本工程旨将rangenet工程部署到TensorRT8，ubuntu20.04中
- 最近正在进行大幅度地改动，本仓库暂时不能很稳定地使用~
- 由于使用了较新的API，本工程只适用于TensorRT8.2.3，但可自行查文档修改相应的API
- 使用过conda环境的torch，然后发现速度会相对较慢(6ms->30ms)
更新的依赖和API
- 将代码部署环境提升到TensorRT8, ubuntu20.04
- 提供docker环境
- 移除boost库
- 使用智能指针管理tensorrt对象和GPU显存的内存回收
- 提供ros例程
更快的运行速度
- 去掉0均值1方差的数据预处理，重新训练模型（毕竟已经有BN层了）
- fix: 每次运行的结果不一样...（就很迷）
├── build
├── devel
├── logs
└── src
└── RangeNetTrt8
├── CMakeLists.txt
├── CMakeLists_v2.txt
├── darknet53
├── docker
├── example
├── include
├── launch
├── LICENSE
├── ops
├── package.xml
├── pics
├── README.md
├── rosbag
├── script
├── src
└── utils
-
nvidia driver
-
创建工作空间
$ git clone https://github.com/Natsu-Akatsuki/RangeNetTrt8 ~/docker_ws/RangeNetTrt8/src- 下载onnx模型
$ wget -c https://www.ipb.uni-bonn.de/html/projects/semantic_suma/darknet53.tar.gz -O ~/docker_ws/RangeNetTrt8/src/darknet53.tar.gz
$ cd ~/docker_ws/RangeNetTrt8/src && tar -xzvf darknet53.tar.gz- 拉取镜像（镜像大小约为20G，需预留足够的空间）
$ docker pull registry.cn-hangzhou.aliyuncs.com/gdut-iidcc/rangenet:1.0- 创建容器
$ cd ~/docker_ws/RanageNetTrt8/src
$ bash script/build_container_rangenet.sh
# 编译和执行
(container root) $ cd /docker_ws/RanageNetTrt8
# 编译前需加CMakelists_v2.txt改名字为CMakelists.txt
(container root) $ catkin_build
(container root) $ source devel/setup.bash
# dem01:
(container root) $ roslaunch rangenet_plusplus rangenet.launch
# 播放包（该模型仅适用于kitti数据集，需自行下载包文件和修改该launch文档）
(container root) $ roslaunch rangenet_plusplus rosbag.launch
# demo2:
# need modify the example/infer.yaml first
(container root) $ ./devel/lib/rangenet_plusplus/inferNOTE
首次运行生成TensorRT模型运行需要一段时间
-
ros1
-
nvidia driver
-
TensorRT 8.2.3（tar包下载）, cuda_11.4.r11.4, cudnn 8.2.4
-
apt package and python package
$ sudo apt install build-essential python3-dev python3-pip apt-utils git cmake libboost-all-dev libyaml-cpp-dev libopencv-dev python3-empy
$ pip install catkin_tools trollius numpy- 创建工作空间
$ git clone https://github.com/Natsu-Akatsuki/RangeNetTrt8 ~/RangeNetTrt8/src- 下载onnx模型
$ wget -c https://www.ipb.uni-bonn.de/html/projects/semantic_suma/darknet53.tar.gz -O ~/RangeNetTrt8/src/darknet53.tar.gz
$ cd ~/RangeNetTrt8/src && tar -xzvf darknet53.tar.gz- 下载libtorch
$ wget -c https://download.pytorch.org/libtorch/cu113/libtorch-cxx11-abi-shared-with-deps-1.10.2%2Bcu113.zip -O libtorch.zip
$ unzip libtorch.zipTIP: 需导入各种环境变量到~/.bashrc
# example
export PATH="/home/helios/.local/bin:$PATH"
CUDA_PATH=/usr/local/cuda/bin
TENSORRT_PATH=${HOME}/application/TensorRT-8.2.3.0/bin
CUDA_LIB_PATH=/usr/local/cuda/lib64
TENSORRT_LIB_PATH=${HOME}/application/TensorRT-8.2.3.0/lib
PYTORCH_LIB_PATH=${HOME}/application/libtorch/lib
export PATH=${PATH}:${CUDA_PATH}:${TENSORRT_PATH}
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${CUDA_LIB_PATH}:${TENSORRT_LIB_PATH}:${PYTORCH_LIB_PATH}- 修改CMakeLists：修改其中的TensorRT, libtorch等依赖库的路径
- 编译和执行
# 编译和执行
$ cd ~/RanageNetTrt8
$ catkin_build
$ source devel/setup.bash
# dem01:
$ roslaunch rangenet_plusplus rangenet.launch
# 播放包（该模型仅适用于kitti数据集，需自行下载包文件和修改该launch文档）
$ roslaunch rangenet_plusplus rosbag.launch
# demo2:
# need modify the example/infer.yaml first
$ ./devel/lib/rangenet_plusplus/infer
NOTE
首次运行生成TensorRT模型运行需要一段时间
- 模型解析出问题（查看是否下载的onnx模型是否完整，是否在解压缩时broken了）
[libprotobuf ERROR google/protobuf/text_format.cc:298] Error parsing text-format onnx2trt_onnx.ModelProto: 1:1: Invalid control characters encountered in text. [libprotobuf ERROR google/protobuf/text_format.cc:298] Error parsing text-format onnx2trt_onnx.ModelProto: 1:14: Message type "onnx2trt_onnx.ModelProto" has no field named "pytorch". Message type "onnx2trt_onnx.ModelProto" has no field named "pytorch"
If you use this library for any academic work, please cite the original paper.
@inproceedings{milioto2019iros,
author = {A. Milioto and I. Vizzo and J. Behley and C. Stachniss},
title = {{RangeNet++: Fast and Accurate LiDAR Semantic Segmentation}},
booktitle = {IEEE/RSJ Intl.~Conf.~on Intelligent Robots and Systems (IROS)},
year = 2019,
codeurl = {https://github.com/PRBonn/lidar-bonnetal},
videourl = {https://youtu.be/wuokg7MFZyU},
}
If you use SuMa++, please cite the corresponding paper:
@inproceedings{chen2019iros,
author = {X. Chen and A. Milioto and E. Palazzolo and P. Giguère and J. Behley and C. Stachniss},
title = {{SuMa++: Efficient LiDAR-based Semantic SLAM}},
booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
year = {2019},
codeurl = {https://github.com/PRBonn/semantic_suma/},
videourl = {https://youtu.be/uo3ZuLuFAzk},
}
Copyright 2019, Xieyuanli Chen, Andres Milioto, Jens Behley, Cyrill Stachniss, University of Bonn.
This project is free software made available under the MIT License. For details see the LICENSE file.
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