dgl/examples/pytorch/hilander

Learning Hierarchical Graph Neural Networks for Image Clustering

This folder contains the official code for Learning Hierarchical Graph Neural Networks for Image Clustering.

Setup

We use python 3.7. The CUDA version needs to be 10.2. Besides DGL (>=0.8), we depend on several packages. To install dependencies using conda:

conda create -n Hilander # create env
conda activate Hilander # activate env
conda install pytorch==1.7.0 torchvision==0.8.0 cudatoolkit=10.2 -c pytorch # install pytorch 1.7 version
conda install -y cudatoolkit=10.2 faiss-gpu=1.6.5 -c pytorch # install faiss gpu version matching cuda 10.2
pip install dgl-cu102 dglgo -f https://data.dgl.ai/wheels/repo.html # install the latest dgl for cuda 10.2
pip install tqdm # install tqdm
git clone https://github.com/yjxiong/clustering-benchmark.git # install clustering-benchmark for evaluation
cd clustering-benchmark
python setup.py install
cd ../

Data

The datasets used for training and test are hosted by several services.

AWS S3 | Google Drive | BaiduPan (pwd: wbmh)

After download, unpack the pickled files into data/.

Training

We provide training scripts for different datasets.

For training on DeepGlint, one can run

bash scripts/train_deepglint.sh

Deepglint is a large-scale dataset, we randomly select 10% of the classes to construct a subset to train.

For training on full iNatualist dataset, one can run

bash scripts/train_inat.sh

For training on re-sampled iNatualist dataset, one can run

bash scripts/train_inat_resampled_1_in_6_per_class.sh

We sample a subset of the full iNat2018-Train to attain a drastically different train-time cluster size distribution as iNat2018-Test, which is named as inat_resampled_1_in_6_per_class.

Inference

In the paper, we have two experiment settings: Clustering with Seen Test Data Distribution and Clustering with Unseen Test Data Distribution.

For Clustering with Seen Test Data Distribution, one can run

bash scripts/test_deepglint_imbd_sampled_as_deepglint.sh

bash scripts/test_inat.sh

Clustering with Seen Test Data Distribution Performance

	IMDB-Test-SameDist	iNat2018-Test
Fp	0.779	0.330
Fb	0.819	0.350
NMI	0.949	0.774

• The results might fluctuate a little due to the randomness introduced by gpu knn building using faiss-gpu.

For Clustering with Unseen Test Data Distribution, one can run

bash scripts/test_deepglint_hannah.sh

bash scripts/test_deepglint_imdb.sh

bash scripts/test_inat_train_on_resampled_1_in_6_per_class.sh

Clustering with Unseen Test Data Distribution Performance

	Hannah	IMDB	iNat2018-Test
Fp	0.741	0.717	0.294
Fb	0.706	0.810	0.352
NMI	0.810	0.953	0.764

• The results might fluctuate a little due to the randomness introduced by gpu knn building using faiss-gpu.