感兴趣区域特征聚合模块核心代码
class SimpleVoxel(nn.Module):
def __init__(self,
num_input_features=4,
use_norm=True,
num_filters=[32, 128],
with_distance=False,
name='VoxelFeatureExtractor'):
super(SimpleVoxel, self).__init__()
self.name = name
self.num_input_features = num_input_features
# features 是 N*K*3 的张量，跟 pointnet++ 的 sample 和 group 很像
# 它在 KITTILiDAR 类中就已经做过了处理
# num_voxels 是 N*1 的张量
def forward(self, features, num_voxels):
#return features
# features: [concated_num_points, num_voxel_size, 3(4)]
# num_voxels: [concated_num_points]
# points_mean 是 K 个近邻点的中心点位置，oncat
# points_mean
# 保存在 coordinate 变量中
points_mean = features[:, :, :self.num_input_features].sum(
dim=1, keepdim=False) / num_voxels.type_as(features).view(-1, 1)
return points_mean.contiguous()
# SimpleVoxel输出一个 N ∗ K ∗ 4 体素化点云， 4 代表点云xyz值和雷达强度项。 Ubuntu 16.04
Python 3.7
pytorch 1.4.0
torchvision 0.5.0
CUDA 10.0 python3.5+
opencv
shapely
mayavi
spconv (v1.0)1.基础安装
pip install opencv-python
pip install shapely
pip install mayavi
pip install scikit-image
pip install numba
pip install matplotlib
pip install Cython
pip install terminaltables
pip install tqdm
pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
pip install pybind11 2.spconv安装
spconv 安装过程涉及cmake安装，参考Ubuntu安装cmake
sudo apt-get install libboost-all-dev
git clone https://github.com/traveller59/spconv.git --recursive
cd spconv && git checkout 7342772
python setup.py bdist_wheel
cd ./dist && pip install *1.mmdet/ops中，编译 C++/CUDA模块
cd mmdet/ops/points_op
python setup.py build_ext --inplace
cd mmdet/ops/iou3d
python setup.py build_ext --inplace
cd mmdet/ops/pointnet2
python setup.py build_ext --inplace2.~/.bashrc中，加环境变量
export NUMBAPRO_CUDA_DRIVER=/usr/lib/x86_64-linux-gnu/libcuda.so
export NUMBAPRO_NVVM=/usr/local/cuda/nvvm/lib64/libnvvm.so
export NUMBAPR
89B2
O_LIBDEVICE=/usr/local/cuda/nvvm/libdevice
export LD_LIBRARY_PATH=/home/ch511/anaconda3/envs/sassd/lib/python3.7/site-packages/spconv;3.安装mmcv
pip install pytest-runner -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install mmcv -i https://pypi.tuna.tsinghua.edu.cn/simple4.安装mmdet
即将本地项目中的mmdet加入site-package中，使得项目可以直接调用。源码中没有setup.py，可以用使用mmdetection的setup.py（将requirement相关注释掉）。
python setup.py develop1.下载数据集
KITTI数据集官网下载如下数据：
- Velodyne point clouds (29 GB): input data to VoxelNet
- Training labels of object data set (5 MB): input label to VoxelNet
- Camera calibration matrices of object data set (16 MB): for visualization of predictions
- Left color images of object data set (12 GB): for visualization of predictions
2.整理KITTI数据集目录
注意需要创建空的velodyne_reduced文件夹，用来放置后续筛选得到的视锥体内的点云数据。
└── KITTI
├── training <-- training data
| ├── image_2
| ├── label_2
| ├── calib
| ├── velodyne
| └── velodyne_reduced # empty folder
└── testing <--- testing data
| ├── image_2
| ├── calib
| ├── velodyne
| └── velodyne_reduced # empty folder3.下载ImageSets
下载地址：https://xiaozhichen.github.io/files/mv3d/imagesets.tar.gz
放置位置：放在KITTI目录下
4.生成数据集
python tools/create_data.pyTrain Model with single GPU
python ./tools/train.py ./configs/car_cfg.pyEval Model with single GPU
python ./tools/test.py ./configs/car_cfg.py ./work_dir/checkpoint_epoch_90.pth