Implementation of OmniNet, Omnidirectional Representations from Transformers, in Pytorch

Last update: Nov 21, 2022

Overview

Omninet - Pytorch

Implementation of OmniNet, Omnidirectional Representations from Transformers, in Pytorch. The authors propose that we should be attending to all the tokens of the previous layers, leveraging recent efficient attention advances to achieve this goal.

Install

$ pip install omninet-pytorch

Usage

import torch
from omninet_pytorch import Omninet

omninet = Omninet(
    dim = 512,                     # model dimension
    depth = 6,                     # depth
    dim_head = 64,                 # dimension per head
    heads = 8,                     # number of heads
    pool_layer_tokens_every = 3,   # key to this paper - every N layers, omni attend to all tokens of all layers
    attn_dropout = 0.1,            # attention dropout
    ff_dropout = 0.1,              # feedforward dropout
    feature_redraw_interval = 1000 # how often to redraw the projection matrix for omni attention net - Performer
)

x = torch.randn(1, 1024, 512)
mask = torch.ones(1, 1024).bool()

omninet(x, mask = mask) # (1, 1024, 512)

Causal case, just use the class OmninetCausal. At the moment, it isn't faithful to the paper (I am using layer axial attention with layer positional embeddings to draw up information), but will fix this once I rework the linear attention CUDA kernel.

import torch
from omninet_pytorch import OmninetCausal

omninet = OmninetCausal(
    dim = 512,                     # model dimension
    depth = 6,                     # depth
    dim_head = 64,                 # dimension per head
    heads = 8,                     # number of heads
    pool_layer_tokens_every = 3,   # key to this paper - every N layers, omni attend to all tokens of all layers
    attn_dropout = 0.1,            # attention dropout
    ff_dropout = 0.1               # feedforward dropout
)

x = torch.randn(1, 1024, 512)
mask = torch.ones(1, 1024).bool()

omninet(x, mask = mask) # (1, 1024, 512)

Citations

@misc{tay2021omninet,
    title   = {OmniNet: Omnidirectional Representations from Transformers}, 
    author  = {Yi Tay and Mostafa Dehghani and Vamsi Aribandi and Jai Gupta and Philip Pham and Zhen Qin and Dara Bahri and Da-Cheng Juan and Donald Metzler},
    year    = {2021},
    eprint  = {2103.01075},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.

StARformer This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations.

14 Dec 9, 2022

Official PyTorch implementation of BlobGAN: Spatially Disentangled Scene Representations

BlobGAN: Spatially Disentangled Scene Representations Official PyTorch Implementation Paper | Project Page | Video | Interactive Demo BlobGAN.mp4 This

148 Dec 29, 2022

ChatBot-Pytorch - A GPT-2 ChatBot implemented using Pytorch and Huggingface-transformers

ChatBot-Pytorch A GPT-2 ChatBot implemented using Pytorch and Huggingface-transf

42 Dec 9, 2022

Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

S2VC Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations. In thi

81 Dec 15, 2022

[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations

CrowdNav with Social-NCE This is an official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations by

125 Dec 23, 2022

Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021

Deep Representation One-class Classification (DROC). This is not an officially supported Google product. Tensorflow 2 implementation of the paper: Lea

137 Dec 23, 2022

Public Implementation of ChIRo from "Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations"

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations This directory contains the model architectures and experimental

35 Dec 5, 2022

Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

Wav2CLIP 🚧 WIP 🚧 Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP 📄 🔗 Ho-Hsiang Wu, Prem Seetharaman

240 Dec 13, 2022

Implementation of the method proposed in the paper "Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation"

Neural Descriptor Fields (NDF) PyTorch implementation for training continuous 3D neural fields to represent dense correspondence across objects, and u

167 Jan 6, 2023

Implementation of OmniNet, Omnidirectional Representations from Transformers, in Pytorch

Related tags

Overview

Omninet - Pytorch

Install

Usage

Citations

You might also like...

PyTorch implementation for paper StARformer: Transformer with State-Action-Reward Representations.

Official PyTorch implementation of BlobGAN: Spatially Disentangled Scene Representations

ChatBot-Pytorch - A GPT-2 ChatBot implemented using Pytorch and Huggingface-transformers

Here is the implementation of our paper S2VC: A Framework for Any-to-Any Voice Conversion with Self-Supervised Pretrained Representations.

[ICCV'21] Official implementation for the paper Social NCE: Contrastive Learning of Socially-aware Motion Representations

Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021

Public Implementation of ChIRo from "Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations"

Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

Implementation of the method proposed in the paper "Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation"

Releases(0.0.6)

0.0.6(Mar 19, 2021)

0.0.5(Mar 2, 2021)

0.0.4(Mar 2, 2021)

0.0.3(Mar 2, 2021)

0.0.2(Mar 2, 2021)

0.0.1(Mar 2, 2021)

Owner

Phil Wang

A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery

Underwater image enhancement

Probabilistic Programming and Statistical Inference in PyTorch

OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion.

Focal Loss for Dense Rotation Object Detection

SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model

Includes PyTorch -> Keras model porting code for ConvNeXt family of models with fine-tuning and inference notebooks.

[NeurIPS-2020] Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID.

Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation（Unfinished）

Python Blood Vessel Topology Analysis

The codes and related files to reproduce the results for Image Similarity Challenge Track 1.

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning, NeurIPS 2021 (Spotlight)

This project generates news headlines using a Long Short-Term Memory (LSTM) neural network.

PyTorch implementation of MoCo: Momentum Contrast for Unsupervised Visual Representation Learning

Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics

A PyTorch toolkit for 2D Human Pose Estimation.

Second Order Optimization and Curvature Estimation with K-FAC in JAX.

Docker containers of baseline agents for the Crafter environment

Apply our monocular depth boosting to your own network!

Pmapper is a super-resolution and deconvolution toolkit for python 3.6+