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Quick Start

Set up a new virtual environment

conda create -n MomAD_env python=3.8 -y
conda activate MomAD_env

Install dependency packpages

MomAD_path="path/to/MomAD"
cd ${MomAD_path}
pip3 install --upgrade pip
pip3 install torch==1.13.0+cu116 torchvision==0.14.0+cu116 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu116
pip3 install -r requirement.txt

🔥 Tips

  1. It is necessary to install the mmcv-full version of mmcv. It is recommended to use cuda113 and torch1.11.0, and to install mmcv version 1.7.2. Use the following command:

    pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html -i https://pypi.tuna.tsinghua.edu.cn/simple

  2. Remember to install flash-attn offline. The download address for flash_attn-0.2.2+cu113torch1.11.0-cp38-cp38-linux_x86_64.whl is here. Move the file to the MomAD folder and execute the following command to install:

    pip install flash_attn-0.2.2+cu113torch1.11.0-cp38-cp38-linux_x86_64.whl

Compile the deformable_aggregation CUDA op

cd projects/mmdet3d_plugin/ops
python3 setup.py develop
cd ../../../

Prepare the data

Download the NuScenes dataset and CAN bus expansion, put CAN bus expansion in /path/to/nuscenes, create symbolic links.

cd ${MomAD_path}
mkdir data
ln -s path/to/nuscenes ./data/nuscenes

Pack the meta-information and labels of the dataset, and generate the required pkl files to data/infos. Note that we also generate map_annos in data_converter, with a roi_size of (30, 60) as default, if you want a different range, you can modify roi_size in tools/data_converter/nuscenes_converter.py.

sh scripts/create_data.sh

Generate anchors by K-means

Gnerated anchors are saved to data/kmeans and can be visualized in vis/kmeans.

sh scripts/kmeans.sh

Download pre-trained weights

Download the required backbone pre-trained weights.

mkdir ckpt
wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O ckpt/resnet50-19c8e357.pth

Commence training and testing

train MomAD 3s

bash ./tools/dist_train.sh \
   projects/configs/MomAD_small_stage2_roboAD.py \
   8 \

train MomAD 6s

bash ./tools/dist_train.sh \
   projects/configs/MomAD_small_stage2_roboAD_6s.py \
   8 \

test MomAD 3s

bash ./tools/dist_test.sh \
    projects/configs/MomAD_small_stage2_roboAD.py \
    work_dirs/MomAD_small_stage2_roboAD/MomAD_3s.pth\
    1 \
    --deterministic \
    --eval bbox

test MomAD 6s

bash ./tools/dist_test.sh \
    projects/configs/MomAD_small_stage2_roboAD_6s.py \
    work_dirs/MomAD_small_stage2_roboAD/MomAD_6s.pth\
    1 \
    --deterministic \
    --eval bbox

Visualization

sh scripts/visualize.sh