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INSTAllation

Requirements

  • Linux
  • Python 3.7+
  • PyTorch ≤ 1.4 (We haven't tested higher version)
  • CUDA 9.0 or higher
  • mmdet==1.1.0
  • mmcv==0.6.2
  • GCC 4.9 or higher
  • NCCL 2

We have tested the following versions of OS and softwares：

  • OS：Ubuntu 16.04
  • CUDA: 10.0/10.1
  • NCCL: 2.1.15/2.2.13/2.3.7/2.4.2
  • GCC(G++): 4.9/5.3/5.4/7.3

Install

a. Create a conda virtual environment and activate it.

conda create -n orientedreppoints python=3.8 -y 
source activate orientedreppoints

b. Make sure your CUDA runtime api version ≤ CUDA driver version. (for example 10.1 ≤ 10.2)

nvcc -V
nvidia-smi

c. Install PyTorch and torchvision following the official instructions, Make sure cudatoolkit version same as CUDA runtime api version, e.g.,

conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch

d. Clone the orientedreppoints_dota repository.

git clone https://github.com/hukaixuan19970627/OrientedRepPoints_DOTA.git
cd OrientedRepPoints_DOTA

e. Install orientedreppoints_dota.

pip install -r requirements.txt
python setup.py develop  #or "pip install -v -e ."

Install DOTA_devkit

cd OrientedRepPoints_DOTA/DOTA_devkit
sudo apt-get install swig
swig -c++ -python polyiou.i
python setup.py build_ext --inplace

Prepare dataset

It is recommended to symlink the dataset root to $orientedreppoints/data. If your folder structure is different, you may need to change the corresponding paths in config files.

orientedreppoints
|——mmdet
|——tools
|——configs
|——data
|  |——dota
|  |  |——trainval_split
|  |  |  |——images
|  |  |  |——labelTxt
|  |  |  |——trainval.json
|  |  |——test_split
|  |  |  |——images
|  |  |  |——test.json
|  |——HRSC2016(OPTINAL)
|  |  |——Train
|  |  |  |——images
|  |  |  |——labelTxt
|  |  |  |——train.txt
|  |  |  |——trainval.json
|  |  |——Test
|  |  |  |——images
|  |  |  |——test.txt
|  |  |  |——test.json
|  |——UCASAOD(OPTINAL)
|  |  |——Train
|  |  |  |——images
|  |  |  |——labelTxt
|  |  |  |——train.txt
|  |  |  |——trainval.json
|  |  |——Test
|  |  |  |——images
|  |  |  |——test.txt
|  |  |  |——test.json

Note:

  • train.txt and test.txt in HRSC2016 and UCASAOD are .txt files recording image names without extension.
  • Without the pre-divided train，test, and val sub-dataset, the partition of UCASAOD dataset follows the rep.