mmpose/projects/rtmpose/examples/onnxruntime

RTMPose inference with ONNXRuntime

This example shows how to run RTMPose inference with ONNXRuntime in Python.

Prerequisites

1. Install onnxruntime inference engine.

Choose one of the following ways to install onnxruntime.

• CPU version

wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-1.8.1.tgz
tar -zxvf onnxruntime-linux-x64-1.8.1.tgz
export ONNXRUNTIME_DIR=$(pwd)/onnxruntime-linux-x64-1.8.1
export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH

• GPU version

pip install onnxruntime-gpu==1.8.1
wget https://github.com/microsoft/onnxruntime/releases/download/v1.8.1/onnxruntime-linux-x64-gpu-1.8.1.tgz
tar -zxvf onnxruntime-linux-x64-gpu-1.8.1.tgz
export ONNXRUNTIME_DIR=$(pwd)/onnxruntime-linux-x64-gpu-1.8.1
export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH

2. Convert model to onnx files

• Install mim tool.

pip install -U openmim

• Download mmpose model.

# choose one rtmpose model
mim download mmpose --config rtmpose-m_8xb64-270e_coco-wholebody-256x192 --dest .

• Clone mmdeploy repo.

git clone https://github.com/open-mmlab/mmdeploy.git

• Convert model to onnx files.

python mmdeploy/tools/deploy.py \
    mmdeploy/configs/mmpose/pose-detection_simcc_onnxruntime_dynamic.py \
    mmpose/rtmpose-m_8xb64-270e_coco-wholebody-256x192.py \
    mmpose/rtmpose-m_simcc-coco-wholebody_pt-aic-coco_270e-256x192-cd5e845c_20230123.pth \
    mmdeploy/demo/resources/human-pose.jpg \
    --work-dir mmdeploy_model/mmpose/ort \
    --device cuda \
    --dump-info

Run demo

Install dependencies

pip install -r requirements.txt

Usage:

python main.py \
    {ONNX_FILE} \
    {IMAGE_FILE} \
    --device {DEVICE} \
    --save-path {SAVE_PATH}

Description of all arguments

• ONNX_FILE: The path of onnx file
• IMAGE_FILE: The path of image file
• DEVICE: The device to run the model, default is cpu
• SAVE_PATH: The path to save the output image, default is output.jpg