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
UNAVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring

UNAVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring Code Summary aggregate.py: this script aggr

1 Dec 28, 2021
Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Shuwa Gesture Toolkit is a framework that detects and classifies arbitrary gestures in short videos

Google 89 Dec 22, 2022
Baseline for the Spoofing-aware Speaker Verification Challenge 2022

Introduction This repository contains several materials that supplements the Spoofing-Aware Speaker Verification (SASV) Challenge 2022 including: calc

40 Dec 28, 2022
EMNLP'2021: SimCSE: Simple Contrastive Learning of Sentence Embeddings

SimCSE: Simple Contrastive Learning of Sentence Embeddings This repository contains the code and pre-trained models for our paper SimCSE: Simple Contr

Princeton Natural Language Processing 2.5k Dec 29, 2022
Implements MLP-Mixer: An all-MLP Architecture for Vision.

MLP-Mixer-CIFAR10 This repository implements MLP-Mixer as proposed in MLP-Mixer: An all-MLP Architecture for Vision. The paper introduces an all MLP (

Sayak Paul 51 Jan 04, 2023
Simple API for UCI Machine Learning Dataset Repository (search, download, analyze)

A simple API for working with University of California, Irvine (UCI) Machine Learning (ML) repository Table of Contents Introduction About Page of the

Tirthajyoti Sarkar 223 Dec 05, 2022
[NeurIPS 2021] Garment4D: Garment Reconstruction from Point Cloud Sequences

Garment4D [PDF] | [OpenReview] | [Project Page] Overview This is the codebase for our NeurIPS 2021 paper Garment4D: Garment Reconstruction from Point

Fangzhou Hong 112 Dec 23, 2022
POCO: Point Convolution for Surface Reconstruction

POCO: Point Convolution for Surface Reconstruction by: Alexandre Boulch and Renaud Marlet Abstract Implicit neural networks have been successfully use

valeo.ai 93 Dec 29, 2022
[SIGGRAPH Asia 2021] Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN

Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN [Paper] [Project Website] [Output resutls] Official Pytorch i

Badour AlBahar 215 Dec 17, 2022
Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data

federated is the source code for the Bachelor's Thesis Privacy-Preserving Federated Learning Applied to Decentralized Data (Spring 2021, NTNU) Federat

Dilawar Mahmood 25 Nov 30, 2022
FairMOT for Multi-Class MOT using YOLOX as Detector

FairMOT-X Project Overview FairMOT-X is a multi-class multi object tracker, which has been tailored for training on the BDD100K MOT Dataset. It makes

Jonathan Tan 33 Dec 28, 2022
LUKE -- Language Understanding with Knowledge-based Embeddings

LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transf

Studio Ousia 587 Dec 30, 2022
Implementation of Vaswani, Ashish, et al. "Attention is all you need."

Attention Is All You Need Paper Implementation This is my from-scratch implementation of the original transformer architecture from the following pape

Brando Koch 195 Dec 30, 2022
Python script that takes an Impulse response .wav and a input .wav to demonstrate audio convolution.

convolver Python script that takes an Impulse response .wav and a input .wav to demonstrate audio convolution. Created by Sean Higley

Sean Higley 1 Feb 23, 2022
Code for "LoRA: Low-Rank Adaptation of Large Language Models"

LoRA: Low-Rank Adaptation of Large Language Models This repo contains the implementation of LoRA in GPT-2 and steps to replicate the results in our re

Microsoft 394 Jan 08, 2023
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation(ICPR 2020) Overview This code is for the paper: Spatial Attention U-Net for Retinal V

Changlu Guo 151 Dec 28, 2022
Official Repository for Machine Learning class - Physics Without Frontiers 2021

PWF 2021 Física Sin Fronteras es un proyecto del Centro Internacional de Física Teórica (ICTP) en Trieste Italia. El ICTP es un centro dedicado a fome

36 Aug 06, 2022
Implementation of OpenAI paper with Simple Noise Scale on Fastai V2

README Implementation of OpenAI paper "An Empirical Model of Large-Batch Training" for Fastai V2. The code is based on the batch size finder implement

13 Dec 10, 2021
Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

Please read the blog post that goes with this code! Jupyter Notebook Setup System Requirements: Python, pip (Optional) virtualenv To start the Jupyter

Denny Britz 863 Dec 15, 2022
FID calculation with proper image resizing and quantization steps

clean-fid: Fixing Inconsistencies in FID Project | Paper The FID calculation involves many steps that can produce inconsistencies in the final metric.

Gaurav Parmar 606 Jan 06, 2023