quick_start.md

Quick Start

Set up a new virtual environment

virtualenv mm_sparse4d --python=python3.8
source mm_sparse4d/bin/activate

Install packpages using pip3

sparse4d_path="path/to/sparse4d"
cd ${sparse4d_path}
pip3 install --upgrade pip
pip3 install -r requirement.txt

Compile the deformable_aggregation CUDA op

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

Prepare the data

Download the NuScenes dataset and create symbolic links.

cd ${sparse4d_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.

pkl_path="data/nuscenes_anno_pkls"
mkdir -p ${pkl_path}
python3 tools/nuscenes_converter.py --version v1.0-mini --info_prefix ${pkl_path}/nuscenes-mini
python3 tools/nuscenes_converter.py --version v1.0-trainval,v1.0-test --info_prefix ${pkl_path}/nuscenes

Generate anchors by K-means

python3 tools/anchor_generator.py --ann_file ${pkl_path}/nuscenes_infos_train.pkl

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
bash local_train.sh sparse4dv3_temporal_r50_1x8_bs6_256x704

# test
bash local_test.sh sparse4dv3_temporal_r50_1x8_bs6_256x704  path/to/checkpoint

For inference-related guidelines, please refer to the tutorial/tutorial.ipynb.