Cosine Annealing With Warmup

Overview

CosineAnnealingWithWarmup

Formulation

The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an initial warmup period of n_warmup steps. Hence, the learning rate at step i is computed as:

Learning rate will be changed as:

Usage

# optimizer, warmup_epochs, warmup_lr, num_epochs, base_lr, final_lr, iter_per_epoch
lr_scheduler = LR_Scheduler(
        optimizer,
        args.warmup_epochs, args.warmup_lr*args.batch_size/256, 
        args.epochs, args.lr*args.batch_size/256, args.final_lr*args.batch_size/256, 
        len(train_loader),
    )

for data in range(train_loader):
  optimizer.zero_grad()
  output = model(data) 
  loss = lossfunc(output,gt)
  loss.backward()
  optimizer.step()
  lr_scheduler.step()

In CV domain [1,2], in order to automatically adapt different batch size you can use a learning rate of lr×BatchSize/256 (linear scaling [4])(we can use larger learning rate while adopting larger batch size, especially, when you use LARS optimizer[3]). Of course, you can modify it according to your specific requirements.

Reference

[1] Jean-Bastien Grill, Florian Strub, Florent Altch´e, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Do- ersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R´emi Munos, and Michal Valko. Bootstrap your own latent: A new approach to self-supervised learning. arXiv:2006.07733v1, 2020.
[2] Chen X, He K. Exploring simple siamese representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 15750-15758.
[3] Yang You, Igor Gitman, and Boris Ginsburg. Large batch training of convolutional networks. arXiv:1708.03888, 2017.
[4] Priya Goyal, Piotr Doll´ar, Ross Girshick, Pieter Noord- huis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Accurate, large minibatch SGD: Training ImageNet in 1 hour. arXiv:1706.02677, 2017.
[5] https://github.com/PatrickHua/SimSiam?utm_source=catalyzex.com

Owner
zhuyun
zhuyun
Nsdf: A mesh SDF with just some code we can directly paste into our raymarcher

nsdf Representing SDFs of arbitrary meshes has been a bit tricky so far. Express

Jan Ivanecky 5 Feb 18, 2022
My implementation of transformers related papers for computer vision in pytorch

vision_transformers This is my personnal repo to implement new transofrmers based and other computer vision DL models I am currenlty working without a

samsja 1 Nov 10, 2021
Original code for "Zero-Shot Domain Adaptation with a Physics Prior"

Zero-Shot Domain Adaptation with a Physics Prior [arXiv] [sup. material] - ICCV 2021 Oral paper, by Attila Lengyel, Sourav Garg, Michael Milford and J

Attila Lengyel 40 Dec 21, 2022
Pytorch implementation of MaskFlownet

MaskFlownet-Pytorch Unofficial PyTorch implementation of MaskFlownet (https://github.com/microsoft/MaskFlownet). Tested with: PyTorch 1.5.0 CUDA 10.1

Daniele Cattaneo 84 Nov 02, 2022
Fast and exact ILP-based solvers for the Minimum Flow Decomposition (MFD) problem, and variants of it.

MFD-ILP Fast and exact ILP-based solvers for the Minimum Flow Decomposition (MFD) problem, and variants of it. The solvers are implemented using Pytho

Algorithmic Bioinformatics Group @ University of Helsinki 4 Oct 23, 2022
Compute FID scores with PyTorch.

FID score for PyTorch This is a port of the official implementation of Fréchet Inception Distance to PyTorch. See https://github.com/bioinf-jku/TTUR f

2.1k Jan 06, 2023
Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)

Weakly- and Semi-Supervised Panoptic Segmentation by Qizhu Li*, Anurag Arnab*, Philip H.S. Torr This repository demonstrates the weakly supervised gro

Qizhu Li 159 Dec 20, 2022
The code for Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation

BiMix The code for Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation arxiv Framework: visualization results: Requiremen

stanley 18 Sep 18, 2022
A library that can print Python objects in human readable format

objprint A library that can print Python objects in human readable format Install pip install objprint Usage op Use op() (or objprint()) to print obj

319 Dec 25, 2022
Deploy recommendation engines with Edge Computing

RecoEdge: Bringing Recommendations to the Edge A one stop solution to build your recommendation models, train them and, deploy them in a privacy prese

NimbleEdge 131 Jan 02, 2023
This repository contains the code for the paper "Hierarchical Motion Understanding via Motion Programs"

Hierarchical Motion Understanding via Motion Programs (CVPR 2021) This repository contains the official implementation of: Hierarchical Motion Underst

Sumith Kulal 40 Dec 05, 2022
A Sign Language detection project using Mediapipe landmark detection and Tensorflow LSTM's

sign-language-detection A Sign Language detection project using Mediapipe landmark detection and Tensorflow LSTM. The project is built for a vocabular

Hashim 4 Feb 06, 2022
git《Investigating Loss Functions for Extreme Super-Resolution》(CVPR 2020) GitHub:

Investigating Loss Functions for Extreme Super-Resolution NTIRE 2020 Perceptual Extreme Super-Resolution Submission. Our method ranked first and secon

Sejong Yang 0 Oct 17, 2022
QT Py Media Knob using rotary encoder & neopixel ring

QTPy-Knob QT Py USB Media Knob using rotary encoder & neopixel ring The QTPy-Knob features: Media knob for volume up/down/mute with "qtpy-knob.py" Cir

Tod E. Kurt 56 Dec 30, 2022
RM Operation can equivalently convert ResNet to VGG, which is better for pruning; and can help RepVGG perform better when the depth is large.

RMNet: Equivalently Removing Residual Connection from Networks This repository is the official implementation of "RMNet: Equivalently Removing Residua

184 Jan 04, 2023
Codes for AAAI22 paper "Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum"

Paper For more details, please see our paper Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum which has been accepted a

14 Sep 30, 2022
MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

Octave Convolution MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution Imag

Meta Research 549 Dec 28, 2022
TensorFlow implementation of "Attention is all you need (Transformer)"

[TensorFlow 2] Attention is all you need (Transformer) TensorFlow implementation of "Attention is all you need (Transformer)" Dataset The MNIST datase

YeongHyeon Park 4 Jan 05, 2022
Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Sartorius - Cell Instance Segmentation https://www.kaggle.com/c/sartorius-cell-instance-segmentation Environment setup Build docker image bash .dev_sc

68 Dec 09, 2022
Machine learning algorithms for many-body quantum systems

NetKet NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and

NetKet 413 Dec 31, 2022