- Linux
- Python 3.5+ (Say goodbye to Python2)
- PyTorch 1.1
- CUDA 9.0+
- NCCL 2+
- GCC 4.9+
- mmcv
We have tested the following versions of OS and softwares:
- OS: Ubuntu 18.04
- CUDA: 9.0/9.2/10.0
- NCCL: 2.1.15/2.2.13/2.3.7/2.4.2
- GCC: 4.9/5.3/5.4/7.3
a. Create a conda virtual environment and activate it. Then install Cython.
conda create -n ROAS_base python=3.7 -y
source activate ROAS_base
conda install cython
b. Install PyTorch stable or nightly and torchvision following the official instructions.
c. Clone the ROAS repository.
git clone https://github.com/SIAnalytics/roas.git
cd roas
d. Compile cuda extensions.
./compile.sh
e. Install ROAS (other dependencies will be installed automatically).
pip install -r requirements.txt
python setup.py develop
# or "pip install -e ."
Note:
-
It is recommended that you run the step e each time you pull some updates from github. If there are some updates of the C/CUDA codes, you also need to run step d. The git commit id will be written to the version number with step e, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.
-
Following the above instructions, ROAS is installed on
dev
mode, any modifications to the code will take effect without installing it again.
sudo apt-get install swig
cd DOTA_devkit
swig -c++ -python polyiou.i
python setup.py build_ext --inplace