An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

Related tags

Deep LearningAWS-OHL-AutoAug
Overview

Automatic Augmentation Zoo

An integration of several popular automatic augmentation methods, including OHL (Online Hyper-Parameter Learning for Auto-Augmentation Strategy) and AWS (Improving Auto Augment via Augmentation Wise Weight Sharing) by Sensetime Research.

We will post updates regularly so you can star 🌟 or watch 👓 this repository for the latest.

Introduction

This repository provides the official implementations of OHL and AWS, and will also integrate some other popular auto-aug methods (like Auto Augment, Fast AutoAugment and Adversarial autoaugment) in pure PyTorch. We use torch.distributed to conduct the distributed training. The model checkpoints will be upload to GoogleDrive or OneDrive soon.

Dependencies

It would be recommended to conduct experiments under:

  • python 3.6.3
  • pytorch 1.1.0, torchvision 0.2.1

All the dependencies are listed in requirements.txt. You may use commands like pip install -r requirements.txt to install them.

Running

  1. Create the directory for your experiment.
cd /path/to/this/repo
mkdir -p exp/aws_search1
  1. Copy configurations into your workspace.
cp scripts/search.sh configs/aws.yaml exp/aws_search1
cd exp/aws_search1
  1. Start searching
# sh ./search.sh  
sh ./search.sh Test 8

An instance of yaml:

version: 0.1.0

dist:
    type: torch
    kwargs:
        node0_addr: auto
        node0_port: auto
        mp_start_method: fork   # fork or spawn; spawn would be too slow for Dalaloader

pipeline:
    type: aws
    common_kwargs:
        dist_training: &dist_training False
#        job_name:         [will be assigned in runtime]
#        exp_root:         [will be assigned in runtime]
#        meta_tb_lg_root:  [will be assigned in runtime]

        data:
            type: cifar100               # case-insensitive (will be converted to lower case in runtime)
#            dataset_root: /path/to/dataset/root   # default: ~/datasets/[type]
            train_set_size: 40000
            val_set_size: 10000
            batch_size: 256
            dist_training: *dist_training
            num_workers: 3
            cutout: True
            cutlen: 16

        model_grad_clip: 3.0
        model:
            type: WRN
            kwargs:
#                num_classes: [will be assigned in runtime]
                bn_mom: 0.5

        agent:
            type: ppo           # ppo or REINFORCE
            kwargs:
                initial_baseline_ratio: 0
                baseline_mom: 0.9
                clip_epsilon: 0.2
                max_training_times: 5
                early_stopping_kl: 0.002
                entropy_bonus: 0
                op_cfg:
                    type: Adam         # any type in torch.optim
                    kwargs:
#                        lr: [will be assigned in runtime] (=sc.kwargs.base_lr)
                        betas: !!python/tuple [0.5, 0.999]
                        weight_decay: 0
                sc_cfg:
                    type: Constant
                    kwargs:
                        base_lr_divisor: 8      # base_lr = warmup_lr / base_lr_divisor
                        warmup_lr: 0.1          # lr at the end of warming up
                        warmup_iters: 10      # warmup_epochs = epochs / warmup_divisor
                        iters: &finetune_lp 350
        
        criterion:
            type: LSCE
            kwargs:
                smooth_ratio: 0.05


    special_kwargs:
        pretrained_ckpt_path: ~ # /path/to/pretrained_ckpt.pth.tar
        pretrain_ep: &pretrain_ep 200
        pretrain_op: &sgd
            type: SGD       # any type in torch.optim
            kwargs:
#                lr: [will be assigned in runtime] (=sc.kwargs.base_lr)
                nesterov: True
                momentum: 0.9
                weight_decay: 0.0001
        pretrain_sc:
            type: Cosine
            kwargs:
                base_lr_divisor: 4      # base_lr = warmup_lr / base_lr_divisor
                warmup_lr: 0.2          # lr at the end of warming up
                warmup_divisor: 200     # warmup_epochs = epochs / warmup_divisor
                epochs: *pretrain_ep
                min_lr: &finetune_lr 0.001

        finetuned_ckpt_path: ~  # /path/to/finetuned_ckpt.pth.tar
        finetune_lp: *finetune_lp
        finetune_ep: &finetune_ep 10
        rewarded_ep: 2
        finetune_op: *sgd
        finetune_sc:
            type: Constant
            kwargs:
                base_lr: *finetune_lr
                warmup_lr: *finetune_lr
                warmup_iters: 0
                epochs: *finetune_ep

        retrain_ep: &retrain_ep 300
        retrain_op: *sgd
        retrain_sc:
            type: Cosine
            kwargs:
                base_lr_divisor: 4      # base_lr = warmup_lr / base_lr_divisor
                warmup_lr: 0.4          # lr at the end of warming up
                warmup_divisor: 200     # warmup_epochs = epochs / warmup_divisor
                epochs: *retrain_ep
                min_lr: 0

Citation

If you're going to to use this code in your research, please consider citing our papers (OHL and AWS).

@inproceedings{lin2019online,
  title={Online Hyper-parameter Learning for Auto-Augmentation Strategy},
  author={Lin, Chen and Guo, Minghao and Li, Chuming and Yuan, Xin and Wu, Wei and Yan, Junjie and Lin, Dahua and Ouyang, Wanli},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={6579--6588},
  year={2019}
}

@article{tian2020improving,
  title={Improving Auto-Augment via Augmentation-Wise Weight Sharing},
  author={Tian, Keyu and Lin, Chen and Sun, Ming and Zhou, Luping and Yan, Junjie and Ouyang, Wanli},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Contact for Issues

References & Opensources

Owner
GitHub Repository
Age and Gender prediction using Keras

cnn_age_gender Age and Gender prediction using Keras Dataset example : Description : UTKFace dataset is a large-scale face dataset with long age span

XN3UR0N 58 May 03, 2022
Unofficial Tensorflow Implementation of ConvNeXt from A ConvNet for the 2020s

Tensorflow Implementation of "A ConvNet for the 2020s" This is the unofficial Tensorflow Implementation of ConvNeXt from "A ConvNet for the 2020s" pap

DK 11 Oct 12, 2022
Anomaly detection related books, papers, videos, and toolboxes

Anomaly Detection Learning Resources Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify

Yue Zhao 6.7k Dec 31, 2022
Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models

Explainable_FIQA_WITH_AMVA Note This is the official repository of the paper: Explainability of the Implications of Supervised and Unsupervised Face I

3 May 08, 2022
Twins: Revisiting the Design of Spatial Attention in Vision Transformers

Twins: Revisiting the Design of Spatial Attention in Vision Transformers Very recently, a variety of vision transformer architectures for dense predic

482 Dec 18, 2022
Supervised Contrastive Learning for Downstream Optimized Sequence Representations

SupCL-Seq 📖 Supervised Contrastive Learning for Downstream Optimized Sequence representations (SupCS-Seq) accepted to be published in EMNLP 2021, ext

Hooman Sedghamiz 18 Oct 21, 2022
CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K

CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city.

96 Jul 05, 2022
Official repository for "On Generating Transferable Targeted Perturbations" (ICCV 2021)

On Generating Transferable Targeted Perturbations (ICCV'21) Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Fatih Porikli Paper:

Muzammal Naseer 46 Nov 17, 2022
Official PyTorch implementation of Spatial Dependency Networks.

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling Đorđe Miladinović   Aleksandar Stanić   Stefan Bauer   Jürgen Schmid

Djordje Miladinovic 34 Jan 19, 2022
An official implementation of the Anchor DETR.

Anchor DETR: Query Design for Transformer-Based Detector Introduction This repository is an official implementation of the Anchor DETR. We encode the

MEGVII Research 276 Dec 28, 2022
Official PyTorch implementation of "Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient".

Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient This repository is the official PyTorch implementation of "Edge Rewiring Go

Shanchao Yang 4 Dec 12, 2022
Distributing reference energies for SMIRNOFF implementations

Warning: This code is currently experimental and under active development. Is it not yet suitable for distribution or use as reference implementation.

Open Force Field Initiative 1 Dec 07, 2021
Dataset used in "PlantDoc: A Dataset for Visual Plant Disease Detection" accepted in CODS-COMAD 2020

PlantDoc: A Dataset for Visual Plant Disease Detection This repository contains the Cropped-PlantDoc dataset used for benchmarking classification mode

Pratik Kayal 109 Dec 29, 2022
The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022

DG-TrajGen The official repository for paper ''Domain Generalization for Vision-based Driving Trajectory Generation'' submitted to ICRA 2022. Our Meth

Wang 25 Sep 26, 2022
A collection of Google research projects related to Federated Learning and Federated Analytics.

Federated Research Federated Research is a collection of research projects related to Federated Learning and Federated Analytics. Federated learning i

Google Research 483 Jan 05, 2023
PyQt6 configuration in yaml format providing the most simple script.

PyamlQt（ぴゃむるきゅーと） PyQt6 configuration in yaml format providing the most simple script. Requirements yaml PyQt6, ( PyQt5 ) Installation pip install Pya

Ar-Ray 7 Aug 15, 2022
Bootstrapped Representation Learning on Graphs

Bootstrapped Representation Learning on Graphs This is the PyTorch implementation of BGRL Bootstrapped Representation Learning on Graphs The main scri

NerDS Lab :: Neural Data Science Lab 55 Jan 07, 2023
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)

[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio

42 Nov 01, 2022
An example of Scatterbrain implementation (combining local attention and Performer)

An example of Scatterbrain implementation (combining local attention and Performer)

HazyResearch 97 Jan 02, 2023
Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for

Abdultawwab Safarji 7 Nov 27, 2022