Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Last update: Dec 28, 2022

Overview

Graph Transformer - Pytorch

Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2. This was recently used by both Costa et al and Bakers lab for transforming MSA and pair-wise embedding into 3d coordinates.

Todo

add rotary embeddings for injecting adjacency information

Install

$ pip install graph-transformer-pytorch

Usage

import torch
from graph_transformer_pytorch import GraphTransformer

model = GraphTransformer(
    dim = 256,
    depth = 6,
    edge_dim = 512,             # optional - if left out, edge dimensions is assumed to be the same as the node dimensions above
    with_feedforwards = True,   # whether to add a feedforward after each attention layer, suggested by literature to be needed
    gated_residual = True       # to use the gated residual to prevent over-smoothing
)

nodes = torch.randn(1, 128, 256)
edges = torch.randn(1, 128, 128, 512)
mask = torch.ones(1, 128).bool()

nodes, edges = model(nodes, edges, mask = mask)

nodes.shape # (1, 128, 256) - project to R^3 for coordinates

Citations

@article {Costa2021.06.02.446809,
    author  = {Costa, Allan and Ponnapati, Manvitha and Jacobson, Joseph M. and Chatterjee, Pranam},
    title   = {Distillation of MSA Embeddings to Folded Protein Structures with Graph Transformers},
    year    = {2021},
    doi     = {10.1101/2021.06.02.446809},
    publisher = {Cold Spring Harbor Laboratory},
    URL     = {https://www.biorxiv.org/content/early/2021/06/02/2021.06.02.446809},
    eprint  = {https://www.biorxiv.org/content/early/2021/06/02/2021.06.02.446809.full.pdf},
    journal = {bioRxiv}
}

@article {Baek2021.06.14.448402,
    author  = {Baek, Minkyung and DiMaio, Frank and Anishchenko, Ivan and Dauparas, Justas and Ovchinnikov, Sergey and Lee, Gyu Rie and Wang, Jue and Cong, Qian and Kinch, Lisa N. and Schaeffer, R. Dustin and Mill{\'a}n, Claudia and Park, Hahnbeom and Adams, Carson and Glassman, Caleb R. and DeGiovanni, Andy and Pereira, Jose H. and Rodrigues, Andria V. and van Dijk, Alberdina A. and Ebrecht, Ana C. and Opperman, Diederik J. and Sagmeister, Theo and Buhlheller, Christoph and Pavkov-Keller, Tea and Rathinaswamy, Manoj K and Dalwadi, Udit and Yip, Calvin K and Burke, John E and Garcia, K. Christopher and Grishin, Nick V. and Adams, Paul D. and Read, Randy J. and Baker, David},
    title   = {Accurate prediction of protein structures and interactions using a 3-track network},
    year    = {2021},
    doi     = {10.1101/2021.06.14.448402},
    publisher = {Cold Spring Harbor Laboratory},
    URL     = {https://www.biorxiv.org/content/early/2021/06/15/2021.06.14.448402},
    eprint  = {https://www.biorxiv.org/content/early/2021/06/15/2021.06.14.448402.full.pdf},
    journal = {bioRxiv}
}

@misc{shi2021masked,
    title   = {Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification}, 
    author  = {Yunsheng Shi and Zhengjie Huang and Shikun Feng and Hui Zhong and Wenjin Wang and Yu Sun},
    year    = {2021},
    eprint  = {2009.03509},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}

Comments

fix mask bug in batch setting

The original implementation accidentally assumes batch size 1 here, and the mask's batch dimension (1) is automatically broadcast to the # of heads. If batch size is greater than 1, we have to handle that explicitly by copying it once for each head.

opened by tachim 1

VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

VSR-Transformer By Jiezhang Cao, Yawei Li, Kai Zhang, Luc Van Gool This paper proposes a new Transformer for video super-resolution (called VSR-Transf

225 Nov 13, 2022

A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

python_graphs This package is for computing graph representations of Python programs for machine learning applications. It includes the following modu

258 Dec 29, 2022

Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch

Transformer in Transformer Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image c

272 Dec 23, 2022

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

12.6k Jan 9, 2023

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.

212 Dec 25, 2022

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

202 Dec 27, 2022

Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

ImageProcessingTransformer Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

61 Jan 1, 2023

Transformer - Transformer in PyTorch

Transformer 完成进度 Embeddings and PositionalEncoding with example. MultiHeadAttent

1 Jan 6, 2022

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

519 Jan 2, 2023

Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Related tags

Overview

Graph Transformer - Pytorch

Install

Usage

Citations

You might also like...

VSR-Transformer - This paper proposes a new Transformer for video super-resolution (called VSR-Transformer).

A static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.

Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image classification, in Pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

Third party Pytorch implement of Image Processing Transformer (Pre-Trained Image Processing Transformer arXiv:2012.00364v2)

Transformer - Transformer in PyTorch

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.

Comments

fix mask bug in batch setting

Releases(0.0.3)

0.0.3(Jan 13, 2022)

0.0.2(Jul 8, 2021)

0.0.1(Jun 21, 2021)

Owner

Phil Wang

A certifiable defense against adversarial examples by training neural networks to be provably robust

Styled text-to-drawing synthesis method. Featured at the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design

Keras Image Embeddings using Contrastive Loss

A fuzzing framework for SMT solvers

DeepCAD: A Deep Generative Network for Computer-Aided Design Models

Code for Robust Contrastive Learning against Noisy Views

Reinfore learning tool box, contains trpo, a3c algorithm for continous action space

Meli Data Challenge 2021 - First Place Solution

BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation

Implementation of CVAE. Trained CVAE on faces from UTKFace Dataset to produce synthetic faces with a given degree of happiness/smileyness.

[CVPR 2022 Oral] TubeDETR: Spatio-Temporal Video Grounding with Transformers

Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Modifications of the official PyTorch implementation of StyleGAN3. Let's easily generate images and videos with StyleGAN2/2-ADA/3!

Pytorch implementation of Rosca, Mihaela, et al. "Variational Approaches for Auto-Encoding Generative Adversarial Networks."

TRIQ implementation

License Plate Detection Application

Code for the paper "Curriculum Dropout", ICCV 2017

Implements MLP-Mixer: An all-MLP Architecture for Vision.

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training @ KDD 2020

Run PowerShell command without invoking powershell.exe