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deep_gcns_torch

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1.0.0

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DeepGCNS：GCN可以像CNN一樣深嗎？

在這項工作中，我們提出了成功培訓非常深入GCN的新方法。我們從CNN借用概念，主要是殘留/密集的連接和擴張的捲積，並將其適應GCN體系結構。通過廣泛的實驗，我們顯示了這些深GCN框架的積極作用。

[Project] [Paper] [幻燈片] [TensorFlow代碼] [Pytorch代碼]

概述

我們進行了廣泛的實驗，以顯示不同的組件（#layers，#filters，#nearest鄰居，擴張等）如何影響DeepGCNs 。我們還提供有關不同類型的深GCN（MRGCN，EDGECONV，GraphSage和Gin）的消融研究。

如何訓練，測試和評估我們的模型

請查看examples文件夾中每個任務的Readme.md中的詳細信息。代碼，數據和驗證模型的所有信息都可以在此處找到。

DeepGCNS（ICCV'2019，TPAMI'2021）：S3DIS，PARTNET，MOTFERNET40，PPI
Deepergcn（Arxiv'2020）：OGB
Gnn'1000（ICML'2021）：OGB

建議的要求

Python> = 3.7
pytorch> = 1.9.0
pytorch_ geometric> = 1.6.0
OGB> = 1.3.1僅用於OGB數據集上的實驗
DGL> = 0.5.3僅用於實驗examples/ogb_eff/ogbn_arxiv_dgl

通過運行安裝環境：

 source deepgcn_env_install.sh

代碼架構

 .
├── 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
└── ...

引用

如果您發現任何有用的話，請引用我們的論文

 @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}
}