pycharm如何创建虚拟环境_pycharm安装后无解释器

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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.

conda create -n hais python=3.7
conda activate hais

2) Clone the repository.

git clone https://github.com/hustvl/HAIS.git --recursive

3) Install the requirements.

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/spconv in step 2) by default.

  For higher version CUDA and pytorch, spconv 1.2 is suggested. Replace HAIS/lib/spconv with this fork of spconv.

git clone https://github.com/outsidercsy/spconv.git --recursive

  Note:  In the provided spconv 1.0 and 1.2, spconv\spconv\functional.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}.whl

5) Compile the external C++ and CUDA ops.

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.