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TensorRT Yu-Net

Description

This sample contains code that performs TensorRT inference on Jetson.

  1. Download ONNX U^2-Net Model from PINTO_model_zoo.
  2. Convert ONNX Model to Serialize engine and inference on Jetson.

Reference

Environment

  • Jetson
    • JetPack 4.6

Download ONNX Model on your Jetson

Clone PINTO_model_zoo repository and download MIRNet model.

git clone https://github.com/PINTO0309/PINTO_model_zoo.git
cd PINTO_model_zoo/144_YuNet
./download.sh

Check trtexec

/usr/src/tensorrt/bin/trtexec --onnx=./saved_model/face_detection_yunet_120x160.onnx

Run Jetson Nano

Install dependency

Install pycuda.
See details:

sudo apt install python3-dev
pip3 install --user cython
pip3 install --global-option=build_ext --global-option="-I/usr/local/cuda/include" --global-option="-L/usr/local/cuda/lib64" pycuda

Clone this repository.

Clone repository.

cd ~
git clone https://github.com/NobuoTsukamoto/tensorrt-examples
cd tensorrt-examples
git submodule update --init --recursive

Convert to Serialize engine file.

Copy face_detection_yunet_120x160.onnx to tensorrt-examples/models.

cp ~/PINTO_model_zoo/saved_model/face_detection_yunet_120x160.onnx ~/tensorrt-examples/models/

cd ~/tensorrt-examples/python/utils
python3 convert_onnxgs2trt.py \
    --model /home/jetson/tensorrt-examples/models/face_detection_yunet_120x160.onnx \
    --output /home/jetson/tensorrt-examples/models/face_detection_yunet_120x160.trt \

Finally you can run the demo.

python3 trt_yunet_capture.py \
    --model ../../models/face_detection_yunet_120x160.trt