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

Related tags

Deep Learning GNAS-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

You might also like...
A weakly-supervised scene graph generation codebase. The implementation of our CVPR2021 paper ``Linguistic Structures as Weak Supervision for Visual Scene Graph Generation''
A weakly-supervised scene graph generation codebase. The implementation of our CVPR2021 paper ``Linguistic Structures as Weak Supervision for Visual Scene Graph Generation''

README.md shall be finished soon. WSSGG 0 Overview 1 Installation 1.1 Faster-RCNN 1.2 Language Parser 1.3 GloVe Embeddings 2 Settings 2.1 VG-GT-Graph

Keren Ye 35 Nov 20, 2022
EdMIPS: Rethinking Differentiable Search for Mixed-Precision Neural Networks

EdMIPS is an efficient algorithm to search the optimal mixed-precision neural network directly without proxy task on ImageNet given computation budgets. It can be applied to many popular network architectures, including ResNet, GoogLeNet, and Inception-V3.

Zhaowei Cai 47 Dec 30, 2022
[ICLR2021oral] Rethinking Architecture Selection in Differentiable NAS

DARTS-PT Code accompanying the paper ICLR'2021: Rethinking Architecture Selection in Differentiable NAS Ruochen Wang, Minhao Cheng, Xiangning Chen, Xi

Ruochen Wang 86 Dec 27, 2022
Rethinking the U-Net architecture for multimodal biomedical image segmentation
Rethinking the U-Net architecture for multimodal biomedical image segmentation

MultiResUNet Rethinking the U-Net architecture for multimodal biomedical image segmentation This repository contains the original implementation of "M

Nabil Ibtehaz 308 Jan 5, 2023
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

What is DeepHyper? DeepHyper is a software package that uses learning, optimization, and parallel computing to automate the design and development of

DeepHyper Team 214 Jan 8, 2023
Model search is a framework that implements AutoML algorithms for model architecture search at scale
Model search is a framework that implements AutoML algorithms for model architecture search at scale

Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers speed up their exploration process for finding the right model architecture for their classification problems (i.e., DNNs with different types of layers).

Google 3.2k Dec 31, 2022
PyTorch implementation of
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Efficient Neural Architecture Search (ENAS) in PyTorch PyTorch implementation of Efficient Neural Architecture Search via Parameters Sharing. ENAS red

Taehoon Kim 2.6k Dec 31, 2022
An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.
An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.

Neural Architecture Search with Random Labels(RLNAS) Introduction This project provides an implementation for Neural Architecture Search with Random L

null 18 Nov 8, 2022
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.
Deep Image Search is an AI-based image search engine that includes deep transfor learning features Extraction and tree-based vectorized search.

Deep Image Search - AI-Based Image Search Engine Deep Image Search is an AI-based image search engine that includes deep transfer learning features Ex

null 139 Jan 1, 2023
Comments
  • About the search time is too long

    About the search time is too long

    Thank you for your wonderful work, I find it very interesting!

    I ran the scripts/search_sbms_cluster.sh directly on one V100 GPU, but I have searched for almost 12 hours and it is not over yet. This is too far from the 2.5 hours described in your article. I want to know whether it is necessary to set an early stop? Do I need to use multiple GPUs? I want to know your experimental hardware settings.

    thanks！

    opened by ZhouAo-ZA 4
  • run error!

    run error!

    when i try to run the sh scripts/search_sbms_cluster.sh [gpu_id] , something wrong happened:

    dgl._ffi.base.DGLError: Cannot assign node feature "h" on device cuda:0 to a graph on device cpu. Call DGLGraph.to() to copy the graph to the same device.

    how can I run the code?

    opened by ZhouAo-ZA 3
  • Run error!

    Run error!

    Hi, when load the datasets, there is a problem as following:

    File "/home/hk416/tjdeng/GNAS-MP/data/SBMs.py", line 160, in __init__ f = pickle.load(f) ModuleNotFoundError: No module named 'dgl.graph' It seems that the version of dgl goes wrong. I use the dgl-cuda11.0-0.7.1. So which version of dgl do you use?

    opened by tjdeng 1
  • How to train cora dataset using this model?

    How to train cora dataset using this model?

    Thank you for your wonderful work, I find it very interesting! I want to explore the performance on one large graph. Could you provide the command line of training cora dataset? thanks!

    opened by lichaoaaron 0
Owner
Shaofei Cai
Retired ICPC contestant, classic algorithm enthusiast.
Shaofei Cai
GitHub
A PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing"

A PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf 2021). Abstract In this work we propose Pathfind

Benedek Rozemberczki 49 Dec 1, 2022
The official repo for CVPR2021——ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search.

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [paper] Introduction This is the official implementation of ViPNAS: Efficient V

Lumin 42 Sep 26, 2022
Neural Message Passing for Computer Vision

Neural Message Passing for Quantum Chemistry Implementation of different models of Neural Networks on graphs as explained in the article proposed by G

Pau Riba 310 Nov 7, 2022
BRepNet: A topological message passing system for solid models

BRepNet: A topological message passing system for solid models This repository contains the an implementation of BRepNet: A topological message passin

Autodesk AI Lab 42 Dec 30, 2022
Message Passing on Cell Complexes

CW Networks This repository contains the code used for the papers Weisfeiler and Lehman Go Cellular: CW Networks (Under review) and Weisfeiler and Leh

Twitter Research 108 Jan 5, 2023
code for paper "Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?"

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? Code for paper: Does Unsupervised Architecture Representation

null 39 Dec 17, 2022
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree

null 35 Jan 3, 2023
Densely Connected Search Space for More Flexible Neural Architecture Search (CVPR2020)

DenseNAS The code of the CVPR2020 paper Densely Connected Search Space for More Flexible Neural Architecture Search. Neural architecture search (NAS)

Jamin Fong 291 Nov 18, 2022
Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets This is the official PyTorch implementation for the paper Rapid Neural A

null 48 Dec 26, 2022
Official DGL implementation of "Rethinking High-order Graph Convolutional Networks"

SE Aggregation This is the implementation for Rethinking High-order Graph Convolutional Networks. Here we show the codes for citation networks as an e

Tianqi Zhang (张天启) 32 Jul 19, 2022