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Installation
1) Environment
- Python 3.x
- Pytorch 1.1 or higher
- CUDA 9.2 or higher
- gcc-5.4 or higher
Create a conda virtual environment and activate it.
代码语言：javascript复制conda create -n hais python=3.7
conda activate hais2) Clone the repository.
代码语言：javascript复制git clone https://github.com/hustvl/HAIS.git --recursive3) Install the requirements.
代码语言：javascript复制cd HAIS
pip install -r requirements.txt
conda install -c bioconda google-sparsehash 4) Install spconv
- Verify the version of spconv.
spconv 1.0, compatible with CUDA < 11 and pytorch < 1.5, is already recursively cloned in
HAIS/lib/spconvin step 2) by default. For higher version CUDA and pytorch, spconv 1.2 is suggested. ReplaceHAIS/lib/spconvwith this fork of spconv.
git clone https://github.com/outsidercsy/spconv.git --recursive Note: In the provided spconv 1.0 and 1.2, spconvspconvfunctional.py is modified to make grad_output contiguous. Make sure you use the modified spconv but not the original one. Or there would be some bugs of optimization.- Install the dependent libraries.
conda install libboost
conda install -c daleydeng gcc-5 # (optional, install gcc-5.4 in conda env)- Compile the spconv library.
cd HAIS/lib/spconv
python setup.py bdist_wheel- Intall the generated .whl file.
cd HAIS/lib/spconv/dist
pip install {wheel_file_name}.whl5) Compile the external C and CUDA ops.
代码语言：javascript复制cd HAIS/lib/hais_ops
export CPLUS_INCLUDE_PATH={conda_env_path}/hais/include:$CPLUS_INCLUDE_PATH
python setup.py build_ext develop{conda_env_path} is the location of the created conda environment, e.g., /anaconda3/envs.
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