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YOLACTv1 ONNX runtime with TRT execution provider

Prerequisites
  • Pytorch (tested with 1.7.0)
  • YOLACT requirements
    • pip3 install -r requirements.txt
  • onnxruntime with TRT execution provider
    • follow detailed instructions: https://github.com/microsoft/onnxruntime/blob/master/BUILD.md#tensorrt
    • one possible issue i see right away, that this build is tested with CUDA 11.0 and cuDNN 8.0. But Jetpack 4.4 supports CUDA 10.0.326 and cuDNN 7.6.3. There could be some trouble building with older versions.
    • i succeded with CUDA 11.1 and cuDNN 8.0
    • add --parallel for faster build
    • onnxruntime build command: ./build.sh --parallel --cudnn_home <path to cuDNN e.g. /usr/lib/x86_64-linux-gnu/> --cuda_home <path to folder for CUDA e.g. /usr/local/cuda-11.1> --use_tensorrt --tensorrt_home <path to TensorRT home>
      • if you get build error complaining about onnx-tensorrt package change return std::move(fields); to return fields; in builtin_op_importers.cpp
    • python wheel build command: ./build.sh --update --build --parallel --build_wheel --use_tensorrt --cuda_home /usr/local/cuda --cudnn_home /usr/lib/aarch64-linux-gnu --tensorrt_home /usr/lib/aarch64-linux-gnu
      • don't forget to change local paths :)
    • copy built python package to the yolact repo later or add <ONNXRuntimeSRCDirectory>/build/Linux/Debug/ to your PYTHONPATH env variable
      • export PYTHONPATH=<ONNXRuntimeSRCDirectory>/build/Linux/Debug:$PYTHONPATH
Execution
  • Clone this repo onnx branch
    • git clone -b onnx https://github.com/simutisernestas/yolact
  • Download model weights from original repo README https://github.com/dbolya/yolact
    • Tested only with Resnet50-FPN (yolact_resnet50_54_800000.pth)
  • Run export_onnx.sh bash script to export onnx model
    • chmod +x export_onnx.sh
    • ./export_onnx.sh
    • This will produce yolact.onnx file
  • Run lab.py script (loading of model will take some time)
    • python3 lab.py
  • If you get error: "Cuda out of memory"
  • Check results directory
Nvidia Jetson TX1/TX2/Nano/Xavier
  • Tested on JetPack SDK 4.4 with CUDA 10.2 and Pytorch 1.7.0
  • On Nano, follow this section of the onnxruntime instruction to build, this would take a while
  • Clone this repo to Nano
  • Download the pre exported .onnx here (exporting this model on Nano should take a long time)
  • Run the lab.py script

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YOLACT execution with TensorRT

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