Implementation of E(n)-Transformer, which extends the ideas of Welling's E(n)-Equivariant Graph Neural Network to attention

Related tags

Deep Learningdeep-learningartificial-intelligencetransformerattention-mechanismequivariance
Overview

E(n)-Equivariant Transformer (wip)

Implementation of E(n)-Equivariant Transformer, which extends the ideas from Welling's E(n)-Equivariant Graph Neural Network with attention.

Install

$ pip install En-transformer

Usage

import torch
from en_transformer import EnTransformer

model = EnTransformer(
    dim = 512,
    depth = 4,
    dim_head = 64,
    heads = 8,
    edge_dim = 4,
    fourier_features = 2
)

feats = torch.randn(1, 16, 512)
coors = torch.randn(1, 16, 3)
edges = torch.randn(1, 16, 16, 4)

feats, coors = model(feats, coors, edges)  # (1, 16, 512), (1, 16, 3)

Todo

  • masking
  • neighborhoods by radius

Citations

@misc{satorras2021en,
    title 	= {E(n) Equivariant Graph Neural Networks}, 
    author 	= {Victor Garcia Satorras and Emiel Hoogeboom and Max Welling},
    year 	= {2021},
    eprint 	= {2102.09844},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
Comments
  • Checkpoint sequential segments should equal number of layers instead of 1?

    Checkpoint sequential segments should equal number of layers instead of 1?

    https://github.com/lucidrains/En-transformer/blob/a37e635d93a322cafdaaf829397c601350b23e5b/en_transformer/en_transformer.py#L527

    Looking at the source code here: https://pytorch.org/docs/stable/_modules/torch/utils/checkpoint.html#checkpoint_sequential

    opened by aced125 2
  • On rotary embeddings

    On rotary embeddings

    Hi @lucidrains, thank you for your amazing work; big fan! I had a quick question on the usage of this repository.

    Based on my understanding, rotary embeddings are a drop-in replacement for the original sinusoidal or learnt PEs in Transformers for sequential data, as in NLP or other temporal applications. If my application is not on sequential data, is there a reason why I should still use rotary embeddings?

    E.g. for molecular datasets such as QM9 (from the En-GNNs paper), would it make sense to have rotary embeddings?

    opened by chaitjo 1
  • Is this line required?

    Is this line required?

    https://github.com/lucidrains/En-transformer/blob/7247e258fab953b2a8b5a73b8dfdfb72910711f8/en_transformer/en_transformer.py#L159

    Is this line required? Does line 157, two lines above, make this line redundant?

    opened by aced125 1
  • Performance drop with checkpointing update

    Performance drop with checkpointing update

    I see a drop in performance (higher loss) when I update checkpointing from checkpoint_sequential(self.layers, 1, inp) to checkpoint_sequential(self.layers, len(self.layers), inp). Is this expected?

    opened by heiidii 0
  • varying number of nodes

    varying number of nodes

    @lucidrains Thank you for your efficient implementation. I was wondering how to use this implementation for the dataset when the number of nodes in each graph is not the same? For example, the datasets of small molecules.

    opened by mohaiminul2810 1
  • Edge model/rep

    Edge model/rep

    Hi,

    Thank you for providing this version of the EnGNN model. This is not really an issue just a query. The original model as implemented here (https://github.com/vgsatorras/egnn) has 3 main steps per layer: edge_feat = self.edge_model(h[row], h[col], radial, edge_attr) coord = self.coord_model(coord, edge_index, coord_diff, edge_feat) h, agg = self.node_model(h, edge_index, edge_feat, node_attr) I am interested in the edge_feat and was wondering what would be an equivalent edge representation in your implementation. Line 335 in EnTransformer.py: qk = self.edge_mlp(qk) seems like the best candidate. Thanks, Pooja

    opened by heiidii 1
  • efficient implementation

    efficient implementation

    Hi, I wonder if relative distances and coordinates can be handled more efficiently using memory efficient attention as in " Self-attention Does Not Need O(n^2) Memory". It is straightforward for the scalar part.

    opened by amrhamedp 2
Releases(1.0.2)
Owner
Phil Wang
Working with Attention. It's all we need.
Phil Wang
GitHub Repository
Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices

EMOShip This repository contains the EMO-Film dataset described in the paper "Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis

1 Nov 18, 2022
RSC-Net: 3D Human Pose, Shape and Texture from Low-Resolution Images and Videos

RSC-Net: 3D Human Pose, Shape and Texture from Low-Resolution Images and Videos Implementation for "3D Human Pose, Shape and Texture from Low-Resoluti

XiangyuXu 42 Nov 10, 2022
Official code for MPG2: Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN

This is the official code for Multi-attribute Pizza Generator (MPG2): Cross-domain Attribute Control with Conditional StyleGAN. Paper Demo Setup Envir

Fangda Han 5 Sep 01, 2022
Molecular AutoEncoder in PyTorch

MolEncoder Molecular AutoEncoder in PyTorch Install $ git clone https://github.com/cxhernandez/molencoder.git && cd molencoder $ python setup.py insta

Carlos Hernández 80 Dec 05, 2022
Official implementation of AAAI-21 paper "Label Confusion Learning to Enhance Text Classification Models"

Description: This is the official implementation of our AAAI-21 accepted paper Label Confusion Learning to Enhance Text Classification Models. The str

101 Nov 25, 2022
Creating Multi Task Models With Keras

Creating Multi Task Models With Keras About The Project! I used the keras and Tensorflow Library, To build a Deep Learning Neural Network to Creating

Srajan Chourasia 4 Nov 28, 2022
rliable is an open-source Python library for reliable evaluation, even with a handful of runs, on reinforcement learning and machine learnings benchmarks.

Open-source library for reliable evaluation on reinforcement learning and machine learning benchmarks. See NeurIPS 2021 oral for details.

Google Research 529 Jan 01, 2023
Implementation of the Point Transformer layer, in Pytorch

Point Transformer - Pytorch Implementation of the Point Transformer self-attention layer, in Pytorch. The simple circuit above seemed to have allowed

Phil Wang 501 Jan 03, 2023
Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

41 Jan 03, 2023
Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation

OSCAR Project Page | Paper This repository contains the codebase used in OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Ma

NVIDIA Research Projects 74 Dec 22, 2022
Fast, differentiable sorting and ranking in PyTorch

Torchsort Fast, differentiable sorting and ranking in PyTorch. Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.)

Teddy Koker 655 Jan 04, 2023
Contour-guided image completion with perceptual grouping (BMVC 2021 publication)

Contour-guided Image Completion with Perceptual Grouping Authors Morteza Rezanejad*, Sidharth Gupta*, Chandra Gummaluru, Ryan Marten, John Wilder, Mic

Sid Gupta 6 Dec 27, 2022
TensorFlow Implementation of Unsupervised Cross-Domain Image Generation

Domain Transfer Network (DTN) TensorFlow implementation of Unsupervised Cross-Domain Image Generation. Requirements Python 2.7 TensorFlow 0.12 Pickle

Yunjey Choi 865 Nov 17, 2022
Vanilla and Prototypical Networks with Random Weights for image classification on Omniglot and mini-ImageNet. Made with Python3.

vanilla-rw-protonets-project Vanilla Prototypical Networks and PNs with Random Weights for image classification on Omniglot and mini-ImageNet. Made wi

Giovani Candido 8 Aug 31, 2022
The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

Will Thompson 166 Jan 04, 2023
The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Energy-based Conditional Generative Adversarial Network (ECGAN) This is the code for the NeurIPS 2021 paper "A Unified View of cGANs with and without

sianchen 22 May 28, 2022
Details about the wide minima density hypothesis and metrics to compute width of a minima

wide-minima-density-hypothesis Details about the wide minima density hypothesis and metrics to compute width of a minima This repo presents the wide m

Nikhil Iyer 9 Dec 27, 2022
Open source repository for the code accompanying the paper 'PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations'.

PatchNets This is the official repository for the project "PatchNets: Patch-Based Generalizable Deep Implicit 3D Shape Representations". For details,

16 May 22, 2022
Predicts an answer in yes or no.

Oui-ou-non-prediction Predicts an answer in 'yes' or 'no'. It is based on the game 'effeuiller la marguerite' in which the person plucks flower petals

Ananya Gupta 1 Jan 15, 2022
机器学习、深度学习、自然语言处理等人工智能基础知识总结。

说明 机器学习、深度学习、自然语言处理基础知识总结。 目前主要参考李航老师的《统计学习方法》一书，也有一些内容例如XGBoost、聚类、深度学习相关内容、NLP相关内容等是书中未提及的。

Peter 445 Dec 12, 2022