conda create -n spconv2 python=3.8
conda activate spconv2
pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install numpy==1.19.5 protobuf==3.19.4 scikit-image==0.19.2 waymo-open-dataset-tf-2-5-0 nuscenes-devkit==1.0.5 spconv-cu111 numba scipy pyyaml easydict fire tqdm shapely matplotlib opencv-python addict pyquaternion awscli open3d pandas future pybind11 tensorboardX tensorboard Cython prefetch-generatorEnvironment we tested:
Ubuntu 18.04
Python 3.8.13
PyTorch 1.8.1
Numba 0.53.1
Spconv 2.1.22 # pip install spconv-cu111
NVIDIA CUDA 11.1
4x 3090 GPUs
TACO currently supports the following datasets:
- Oxford Radar RobotCar
- [NuScenes]
- [KITTI-360]
Organize the dataset directories as follows:
- (QE)Oxford
data_root
├── 2019-01-11-14-02-26-radar-oxford-10k
│ ├── velodyne_left
│ │ ├── xxx.bin
│ │ ├── xxx.bin
│ │ ├── …
│ ├── id_label
│ │ ├── xxx.txt
│ │ ├── xxx.txt
│ │ ├── …
│ ├── label_mot
│ │ ├── xxx.txt
│ │ ├── xxx.txt
│ │ ├── …
│ ├── label_m
│ │ ├── xxx.txt
│ │ ├── xxx.txt
│ │ ├── …
│ ├── velodyne_left_calibrateFalse.h5
│ ├── velodyne_left_False.h5
│ ├── rot_tr.bin
│ ├── tr.bin
│ ├── tr_add_mean.bin
├── …
├── (QE)Oxford_pose_stats.txt
├── train_split.txt
├── valid_split.txt
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --master_addr 127.0.0.34 --master_port 29503 train_ddp.py
python Eval_Loc.py
python Eval_Det.py
%## 🌟 Visualization
%## 🤗 Model zoo
%## 🙏 Acknowledgements