Pointpillar_tensorrt

pointpillar mmdeteion3d model Tensorrt deployment, improved and faster

This repository contains sources and model for PointPillars inference using TensorRT. The model is created with mmdetection3d.

Overall inference has five phases:

• Convert points cloud into 4-channle voxels
• Extend 4-channel voxels to 10-channel voxel features
• Run pfe TensorRT engine to get 64-channel voxel features
• Run rpn backbone TensorRT engine to get 3D-detection raw data
• Parse bounding box, class type and direction

Model && Data

The demo use the waymo data from Waymo Open Dataset. The onnx file can be converted by onnx_tools If you want use my onnx transform code,you need to git clone mmdetection3d v1.0.0rc0(https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0rc0).

Prerequisites

To build the pointpillars inference, TensorRT and CUDA are needed.

Environments

• NVIDIA RTX 3060 && RTX 3070ti
• in TensorRT 8.2.3 && TensorRT 8.6 && TensorRT 7.2.1

Compile && Run

$ mkdir build && cd build
$ cmake .. && make 
$ cd ..
$ ./ApolloPP

Visualization

You should install open3d in python environment.

$ cp -r ./Pointpillar_tensorrt/data/waymo_pcd ./Pointpillar_tensorrt/tools/pcd_bbox_display/waymo_pcd
$ cp -r ./Pointpillar_tensorrt/data/bbox ./Pointpillar_tensorrt/tools/pcd_bbox_display/bbox
$ cd tools/pcd_bbox_display
$ python read_apollo.py

trt fp16	pytorch

Performance in RTX3070ti of FP16

| Function(unit:ms) | NVIDIA RTX 3070ti Laptop GPU |
| ----------------- | --------------------------- |
| Preprocess        | 0.611175 ms                 |
| AnchorMask        | 0.30804  ms                 |
| Pfe               | 5.21747  ms                 |
| Scatter           | 0.089236 ms                 |
| Backbone          | 8.74011  ms                 |
| Postprocess       | 0.324388 ms                 |
| Summary           | 15.2936  ms                 |

Note

• The waymo pretrained model in this project is trained only using 4-channel (x, y, z, i), which is different from the mmdetection3d pretrained_model.
• The demo will cache the onnx file to improve performance. If a new onnx will be used, please remove the cache file in "./model".
• The improvement point of this article is to perform the anchor operation as post-processing, and only export the backbone to the convolution output as an onnx model.
• Deployment environment： docker run --gpus all -it --name env_pyt_1.12 -v $(pwd):/app nvcr.io/nvidia/pytorch:22.03-py3 opencv install:apt install libopencv-dev yaml install:apt-get update&& apt-get install libyaml-cpp-dev

References

• PointPillars: Fast Encoders for Object Detection from Point Clouds
• mmdetection3d
• MMDet3d-PointPillars