• Ubuntu

  Install Ubuntu on your RK3588 device. (tested on Ubuntu 20.04 and OrangePi5/Firefly ROC RK3588S devices)

  For installing Ubuntu on Firefly you can use their manual[1][2].

  For installing Ubuntu on OrangePi you can use their manual.

  Or use ours README's for them (select the one below).

  OrangePi	Firefly

Install miniconda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh
bash Miniconda3-latest-Linux-aarch64.sh

Then rerun terminal session:

source ~/.bashrc

Create conda env with python3.8

conda create -n <env-name> python=3.8
conda activate <env-name>

Clone repository:

git clone https://github.com/Applied-Deep-Learning-Lab/Keypoints_HRNet_RK3588 
cd Keypoints_HRNet_RK3588

Install RKNN-Toolkit2-Lite

pip install install/rknn_toolkit_lite2-1.5.0-cp38-cp38-linux_aarch64.whl

In created conda enviroment also install requirements from the same directory

pip install -r install/requirements.txt

main.py runs inference like:

python3 main.py weights/human_pose.rknn \
                images/human.jpg

Inference results are saved to the ./results folder by default.

• Preparation Host PC

  For training model we use MMPose by OpenMMLab.

  Step 0. You will also need conda on the host PC.

  conda create -n openmmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
  conda activate openmmlab
  

  Step 1. Install MMCV using MIM.

  sudo apt-get update
  pip3 install -U openmim
  mim install mmcv-full==1.7.0
  

  Step 2. Install MMPose.

  git clone --depth 1 --branch v0.29.0 https://github.com/open-mmlab/mmpose.git
  cd mmpose
  pip install -r requirements.txt
  pip install -v -e .
  pip install numpy==1.23.5
  

• Convert pytorch to onnx

  Inside mmpose folder and conda 'openmmlab' environment:

  python tools/deployment/pytorch2onnx.py <path/to/config.py> \
    				<path/to/model.pth> \
    				--output-file <path/to/model.onnx> \
    				--shape 1 3 <model_size> <model_size>
  

  Example:

  mim download mmpose --config associative_embedding_hrnet_w32_coco_512x512  --dest .
  python tools/deployment/pytorch2onnx.py associative_embedding_hrnet_w32_coco_512x512.py \
    				hrnet_w32_coco_512x512-bcb8c247_20200816.pth \
    				--output-file human_pose.onnx \
    				--shape 1 3 512 512
  

• Convert onnx to rknn

  Step 1. Create conda environment

  conda create -n rknn python=3.8
  conda activate rknn
  

  Step 2. Install RKNN-Toolkit2

  git clone https://github.com/Applied-Deep-Learning-Lab/Keypoints_HRNet_RK3588
  cd Keypoints_HRNet_RK3588
  pip install install/rknn_toolkit2-1.5.0+1fa95b5c-cp38-cp38-linux_x86_64.whl
  

  Step 3. For convert your .onnx model to .rknn run onnx2rknn.py like:

  python onnx2rknn.py <path/to/model.onnx>
  
  # For more precise conversion settings, 
  # check the additional options in the help:
  # python onnx2rknn.py -h
  

  Example:

  python onnx2rknn.py human_pose.onnx