c0980efd
$ git submodule update --init
$ mkdir -p build
$ cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=OFF -DNCNN_BUILD_TOOLS=ON -DCMAKE_TOOLCHAIN_FILE=../toolchains/pi3.toolchain.cmake ..
$ make -j2
$ make install 

Convert model, build and run the demo

1. convert pytorch model to ncnn model via onnx

On your training platform:

$ cd BiSeNet/
$ python tools/export_onnx.py --aux-mode eval --config configs/bisenetv2_city.py --weight-path /path/to/your/model.pth --outpath ./model_v2.onnx 
$ python -m onnxsim model_v2.onnx model_v2_sim.onnx

Then copy your model_v2_sim.onnx from training platform to raspberry device.

On raspberry device:

$ /path/to/ncnn/build/tools/onnx/onnx2ncnn model_v2_sim.onnx model_v2_sim.param model_v2_sim.bin
$ cd BiSeNet/ncnn/
$ mkdir -p models
$ mv model_v2_sim.param models/
$ mv model_v2_sim.bin models/

2. compile demo code

On raspberry device:

$ mkdir -p BiSeNet/ncnn/build
$ cd BiSeNet/ncnn/build
$ cmake .. -DNCNN_ROOT=/path/to/ncnn/build/install
$ make

3. run demo

./segment