Download our dataset from (LINK) and make sure the structure of data as follows:
Notice: some version of data may have slightly different folder structure. You may need to use soft link (ln -s) and change the path related code.
Bench2DriveZoo-Jittor
├── ...
├── data/
| ├── bench2drive/
| | ├── v1/ # Bench2Drive base
| | | ├── Accident_Town03_Route101_Weather23/
| | | ├── Accident_Town03_Route102_Weather20/
| | | └── ...
| | └── maps/ # maps of Towns
| | ├── Town01_HD_map.npz
| | ├── Town02_HD_map.npz
| | └── ...
| ├── others
| | └── b2d_motion_anchor_infos_mode6.pkl # motion anchors for UniAD
| └── splits
| └── bench2drive_base_train_val_split.json # trainval_split of Bench2Drive base
Run the following command:
cd mmcv/datasets
python prepare_B2D.py --workers 16 # workers used to prepare data
The command will generate b2d_infos_train.pkl
, b2d_infos_val.pkl
, b2d_map_infos.pkl
under data/infos
.
Note: This command will be by default use all routes except those in data/splits/bench2drive_base_train_val_split.json as the training set. It will take about 1 hour to generate all the data with 16 workers for Base set (1000 clips).
After installing and data preparing, the structure of our code will be as follows:
Bench2DriveZoo-Jittor
├── adzoo/
| ├── bevformer/
| ├── uniad/
| └── vad/
├── ckpts/
| ├── r101_dcn_fcos3d_pretrain.pth # pretrain weights for bevformer
| ├── resnet50-19c8e357.pth # image backbone pretrain weights for vad
| ├── bevformer_base_b2d.pth # download weights you need
| ├── uniad_base_b2d.pth # download weights you need
| └── ...
├── data/
| ├── bench2drive/
| | ├── v1/ # Bench2Drive base
| | | ├── Accident_Town03_Route101_Weather23/
| | | ├── Accident_Town03_Route102_Weather20/
| | | └── ...
| | └── maps/ # maps of Towns
| | ├── Town01_HD_map.npz
| | ├── Town02_HD_map.npz
| | └── ...
│ ├── infos/
│ │ ├── b2d_infos_train.pkl
│ │ ├── b2d_infos_val.pkl
| | └── b2d_map_infos.pkl
| ├── others
| | └── b2d_motion_anchor_infos_mode6.pkl # motion anchors for UniAD
| └── splits
| └── bench2drive_base_train_val_split.json # trainval_split of Bench2Drive base
├── docs/
├── mmcv/
├── team_code/ # for Closed-loop Evaluation in CARLA