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Step-by-step installation instructions

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