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Convert pointpillars Pytorch Model To ONNX for TensorRT Inference
| Date | Stars |
|---|---|
| 2026-07-24 | 408 |
| 2026-07-25 | 408 |
| 2026-07-28 | 408 |
| 2026-07-30 | 408 |
| 2026-08-06 | 408 |
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# PointPillars Pytorch Model Convert To ONNX, And Using TensorRT to Load this IR(ONNX) for Fast Speeding Inference Welcome to PointPillars(This is origin from nuTonomy/second.pytorch ReadMe.txt). This repo demonstrates how to reproduce the results from [_PointPillars: Fast Encoders for Object Detection from Point Clouds_](https://arxiv.org/abs/1812.05784) (to be published at CVPR 2019) on the [KITTI dataset](http://www.cvlibs.net/datasets/kitti/) by making the minimum required changes from the preexisting open source codebase [SECOND](https://github.com/traveller59/second.pytorch). Meanwhile, This part of the code also refers to the open source k0suke-murakami (https://github.com/k0suke-murakami/train_point_pillars) this code. This is not an official nuTonomy codebase, but it can be used to match the published PointPillars results. **WARNING: This code is not being actively maintained. This code can be used to reproduce the results in the first version of the paper, https://arxiv.org/abs/1812.05784v1. For an actively maintained repository that can also reproduce PointPillars results on nuScenes, we recommend using [SECOND](https://github.com/traveller59/second.pytorch). We are not the owners of the repository, but we have worked with the author and endorse his code.**  ## Getting Started This is a fork of [SECOND for KITTI object detection](https://github.com/traveller59/second.pytorch) and the relevant subset of the original README is reproduced here. ### Docker Environments If you do not waste time on pointpillars envs, please pull my docker virtual environments : ```bash docker pull smallmunich/suke_pointpillars:v1 ``` Attention: when you launch this docker envs, please run this command : ```bash conda activate pointpillars ``` And Then, you can run train or evaluation or onnx model generate command line. ### Install #### 1. Clone code ```bash git clone https://github.com/SmallMunich/nutonomy_pointpillars.git ``` #### 2. Install Python packages It is recommend to use the Anaconda package manager. First, use Anaconda to configure as many packages as possible. ```bash conda create -n pointpillars python=3.6 anaconda source activate pointpillars conda install shapely pybind11 protobuf scikit-image numba pillow conda install pytorch torchvision -c pytorch conda install google-sparsehash -c bioconda ``` Then use pip for the packages missing from Anaconda. ```bash pip install --upgrade pip pip install fire tensorboardX ``` Finally, install SparseConvNet. This is not required for PointPillars, but the general SECOND code base expects this to be correctly configured. However, I suggest you install the spconv instead of SparseConvNet. ```bash git clone [email protected]:facebookresearch/SparseConvNet.git cd SparseConvNet/ bash build.sh # NOTE: if bash build.sh fails, try bash develop.sh instead ``` Additionally, you may need to install Boost geometry: ```bash sudo apt-get install libboost-all-dev ``` #### 3. Setup cuda for numba You need to add following environment variables for numba to ~/.bashrc: ```bash export NUMBAPRO_CUDA_DRIVER=/usr/lib/x86_64-linux-gnu/libcuda.so export NUMBAPRO_NVVM=/usr/local/cuda/nvvm/lib64/libnvvm.so export NUMBAPRO_LIBDEVICE=/usr/local/cuda/nvvm/libdevice ``` #### 4. PYTHONPATH Add nutonomy_pointpillars/ to your PYTHONPATH. ```bash export PYTHONPATH=$PYTHONPATH:/your_root_path/nutonomy_pointpillars/ ``` ### Prepare dataset #### 1. Dataset preparation Download KITTI dataset and create some directories first: ```plain └── KITTI_DATASET_ROOT ├── training <-- 7481 train data | ├── image_2 <-- for visualization | ├── calib | ├── label_2 | ├── velodyne | └── velodyne_reduced <-- empty directory └── testing <-- 7580 test data ├── image_2 <-- for visualization ├──
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c613532f969b3240, topic:tensorrt, topic:onnx
matched fp:c613532f969b3240, topic:pytorch