Following https://mmdetection3d.readthedocs.io/en/latest/getting_started.html#installation
a. Create a conda virtual environment and activate it.
conda create -n open-mmlab python=3.8 -y
conda activate open-mmlab
b. Install PyTorch and torchvision following the official instructions.
pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html
# Recommended torch>=1.9
c. Install gcc>=5 in conda env (optional).
conda install -c omgarcia gcc-6 # gcc-6.2
c. Install mmcv-full.
pip install mmcv-full==1.4.0
# pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
d. Install mmdet and mmseg.
pip install mmdet==2.14.0
pip install mmsegmentation==0.14.1
e. Install mmdet3d from source code.
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
git checkout v0.17.1 # Other versions may not be compatible.
python setup.py install
f. Install Detectron2 and Timm.
pip install einops fvcore seaborn iopath==0.1.9 timm==0.6.13 typing-extensions==4.5.0 pylint ipython==8.12 numpy==1.19.5 matplotlib==3.5.2 numba==0.48.0 pandas==1.4.4 scikit-image==0.19.3 setuptools==59.5.0
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
g. Clone BEVFormer.
git clone https://github.com/fundamentalvision/BEVFormer.git
h. Prepare pretrained models.
cd bevformer
mkdir ckpts
cd ckpts & wget https://github.com/zhiqi-li/storage/releases/download/v1.0/r101_dcn_fcos3d_pretrain.pth
note: this pretrained model is the same model used in detr3d