Contrastive General Graph Matching with Adaptive Augmentation Sampling

Official implementation of IJCAI'24 paper Contrastive General Graph Matching with Adaptive Augmentation Sampling

Setup (Ubuntu)

The codebase is verified on Ubuntu 16.04 with Python 3.7, Pytorch 1.6, CUDA 10.1, CUDNN 7, and torch-geometric 1.6.3.

1. Set up Pytorch 1.6 with support for GPU.

2. Install ninja-build with: apt-get install ninja-build

3. Add Python libraries:

   pip install tensorboardX scipy easydict pyyaml xlrd xlwt pynvml pygmtools wandb

4. Prepare LPMP build essentials:

   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. Download and compile LPMP:

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

   For a successful LPMP build, gcc-9 may be necessary. To install and set up gcc-9, follow this example:

   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 by running:

   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 gcc-9 was used for LPMP, revert to gcc-7 afterwards, as shown below:

   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

8. If there was an error related to the package, please check requirements.txt for the correct version.

Datasets

The datasets below are obtainable through pygmtools automatically or manual download in case of any download issues.

1. PascalVOC-Keypoint

   • Get the VOC2011 dataset and ensure to have it as: data/PascalVOC/TrainVal/VOCdevkit/VOC2011
   • Retrieve keypoint annotations for VOC2011 from the Berkeley server or google drive to ensure they looks like: data/PascalVOC/annotations
   • The train/test split is available in data/PascalVOC/voc2011_pairs.npz

2. SPair-71k

   • Obtain the dataset from this link.

   • Ensure images are placed in data/SPair-71k/JPEGImages, and annotations in data/SPair-71k/ImageAnnotation.

3. Willow ObjectClass

   • The Willow dataset can be found on this page.

• After downloading, unzip and store images and annotations are in data/WillowObject/WILLOW-ObjectClass

4. Synthetic Dataset

   Due to the size limitations of the supplementary material, the synthetic dataset was designed to be automatically generated and saved during the execution of the experiments.

   python train_eval_gcgm.py --cfg experiments/Synthetic/GCGM/synthetic_gcgm.yaml

Training and Validation Splits

To simplify the implementation process, we perform training and validation splits dynamically, controlled by random seeds. The five random seeds used to report the experimental results are 0, 1, 2, 3, 123. Please update these values in the configuration file, under the RANDOM_SEED field.

Run the Experiment

Training and evaluation GCGM:

python train_eval_gcgm.py --cfg experiments/GCGM/willow_gcgm_ngmv2.yaml

Training and evaluation other baselines:

python train_eval_baseline.py --cfg experiments/SCGM/willow_scgm_ngmv2.yaml