Think Match

ThinkMatch is developed and maintained by ThinkLab at Shanghai Jiao Tong University. This repository is developed for the following purposes:

• Providing modules for developing deep graph matching algorithms to facilitate future research.
• Providing implementation of state-of-the-art deep graph matching methods.
• Benchmarking existing deep graph matching algorithms under different dataset & experiment settings, for the purpose of fair comparison.

Introduction to Graph Matching

Graph Matching (GM) is a fundamental yet challenging problem in computer vision, pattern recognition and data mining. GM aims to find node-to-node correspondence among multiple graphs, by solving an NP-hard combinatorial problem named Quadratic Assignment Problem (QAP). Recently, there is growing interest in developing deep learning based graph matching methods.

Graph matching techniques have been applied to the following applications:

• Bridging movie and synopses

  

• Image correspondence

  

• Molecules matching

  

• and more...

In this repository, we mainly focus on image keypoint matching because it is a popular testbed for existing graph matching methods.

Readers are referred to the following survey for more technical details about graph matching:

• Junchi Yan, Xu-Cheng Yin, Weiyao Lin, Cheng Deng, Hongyuan Zha, Xiaokang Yang. "A Short Survey of Recent Advances in Graph Matching." ICMR 2016.

Deep Graph Matching Algorithms

ThinkMatch currently contains pytorch source code of the following deep graph matching methods:

• GMN
  • Andrei Zanfir and Cristian Sminchisescu. "Deep Learning of Graph Matching." CVPR 2018. [paper]
• PCA-GM & IPCA-GM
  • Runzhong Wang, Junchi Yan and Xiaokang Yang. "Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach." TPAMI 2020. [paper], [project page]
  • Runzhong Wang, Junchi Yan and Xiaokang Yang. "Learning Combinatorial Embedding Networks for Deep Graph Matching." ICCV 2019. [paper]
• NGM & NGM-v2
  • Runzhong Wang, Junchi Yan, Xiaokang Yang. "Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching." TPAMI 2021. [paper], [project page]
• CIE-H
  • Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li. "Learning deep graph matching with channel-independent embedding and Hungarian attention." ICLR 2020. [paper]
• GANN
  • Runzhong Wang, Junchi Yan and Xiaokang Yang. "Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning." NeurIPS 2020. [paper]
  • Runzhong Wang, Shaofei Jiang, Junchi Yan and Xiaokang Yang. "Robust Self-supervised Learning of Deep Graph Matching with Mixture of Modes." Submitted to TPAMI. [project page]
• BBGM
  • Michal Rolínek, Paul Swoboda, Dominik Zietlow, Anselm Paulus, Vít Musil, Georg Martius. "Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers." ECCV 2020. [paper]

Deep Graph Matching Benchmarks

PascalVOC - 2GM

model	year	aero	bike	bird	boat	bottle	bus	car	cat	chair	cow	table	dog	horse	mbkie	person	plant	sheep	sofa	train	tv	mean
GMN	2018	0.4163	0.5964	0.6027	0.4795	0.7918	0.7020	0.6735	0.6488	0.3924	0.6128	0.6693	0.5976	0.6106	0.5975	0.3721	0.7818	0.6800	0.4993	0.8421	0.9141	0.6240
PCA-GM	2019	0.4979	0.6193	0.6531	0.5715	0.7882	0.7556	0.6466	0.6969	0.4164	0.6339	0.5073	0.6705	0.6671	0.6164	0.4447	0.8116	0.6782	0.5922	0.7845	0.9042	0.6478
NGM	2019	0.5010	0.6350	0.5790	0.5340	0.7980	0.7710	0.7360	0.6820	0.4110	0.6640	0.4080	0.6030	0.6190	0.6350	0.4560	0.7710	0.6930	0.6550	0.7920	0.8820	0.6413
NHGM	2019	0.5240	0.6220	0.5830	0.5570	0.7870	0.7770	0.7440	0.7070	0.4200	0.6460	0.5380	0.6100	0.6190	0.6080	0.4680	0.7910	0.6680	0.5510	0.8090	0.8870	0.6458
IPCA-GM	2020	0.5378	0.6622	0.6714	0.6120	0.8039	0.7527	0.7255	0.7252	0.4455	0.6524	0.5430	0.6724	0.6790	0.6421	0.4793	0.8435	0.7079	0.6398	0.8380	0.9083	0.6770
CIE-H	2020	0.5250	0.6858	0.7015	0.5706	0.8207	0.7700	0.7073	0.7313	0.4383	0.6994	0.6237	0.7018	0.7031	0.6641	0.4763	0.8525	0.7172	0.6400	0.8385	0.9168	0.6892
BBGM	2020	0.6187	0.7106	0.7969	0.7896	0.8740	0.9401	0.8947	0.8022	0.5676	0.7914	0.6458	0.7892	0.7615	0.7512	0.6519	0.9818	0.7729	0.7701	0.9494	0.9393	0.7899
NGM-v2	2021	0.6184	0.7118	0.7762	0.7875	0.8733	0.9363	0.8770	0.7977	0.5535	0.7781	0.8952	0.7880	0.8011	0.7923	0.6258	0.9771	0.7769	0.7574	0.9665	0.9323	0.8011
NHGM-v2	2021	0.5995	0.7154	0.7724	0.7902	0.8773	0.9457	0.8903	0.8181	0.5995	0.8129	0.8695	0.7811	0.7645	0.7750	0.6440	0.9872	0.7778	0.7538	0.9787	0.9280	0.8040

Willow Object Class - 2GM & MGM

model	year	remark	Car	Duck	Face	Motorbike	Winebottle	mean
GMN	2018	-	0.6790	0.7670	0.9980	0.6920	0.8310	0.7934
PCA-GM	2019	-	0.8760	0.8360	1.0000	0.7760	0.8840	0.8744
NGM	2019	-	0.8420	0.7760	0.9940	0.7680	0.8830	0.8530
NHGM	2019	-	0.8650	0.7220	0.9990	0.7930	0.8940	0.8550
NMGM	2019	-	0.7850	0.9210	1.0000	0.7870	0.9480	0.8880
IPCA-GM	2020	-	0.9040	0.8860	1.0000	0.8300	0.8830	0.9006
CIE-H	2020	-	0.8581	0.8206	0.9994	0.8836	0.8871	0.8898
BBGM	2020	-	0.9680	0.8990	1.0000	0.9980	0.9940	0.9718
GANN-MGM	2020	self-supervised	0.9600	0.9642	1.0000	1.0000	0.9879	0.9906
NGM-v2	2021	-	0.9740	0.9340	1.0000	0.9860	0.9830	0.9754
NHGM-v2	2021	-	0.9740	0.9390	1.0000	0.9860	0.9890	0.9780
NMGM-v2	2021	-	0.9760	0.9447	1.0000	1.0000	0.9902	0.9822

ThinkMatch includes the flowing datasets with the provided benchmarks:

• PascalVOC-Keypoint
• Willow-Object-Class
• CUB2011
• IMC-PT-SparseGM

TODO We also plan to include the following datasets in the future:

• SPair-21k
• Synthetic data

ThinkMatch also supports the following graph matching settings:

• 2GM namely Two-Graph Matching where every time only a pair of two graphs is matched.
• MGM namely Multi-Graph Matching where more than two graphs are jointly matched.
• MGM3 namely Multi-Graph Matching with a Mixture of Modes, where multiple graphs are jointly considered, and at the same time the graphs may come from different categories.

Get Started

Docker (RECOMMENDED)

1. We maintain a prebuilt image at dockerhub: runzhongwang/thinkmatch:torch1.6.0-cuda10.1-cudnn7-pyg1.6.3. It can be used by docker or other container runtimes that support docker images e.g. singularity.
2. We also provide a Dockerfile to build your own image (you may need docker and nvidia-docker installed on your computer).

Manual configuration (for Ubuntu)

This repository is developed and tested with Ubuntu 16.04, Python 3.7, Pytorch 1.6, cuda10.1, cudnn7 and torch-geometric 1.6.3.

1. Install and configure Pytorch 1.6 (with GPU support).
2. Install ninja-build: apt-get install ninja-build
3. Install python packages:

   pip install tensorboardX scipy easydict pyyaml xlrd xlwt pynvml

4. Install building tools for LPMP:

   apt-get install -y findutils libhdf5-serial-dev git wget libssl-dev
   
   wget https://github.com/Kitware/CMake/releases/download/v3.19.1/cmake-3.19.1.tar.gz && tar zxvf cmake-3.19.1.tar.gz
   cd cmake-3.19.1 && ./bootstrap && make && make install

5. Install and build LPMP:

   python -m pip install git+https://[email protected]/rogerwwww/lpmp.git

   You may need gcc-9 to successfully build LPMP. Here we provide an example installing and configuring gcc-9:

   apt-get update
   apt-get install -y software-properties-common
   add-apt-repository ppa:ubuntu-toolchain-r/test
   
   apt-get install -y gcc-9 g++-9
   update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 --slave /usr/bin/g++ g++ /usr/bin/g++-9

6. Install torch-geometric:

   export CUDA=cu101
   export TORCH=1.6.0
   /opt/conda/bin/pip install torch-scatter==2.0.5 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
   /opt/conda/bin/pip install torch-sparse==0.6.8 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
   /opt/conda/bin/pip install torch-cluster==1.5.8 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
   /opt/conda/bin/pip install torch-spline-conv==1.2.0 -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
   /opt/conda/bin/pip install torch-geometric==1.6.3

7. If you have configured gcc-9 to build LPMP, be sure to switch back to gcc-7 because this code repository is based on gcc-7. Here is also an example:

   update-alternatives --remove gcc /usr/bin/gcc-9
   update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-7 60 --slave /usr/bin/g++ g++ /usr/bin/g++-7

Available datasets

1. PascalVOC-Keypoint

   1. Download VOC2011 dataset and make sure it looks like data/PascalVOC/VOC2011
   2. Download keypoint annotation for VOC2011 from Berkeley server or google drive and make sure it looks like data/PascalVOC/annotations
   3. The train/test split is available in data/PascalVOC/voc2011_pairs.npz

   Please cite the following papers if you use PascalVOC-Keypoint dataset:

   @article{EveringhamIJCV10,
     title={The pascal visual object classes (voc) challenge},
     author={Everingham, Mark and Van Gool, Luc and Williams, Christopher KI and Winn, John and Zisserman, Andrew},
     journal={International Journal of Computer Vision},
     volume={88},
     pages={303–338},
     year={2010}
   }
   
   @inproceedings{BourdevICCV09,
     title={Poselets: Body part detectors trained using 3d human pose annotations},
     author={Bourdev, L. and Malik, J.},
     booktitle={International Conference on Computer Vision},
     pages={1365--1372},
     year={2009},
     organization={IEEE}
   }
   

2. Willow-Object-Class

   1. Download Willow-ObjectClass dataset
   2. Unzip the dataset and make sure it looks like data/WILLOW-ObjectClass

   Please cite the following paper if you use Willow-Object-Class dataset:

   @inproceedings{ChoICCV13,
     author={Cho, Minsu and Alahari, Karteek and Ponce, Jean},
     title = {Learning Graphs to Match},
     booktitle = {International Conference on Computer Vision},
     pages={25--32},
     year={2013}
   }
   

3. CUB2011

   1. Download CUB-200-2011 dataset.
   2. Unzip the dataset and make sure it looks like data/CUB_200_2011

   Please cite the following report if you use CUB2011 dataset:

   @techreport{CUB2011,
     Title = {{The Caltech-UCSD Birds-200-2011 Dataset}},
     Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.},
     Year = {2011},
     Institution = {California Institute of Technology},
     Number = {CNS-TR-2011-001}
   }
   

4. IMC-PT-SparseGM

   1. Download the IMC-PT-SparseGM dataset from google drive or baidu drive (code: 0576)
   2. Unzip the dataset and make sure it looks like data/IMC_PT_SparseGM/annotation

   Please cite the following papers if you use IMC-PT-SparseGM dataset:

   @article{JinIJCV21,
     title={Image Matching across Wide Baselines: From Paper to Practice},
     author={Jin, Yuhe and Mishkin, Dmytro and Mishchuk, Anastasiia and Matas, Jiri and Fua, Pascal and Yi, Kwang Moo and Trulls, Eduard},
     journal={International Journal of Computer Vision},
     pages={517--547},
     year={2021}
   }
   
   @unpublished{WangPAMIsub21,
     title={Robust Self-supervised Learning of Deep Graph Matching with Mixture of Modes},
     author={Wang, Runzhong and Jiang, Shaofei and Yan, Junchi and Yang, Xiaokang},
     note={submitted to IEEE Transactions of Pattern Analysis and Machine Intelligence},
     year={2021}
   }
   

Run the Experiment

Run training and evaluation

python train_eval.py --cfg path/to/your/yaml

and replace path/to/your/yaml by path to your configuration file, e.g.

python train_eval.py --cfg experiments/vgg16_pca_voc.yaml

Default configuration files are stored inexperiments/ and you are welcomed to try your own configurations. If you find a better yaml configuration, please let us know by raising an issue or a PR and we will update the benchmark!

Pretrained Models

ThinkMatch provides pretrained models. The model weights are available via google drive.

To use the pretrained models, firstly download the weight files, then add the following line to your yaml file:

PRETRAINED_PATH: path/to/your/pretrained/weights