onnxruntime.md

ONNX Runtime Support

Introduction of ONNX Runtime

ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. Check its github for more information.

Installation

Please note that only onnxruntime>=1.8.1 of CPU version on Linux platform is supported by now.

• Install ONNX Runtime python package

pip install onnxruntime==1.8.1

Build custom ops

Prerequisite

• Download onnxruntime-linux from ONNX Runtime releases, extract it, expose ONNXRUNTIME_DIR and finally add the lib path to LD_LIBRARY_PATH as below:

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
cd onnxruntime-linux-x64-1.8.1
export ONNXRUNTIME_DIR=$(pwd)
export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH

Note:

• If you want to save onnxruntime env variables to bashrc, you could run

  echo '# set env for onnxruntime' >> ~/.bashrc
  echo "export ONNXRUNTIME_DIR=${ONNXRUNTIME_DIR}" >> ~/.bashrc
  echo 'export LD_LIBRARY_PATH=$ONNXRUNTIME_DIR/lib:$LD_LIBRARY_PATH' >> ~/.bashrc
  source ~/.bashrc

Build on Linux

cd ${MMDEPLOY_DIR} # To MMDeploy root directory
mkdir -p build && cd build
cmake -DMMDEPLOY_TARGET_BACKENDS=ort -DONNXRUNTIME_DIR=${ONNXRUNTIME_DIR} ..
make -j$(nproc)

How to convert a model

• You could follow the instructions of tutorial How to convert model

How to add a new custom op

Reminder

• The custom operator is not included in supported operator list in ONNX Runtime.
• The custom operator should be able to be exported to ONNX.

Main procedures

Take custom operator roi_align for example.

1. Create a roi_align directory in ONNX Runtime source directory ${MMDEPLOY_DIR}/csrc/backend_ops/onnxruntime/
2. Add header and source file into roi_align directory ${MMDEPLOY_DIR}/csrc/backend_ops/onnxruntime/roi_align/
3. Add unit test into tests/test_ops/test_ops.py Check here for examples.

Finally, welcome to send us PR of adding custom operators for ONNX Runtime in MMDeploy. 🤓

References

• How to export Pytorch model with custom op to ONNX and run it in ONNX Runtime
• How to add a custom operator/kernel in ONNX Runtime