LogAvgExp - Pytorch Implementation of LogAvgExp

Related tags

Deep Learningpytorchlogsumexp
Overview

LogAvgExp - Pytorch

Implementation of LogAvgExp for Pytorch

Install

$ pip install logavgexp-pytorch

Usage

import torch
from logavgexp_pytorch import logavgexp

# basically it is an improved logsumexp (differentiable max)
# normalized for length

x = torch.arange(1000)
y = logavgexp(x, dim = 0, temp = 0.01) # ~998.8

# more than 1 dimension

x = torch.randn(1, 2048, 5)
y = logavgexp(x, dim = 1, temp = 0.2) # (1, 5)

# keep dimension

x = torch.randn(1, 2048, 5)
y = logavgexp(x, dim = 1, temp = 0.2, keepdim = True) # (1, 1, 5)

# masking (False for mask out with large negative value)

x = torch.randn(1, 2048, 5)
m = torch.randint(0, 2, (1, 2048, 1)).bool()

y = logavgexp(x, mask = m, dim = 1, temp = 0.2, keepdim = True) # (1, 1, 5)

With learned temperature

# learned temperature
import torch
from torch import nn
from logavgexp_pytorch import logavgexp

learned_temp = nn.Parameter(torch.ones(1) * -5).exp().clamp(min = 1e-8) # make sure temperature can't hit 0

x = torch.randn(1, 2048, 5)
y = logavgexp(x, temp = learned_temp, dim = -1) # (1, 5)

Or you can use the LogAvgExp class to handle the learned temperature parameter

import torch
from logavgexp_pytorch import LogAvgExp

logavgexp = LogAvgExp(
    temp = 0.01,
    dim = 1,
    learned_temp = True
)

x = torch.randn(1, 2048, 5)
y = logavgexp(x) # (1, 5)

Citations

@misc{lowe2021logavgexp,
    title   = {LogAvgExp Provides a Principled and Performant Global Pooling Operator}, 
    author  = {Scott C. Lowe and Thomas Trappenberg and Sageev Oore},
    year    = {2021},
    eprint  = {2111.01742},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
You might also like...
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code here will be included in upstream Pytorch eventually. The intention of Apex is to make up-to-date utilities available to users as quickly as possible.

NVIDIA Corporation 6.9k Jan 3, 2023
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch
Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

30 Days Of Machine Learning Using Pytorch Objective of the repository is to learn and build machine learning models using Pytorch. List of Algorithms

Mayur 119 Nov 24, 2022
Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch
Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch

Pytorch Lightning 1.4k Jan 1, 2023
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with Source: https://www.ka

Mamy Ratsimbazafy 360 Dec 10, 2022
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pu

Ritchie Ng 9.2k Jan 2, 2023
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks

Amazon Forest Computer Vision Satellite Image tagging code using PyTorch / Keras Here is a sample of images we had to work with Source: https://www.ka

Mamy Ratsimbazafy 359 Jan 5, 2023
A bunch of random PyTorch models using PyTorch's C++ frontend
A bunch of random PyTorch models using PyTorch's C++ frontend

PyTorch Deep Learning Models using the C++ frontend Gettting started Clone the repo 1. https://github.com/mrdvince/pytorchcpp 2. cd fashionmnist or

Vince 0 Jul 13, 2021
PyTorch Autoencoders - Implementing a Variational Autoencoder (VAE) Series in Pytorch.

PyTorch Autoencoders Implementing a Variational Autoencoder (VAE) Series in Pytorch. Inspired by this repository Model List check model paper conferen

Subin An 8 Nov 21, 2022
PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices.

PyTorch-LIT PyTorch-LIT is the Lite Inference Toolkit (LIT) for PyTorch which focuses on easy and fast inference of large models on end-devices. With

Amin Rezaei 157 Dec 11, 2022
Comments
  • Enhancement: 2d Pooling

    Enhancement: 2d Pooling

    Could put in 2d pooling for an easy to drop in alternative to AvgPool2d by using pixel_unshuffle for cases where there's exact divisions of an input shape, or padding and unfold for overlapping windows / strides that aren't equal to the window size.

    I don't know how fast Unfold is or if there's a better way to do the operation; I tried checking the PyTorch github to see how it does AvgPool2d for strides that aren't equal to the window size behind the scenes but I can never seem to figure out which version of the operation is the one that's used, it's defined in so many places it's beyond me.

    I've once seen an alternative to pixel unshuffle that used grouped conv2d, a kernel that put each position into its own channel output, and stride, but I can't seem to find it again. It was useful because you could adjust the stride and then it'd be like Unfold, but I never got around to testing if it was actually faster.

    opened by torridgristle 6
  • learned temperature stagnates at a low value (a high value is expected)

    learned temperature stagnates at a low value (a high value is expected)

    Hi,

    Big thanks for your pytorch implementation of the logavgexp !

    I noticed that it is easy for logavgexp to reproduce the max operator (temperature goes nicely to 0); but it has trouble reproducing the mean operator, the temperature stagnates in the following example at 0.35; do your have an explanation for that or ways to circumvent this issue? Thanks !

    import torch
torch.manual_seed(12345)
from logavgexp_pytorch import LogAvgExp

B = 10
N = 20
x = torch.randn(B,N)
#y, _ = x.max(dim=-1, keepdim=True)
y    = x.mean(dim=-1, keepdim=True)
logavgexp = LogAvgExp(
    temp = 1,
    dim = 1,
    learned_temp = True,
    keepdim = True)

optimizer = torch.optim.Adam(logavgexp.parameters(), lr=0.01)
loss_func = torch.nn.MSELoss()
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
                                                       factor=0.5,
                                                       patience=100,
                                                       verbose=True)

for i in range(10000):
    prediction = logavgexp(x)
    loss = loss_func(prediction, y) 
    optimizer.zero_grad()
    loss.backward()        
    optimizer.step()
    scheduler.step(loss, epoch=i)
    print(f"ite: {i}, loss: {loss.item():.2e}, temperature: {logavgexp.temp.exp().item():.4f}")
    opened by ldv1 0
Releases(0.0.6)
Owner
Phil Wang
Working with Attention. It's all we need
Phil Wang
GitHub Repository
Implementation for paper "Towards the Generalization of Contrastive Self-Supervised Learning"

Contrastive Self-Supervised Learning on CIFAR-10 Paper "Towards the Generalization of Contrastive Self-Supervised Learning", Weiran Huang, Mingyang Yi

Weiran Huang 13 Nov 30, 2022
PyTorch GPU implementation of the ES-RNN model for time series forecasting

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm A GPU-enabled version of the hybrid ES-RNN model by Slawek et al that won the M4 time-series

Kaung 305 Jan 03, 2023
NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size

NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size Xuanyi Dong, Lu Liu, Katarzyna Musial, Bogdan Gabrys in IEEE Transactions o

D-X-Y 137 Dec 20, 2022
Notes taking website build with Docker + Django + React.

Notes website. Try it in browser! / But how to run? Description. This is monorepository with notes website. Website provides web interface for creatin

Kirill Zhosul 2 Jul 27, 2022
A PyTorch-centric hybrid classical-quantum machine learning framework

torchquantum A PyTorch-centric hybrid classical-quantum dynamic neural networks framework. News Add a simple example script using quantum gates to do

MIT HAN Lab 400 Jan 02, 2023
CS_Final_Metal_surface_detection - This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021.

CS_Final_Metal_surface_detection This is a final project for CoderSchool Machine Learning bootcamp on 29/12/2021. The project is based on the dataset

Cuong Vo 1 Dec 29, 2021
METER: Multimodal End-to-end TransformER

METER Code and pre-trained models will be publicized soon. Citation @article{dou2021meter, title={An Empirical Study of Training End-to-End Vision-a

Zi-Yi Dou 257 Jan 06, 2023
The implement of papar "Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization"

SIGIR2021-EGLN The implement of paper "Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization" Neural graph based Col

15 Dec 27, 2022
Unofficial Pytorch Implementation of WaveGrad2

WaveGrad 2 — Unofficial PyTorch Implementation WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis Unofficial PyTorch+Lightning Implementati

MINDs Lab 104 Nov 29, 2022
[ICML 2022] The official implementation of Graph Stochastic Attention (GSAT).

Graph Stochastic Attention (GSAT) The official implementation of GSAT for our paper: Interpretable and Generalizable Graph Learning via Stochastic Att

85 Nov 27, 2022
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

PyTorch Image Models Sponsors What's New Introduction Models Features Results Getting Started (Documentation) Train, Validation, Inference Scripts Awe

Ross Wightman 22.9k Jan 09, 2023
An implementation for the loss function proposed in Decoupled Contrastive Loss paper.

Decoupled-Contrastive-Learning This repository is an implementation for the loss function proposed in Decoupled Contrastive Loss paper. Requirements P

Ramin Nakhli 71 Dec 04, 2022
Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN

Interpretable Control Exploration and Counterfactual Explanation (ICE) on StyleGAN Which Style Makes Me Attractive? Interpretable Control Discovery an

Bo Li 11 Dec 01, 2022
Code for: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification Prerequisite PyTorch = 1.2.0 Python3 torch

16 Dec 14, 2022
Faster Convex Lipschitz Regression

Faster Convex Lipschitz Regression This reepository provides a python implementation of our Faster Convex Lipschitz Regression algorithm with GPU and

Ali Siahkamari 0 Nov 19, 2021
Automatic detection and classification of Covid severity degree in LUS (lung ultrasound) scans

Final-Project Final project in the Technion, Biomedical faculty, by Mor Ventura, Dekel Brav & Omri Magen. Subproject 1: Automatic Detection of LUS Cha

Mor Ventura 1 Dec 18, 2021
Reinforcement Learning for Portfolio Management

qtrader Reinforcement Learning for Portfolio Management Why Reinforcement Learning? Learns the optimal action, rather than models the market. Adaptive

Angelos Filos 406 Jan 01, 2023
ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'

ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'

Yanda Meng 14 May 13, 2022
[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation [Paper] Prerequisites To install requirements: pip install -r requirements.txt

Guangrui Li 84 Dec 26, 2022
Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring

Enhancing Column Generation by a Machine-Learning-BasedPricing Heuristic for Graph Coloring (to appear at AAAI 2022) We propose a machine-learning-bas

YunzhuangS 2 May 02, 2022