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https://github.com/lightaime/deep_gcns_torch
Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
https://github.com/lightaime/deep_gcns_torch
3d-point-clouds bioinformatics cheminformatics computer-vision data-mining deep-gcns deep-learning geometric-deep-learning graph-convolutional-networks graph-neural-networks pytorch science-research social-network
Last synced: 2 days ago
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Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
- Host: GitHub
- URL: https://github.com/lightaime/deep_gcns_torch
- Owner: lightaime
- License: mit
- Created: 2019-07-30T19:44:00.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-07-31T21:01:32.000Z (over 2 years ago)
- Last Synced: 2025-01-12T17:04:48.817Z (9 days ago)
- Topics: 3d-point-clouds, bioinformatics, cheminformatics, computer-vision, data-mining, deep-gcns, deep-learning, geometric-deep-learning, graph-convolutional-networks, graph-neural-networks, pytorch, science-research, social-network
- Language: Python
- Homepage:
- Size: 7.02 MB
- Stars: 1,144
- Watchers: 17
- Forks: 156
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepGCNs: Can GCNs Go as Deep as CNNs?
In this work, we present new ways to successfully train very deep GCNs. We borrow concepts from CNNs, mainly residual/dense connections and dilated convolutions, and adapt them to GCN architectures. Through extensive experiments, we show the positive effect of these deep GCN frameworks.
[[Project]](https://www.deepgcns.org/) [[Paper]](https://arxiv.org/abs/1904.03751) [[Slides]](https://docs.google.com/presentation/d/1L82wWymMnHyYJk3xUKvteEWD5fX0jVRbCbI65Cxxku0/edit?usp=sharing) [[Tensorflow Code]](https://github.com/lightaime/deep_gcns) [[Pytorch Code]](https://github.com/lightaime/deep_gcns_torch)
## Overview
We do extensive experiments to show how different components (#Layers, #Filters, #Nearest Neighbors, Dilation, etc.) effect `DeepGCNs`. We also provide ablation studies on different type of Deep GCNs (MRGCN, EdgeConv, GraphSage and GIN).
## How to train, test and evaluate our models
Please look the details in `Readme.md` of each task inside `examples` folder.
All the information of code, data, and pretrained models can be found there.
* DeepGCNs ([ICCV'2019](https://arxiv.org/abs/1904.03751), [TPAMI'2021](https://arxiv.org/abs/1910.06849)): [S3DIS](examples/sem_seg_dense), [PartNet](examples/part_sem_seg), [ModelNet40](examples/modelnet_cls), [PPI](/examples/ppi)
* DeeperGCN ([Arxiv'2020](https://arxiv.org/abs/2006.07739)): [OGB](examples/ogb)
* GNN'1000 ([ICML'2021](https://arxiv.org/abs/2106.07476)): [OGB](examples/ogb_eff)
## Recommended Requirements
* [Python>=3.7](https://www.python.org/)
* [Pytorch>=1.9.0](https://pytorch.org)
* [pytorch_geometric>=1.6.0](https://pytorch-geometric.readthedocs.io/en/latest/)
* [ogb>=1.3.1](https://github.com/snap-stanford/ogb) only used for experiments on OGB datasets
* [dgl>=0.5.3](https://github.com/dmlc/dgl) only used for the experiment `examples/ogb_eff/ogbn_arxiv_dgl`
Install enviroment by runing:
```
source deepgcn_env_install.sh
```
## Code Architecture
.
├── misc # Misc images
├── utils # Common useful modules
├── gcn_lib # gcn library
│ ├── dense # gcn library for dense data (B x C x N x 1)
│ └── sparse # gcn library for sparse data (N x C)
├── eff_gcn_modules # modules for mem efficient gnns
├── examples
│ ├── modelnet_cls # code for point clouds classification on ModelNet40
│ ├── sem_seg_dense # code for point clouds semantic segmentation on S3DIS (data type: dense)
│ ├── sem_seg_sparse # code for point clouds semantic segmentation on S3DIS (data type: sparse)
│ ├── part_sem_seg # code for part segmentation on PartNet
│ ├── ppi # code for node classification on PPI dataset
│ └── ogb # code for node/graph property prediction on OGB datasets
│ └── ogb_eff # code for node/graph property prediction on OGB datasets with memory efficient GNNs
└── ...
## Citation
Please cite our paper if you find anything helpful,
```
@InProceedings{li2019deepgcns,
title={DeepGCNs: Can GCNs Go as Deep as CNNs?},
author={Guohao Li and Matthias Müller and Ali Thabet and Bernard Ghanem},
booktitle={The IEEE International Conference on Computer Vision (ICCV)},
year={2019}
}
```
```
@article{li2021deepgcns_pami,
title={Deepgcns: Making gcns go as deep as cnns},
author={Li, Guohao and M{\"u}ller, Matthias and Qian, Guocheng and Perez, Itzel Carolina Delgadillo and Abualshour, Abdulellah and Thabet, Ali Kassem and Ghanem, Bernard},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2021},
publisher={IEEE}
}
```
```
@misc{li2020deepergcn,
title={DeeperGCN: All You Need to Train Deeper GCNs},
author={Guohao Li and Chenxin Xiong and Ali Thabet and Bernard Ghanem},
year={2020},
eprint={2006.07739},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
```
@InProceedings{li2021gnn1000,
title={Training Graph Neural Networks with 1000 layers},
author={Guohao Li and Matthias Müller and Bernard Ghanem and Vladlen Koltun},
booktitle={International Conference on Machine Learning (ICML)},
year={2021}
}
```
## License
MIT License
## Contact
For more information please contact [Guohao Li](https://ghli.org), [Matthias Muller](https://matthias.pw/), [Guocheng Qian](https://www.gcqian.com/).