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
Reimplementation of Dynamic Multi-scale filters for Semantic Segmentation.

Paddle implementation of Dynamic Multi-scale filters for Semantic Segmentation.

Hongqiang.Wang 2 Nov 01, 2021
Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments

Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments Paper: arXiv (ICRA 2021) Video : https://youtu.be/CC

Sachini Herath 68 Jan 03, 2023
UNAVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring

UNAVOIDS: Unsupervised and Nonparametric Approach for Visualizing Outliers and Invariant Detection Scoring Code Summary aggregate.py: this script aggr

1 Dec 28, 2021
Gin provides a lightweight configuration framework for Python

Gin Config Authors: Dan Holtmann-Rice, Sergio Guadarrama, Nathan Silberman Contributors: Oscar Ramirez, Marek Fiser Gin provides a lightweight configu

Google 1.7k Jan 03, 2023
tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series classification, regression and forecasting.

Time series Timeseries Deep Learning Pytorch fastai - State-of-the-art Deep Learning with Time Series and Sequences in Pytorch / fastai

timeseriesAI 2.8k Jan 08, 2023
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023
custom pytorch implementation of MoCo v3

MoCov3-pytorch custom implementation of MoCov3 [arxiv]. I made minor modifications based on the official MoCo repository [github]. No ViT part code an

39 Nov 14, 2022
This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams.

Mutli-agent task allocation This code uses generative adversarial networks to generate diverse task allocation plans for Multi-agent teams. To change

Biorobotics Lab 5 Oct 12, 2022
Stock-history-display - something like a easy yearly review for your stock performance

Stock History Display Available on Heroku: https://stock-history-display.herokua

LiaoJJ 1 Jan 07, 2022
Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt) Task Training huge unsupervised deep neural networks yields to strong progress in

2 Aug 05, 2022
Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes

Pruner for nested cross-validation - Sphinx-Doc Nested cross-validation is necessary to avoid biased model performance in embedded feature selection i

1 Dec 15, 2021
Optimizing DR with hard negatives and achieving SOTA first-stage retrieval performance on TREC DL Track (SIGIR 2021 Full Paper).

Optimizing Dense Retrieval Model Training with Hard Negatives Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma 🔥 News 2021-10

Jingtao Zhan 99 Dec 27, 2022
Reproducing-BowNet: Learning Representations by Predicting Bags of Visual Words

Reproducing-BowNet Our reproducibility effort based on the 2020 ML Reproducibility Challenge. We are reproducing the results of this CVPR 2020 paper:

6 Mar 16, 2022
Embracing Single Stride 3D Object Detector with Sparse Transformer

SST: Single-stride Sparse Transformer This is the official implementation of paper: Embracing Single Stride 3D Object Detector with Sparse Transformer

TuSimple 385 Dec 28, 2022
[CVPR'21] Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation

Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation Weixiang Yang, Qi Li, Wenxi Liu, Yuanlong Yu, Y

118 Dec 26, 2022
Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction, ICCV-2021".

HF2-VAD Offcial implementation of "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictio

76 Dec 21, 2022
Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021]

Robust Instance Segmentation through Reasoning about Multi-Object Occlusion [CVPR 2021] Abstract Analyzing complex scenes with DNN is a challenging ta

Irene Yuan 24 Jun 27, 2022
An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

Zou 33 Jan 03, 2023
A general, feasible, and extensible framework for classification tasks.

Pytorch Classification A general, feasible and extensible framework for 2D image classification. Features Easy to configure (model, hyperparameters) T

Eugene 26 Nov 22, 2022
[ICCV '21] In this repository you find the code to our paper Keypoint Communities

Keypoint Communities In this repository you will find the code to our ICCV '21 paper: Keypoint Communities Duncan Zauss, Sven Kreiss, Alexandre Alahi,

Duncan Zauss 262 Dec 13, 2022