深度学习-ubuntu18.04+RTX3080+cuda11.2+cudnn8.1.0下安装polarstream全纪录

１、安装

• 创建一个python3.7的虚拟环境

conda create --name polarstream python=3.7 

• 激活虚拟环境

source activate polarstream

以下操作均在虚拟环境中进行

• 安装与cuda和python版本对应的torch版本,参考https://blog.csdn.net/didadifish/article/details/127487635

pip install torch-1.8.1+cu111-cp37-cp37m-linux_x86_64.whl

• 安装与torch版本对应的torchvision版本,参考https://wenku.csdn.net/answer/638aee6ce78a11edbcb5fa163eeb3507

pip install torchvision==0.9.1

• 拉取polarstream并进入

git clone https://github.com/qchenclaire/polarstream
cd polarstream

• 安装依赖环境

pip install -r requirements.txt
requirements.txt的内容如下:
numba                                                                                                 
xlwt 
fire                                                                                                      
protobuf                                                                                                  
opencv-python                                                                                          
opencv-contrib-python                                                                                     
pybind11                                                                                                                                                                                                                                                                                                       easydict                                                                                                  
open3d-python                                                                                             
terminaltables                                                                                          
pytest-runner                                                                                             
addict                                                                                               
pycocotools                                                                                               
imagecorruptions                                                                                          
objgraph                                                                                                  
cachetools
descartes
jupyter
matplotlib 
motmetrics==1.1.3
numpy
pandas==0.24
Pillow==7.1.0 
pyquaternion==0.9.5 
scikit-learn     
Shapely
tqdm
pyyaml 
requests 
detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu111/torch1.8/index.html
/home/user/tools/torch_cluster-1.5.9-cp37-cp37m-linux_x86_64.whl
/home/user/tools/torch_scatter-2.0.6-cp37-cp37m-linux_x86_64.whl
/home/user/tools/torch_sparse-0.6.10-cp37-cp37m-linux_x86_64.whl
/home/user/tools/torch_spline_conv-1.2.1-cp37-cp37m-linux_x86_64.whl
torchgeometry

• 添加polarstream的路径到PYTHONPATH,PYTHONPATH是Python搜索路径,默认我们import的模块都会从PYTHONPATH里面寻找。参考https://blog.csdn.net/m0_48134027/article/details/122153674

export PYTHONPATH="${PYTHONPATH}:/home/user/catkin_ws/polarstream"

• 安装nuscenes-devkit

git clone https://github.com/tianweiy/nuscenes-devkit
#添加到python搜索路经
export PYTHONPATH="${PYTHONPATH}:/home/user/catkin_ws/nuscenes-devkit/python-sdk"

• 安装Cuda扩展

bash setup.sh 

• 安装APEX
  Note: apex build is also gpu-specific.

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

• 安装spconv2.x

pip install spconv-cu114

２、基于nuScenes数据集训练
2.1 数据准备

• 数据组织
  基于关键帧训练,数据集按以下结构组织,其中maps未使用,sweeps可以为空.
  
• 创建数据
  将/home/user/catkin_ws/polarstream/det3d/datasets/nuscenes/nusc_common.py中399行的print(cnt / (nsweeps - 1))注释
  将/home/mdj/catkin_ws/polarstream/tools/generate_instance_ids.py中62行改为info_path = ‘/home/user/data/nuSenses/v1.0-trainval01_blobs/infos_val_01sweeps_withvelo_filter_True.pkl’
  执行以下两条命令:

python tools/create_data.py nuscenes_data_prep --root_path=/home/user/data/nuSenses/v1.0-trainval01_blobs --version="v1.0-trainval" --nsweeps=1

生成文件infos_train_01sweeps_withvelo_filter_True.pkl与infos_val_01sweeps_withvelo_filter_True.pkl

python tools/generate_instance_ids.py

生成instance_all
2.2 单GPU训练
将/home/user/catkin_ws/polarstream/configs/nusc/pp/polarstream_det_n_seg_1_sector.py中第5行设为nsweeps = 1,第120行设为data_root = “/home/user/data/nuSenses/v1.0-trainval01_blobs”
执行

python tools/train.py /home/user/catkin_ws/polarstream/configs/nusc/pp/polarstream_det_n_seg_1_sector.py --work_dir /home/user/catkin_ws/polarstream

开始训练
如图所示为训练过程