README.md

1. Installation

1.1. Docker Installation

docker pull yanglei2024/op-bevheight:base
cd V2X-Radar/CodeBase/BEVHeight
python setup.py develop

1.2. Local Installation

a. Install pytorch(v1.9.0).

b. Install mmcv-full==1.4.0 mmdet==2.19.0 mmdet3d==0.18.1.

c. Install pypcd

git clone https://github.com/klintan/pypcd.git
cd pypcd
python setup.py install

d. Install requirements.

pip install -r requirements.txt

e. Install V2X-Radar/CodeBase/BEVHeight (gpu required).

python setup.py develop

2. Data Preparation

2.1. V2X-Radar-I / V2X-Radar-V Dataset

a. Download V2X-Radar-I / V2X-Radar-V dataset from official website.

We provide mini and full versions (release soon)

V2X-Radar
├── data
│   ├── v2x-radar
│   │   ├── v2x-radar-i   # KITTI Format
│   │   │   ├── training
│   │   │   │   ├── velodyne
│   │   │   │   ├── radar
│   │   │   │   ├── calib
│   │   │   │   ├── image_1
│   │   │   │   │   ├── image_2
│   │   │   │   ├── image_3
│   │   │   │   ├── label_2
│   │   │   ├── ImageSets
│   │   │   │   ├── train.txt
│   │   │   │   ├── val.txt
│   │   ├── v2x-radar-v # KITTI Format
│   │   │   ├── training
│   │   │   │   ├── velodyne
│   │   │   │   ├── radar 
│   │   │   │   ├── calib
│   │   │   │   ├── image_2
│   │   │   │   ├── label_2
│   │   │   ├── ImageSets
│   │   │   │   ├── train.txt
│   │   │   │   ├── val.txt
│   │   ├── ...

b. Prepare infos for V2X-Radar-I / V2X-Radar-V datasets.

python scripts/gen_info_v2x_radar_i.py
python scripts/gen_info_v2x_radar_v.py

2.2. DAIR-V2X-I Dataset

a. Download DAIR-V2X-I dataset from official website.

b. Convert the dataset to KITTI format.

ln -s [single-infrastructure-side root] ./data/dair-v2x
python scripts/data_converter/dair2kitti.py --source-root data/dair-v2x-i --target-root data/dair-v2x-i-kitti

The directory will be as follows.

V2X-Radar
├── data
│   ├── dair-v2x-i
│   │   ├── velodyne
│   │   ├── image
│   │   ├── calib
│   │   ├── label
|   |   └── data_info.json
|   ├── dair-v2x-i-kitti
|   |   ├── training
|   |   |   ├── calib
|   |   |   ├── label_2
|   |   |   └── images_2
|   |   └── ImageSets
|   |        ├── train.txt
|   |        └── val.txt
|   |...
|...

c. Prepare infos for DAIR-V2X-I dataset.

python scripts/gen_info_dair.py

2.3. Rope3D Dataset

a. Download Rope3D dataset from official website.

b. Convert the dataset to KITTI format.

ln -s [rope3d root] ./data/rope3d
python scripts/data_converter/rope2kitti.py --source-root data/rope3d --target-root data/rope3d-kitti

The directory will be as follows.

V2X-Radar
├── data
|   ├── rope3d
|   |   ├── training
|   |   ├── validation
|   |   ├── training-image_2a
|   |   ├── training-image_2b
|   |   ├── training-image_2c
|   |   ├── training-image_2d
|   |   └── validation-image_2
|   ├── rope3d-kitti
|   |   ├── training
|   |   |   ├── calib
|   |   |   ├── denorm
|   |   |   ├── label_2
|   |   |   └── images_2
|   |   └── map_token2id.json
|   |...  
├── ...

c. Prepare infos for Rope3D dataset.

python scripts/gen_info_rope3d.py

2.4. KITTI Dataset

a. Download KITTI dataset from official website.

b. Prepare infos for KITTI dataset.

python scripts/gen_info_kitti.py --data_root data/kitti

2.5. Visualize the dataset in KITTI format

python scripts/data_converter/visual_tools_kitti.py --data_root ../../data/kitti --demo_dir ./demo
python scripts/data_converter/visual_tools_v2x_radar.py --data_root ../../data/v2x-radar/v2x-radar-i --demo_dir ./demo

3. Train and Eval

3.1. Eval BEVDepth / BEVHeight / BEVHeight++ with 8 GPUs

python [EXP_PATH] --amp_backend native -b 8 --gpus 8

3.2. Eval BEVDepth / BEVHeight / BEVHeight++ with 8 GPUs

python [EXP_PATH] --ckpt_path [CKPT_PATH] -e -b 8 --gpus 8