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OpenStereo Deployment Guide

English | 中文

Model Export

First, you need to convert the model checkpoint to a common IR (e.g., ONNX format). We provide the export.py script, which you can use to export a simplified ONNX model in the root directory of OpenStereo with the following command:

python deploy/export.py --config cfgs/psmnet/psmnet_kitti15.yaml --weights output/KittiDataset/PSMNet/psmnet_kitti15/default/ckpt/checkpoint_epoch_0.pth --device 0 --simplify --half --include onnx

If you want to deploy on NVIDIA devices using our C++ example code, you can add engine after the --include parameter to export TensorRT's IR:

python deploy/export.py --config cfgs/psmnet/psmnet_kitti15.yaml --weights output/KittiDataset/PSMNet/psmnet_kitti15/default/ckpt/checkpoint_epoch_0.pth --device 0 --simplify --half --include onnx engine

For more usage help, run:

python deploy/export.py -h

Performance Evaluation

We provide the trt_profile.sh script to evaluate the model's performance on the device:

bash deploy/trt_profile.sh --onnx output/KittiDataset/PSMNet/psmnet_kitti15/default/ckpt/checkpoint_epoch_0.onnx --fp16 --verbose
bash deploy/trt_profile.sh --loadEngine output/KittiDataset/PSMNet/psmnet_kitti15/default/ckpt/checkpoint_epoch_0.engine --fp16 --verbose

C++ Deployment Example

To run our C++ deployment example, you need to install some necessary third-party libraries:

apt-get update
apt-get install libyaml-cpp-dev libopencv-dev python3-opencv

Additionally, make sure you have installed the CUDA Toolkit and TensorRT:

Once ready, navigate to the deploy/cpp folder in the project root directory and run the following commands to build:

cd <OpenStereoROOT>/deploy/cpp
mkdir build && cd build

Build using the system library's TensorRT:

cmake .. && make

Or build using the downloaded TensorRT tar package:

cmake -DTENSORRT_ROOT=<path_to_tensorrt> .. && make

Remember specify your CUDA architecture

cmake -DTENSORRT_ROOT=<path_to_tensorrt> -DCMAKE_CUDA_ARCHITECTURES=<your_cuda_architecture> .. && make

Then, you can perform inference with the following command:

./main <cfg_path> <engine_path> <left_image_path> <right_image_path> <options>

The required header files and dynamic libraries for deployment are packaged in build/libopenstereo, which can be conveniently included in your project.

Note: When building with the TensorRT tar package, ensure that the LD_LIBRARY_PATH is set correctly. You can check if the TensorRT dynamic library is linked correctly with the following command:

ldd main | grep libnv*