IsoGCN code for ICLR2021

Related tags

Deep Learningmachine-learningdeep-learningphysical-simulationgraph-neural-network
Overview

IsoGCN

The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional Networks [arXiv].

isogcn

Please cite us as:

@inproceedings{
horie2021isometric,
title={Isometric Transformation Invariant and Equivariant Graph Convolutional Networks},
author={Masanobu Horie and Naoki Morita and Toshiaki Hishinuma and Yu Ihara and Naoto Mitsume},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=FX0vR39SJ5q}
}

General notice

If some of the following steps not working, please modify User settings section in the Makefile to fit with your environment.

Installation

You can either locally install locally or use Docker image. However, to generate an anisotropic nonlinear heat equation dataset, we recommend using Docker.

Local install

We are using poetry, thus first install it following the instruction on https://python-poetry.org/docs/ . Then, update the Makefile to PYTHON ?= 'poetry run python3' or explicitly specify the PYTHON environment variable on the execution of the make command.

For GPU environment,

PYTHON='poetry run python3' make poetry
poetry install
PYTHON='poetry run python3' make install_pyg_gpu

For CPU environment,

PYTHON='poetry run python3' make poetry
poetry install
PYTHON='poetry run python3' make install_pyg_cpu

, and set GPU_ID = -1 in the Makefile.

Also, optionally, please install FrontISTR and gmsh to generate an anisotropic nonlinear heat equation dataset (which are installed in the Docker image).

Docker image

Please download the docker image via https://savanna.ritc.jp/~horiem/isogcn_iclr2021/images/isogcn.tar, then place the image in the images directory. After that, plsease make in to login the docker befor perfroming all the following processes.

Differential operator dataset

Data generation

make differential_data

Training IsoGCN

make scalar2grad  # Scalar to gradient task
make scalar2grad  ADJ=5  # Scalar to gradient task with # hops = 5
make scalar2hessian  # Scalar to Hessian task
make grad2laplacian  # Gradient to Laplacian task
make grad2hessian  # Gradient to Hessian task

Training baseline models

make scalar2grad_baseline BASELINE_NAME=gcn  # BASELINE_NAME=[cluster_gcn, gcn, gcnii, gin, sgcn]

Similarly, one can perform baseline model trainings for other tasks.

Anisotropic nonlinear heat equation dataset

Run whole process with small data to check the process (Optional)

It generates a small dataset to simulate the whole process of data generation, preprocessing, training, and inference. This process requires either FrontISTR installed locally or Docker image.

make small_heat_nl_tensor_pipeline

Dataset download

The dataset containing finite element analysis results is generated from the ABC dataset using gmsh for meshing and FrontISTR for analysis.

Please download the dataset you need. (Note: To perform only training, you need only 'preprocessed' data.) The dataset can be downloaded via:

After download finished, please merge the split archives with:

cat train_50.tar.gz.parta* > train.tar.gz

, extract them with tar xvf *.tar.gz, then place them in the corresponding data/heat_nl_tensor/(raw|interim|preprocessed) directory.

Training IsoGCN

make heat_nl_tensor

Training baseline models

make heat_nl_tensor_baseline BASELINE_NAME=gcn  # BASELINE_NAME=[cluster_gcn, gcn, gcnii, gin, sgcn]

IsoGCN core implementation

The core implementation of the IsoGCN layer is separated in the library SiML and can be found here. Also, the code to generate IsoAMs is separated in the library Femio and can be found here.

License

Apache License 2.0.

Owner
horiem
A researcher, an engineer, and a Ph.D. student in machine learning + physical simulation.
horiem
GitHub Repository
Source code and notebooks to reproduce experiments and benchmarks on Bias Faces in the Wild (BFW).

Face Recognition: Too Bias, or Not Too Bias? Robinson, Joseph P., Gennady Livitz, Yann Henon, Can Qin, Yun Fu, and Samson Timoner. "Face recognition:

Joseph P. Robinson 41 Dec 12, 2022
1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking

Instead, two models for appearance modeling are included, together with the open-source BAGS model and the full set of code for inference. With this code, you can achieve around 79 Oct 08, 2022

Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing

Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing Paper Introduction Multi-task indoor scene understanding is widely considered a

62 Dec 05, 2022
An extremely simple, intuitive, hardware-friendly, and well-performing network structure for LiDAR semantic segmentation on 2D range image. IROS21

FIDNet_SemanticKITTI Motivation Implementing complicated network modules with only one or two points improvement on hardware is tedious. So here we pr

YimingZhao 54 Dec 12, 2022
PyTorch wrappers for using your model in audacity!

audacitorch This package contains utilities for prepping PyTorch audio models for use in Audacity. More specifically, it provides abstract classes for

Hugo Flores García 130 Dec 14, 2022
(CVPR 2021) Lifting 2D StyleGAN for 3D-Aware Face Generation

Lifting 2D StyleGAN for 3D-Aware Face Generation Official implementation of paper "Lifting 2D StyleGAN for 3D-Aware Face Generation". Requirements You

Yichun Shi 66 Nov 29, 2022
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

HEP Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior Implementation Python3 PyTorch=1.0 NVIDIA GPU+CUDA Training process The

FengZhang 34 Dec 04, 2022
RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems

RCD: Relation Map Driven Cognitive Diagnosis for Intelligent Education Systems This is our implementation for the paper: Weibo Gao, Qi Liu*, Zhenya Hu

BigData Lab @USTC 中科大大数据实验室 10 Oct 16, 2022
Build a small, 3 domain internet using Github pages and Wikipedia and construct a crawler to crawl, render, and index.

TechSEO Crawler Build a small, 3 domain internet using Github pages and Wikipedia and construct a crawler to crawl, render, and index. Play with the r

JR Oakes 57 Nov 24, 2022
A3C LSTM Atari with Pytorch plus A3G design

NEWLY ADDED A3G A NEW GPU/CPU ARCHITECTURE OF A3C FOR SUBSTANTIALLY ACCELERATED TRAINING!! RL A3C Pytorch NEWLY ADDED A3G!! New implementation of A3C

David Griffis 532 Jan 02, 2023
TextureGAN in Pytorch

TextureGAN This code is our PyTorch implementation of TextureGAN [Project] [Arxiv] TextureGAN is a generative adversarial network conditioned on sketc

Patsorn 147 Dec 14, 2022
CPF: Learning a Contact Potential Field to Model the Hand-object Interaction

Contact Potential Field This repo contains model, demo, and test codes of our paper: CPF: Learning a Contact Potential Field to Model the Hand-object

Lixin YANG 99 Dec 26, 2022
The code for Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation

BiMix The code for Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation arxiv Framework: visualization results: Requiremen

stanley 18 Sep 18, 2022
A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks A Research-oriented Federated Learning Library and Benchmark Platform

FedML-AI 175 Dec 01, 2022
Reproduce ResNet-v2(Identity Mappings in Deep Residual Networks) with MXNet

Reproduce ResNet-v2 using MXNet Requirements Install MXNet on a machine with CUDA GPU, and it's better also installed with cuDNN v5 Please fix the ran

Wei Wu 531 Dec 04, 2022
v objective diffusion inference code for JAX.

v-diffusion-jax v objective diffusion inference code for JAX, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The models

Katherine Crowson 186 Dec 21, 2022
Realtime_Multi-Person_Pose_Estimation

Introduction Multi Person PoseEstimation By PyTorch Results Require Pytorch Installation git submodule init && git submodule update Demo Download conv

tensorboy 1.3k Jan 05, 2023
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
Fast mesh denoising with data driven normal filtering using deep variational autoencoders

Fast mesh denoising with data driven normal filtering using deep variational autoencoders This is an implementation for the paper entitled "Fast mesh

9 Dec 02, 2022
Using VideoBERT to tackle video prediction

VideoBERT This repo reproduces the results of VideoBERT (https://arxiv.org/pdf/1904.01766.pdf). Inspiration was taken from https://github.com/MDSKUL/M

75 Dec 14, 2022