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INSTALL.md

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Installation

Here we provide two ways for installation

  • docker
  • step-by-step

Docker Installation

a. Pull docker from dockerhub

docker pull csuhan/s2anet:latest

b. Run docker.

sudo docker run --gpus all -it -v your/path/to/dataset:/s2anet/data csuhan/s2anet:latest

Note:

  • Make sure you have installed docker, and GPUs are available in docker.

Step-by-step Installation

Requirements

  • Linux
  • Python 3.5+ (Python 2 is not supported)
  • PyTorch 1.3 or higher
  • CUDA 9.0 or higher
  • NCCL 2
  • GCC(G++) 4.9 or higher
  • mmcv==0.2.14

Note some cuda extensions, e.g., box_iou_rotated and nms_rotated require pytorch>=1.3 and gcc>=4.9.

We have tested the following versions of OS and softwares:

  • OS: CentOS 7.2
  • CUDA: 10.0-10.1
  • NCCL: 2.1.15/2.2.13/2.3.7/2.4.2
  • GCC(G++): 4.9
  • pytorch: 1.3.1

Install

a. Create a conda virtual environment and activate it.

conda create -n s2anet python=3.7 -y
conda activate s2anet

b. Install PyTorch stable or nightly and torchvision following the official instructions, e.g.,

conda install pytorch=1.3 torchvision cudatoolkit=10.0 -c pytorch

c. Clone the s2anet repository.

git clone https://github.com/csuhan/s2anet.git
cd s2anet

d. Install s2anet

# optional
pip install -r requirements.txt

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

Install DOTA_devkit

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

Prepare datasets

For DOTA, we provide scripts to split the original images into chip images (e.g., 1024*1024), and convert annotations to mmdet's format. Please refer to DOTA_devkit/prepare_dota1_ms.py.

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

mmdetection
├── mmdet
├── tools
├── configs
├── data
│   ├── dota_1024
│   │   ├── trainval_split
│   │   │    │─── images
│   │   │    │─── labelTxt
│   │   │    │─── trainval_s2anet.pkl
│   │   ├── test_split
│   │   │    │─── images
│   │   │    │─── test_s2anet.pkl
│   ├── HRSC2016 (optional)
│   │   ├── Train
│   │   │    │─── AllImages
│   │   │    │─── Annotations
│   │   │    │─── train.txt
│   │   ├── Test
│   │   │    │─── AllImages
│   │   │    │─── Annotations
│   │   │    │─── test.txt

Note train.txt and test.txt in HRSC2016 are .txt files recording image names without extension.

For example:

P00001
P00002
...