SiamTracker-with-TensorRT

The modify PyTorch version of Siam-trackers which are speed-up by TensorRT or ONNX. [Updating...]
Examples demonstrating how to optimize PyTorch models with ONNX or TensorRT and do inference on NVIDIA Jetson platforms.

• All reported speeds contain pre-process process.

Dataset	Tracker	Platform	Origin Success(%)/Speed(fps)	TensorRT-FP32 Success(%)/Speed(fps)	TensorRT-FP16 Success(%)/Speed(fps)	ONNX Success(%)/Speed(fps)
UAV123	LightTrack	Jetson AGX Xavier	62.6/15~	61.7/38~	61.3/42~	61.6/36~

• For fixed image size of 256x256, the performance of ONNX and TensorRT model is not equal to PyTorch model which input size is either of 288x288 or 256x256. If the input size is same, they are equal.

Installation

Prerequisites

The code in this repository was tested on Jetson AGX Xavier DevKits. In order to go futher, first make sure you have the proper version of image (JetPack) installed on the target Jetson system. If not, you can refer to this: Set up Jetson Xavier family , we use JetPack4.6 in this repo.

• More specifically, the target Jetson system must have ONNX and TensorRT libraries installed. Check which version of TensorRT has been installed on your Jetson system:

  sudo -H pip3 install jetson-stats
  jetson_release -v

• Install ONNX

  sudo apt-get install protobuf-compiler libprotoc-dev
  pip3 install onnx

• Install ONNX Runtime. Jetson_Zoo has provided pre-build version on Jetson systems, you can download proper version on this website, more details refer to Set up Jetson Xavier family.

  # Download pip wheel from location mentioned above
  $ wget https://nvidia.box.com/s/bfs688apyvor4eo8sf3y1oqtnarwafww -O onnxruntime_gpu-1.6.0-cp36-cp36m-linux_aarch64.whl
  # Install pip wheel
  $ pip3 install onnxruntime_gpu-1.6.0-cp36-cp36m-linux_aarch64.whl

• Install pycuda pip3 install pycuda

Convert PyTorch Model to ONNX

• In LightTrack repo, first modify the loaded PyTorch model path in LightTrack/tracking/lighttrack_onnx.py. To obtain ONNX model:

  python3 tracking/lighttrack_onnx.py

Build engine

• If the TensorRT libraries has been installed, trtexec exists in /usr/src/tensorrt/bin, then run:

  /usr/src/tensorrt/bin/trtexec --onnx=/path/to/your/onnx/model --saveEngine=LightTrack.trt --fp16

Test TensorRT model

• In LightTrack repo, modify engine path in lib/models/lighttrack_speed_trt.py, then run:

  python3 tracking/test_lighttrack_trt_uav.py --arch LightTrackM_Subnet --dataset UAV123 --stride 16 --even 0 --path_name back_04502514044521042540+cls_211000022+reg_100000111_ops_32

Evaluate performance

• Put the results folder under pysot, evaluation please refer to pysot.