Simple sinc interpolation in PyTorch.

Related tags

Deep Learningdspupsamplepytorch-implementationdownsampling
Overview

Kazane: simple sinc interpolation for 1D signal in PyTorch

Kazane utilize FFT based convolution to provide fast sinc interpolation for 1D signal when your sample rate only needs to change by an integer amounts; If you need to change by a fraction amounts, checkout julius.

Installation

pip install kazane

or

pip install git+https://github.com/yoyololicon/kazane

for latest version.

Usage

import kazane
import torch

signal = torch.randn(8, 2, 44100)

# downsample by an amount of 3
decimater = kazane.Decimate(3)
resampled_signal = decimater(signal)

# upsample by an amount of 2
upsampler = kazane.Upsample(2)
resampled_signal = upsampler(signal)

# you can also control number of zeros, roll-off frequency of the sinc interpolation kernel
decimater = kazane.Decimate(3, num_zeros=24, roll_off=0.9)

# use other types of window function for the sinc kernel
upsampler = kazane.Upsample(2, window_func=torch.blackman_window)

Benchmarks on CUDA

Using the benchmark scripts at bench, you can see that FFT can gives some speed improvements when the sample rate changes with some common integer numbers.

[---------- Down sample ----------]
               |  julius  |  kazane
2 threads: ------------------------
      rate: 2  |   52.2   |   52.4 
      rate: 3  |   66.5   |   36.1 
      rate: 5  |   94.8   |   30.0 
      rate: 7  |  121.7   |   42.3 

Times are in milliseconds (ms).

[----------- Up sample -----------]
               |  julius  |  kazane
2 threads: ------------------------
      rate: 2  |   48.8   |   39.0 
      rate: 3  |   68.1   |   51.6 
      rate: 5  |  112.5   |   78.9 
      rate: 7  |  159.4   |  108.0 

Times are in milliseconds (ms).
You might also like...
Asymmetric Bilateral Motion Estimation for Video Frame Interpolation, ICCV2021
Asymmetric Bilateral Motion Estimation for Video Frame Interpolation, ICCV2021

ABME (ICCV2021) Junheum Park, Chul Lee, and Chang-Su Kim Official PyTorch Code for "Asymmetric Bilateral Motion Estimation for Video Frame Interpolati

Junheum Park 86 Dec 28, 2022
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE - Real Time Video Interpolation arXiv | YouTube | Colab | Tutorial | Demo Table of Contents Introduction Collection Usage Evaluation Training and

hzwer 3k Jan 4, 2023
N-gram models- Unsmoothed, Laplace, Deleted Interpolation

N-gram models- Unsmoothed, Laplace, Deleted Interpolation

Ravika Nagpal 1 Jan 4, 2022
Interpolation-based reduced-order models
Interpolation-based reduced-order models

Interpolation-reduced-order-models Interpolation-based reduced-order models High-fidelity computational fluid dynamics (CFD) solutions are time consum

Donovan Blais 1 Jan 10, 2022
RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation
RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

RIFE - Real-Time Intermediate Flow Estimation for Video Frame Interpolation YouTube | BiliBili 16X interpolation results from two input images: Introd

旷视天元 MegEngine 28 Dec 9, 2022
Video Frame Interpolation with Transformer (CVPR2022)

VFIformer Official PyTorch implementation of our CVPR2022 paper Video Frame Interpolation with Transformer Dependencies python = 3.8 pytorch = 1.8.0

DV Lab 63 Dec 16, 2022
Simple-Image-Classification - Simple Image Classification Code (PyTorch)
Simple-Image-Classification - Simple Image Classification Code (PyTorch)

Simple-Image-Classification Simple Image Classification Code (PyTorch) Yechan Kim This repository contains: Python3 / Pytorch code for multi-class ima

Yechan Kim 8 Oct 29, 2022
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps Here is the code for ssbassline model. We also provide OCR results/features/mode

ZephyrZhuQi 51 Nov 18, 2022
The official repository for our paper "The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers". We significantly improve the systematic generalization of transformer models on a variety of datasets using simple tricks and careful considerations.

Codebase for training transformers on systematic generalization datasets. The official repository for our EMNLP 2021 paper The Devil is in the Detail:

Csordás Róbert 57 Nov 21, 2022
Releases(v1.0.1)
Owner
Chin-Yun Yu
I'm a Djentle man. When I hear 0000000 I click like.
Chin-Yun Yu
GitHub Repository
Model Serving Made Easy

The easiest way to build Machine Learning APIs BentoML makes moving trained ML models to production easy: Package models trained with any ML framework

BentoML 4.4k Jan 08, 2023
JAX code for the paper "Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation"

Optimal Model Design for Reinforcement Learning This repository contains JAX code for the paper Control-Oriented Model-Based Reinforcement Learning wi

Evgenii Nikishin 43 Sep 28, 2022
CLOOB training (JAX) and inference (JAX and PyTorch)

cloob-training Pretrained models There are two pretrained CLOOB models in this repo at the moment, a 16 epoch and a 32 epoch ViT-B/16 checkpoint train

Katherine Crowson 64 Nov 27, 2022
A solution to ensure Crowd Management with Contactless and Safe systems.

CovidTrack A Solution to ensure Crowd Management with Contactless and Safe systems. ML Model Mask Detection Social Distancing Detection Analytics Page

Om Khare 1 Nov 10, 2021
The official implementation of our CVPR 2021 paper - Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

Graph Optimizer This repo contains the official implementation of our CVPR 2021 paper - Hybrid Rotation Averaging: A Fast and Robust Rotation Averagin

Chenyu 109 Dec 23, 2022
An Inverse Kinematics library aiming performance and modularity

IKPy Demo Live demos of what IKPy can do (click on the image below to see the video): Also, a presentation of IKPy: Presentation. Features With IKPy,

Pierre Manceron 481 Jan 02, 2023
Source code for The Power of Many: A Physarum Swarm Steiner Tree Algorithm

Physarum-Swarm-Steiner-Algo Source code for The Power of Many: A Physarum Steiner Tree Algorithm Code implements ideas from the following papers: Sher

Sheryl Hsu 2 Mar 28, 2022
Much faster than SORT(Simple Online and Realtime Tracking), a little worse than SORT

QSORT QSORT(Quick + Simple Online and Realtime Tracking) is a simple online and realtime tracking algorithm for 2D multiple object tracking in video s

Yonghye Kwon 8 Jul 27, 2022
OptNet: Differentiable Optimization as a Layer in Neural Networks

OptNet: Differentiable Optimization as a Layer in Neural Networks This repository is by Brandon Amos and J. Zico Kolter and contains the PyTorch sourc

CMU Locus Lab 428 Dec 24, 2022
Adversarial Attacks are Reversible via Natural Supervision

Adversarial Attacks are Reversible via Natural Supervision ICCV2021 Citation @InProceedings{Mao_2021_ICCV, author = {Mao, Chengzhi and Chiquier

Computer Vision Lab at Columbia University 20 May 22, 2022
The code of “Similarity Reasoning and Filtration for Image-Text Matching” [AAAI2021]

SGRAF PyTorch implementation for AAAI2021 paper of “Similarity Reasoning and Filtration for Image-Text Matching”. It is built on top of the SCAN and C

Ronnie_IIAU 149 Dec 22, 2022
NIMA: Neural IMage Assessment

PyTorch NIMA: Neural IMage Assessment PyTorch implementation of Neural IMage Assessment by Hossein Talebi and Peyman Milanfar. You can learn more from

Kyryl Truskovskyi 293 Dec 30, 2022
Rasterize with the least efforts for researchers.

utils3d Rasterize and do image-based 3D transforms with the least efforts for researchers. Based on numpy and OpenGL. It could be helpful when you wan

Ruicheng Wang 8 Dec 15, 2022
A pytorch &keras implementation and demo of Fastformer.

Fastformer Notes from the authors Pytorch/Keras implementation of Fastformer. The keras version only includes the core fastformer attention part. The

153 Dec 28, 2022
R interface to fast.ai

R interface to fastai The fastai package provides R wrappers to fastai. The fastai library simplifies training fast and accurate neural nets using mod

113 Dec 20, 2022
PyTorch implementation of paper A Fast Knowledge Distillation Framework for Visual Recognition.

FKD: A Fast Knowledge Distillation Framework for Visual Recognition Official PyTorch implementation of paper A Fast Knowledge Distillation Framework f

Zhiqiang Shen 129 Dec 24, 2022
Unoffical reMarkable AddOn for Firefox.

reMarkable for Firefox (Download) This repo converts the offical reMarkable Chrome Extension into a Firefox AddOn published here under the name "Unoff

Jelle Schutter 45 Nov 28, 2022
This is a JAX implementation of Neural Radiance Fields for learning purposes.

learn-nerf This is a JAX implementation of Neural Radiance Fields for learning purposes. I've been curious about NeRF and its follow-up work for a whi

Alex Nichol 62 Dec 20, 2022
Film review classification

Film review classification Решение задачи классификации отзывов на фильмы на положительные и отрицательные с помощью рекуррентных нейронных сетей 1. З

Nikita Dukin 3 Jan 21, 2022
Implementation of Neural Style Transfer in Pytorch

PytorchNeuralStyleTransfer Code to run Neural Style Transfer from our paper Image Style Transfer Using Convolutional Neural Networks. Also includes co

Leon Gatys 396 Dec 01, 2022