Ranger - a synergistic optimizer using RAdam (Rectified Adam), Gradient Centralization and LookAhead in one codebase

Overview

Ranger-Deep-Learning-Optimizer


Ranger - a synergistic optimizer combining RAdam (Rectified Adam) and LookAhead, and now GC (gradient centralization) in one optimizer.

quick note - Ranger21 is now in beta and is Ranger with a host of new improvements.

Recommend you compare results with Ranger21: https://github.com/lessw2020/Ranger21

Latest version 20.9.4 - updates Gradient Centralization to GC2 (thanks to GC developer) and removes addcmul_ deprecation warnings in PyTorch 1.60.



*Latest version is in ranger2020.py - looking at a few other additions before integrating into the main ranger.py.

What is Gradient Centralization? = "GC can be viewed as a projected gradient descent method with a constrained loss function. The Lipschitzness of the constrained loss function and its gradient is better so that the training process becomes more efficient and stable." Source paper: https://arxiv.org/abs/2004.01461v2
Ranger now uses Gradient Centralization by default, and applies it to all conv and fc layers by default. However, everything is customizable so you can test with and without on your own datasets. (Turn on off via "use_gc" flag at init).

Best training results - use a 75% flat lr, then step down and run lower lr for 25%, or cosine descend last 25%.


Per extensive testing - It's important to note that simply running one learning rate the entire time will not produce optimal results.
Effectively Ranger will end up 'hovering' around the optimal zone, but can't descend into it unless it has some additional run time at a lower rate to drop down into the optimal valley.

Full customization at init:


Ranger will now print out id and gc settings at init so you can confirm the optimizer settings at train time:

/////////////////////

Medium article with more info:
https://medium.com/@lessw/new-deep-learning-optimizer-ranger-synergistic-combination-of-radam-lookahead-for-the-best-of-2dc83f79a48d

Multiple updates: 1 - Ranger is the optimizer we used to beat the high scores for 12 different categories on the FastAI leaderboards! (Previous records all held with AdamW optimizer).

2 - Highly recommend combining Ranger with: Mish activation function, and flat+ cosine anneal training curve.

3 - Based on that, also found .95 is better than .90 for beta1 (momentum) param (ala betas=(0.95, 0.999)).

Fixes: 1 - Differential Group learning rates now supported. This was fix in RAdam and ported here thanks to @sholderbach. 2 - save and then load may leave first run weights stranded in memory, slowing down future runs = fixed.

Installation

Clone the repo, cd into it and install it in editable mode (-e option). That way, these is no more need to re-install the package after modification.

git clone https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
cd Ranger-Deep-Learning-Optimizer
pip install -e . 

Usage

from ranger import Ranger  # this is from ranger.py
from ranger import RangerVA  # this is from ranger913A.py
from ranger import RangerQH  # this is from rangerqh.py

# Define your model
model = ...
# Each of the Ranger, RangerVA, RangerQH have different parameters.
optimizer = Ranger(model.parameters(), **kwargs)

Usage and notebook to test are available here: https://github.com/lessw2020/Ranger-Mish-ImageWoof-5

Citing this work

We recommend you use the following to cite Ranger in your publications:

@misc{Ranger,
  author = {Wright, Less},
  title = {Ranger - a synergistic optimizer.},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer}}
}
Owner
Less Wright
Principal Software Engineer at Audere PM/Test/Dev at Microsoft Software Architect at X10 Wireless
Less Wright
Out-of-Distribution Generalization of Chest X-ray Using Risk Extrapolation

OoD_Gen-Chest_Xray Out-of-Distribution Generalization of Chest X-ray Using Risk Extrapolation Requirements (Installations) Install the following libra

Enoch Tetteh 2 Oct 01, 2022
UPSNet: A Unified Panoptic Segmentation Network

UPSNet: A Unified Panoptic Segmentation Network Introduction UPSNet is initially described in a CVPR 2019 oral paper. Disclaimer This repository is te

Uber Research 622 Dec 26, 2022
Code repository for the paper Computer Vision User Entity Behavior Analytics

Computer Vision User Entity Behavior Analytics Code repository for "Computer Vision User Entity Behavior Analytics" Code Description dataset.csv As di

Sameer Khanna 2 Aug 20, 2022
Streaming over lightweight data transformations

Description Data augmentation libarary for Deep Learning, which supports images, segmentation masks, labels and keypoints. Furthermore, SOLT is fast a

Research Unit of Medical Imaging, Physics and Technology 256 Jan 08, 2023
Fast and Simple Neural Vocoder, the Multiband RNNMS

Multiband RNN_MS Fast and Simple vocoder, Multiband RNN_MS. Demo Quick training How to Use System Details Results References Demo ToDO: Link super gre

tarepan 5 Jan 11, 2022
CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes Implementation of CoSMA: Convolutional Semi-Regular Mesh Autoencoder arXiv p

Fraunhofer SCAI 10 Oct 11, 2022
Code associated with the paper "Deep Optics for Single-shot High-dynamic-range Imaging"

Deep Optics for Single-shot High-dynamic-range Imaging Code associated with the paper "Deep Optics for Single-shot High-dynamic-range Imaging" CVPR, 2

Stanford Computational Imaging Lab 40 Dec 12, 2022
DeiT: Data-efficient Image Transformers

DeiT: Data-efficient Image Transformers This repository contains PyTorch evaluation code, training code and pretrained models for DeiT (Data-Efficient

Facebook Research 3.2k Jan 06, 2023
(CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

ClassSR (CVPR2021) ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic Paper Authors: Xiangtao Kong, Hengyuan

Xiangtao Kong 308 Jan 05, 2023
Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

Summary Explorer Summary Explorer is a tool to visually inspect the summaries from several state-of-the-art neural summarization models across multipl

Webis 42 Aug 14, 2022
MEND: Model Editing Networks using Gradient Decomposition

MEND: Model Editing Networks using Gradient Decomposition Setup Environment This codebase uses Python 3.7.9. Other versions may work as well. Create a

Eric Mitchell 141 Dec 02, 2022
CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation

CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation We propose a novel approach to translate unpaired contrast computed

Nicolae Catalin Ristea 13 Jan 02, 2023
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Autoformer (NeurIPS 2021) Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting Time series forecasting is a c

THUML @ Tsinghua University 847 Jan 08, 2023
Keeper for Ricochet Protocol, implemented with Apache Airflow

Ricochet Keeper This repository contains Apache Airflow DAGs for executing keeper operations for Ricochet Exchange. Usage You will need to run this us

Ricochet Exchange 5 May 24, 2022
Image Captioning on google cloud platform based on iot

Image-Captioning-on-google-cloud-platform-based-on-iot - Image Captioning on google cloud platform based on iot

Shweta_kumawat 1 Jan 20, 2022
Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking Part-Aware Measurement for Robust Multi-View Multi-Human 3D P

19 Oct 27, 2022
Code for CVPR2021 "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Visualizing Adapted Knowledge in Domain Transfer @inproceedings{hou2021visualizing, title={Visualizing Adapted Knowledge in Domain Transfer}, auth

Yunzhong Hou 80 Dec 25, 2022
PyTorch implementation of PNASNet-5 on ImageNet

PNASNet.pytorch PyTorch implementation of PNASNet-5. Specifically, PyTorch code from this repository is adapted to completely match both my implemetat

Chenxi Liu 314 Nov 25, 2022
pytorch implementation of Attention is all you need

A Pytorch Implementation of the Transformer: Attention Is All You Need Our implementation is largely based on Tensorflow implementation Requirements N

230 Dec 07, 2022