This is the official PyTorch implementation and models for our CVPR 2021 paper: Deep Graph Matching under Quadratic Constraint.

It also contains the configuration files to reproduce the results of qc-DGM_1 reported in the paper on Pascal VOC Keypoint and Willow Object Class dataset.

1. pytorch (GPU version) >= 1.1
2. ninja-build: apt-get install ninja-build
3. python packages: pip install tensorboardX scipy easydict pyyaml
4. Download dataset:
   1. Pascal VOC Keypoint:
   • Download and tar VOC2011 keypoints, and the path looks like: ./data/PascalVOC/VOC2011.
   • Download and tar Berkeley annotation, and the path looks like: ./data/PascalVOC/annotations.
   • The train/test split of Pascal VOC Keypoint is available in: ./data/PascalVOC/voc2011_pairs.npz.
   2. Willow Object Class dataset:
   • Download and unzip Willow ObjectClass dataset, and the path looks like: ./data/WILLOW-ObjectClass.

1. Run training and evaluation on Pascal VOC Keypoint:

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

   or you could replace the default ./experiments/QCDGM_voc.yaml with path to your own configuration file.

2. Run training and evaluation on Willow Object Class dataset:

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

   or you could replace the default ./experiments/QCDGM_willow.yaml with path to your own configuration file.

1. Run evaluation on Pascal VOC Keypoint on epoch k:

   python eval.py --cfg ./experiments/QCDGM_voc.yaml --epoch k

   or you could replace the default ./experiments/QCDGM_voc.yaml with path to your own configuration file.

2. Run evaluation on Willow Object Class dataset on epoch k:

   python eval.py --cfg ./experiments/QCDGM_willow.yaml --epoch k

   or you could replace the default ./experiments/QCDGM_voc.yaml with path to your own configuration file.

We report the performance on Pascal VOC Keypoint and Willow Object Class datasets.

Pascal VOC Keypoint

For the convenience of evaluation, our trained parameter file is also provided by BaiduYun download link with extracting code vocc. Download the parameter file with path to ./output/QCDGM_voc/params/ and run evaluation on Pascal VOC Keypoint.

Willow Object Class

For the convenience of evaluation, our trained parameter file is also provided by BaiduYun download link with extracting code will. Download the parameter file with path to ./output/QCDGM_willow/params/ and run evaluation on Willow Object Class dataset.

@InProceedings{Gao_2021_CVPR,
author = {Gao, Quankai and Wang, Fudong and Xue, Nan and Yu, Jin-Gang and Xia, Gui-Song},
title = {Deep Graph Matching under Quadratic Constraint},
booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
year = {2021}
}