SoGCN: Second-Order Graph Convolutional Networks

Related tags

Deep Learningmachine-learningdeep-learningartificial-intelligencegcngnn
Overview

SoGCN: Second-Order Graph Convolutional Networks

This is the authors' implementation of paper "SoGCN: Second-Order Graph Convolutional Networks" in PyTorch. All the hyper-parameters and experiment settings have been included in this repo.

Requirements

For the GNN benchmarking part, our experiments are based on GNN Benchmark. Please follow the instructions in Benchmark Installation to install the running environment. Our code is tested with PyTorch 1.3.1 + Cuda Toolkit 10.0.

For the experiments on OGB Open Graph Benchmark, we built our models based on offical code. Please follow the instructions in Getting Started to configure the running environment. Our code is tested with PyTorch 1.4.0 + Cuda Toolkit 10.1.

Our experiments is conducted on a 4-core Nvidia Quadro P6000 GPU running on Ubuntu 18.04.2 LTS.

Reproduce Results

For SGS and GNN benchmark datasets, we provide a script named 'scripts/exp.py' to run a series of model training in screen sessions. You can type python scripts/exp.py -h to view its usage. To OGB benchmark dataset, we provide shell scripts 'scripts/run_ogbn_proteins.sh' and 'scripts/run_ogbg_molhiv.sh' to reproduce results with our hyper-parameter settings.

For convenience, below presents the commands to reproduce our results.

Synthetic Graph Spectrum Dataset

To train models on SGS datasets, run the following commands:

## High-Pass
python scripts/exp.py -a start -e highpass_sogcn -t SGS -g 1111 --dataset SGS_HIGH_PASS --config 'configs/SGS_node_regression_SoGCN.json'
python scripts/exp.py -a start -e highpass_sogcn -t SGS -g 1111 --dataset SGS_HIGH_PASS --config 'configs/SGS_node_regression_GCN.json'
python scripts/exp.py -a start -e highpass_sogcn -t SGS -g 1111 --dataset SGS_HIGH_PASS --config 'configs/SGS_node_regression_GIN.json'

## Low-Pass
python scripts/exp.py -a start -e lowpass_sogcn -t SGS -g 1111 --dataset SGS_LOW_PASS --config 'configs/SGS_node_regression_SoGCN.json'
python scripts/exp.py -a start -e lowpass_sogcn -t SGS -g 1111 --dataset SGS_LOW_PASS --config 'configs/SGS_node_regression_GCN.json'
python scripts/exp.py -a start -e lowpass_sogcn -t SGS -g 1111 --dataset SGS_LOW_PASS --config 'configs/SGS_node_regression_GIN.json'

## Band-Pass
python scripts/exp.py -a start -e bandpass_sogcn -t SGS -g 1111 --dataset SGS_BAND_PASS --config 'configs/SGS_node_regression_SoGCN.json'
python scripts/exp.py -a start -e bandpass_sogcn -t SGS -g 1111 --dataset SGS_BAND_PASS --config 'configs/SGS_node_regression_GCN.json'
python scripts/exp.py -a start -e bandpass_sogcn -t SGS -g 1111 --dataset SGS_BAND_PASS --config 'configs/SGS_node_regression_GIN.json'

Note the results will be saved to '_out/SGS_node_regression/'.

Open Graph Benchmarks

Running the following commands will reproduce our results on Open Graph Benchmark datasets:

scripts/run_ogbn_proteins.sh <log_dir> [<gpu_id>] [--test]
scripts/run_ogbg_molhiv.sh <log_dir> [<gpu_id>] [--test]

where log_dir specifies the folder to load or save output logs. The downloaded log files will be saved in _out/protein_nodeproppred and _out/molhiv_graphproppred for ogbn-protein and ogbn-molhiv datasets, respectively. gpu_id specifies the GPU device to run our models. Add --test if you only want to reload the log files and read out the testing results. The OGB dataset will be automatically downloaded into data/OGB directory at the first run.

To download the saved log files for ogb datasets, please run the following scripts:

bash scripts/download_logfiles_ogb.sh

GNN Benchmarks

To test on our pretrained models on GNN benchmarks, please follow the steps as below:

  1. Download our pretrained models.
# make sure the commands below are executed in the root directory of this project
bash scripts/download_pretrained_molecules.sh
bash scripts/download_pretrained_superpixels.sh
bash scripts/download_pretrained_SBMs.sh

Pretrained models will be downloaded to '_out/molecules_graph_regression', '_out/superpixels_graph_classification', '_out/SBMs_node_classification', respectively.

  1. Type the commands for different tasks

Molecules Graph Regression

## ZINC
python main_molecules_graph_regression.py --model SoGCN --dataset ZINC --gpu_id 0 --test True --out_dir _out/molecules_graph_regression/zinc_sogcn
python main_molecules_graph_regression.py --model SoGCN --dataset ZINC --gpu_id 0 --test True --out_dir _out/molecules_graph_regression/zinc_sogcn_gru

Superpixels Graph Classification

## MNIST
python main_superpixels_graph_classification.py --model SoGCN --dataset MNIST --gpu_id 0 --test True --out_dir _out/superpixels_graph_classification/mnist_sogcn
python main_superpixels_graph_classification.py --model SoGCN --dataset MNIST --gpu_id 0 --test True --out_dir _out/superpixels_graph_classification/mnist_sogcn_gru


## CIFAR10
python main_superpixels_graph_classification.py --model SoGCN --dataset CIFAR10 --gpu_id 0 --test True --out_dir _out/superpixels_graph_classification/cifar10_sogcn
python main_superpixels_graph_classification.py --model SoGCN --dataset CIFAR10 --gpu_id 0 --test True --out_dir _out/superpixels_graph_classification/cifar10_sogcn_gru

SBMs Node Classification

## CLUSTER
python main_SBMs_node_classification.py --model SoGCN --dataset SBM_CLUSTER  --verbose True --gpu_id 0 --test True --out_dir _out/SBMs_node_classification/cluster_sogcn
python main_SBMs_node_classification.py --model SoGCN --dataset SBM_CLUSTER  --verbose True --gpu_id 0 --test True --out_dir _out/SBMs_node_classification/cluster_sogcn_gru

## PATTERN
python main_SBMs_node_classification.py --model SoGCN --dataset SBM_PATTERN  --verbose True --gpu_id 0 --test True --out_dir _out/SBMs_node_classification/pattern_sogcn
python main_SBMs_node_classification.py --model SoGCN --dataset SBM_PATTERN  --verbose True --gpu_id 0 --test True --out_dir _out/SBMs_node_classification/pattern_sogcn_gru
Owner
Yuehao
PhD in Computer Science & Engineering @ CUHK. Research interest includes Graphics + Vision + Machine Learning.
Yuehao
GitHub Repository
The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution.

WSRGlow The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution. Audio sa

Kexun Zhang 96 Jan 03, 2023
PyTorch implementation of SwAV (Swapping Assignments between Views)

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments This code provides a PyTorch implementation and pretrained models for SwAV

Meta Research 1.7k Jan 04, 2023
Official implementation for the paper: "Multi-label Classification with Partial Annotations using Class-aware Selective Loss"

Multi-label Classification with Partial Annotations using Class-aware Selective Loss Paper | Pretrained models Official PyTorch Implementation Emanuel

99 Dec 27, 2022
The official implementation for "FQ-ViT: Fully Quantized Vision Transformer without Retraining".

FQ-ViT [arXiv] This repo contains the official implementation of "FQ-ViT: Fully Quantized Vision Transformer without Retraining". Table of Contents In

132 Jan 08, 2023
Implementation of Common Image Evaluation Metrics by Sayed Nadim (sayednadim.github.io). The repo is built based on full reference image quality metrics such as L1, L2, PSNR, SSIM, LPIPS. and feature-level quality metrics such as FID, IS. It can be used for evaluating image denoising, colorization, inpainting, deraining, dehazing etc. where we have access to ground truth.

Image Quality Evaluation Metrics Implementation of some common full reference image quality metrics. The repo is built based on full reference image q

Sayed Nadim 10 Jan 01, 2023
This is an open source python repository for various python tests

Welcome to Py-tests This is an open source python repository for various python tests. This is in response to the hacktoberfest2021 challenge. It is a

Yada Martins Tisan 3 Oct 31, 2021
Automatic labeling, conversion of different data set formats, sample size statistics, model cascade

Simple Gadget Collection for Object Detection Tasks Automatic image annotation Conversion between different annotation formats Obtain statistical info

llt 4 Aug 24, 2022
View model summaries in PyTorch!

torchinfo (formerly torch-summary) Torchinfo provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensor

Tyler Yep 1.5k Jan 05, 2023
Improving 3D Object Detection with Channel-wise Transformer

"Improving 3D Object Detection with Channel-wise Transformer" Thanks for the OpenPCDet, this implementation of the CT3D is mainly based on the pcdet v

Hualian Sheng 107 Dec 20, 2022
AI-Fitness-Tracker - AI Fitness Tracker With Python

AI-Fitness-Tracker We have build a AI based Fitness Tracker using OpenCV and Pyt

Sharvari Mangale 5 Feb 09, 2022
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库，帮助大家挑选或训练出更适合自己科研或者业务的模型结构

BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库，帮助大家挑选或训练出更适合自己科研或者业务的模型结构。 文档地址：https://basecls.readthedocs.io 安装 安装环境 BaseCls 需要 Python = 3.6。 BaseCls 依赖 M

MEGVII Research 28 Dec 23, 2022
Code for "Universal inference meets random projections: a scalable test for log-concavity"

How to use this repository This repository contains code to replicate the results of "Universal inference meets random projections: a scalable test fo

Robin Dunn 0 Nov 21, 2021
Node Editor Plug for Blender

NodeEditor Blender的程序化建模插件 Show Current 基本框架：自定义的tree-node-socket、tree中的node与socket采用字典查询、基于socket入度的拓扑排序 数据传递和处理依靠Tree中的字典，socket传递字典key TODO 增加更多的节点

Cuimi 11 Dec 03, 2022
The official implementation of paper Siamese Transformer Pyramid Networks for Real-Time UAV Tracking, accepted by WACV22

SiamTPN Introduction This is the official implementation of the SiamTPN (WACV2022). The tracker intergrates pyramid feature network and transformer in

Robotics and Intelligent Systems Control @ NYUAD 29 Jan 08, 2023
Learning Representational Invariances for Data-Efficient Action Recognition

Learning Representational Invariances for Data-Efficient Action Recognition Official PyTorch implementation for Learning Representational Invariances

Virginia Tech Vision and Learning Lab 27 Nov 22, 2022
Investigating automatic navigation towards standard US views integrating MARL with the virtual US environment developed in CT2US simulation

AutomaticUSnavigation Investigating automatic navigation towards standard US views integrating MARL with the virtual US environment developed in CT2US

Cesare Magnetti 6 Dec 05, 2022
Affine / perspective transformation in Pose Estimation with Tensorflow 2

Pose Transformation Affine / Perspective transformation in Pose Estimation with Tensorflow 2 Introduction 이 repo는 pose estimation을 연구하고 개발하는 데 도움이 되기

Kim Junho 1 Dec 22, 2021
Apollo optimizer in tensorflow

Apollo Optimizer in Tensorflow 2.x Notes: Warmup is important with Apollo optimizer, so be sure to pass in a learning rate schedule vs. a constant lea

Evan Walters 1 Nov 09, 2021
Official code for "Distributed Deep Learning in Open Collaborations" (NeurIPS 2021)

Distributed Deep Learning in Open Collaborations This repository contains the code for the NeurIPS 2021 paper "Distributed Deep Learning in Open Colla

Yandex Research 96 Sep 15, 2022
A PyTorch Lightning Callback for pushing models to the Hugging Face Hub 🤗⚡️

hf-hub-lightning A callback for pushing lightning models to the Hugging Face Hub. Note: I made this package for myself, mostly...if folks seem to be i

Nathan Raw 27 Dec 14, 2022