VoxelFormer

This is the official implementation of the paper "VoxelFormer: Bird’s-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection"

Install

a. Create a conda virtual environment and activate it.

conda create --name VoxelFormer -y python=3.8
conda activate VoxelFormer
conda install -y pip

b. Install PyTorch and torchvision.

pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 -f https://download.pytorch.org/whl/torch_stable.html
# Recommended torch==1.7

c. Install mmcv-full.

pip install openmim
mim install mmcv-full==1.4.0

d. Install mmdet and mmseg.

pip install mmdet==2.24.1
pip install mmsegmentation==0.20.2

e. Install mmdet3d from source code.

git clone  https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
git checkout v0.17.1
pip install -e .
cd ..

f. Install Dual-View Attn from source code.

cd projects/mmdet3d_plugin/models/dv_attn
rm -rf build
python setup.py develop
cd ../../../..

g. Install other requirements.

pip install -r requirements.txt

h. Prepare pretrained models.

mkdir ckpts

Download the pre-trained resnet101 and vovnet in this folder. You can download them from resnet101 and vovnet.

Nuscenes

Download nuScenes V1.0 full dataset data HERE. Prepare nuscenes data by running

Prepare nuScenes data

python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag voxel_nuscenes

Using the above code will generate voxel_nuscenes_infos_{train,val}.pkl.

python tools/voxel_generate_sweep_pkl.py --split train
python tools/voxel_generate_sweep_pkl.py --split val
# python tools/voxel_generate_sweep_pkl.py --split test

Using the above code will generate voxel_nuscenes_temporal_infos_{train,val}.pkl.

(optional)

python tools/voxel_generate_demo_pkl.py --split train
python tools/voxel_generate_demo_pkl.py --split val
# python tools/voxel_generate_demo_pkl.py --split test

Using the above code will generate voxel_nuscenes_temporal_infos_{train,val}_demo.pkl.

Folder structure

VoxelFormer
├── projects/
├── tools/
├── configs/
├── ckpts/
│   ├── fcos3d-res101.pth
│   ├── fcos3d_vovnet_imgbackbone-remapped.pth
├── mmdetection3d/
├── data/
│   ├── nuscenes/
│   │   ├── maps/
│   │   ├── samples/
│   │   ├── sweeps/
│   │   ├── v1.0-test/
|   |   ├── v1.0-trainval/
|   |   ├── voxel_nuscenes_infos_trian.pkl
|   |   ├── voxel_nuscenes_infos_val.pkl
|   |   ├── voxel_nuscenes_temporal_infos_trian.pkl
|   |   ├── voxel_nuscenes_temporal_infos_val.pkl

Train We provide four config files for training in projects/configs/voxelformer/. You can train a model by:

bash tools/dist_train.sh $config_path $gpu_num $port --work-dir work_dirs/$exp_save_path

For inference, use:

bash tools/dist_test.sh $config_path $path_to_ckpt $gpu_nm --eval bbox

Lizhuoling/VoxelFormer-public

VoxelFormer

Install

Nuscenes