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mmpose/projects/rtmpose/examples/onnxruntime/README_CN.md

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使用ONNXRuntime进行RTMPose推理

本示例展示了如何在Python中用ONNXRuntime推理RTMPose模型。

准备

1. 安装onnxruntime推理引擎.

选择以下方式之一来安装onnxruntime。

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

2. 将模型转换为onnx文件

  • 安装mim工具
pip install -U openmim
  • 下载mmpose模型
# choose one rtmpose model
mim download mmpose --config rtmpose-m_8xb64-270e_coco-wholebody-256x192 --dest .
  • 克隆mmdeploy仓库
git clone https://github.com/open-mmlab/mmdeploy.git
  • 将模型转换为onnx文件
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

运行

安装依赖

pip install -r requirements.txt

用法：

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

参数解释

  • ONNX_FILE: onnx文件的路径
  • IMAGE_FILE: 图像文件的路径
  • DEVICE: 运行模型的设备，默认为`cpu'
  • SAVE_PATH: 保存输出图像的路径，默认为 "output.jpg"