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INSTALL.md

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Installation

Modified from bevformer and mmdetection3d.

a. Env: Create a conda virtual environment and activate it.

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

b. Torch: Install PyTorch and torchvision following the official instructions.

conda install cudatoolkit=11.1.1 -c conda-forge
# We use cuda-11.1 by default
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. GCC: Make sure gcc>=5 in conda env.

# If gcc is not installed:
# conda install -c omgarcia gcc-6 # gcc-6.2

export PATH=YOUR_GCC_PATH/bin:$PATH
# Eg: export PATH=/mnt/gcc-5.4/bin:$PATH

d. CUDA: Before installing MMCV family, you need to set up the CUDA_HOME (for compiling some operators on the gpu).

export CUDA_HOME=YOUR_CUDA_PATH/
# Eg: export CUDA_HOME=/mnt/cuda-11.1/

e. Install mmcv-full.

pip install mmcv-full==1.4.0
# If it's not working, try:
# pip install mmcv-full==1.4.0 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html

f. Install mmdet and mmseg.

pip install mmdet==2.14.0
pip install mmsegmentation==0.14.1

g. Install mmdet3d from source code.

cd ~
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
git checkout v0.17.1
pip install scipy==1.7.3
pip install scikit-image==0.20.0
pip install -v -e .

h. Install UniAD.

cd ~
git clone https://github.com/OpenDriveLab/UniAD.git
cd UniAD
pip install -r requirements.txt

i. Prepare pretrained weights.

mkdir ckpts && cd ckpts

# Pretrained weights of bevformer
# Also the initial state of training stage1 model
wget https://github.com/zhiqi-li/storage/releases/download/v1.0/bevformer_r101_dcn_24ep.pth

# Pretrained weights of stage1 model (perception part of UniAD)
wget https://github.com/OpenDriveLab/UniAD/releases/download/v1.0/uniad_base_track_map.pth

# Pretrained weights of stage2 model (fully functional UniAD)
wget https://github.com/OpenDriveLab/UniAD/releases/download/v1.0.1/uniad_base_e2e.pth

-> Next Page: Prepare The Dataset