Official implementation of Rethinking Graph Neural Architecture Search from Message-passing (CVPR2021)

Related tags

Deep LearningGNAS-MP
Overview

Rethinking Graph Neural Architecture Search from Message-passing

Intro

The GNAS can automatically learn better architecture with the optimal depth of message passing on the graph. Specifically, we design Graph Neural Architecture Paradigm (GAP) with tree-topology computation procedure and two types of fine-grained atomic operations (feature filtering & neighbor aggregation) from message-passing mechanism to construct powerful graph network search space. Feature filtering performs adaptive feature selection, and neighbor aggregation captures structural information and calculates neighbors’ statistics. Experiments show that our GNAS can search for better GNNs with multiple message-passing mechanisms and optimal message-passing depth.

Getting Started

0. Prerequisites

  • Linux
  • NVIDIA GPU + CUDA CuDNN

1. Setup Python environment for GPU

# clone Github repo
conda install git
git clone https://github.com/phython96/GNAS-MP.git
cd GNAS-MP

# Install python environment
conda env create -f environment_gpu.yml
conda activate gnas

2. Download datasets

The datasets are provided by project benchmarking-gnns, you can click here to download all the required datasets.

3. Searching

We have provided scripts for easily searching graph neural networks on five datasets.

# searching on ZINC dataset at graph regression task
sh scripts/search_molecules_zinc.sh [gpu_id]

# searching on SBMs_PATTERN dataset at node classification task
sh scripts/search_sbms_pattern.sh [gpu_id]

# searching on SBMs_CLUSTER dataset at node classification task
sh scripts/search_sbms_cluster.sh [gpu_id]

# searching on MNIST dataset at graph classification task
sh scripts/search_superpixels_mnist.sh [gpu_id]

# searching on CIFAR10 dataset at graph classification task
sh scripts/search_superpixels_cifar10.sh [gpu_id]

When the search procedure is finished, you need to copy the searched genotypes from file "./save/[data_name]_search.txt" to "./configs/genotypes.py".

For example, we have searched on MNIST dataset, and obtain genotypes result file "./save/MNIST_search.txt".

Epoch : 19
[Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_dense', 2, 1), ('f_sparse', 3, 2), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 6), ('f_identity', 8, 7), ('f_dense', 9, 7)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_dense', 2, 0), ('f_dense', 3, 0), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_identity', 7, 5), ('f_identity', 8, 6), ('f_sparse', 9, 8)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 0), ('f_sparse', 3, 0), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 4), ('f_sparse', 8, 6), ('f_identity', 9, 4)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 1), ('f_sparse', 3, 1), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 4), ('f_identity', 8, 7), ('f_sparse', 9, 4)], concat_node=None)]
Epoch : 20
[Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_dense', 2, 1), ('f_sparse', 3, 2), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 6), ('f_identity', 8, 7), ('f_sparse', 9, 8)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 1), ('f_dense', 3, 0), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_identity', 7, 5), ('f_identity', 8, 6), ('f_sparse', 9, 8)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 0), ('f_sparse', 3, 0), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 4), ('f_dense', 8, 4), ('f_sparse', 9, 6)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 1), ('f_sparse', 3, 2), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 4), ('f_sparse', 8, 6), ('f_sparse', 9, 8)], concat_node=None)]
Epoch : 21
[Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_dense', 2, 1), ('f_sparse', 3, 2), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 6), ('f_identity', 8, 7), ('f_sparse', 9, 8)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_dense', 2, 0), ('f_dense', 3, 0), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_identity', 7, 5), ('f_identity', 8, 6), ('f_identity', 9, 6)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 0), ('f_sparse', 3, 0), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 6), ('f_identity', 8, 4), ('f_identity', 9, 7)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 1), ('f_sparse', 3, 2), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 4), ('f_sparse', 8, 6), ('f_identity', 9, 4)], concat_node=None)]

Copy the fourth line from the above file and paste it into "./configs/genotypes.py" with the prefix "MNIST = ".

MNIST_Net = [Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_dense', 2, 1), ('f_sparse', 3, 2), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 6), ('f_identity', 8, 7), ('f_sparse', 9, 8)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 1), ('f_dense', 3, 0), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_identity', 7, 5), ('f_identity', 8, 6), ('f_sparse', 9, 8)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 0), ('f_sparse', 3, 0), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 4), ('f_dense', 8, 4), ('f_sparse', 9, 6)], concat_node=None), Genotype(alpha_cell=[('f_sparse', 1, 0), ('f_sparse', 2, 1), ('f_sparse', 3, 2), ('a_max', 4, 1), ('a_max', 5, 2), ('a_max', 6, 3), ('f_sparse', 7, 4), ('f_sparse', 8, 6), ('f_sparse', 9, 8)], concat_node=None)]

4. Training

Before training, you must confim that there is a genotype of searched graph neural network in file "./configs/genotypes.py".

We provided scripts for easily training graph neural networks searched by GNAS.

# training on ZINC dataset at graph regression task
sh scripts/train_molecules_zinc.sh [gpu_id]

# training on SBMs_PATTERN dataset at node classification task
sh scripts/train_sbms_pattern.sh [gpu_id]

# training on SBMs_CLUSTER dataset at node classification task
sh scripts/train_sbms_cluster.sh [gpu_id]

# training on MNIST dataset at graph classification task
sh scripts/train_superpixels_mnist.sh [gpu_id]

# training on CIFAR10 dataset at graph classification task
sh scripts/train_superpixels_cifar10.sh [gpu_id]

Results

Visualization

Here, we show 4-layer graph neural networks searched by GNAS on five datasets at three graph tasks.

Reference

to be updated

Owner
Shaofei Cai
Retired ICPC contestant, classic algorithm enthusiast.
Shaofei Cai
GitHub Repository
Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks

Jina AI 794 Dec 31, 2022
This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].

CG3 This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning]. R

12 Oct 28, 2022
ColossalAI-Examples - Examples of training models with hybrid parallelism using ColossalAI

ColossalAI-Examples This repository contains examples of training models with Co

HPC-AI Tech 185 Jan 09, 2023
Simple codebase for flexible neural net training

neural-modular Simple codebase for flexible neural net training. Allows for seamless exchange of models, dataset, and optimizers. Uses hydra for confi

Jannik Kossen 7 Apr 05, 2022
A modern pure-Python library for reading PDF files

pdf A modern pure-Python library for reading PDF files. The goal is to have a modern interface to handle PDF files which is consistent with itself and

6 Apr 06, 2022
MINOS: Multimodal Indoor Simulator

MINOS Simulator MINOS is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environ

194 Dec 27, 2022
pytorch implementation for PointNet

PointNet.pytorch This repo is implementation for PointNet in pytorch. The model is in pointnet/model.py. It is teste

Fei Xia 1.7k Dec 30, 2022
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)

AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f

Junxiao Song 2.8k Dec 26, 2022
Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks

Hidden-Fold Networks (HFN): Random Recurrent Residuals Using Sparse Supermasks by Ángel López García-Arias, Masanori Hashimoto, Masato Motomura, and J

Ángel López García-Arias 4 May 19, 2022
Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

14 Nov 06, 2022
The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue. How do I cite D-REX? For now, cite

Alon Albalak 6 Mar 31, 2022
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

MSVCL_MICCAI2021 Installation Please follow the instruction in pytorch-CycleGAN-and-pix2pix to install. Example Usage An example of vendor-styles tran

Jaron Lee 11 Oct 19, 2022
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

MI-AOD Language: 简体中文 | English Introduction This is the code for Multiple Instance Active Learning for Object Detection (The PDF is not available tem

Tianning Yuan 269 Dec 21, 2022
The fastai book, published as Jupyter Notebooks

English / Spanish / Korean / Chinese / Bengali / Indonesian The fastai book These notebooks cover an introduction to deep learning, fastai, and PyTorc

fast.ai 17k Jan 07, 2023
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet 中文版 Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023
"NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search".

NAS-Bench-301 This repository containts code for the paper: "NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search". The

AutoML-Freiburg-Hannover 57 Nov 30, 2022
💃 VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena

💃 VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena.

Heidelberg-NLP 17 Nov 07, 2022
LRBoost is a scikit-learn compatible approach to performing linear residual based stacking/boosting.

LRBoost is a sckit-learn compatible package for linear residual boosting. LRBoost combines a linear estimator and a non-linear estimator to leverage t

Andrew Patton 5 Nov 23, 2022
This repository accompanies the ACM TOIS paper "What can I cook with these ingredients?" - Understanding cooking-related information needs in conversational search

In this repository you find data that has been gathered when conducting in-situ experiments in a conversational cooking setting. These data include tr

6 Sep 22, 2022
Official Pytorch implementation of 'RoI Tanh-polar Transformer Network for Face Parsing in the Wild.'

Official Pytorch implementation of 'RoI Tanh-polar Transformer Network for Face Parsing in the Wild.'

Jie Shen 125 Jan 08, 2023