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https://github.com/dingjiansw101/RoITransformer_DOTA
https://github.com/dingjiansw101/RoITransformer_DOTA
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/dingjiansw101/RoITransformer_DOTA
- Owner: dingjiansw101
- License: mit
- Created: 2019-06-13T04:55:06.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-01-31T02:41:08.000Z (almost 5 years ago)
- Last Synced: 2024-08-01T22:40:25.453Z (5 months ago)
- Language: Python
- Size: 416 KB
- Stars: 194
- Watchers: 9
- Forks: 42
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-MXNet - RoITransformer
README
## Disclaimer
This is the official repo of paper [Learning RoI Transformer for Detecting Oriented Objects in Aerial Images](https://arxiv.org/abs/1812.00155)
This code is based on deformable convolution network
We refactored the code and retrained the model. There are slight differences in the final accuracy.
[mmdetection version](https://github.com/dingjiansw101/AerialDetection) is finished, it is more faster and accurate, we recommend you to use the new version.
## Requirements: Software
1. MXNet from [the offical repository](https://github.com/dmlc/mxnet).
2. Python 2.7. We recommend using Anaconda2 as it already includes many common packages. We do not support Python 3 yet, if you want to use Python 3 you need to modify the code to make it work.
3. Python packages might missing: cython, opencv-python >= 3.2.0, easydict. If `pip` is set up on your system, those packages should be able to be fetched and installed by running
```
pip install -r requirements.txt
```
4. For Windows users, Visual Studio 2015 is needed to compile cython module.
## Installation
1. Clone the RoI Transformer repository, and we'll call the directory that you cloned RoI Transformer as ${RoI_ROOT}
```
git clone https://github.com/dingjiansw101/RoITransformer_DOTA.git
```
2. For Windows users, run ``cmd .\init.bat``. For Linux user, run `sh ./init.sh`. The scripts will build cython module automatically and create some folders.
3. Install MXNet:
**Note: The MXNet's Custom Op cannot execute parallelly using multi-gpus after this [PR](https://github.com/apache/incubator-mxnet/pull/6928). We strongly suggest the user rollback to version [MXNet@(commit 998378a)](https://github.com/dmlc/mxnet/tree/998378a) for training (following Section 3.2 - 3.5).**
***Build from source (Since there are custom c++ operators, We need to complie the MXNet from source.)***
3.1 Clone MXNet and checkout to [MXNet@(commit 998378a)](https://github.com/dmlc/mxnet/tree/998378a) by
```
git clone --recursive https://github.com/dmlc/mxnet.git
git checkout 998378a
git submodule update
# if it's the first time to checkout, just use: git submodule update --init --recursive
```
3.2 Copy the c++ operators to MXNet source
```
cp ${RoI_ROOT}/fpn/operator_cxx/* ${MXNET_ROOT}/src/operator/contrib
```
3.3 Compile MXNet
```
cd ${MXNET_ROOT}
make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
```
3.4 Install the MXNet Python binding by
***Note: If you will actively switch between different versions of MXNet, please follow 3.5 instead of 3.4***
```
cd python
sudo python setup.py install
```
3.5 For advanced users, you may put your Python packge into `./external/mxnet/$(YOUR_MXNET_PACKAGE)`, and modify `MXNET_VERSION` in `./experiments/rfcn/cfgs/*.yaml` to `$(YOUR_MXNET_PACKAGE)`. Thus you can switch among different versions of MXNet quickly.
4. Compile dota_kit
```
sudo apt-get install swig
cd ${RoI_ROOT}/dota_kit
swig -c++ -python polyiou.i
python setup.py build_ext --inplace
cd ${RoI_ROOT}/dota_kit/poly_nms_gpu
make -j16
```
## Prepare DOTA Data:
1. Prepare script
put your original dota data (before split) in path_to_data
make sure it looks like
```
path_to_data/train/images,
path_to_data/train/labelTxt,
path_to_data/val/images,
path_to_data/val/labelTxt,
path_to_data/test/images
cd ${RoI_ROOT}/prepare_data
python prepare_data.py --data_path path_to_data --num_process 32
```
2. Create soft link
```
cd ${RoI_ROOT}
mkdir -p data
cd data
ln -s path_to_data dota_1024
```
## Pretrained Models
We provide trained convnet models.
1. To use the demo with our pre-trained RoI Transformer models for DOTA, please download manually from [Google Drive](https://drive.google.com/drive/folders/1kUBsH2v5DK6QjqDoMmyx16bW7gUlEgn1?usp=sharing), or [BaiduYun](https://pan.baidu.com/s/14KBADK41S5hOO8NQVQlbWA) (Extraction code: fucc)
and put it under the following folder.
Make sure it look like this:
```
./output/rcnn/DOTA/resnet_v1_101_dota_RoITransformer_trainval_rcnn_end2end/train/rcnn_dota-0040.params
./output/fpn/DOTA/resnet_v1_101_dota_rotbox_light_head_RoITransformer_trainval_fpn_end2end/train/fpn_DOTA_oriented-0008.params
```
## Training & Testing
```
cd ${RoI_ROOT}
```
1. training
Please download ImageNet-pretrained ResNet-v1-101 model manually from [OneDrive](https://1drv.ms/u/s!Am-5JzdW2XHzhqMEtxf1Ciym8uZ8sg), or [BaiduYun](https://pan.baidu.com/s/1YuB5ib7O-Ori1ZpiGf8Egw#list/path=%2F), or [Google drive](https://drive.google.com/open?id=1b6P-UMaBBpMPlcgvc38dMToPAa_Gyu6F), and put it under folder `./model`. Make sure it look like this:
```
./model/pretrained_model/resnet_v1_101-0000.params
```
Start training (we use the Light-head R-CNN + RoI Transformer (without FPN) for example, you may choose other models)
```
sh train_dota_light_RoITransformer.sh
```
2. testing
Start testing
```
sh test_dota_light_RoITransformer.sh
```
---------------------------------------------------
© Microsoft, 2017. Licensed under an MIT license.
If you find RoI Transformer and DOTA data useful in your research, please consider citing:
```
@inproceedings{ding2019learning,
title={Learning RoI Transformer for Oriented Object Detection in Aerial Images},
author={Ding, Jian and Xue, Nan and Long, Yang and Xia, Gui-Song and Lu, Qikai},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={2849--2858},
year={2019}
}
@inproceedings{xia2018dota,
title={DOTA: A large-scale dataset for object detection in aerial images},
author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={3974--3983},
year={2018}
}
```