first commit

.gitignore

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+# IntelliJ project files
+.idea
+*.iml
+out
+gen
+
+### Vim template
+[._]*.s[a-w][a-z]
+[._]s[a-w][a-z]
+*.un~
+Session.vim
+.netrwhist
+*~
+
+### IPythonNotebook template
+# Temporary data
+.ipynb_checkpoints/
+
+### Python template
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+#lib/
+#lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+*.ipynb
+*.params
+*.json
+.vscode/
+
+lib/dataset/pycocotools/*.c
+lib/dataset/pycocotools/*.cpp
+lib/nms/*.c
+lib/nms/*.cpp
+
+data
+external
+output
+model
+
+.db

LICENSE

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+MIT License
+
+Copyright (c) 2017 Microsoft
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+This is the official code for [Learning RoI Transformer for Detecting Oriented Objects in Aerial Images](https://arxiv.org/abs/1812.00155)
+
+This code is based on deformable convolution network
+
+mmdetection version is on the way
+
+Since there are custom c++ operators, We need to complie the MXNet source.
+
+## 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 Deformable ConvNets repository, and we'll call the directory that you cloned Deformable-ConvNets as ${DCN_ROOT}.
+```
+git clone https://github.com/msracver/Deformable-ConvNets.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).**
+
+	***Quick start***
+
+	3.1 Install MXNet and all dependencies by
+	```
+	pip install -r requirements.txt
+	```
+	If there is no other error message, MXNet should be installed successfully.
+
+	***Build from source (alternative way)***
+
+	3.2 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
+	```
+	3.3 Copy the c++ operators to MXNet source
+	```
+	cp fpn/operator_cxx/* mxnet/src/operator/contrib
+	```
+	3.3 Compile MXNet
+	```
+	cd ${MXNET_ROOT}
+	make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
+	```
+	3.4 Install the MXNet Python (python 2.7) binding by
+
+	***Note: If you will actively switch between different versions of MXNet, please follow 3.5 instead of 3.4***
+	```
+	cd python
+	sudo python setup.py install
+	```
+	3.5 For advanced users, you may put your Python packge into `./external/mxnet/$(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.
+
+complie dota_kit
+
+
+## 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 prepare_data
+   python prepare_data.py --data_path path_to_data --num_process 32
+
+2.soft link
+   mkdir 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
+1.training
+  sh train_dota_light_fpn_RoITransformer.sh
+
+2.testing
+  sh test_dota_light_fpn_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}
+}
+```
+