exponential adaptive pooling for PyTorch

Related tags

Deep LearningadaPool
Overview

AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling

supported versions Library GitHub license

Abstract

Pooling layers are essential building blocks of Convolutional Neural Networks (CNNs) that reduce computational overhead and increase the receptive fields of proceeding convolutional operations. They aim to produce downsampled volumes that closely resemble the input volume while, ideally, also being computationally and memory efficient. It is a challenge to meet both requirements jointly. To this end, we propose an adaptive and exponentially weighted pooling method named adaPool. Our proposed method uses a parameterized fusion of two sets of pooling kernels that are based on the exponent of the Dice-Sørensen coefficient and the exponential maximum, respectively. A key property of adaPool is its bidirectional nature. In contrast to common pooling methods, weights can be used to upsample a downsampled activation map. We term this method adaUnPool. We demonstrate how adaPool improves the preservation of detail through a range of tasks including image and video classification and object detection. We then evaluate adaUnPool on image and video frame super-resolution and frame interpolation tasks. For benchmarking, we introduce Inter4K, a novel high-quality, high frame-rate video dataset. Our combined experiments demonstrate that adaPool systematically achieves better results across tasks and backbone architectures, while introducing a minor additional computational and memory overhead.


[arXiv preprint -- coming soon]

Original
adaPool

Dependencies

All parts of the code assume that torch is of version 1.4 or higher. There might be instability issues on previous versions.

This work relies on the previous repo for exponential maximum pooling (alexandrosstergiou/SoftPool). Before opening an issue please do have a look at that repository as common problems in running or installation have been addressed.

! Disclaimer: This repository is heavily structurally influenced on Ziteng Gao's LIP repo https://github.com/sebgao/LIP

Installation

You can build the repo through the following commands:

$ git clone https://github.com/alexandrosstergiou/adaPool.git
$ cd adaPool-master/pytorch
$ make install
--- (optional) ---
$ make test

Usage

You can load any of the 1D, 2D or 3D variants after the installation with:

# Ensure that you import `torch` first!
import torch
import adapool_cuda

# For function calls
from adaPool import adapool1d, adapool2d, adapool3d, adaunpool
from adaPool import edscwpool1d, edscwpool2d, edscwpool3d
from adaPool import empool1d, empool2d, empool3d
from adaPool import idwpool1d, idwpool2d, idwpool3d

# For class calls
from adaPool import AdaPool1d, AdaPool2d, AdaPool3d
from adaPool import EDSCWPool1d, EDSCWPool2d, EDSCWPool3d
from adaPool import EMPool1d, EMPool2d, EMPool3d
from adaPool import IDWPool1d, IDWPool2d, IDWPool3d
  • (ada/edscw/em/idw)pool<x>d: Are functional interfaces for each of the respective pooling methods.
  • (Ada/Edscw/Em/Idw)Pool<x>d: Are the class version to create objects that can be referenced in the code.

Citation

@article{stergiou2021adapool,
  title={AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling},
  author={Stergiou, Alexandros and Poppe, Ronald},
  journal={arXiv preprint},
  year={2021}}

Licence

MIT

You might also like...
PyTorch implementation of ARM-Net: Adaptive Relation Modeling Network for Structured Data.
PyTorch implementation of ARM-Net: Adaptive Relation Modeling Network for Structured Data.

A ready-to-use framework of latest models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, and etc.

null 48 Nov 30, 2022
PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

Convolutional Networks with Adaptive Inference Graphs (ConvNet-AIG) This repository contains a PyTorch implementation of the paper Convolutional Netwo

Andreas Veit 176 Dec 7, 2022
Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

Pixel-Level Cycle Association This is the Pytorch implementation of our NeurIPS 2020 Oral paper Pixel-Level Cycle Association: A New Perspective for D

null 87 Oct 19, 2022
[CVPR 2021] Official PyTorch Implementation for
[CVPR 2021] Official PyTorch Implementation for "Iterative Filter Adaptive Network for Single Image Defocus Deblurring"

IFAN: Iterative Filter Adaptive Network for Single Image Defocus Deblurring Checkout for the demo (GUI/Google Colab)! The GUI version might occasional

Junyong Lee 173 Dec 30, 2022
an implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation using PyTorch
an implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation using PyTorch

revisiting-sepconv This is a reference implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation [1] using PyTorch. Given two f

Simon Niklaus 59 Dec 22, 2022
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch
An implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 984 Dec 16, 2022
Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization'
Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization'

pytorch-AdaIN This is an unofficial pytorch implementation of a paper, Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization [Hua

Naoto Inoue 873 Jan 6, 2023
This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).
This is an official PyTorch implementation of Task-Adaptive Neural Network Search with Meta-Contrastive Learning (NeurIPS 2021, Spotlight).

NeurIPS 2021 (Spotlight): Task-Adaptive Neural Network Search with Meta-Contrastive Learning This is an official PyTorch implementation of Task-Adapti

Wonyong Jeong 15 Nov 21, 2022
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

null 15 Nov 18, 2022
Comments
  • Installation issue on Google Colab

    Installation issue on Google Colab

    Hi, Thanks for providing a Cuda optimized implementation. While building the lib I encountered an issue with "inf" at limits.cuh.

    CUDA/limits.cuh(119): error: identifier "inf" is undefined

CUDA/limits.cuh(120): error: identifier "inf" is undefined

CUDA/limits.cuh(128): error: identifier "inf" is undefined

CUDA/limits.cuh(129): error: identifier "inf" is undefined

4 errors detected in the compilation of "CUDA/adapool_cuda_kernel.cu".
error: command '/usr/local/cuda/bin/nvcc' failed with exit status 1
Makefile:2: recipe for target 'install' failed
make: *** [install] Error 1

    The following notebook provides more details with environment informations: https://colab.research.google.com/drive/1T6Nxe2qbjKxXzo2IimFMYBn52qbthlZB?usp=sharing

    opened by okbalefthanded 2
  • Solution: Unresolved extern function '_Z3powdi'”

    Solution: Unresolved extern function '_Z3powdi'”

    cuda11. 0

    When I tried to build your project on win10, I encountered the following problems： “ptxas fatal : Unresolved extern function '_Z3powdi'”

    Reason: Wrong use of pow function in Cu code Solution: for example, pow (x, 2) can be changed to X * X

    opened by Culturenotes 1
  • Does AdaPool2d's beta require fixed image size?

    Does AdaPool2d's beta require fixed image size?

    I'm currently running AdaPool2d as a replacement of MaxPool2d in Resnet's stem similar on how you did it in SoftPool. However, I keep on getting an assertionError in line 1325 as shown below:

    assert isinstance(beta, tuple) or torch.is_tensor(beta), 'Agument `beta` can only be initialized with Tuple or Tensor type objects and should correspond to size (oH, oW)'

    Does this mean beta requires a fixed image size, e.g. (224,244)? Or is there a way to make it adaptive across varying image size (e.g. object detection)?

    opened by johnanthonyjose 1
  • The version of pytorch and how to deal with `nan_to_num` function in lower versions

    The version of pytorch and how to deal with `nan_to_num` function in lower versions

    Thank you for this amazing project. I saw it from SoftPool. After installing it, make test, but I got AttributeError: module 'torch' has no attribute 'nan_to_num', after I checked, this function used in idea.py was introduced in Pytorch 1.8.0, so the torch version in the README may need to be updated, or is there an easy way to be compatible with lower versions?

    opened by MaxChanger 1
Releases(v0.2)
Owner
Alexandros Stergiou
Computer Vision and Machine Learning Researcher
Alexandros Stergiou
GitHub Repository
Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

Fuhang 5 Jan 18, 2022
Extending JAX with custom C++ and CUDA code

Extending JAX with custom C++ and CUDA code This repository is meant as a tutorial demonstrating the infrastructure required to provide custom ops in

Dan Foreman-Mackey 237 Dec 23, 2022
Official Implementation of "Learning Disentangled Behavior Embeddings"

DBE: Disentangled-Behavior-Embedding Official implementation of Learning Disentangled Behavior Embeddings (NeurIPS 2021). Environment requirement The

Mishne Lab 12 Sep 28, 2022
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning This is the official PyTorch implementation for UniMoCo pape

dddzg 49 Jan 02, 2023
A video scene detection algorithm is designed to detect a variety of different scenes within a video

Scene-Change-Detection - A video scene detection algorithm is designed to detect a variety of different scenes within a video. There is a very simple definition for a scene: It is a series of logical

1 Jan 04, 2022
Deploy optimized transformer based models on Nvidia Triton server

🤗 Hugging Face Transformer submillisecond inference 🤯 and deployment on Nvidia Triton server Yes, you can perfom inference with transformer based mo

Lefebvre Sarrut Services 1.2k Jan 05, 2023
Package for extracting emotions from social media text. Tailored for financial data.

EmTract: Extracting Emotions from Social Media Text Tailored for Financial Contexts EmTract is a tool that extracts emotions from social media text. I

13 Nov 17, 2022
Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling

Caffe SegNet This is a modified version of Caffe which supports the SegNet architecture As described in SegNet: A Deep Convolutional Encoder-Decoder A

Alex Kendall 1.1k Jan 02, 2023
A decent AI that solves daily Wordle puzzles. Works with different websites with similar wordlists,.

Wordle-AI A decent AI that solves daily "Wordle" puzzles. Works with different websites with similar wordlists. When prompted with "Word:" enter the w

Ethan 1 Feb 10, 2022
Multiple paper open-source codes of the Microsoft Research Asia DKI group

📫 Paper Code Collection (MSRA DKI Group) This repo hosts multiple open-source codes of the Microsoft Research Asia DKI Group. You could find the corr

Microsoft 249 Jan 08, 2023
Dungeons and Dragons randomized content generator

Component based Dungeons and Dragons generator Supports Entity/Monster Generation NPC Generation Weapon Generation Encounter Generation Environment Ge

Zac 3 Dec 04, 2021
Resco: A simple python package that report the effect of deep residual learning

resco Description resco is a simple python package that report the effect of dee

Pierre-Arthur Claudé 1 Jun 28, 2022
Voice Conversion by CycleGAN (语音克隆/语音转换)：CycleGAN-VC3

CycleGAN-VC3-PyTorch 中文说明 | English This code is a PyTorch implementation for paper: CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-spectr

Kun Ma 110 Dec 24, 2022
Mscp jamf - Build compliance in jamf

mscp_jamf Build compliance in Jamf. This will build the following xml pieces to

Bob Gendler 3 Jul 25, 2022
A highly efficient, fast, powerful and light-weight anime downloader and streamer for your favorite anime.

AnimDL - Download & Stream Your Favorite Anime AnimDL is an incredibly powerful tool for downloading and streaming anime. Core features Abuses the dev

KR 759 Jan 08, 2023
CUda Matrix Multiply library.

cumm CUda Matrix Multiply library. cumm is developed during learning of CUTLASS, which use too much c++ template and make code unmaintainable. So I de

49 Dec 27, 2022
A minimal implementation of Gaussian process regression in PyTorch

pytorch-minimal-gaussian-process In search of truth, simplicity is needed. There exist heavy-weighted libraries, but as you know, we need to go bare b

Sangwoong Yoon 38 Nov 25, 2022
A list of all papers and resoureces on Semantic Segmentation

Semantic-Segmentation A list of all papers and resoureces on Semantic Segmentation. Dataset importance SemanticSegmentation_DL Some implementation of

Alan Tang 1.1k Dec 12, 2022
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning This is a small repo illustrating how to use WebDataset on ImageNet. usi

50 Dec 16, 2022
This repository contains code for the paper "Decoupling Representation and Classifier for Long-Tailed Recognition", published at ICLR 2020

Classifier-Balancing This repository contains code for the paper: Decoupling Representation and Classifier for Long-Tailed Recognition Bingyi Kang, Sa

Facebook Research 820 Dec 26, 2022