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
PyTorch-based framework for Deep Hedging

PFHedge: Deep Hedging in PyTorch PFHedge is a PyTorch-based framework for Deep Hedging. PFHedge Documentation Neural Network Architecture for Efficien

139 Dec 30, 2022
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack

FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack Case study of the FCA. The code can be find in FCA. Cas

IDRL 21 Dec 15, 2022
PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short-Term Transformer for Online Action Detection".

Long Short-Term Transformer for Online Action Detection Introduction This is a PyTorch implementation for our NeurIPS 2021 Spotlight paper "Long Short

77 Dec 16, 2022
High frequency AI based algorithmic trading module.

Flow Flow is a high frequency algorithmic trading module that uses machine learning to self regulate and self optimize for maximum return. The current

59 Dec 14, 2022
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data

Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data arXiv This is the code base for weakly supervised NER. We provide a

Amazon 92 Jan 04, 2023
Face Alignment using python

Face Alignment Face Alignment using python Input Image Aligned Face Aligned Face Aligned Face Input Image Aligned Face Input Image Aligned Face Instal

Sajjad Aemmi 28 Nov 23, 2022
Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution

Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution Abstract Within the Latin (and ancient Greek) production, it is well

4 Dec 03, 2022
Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

shindo.py Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data stored in NumPy array Introduction Japa

RR_Inyo 3 Sep 23, 2022
[Preprint] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang

Chasing Sparsity in Vision Transformers: An End-to-End Exploration Codes for [Preprint] Chasing Sparsity in Vision Transformers: An End-to-End Explora

VITA 64 Dec 08, 2022
Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning This is a PyTorch implementation of the original Lua code release. Overview This

Minqi 297 Dec 12, 2022
A Python library for generating new text from existing samples.

ReMarkov is a Python library for generating text from existing samples using Markov chains. You can use it to customize all sorts of writing from birt

8 May 17, 2022
for a paper about leveraging discourse markers for training new models

TSLM-DISCOURSE-MARKERS Scope This repository contains: (1) Code to extract discourse markers from wikipedia (TSA). (1) Code to extract significant dis

International Business Machines 6 Nov 02, 2022
Implementation of Memory-Efficient Neural Networks with Multi-Level Generation, ICCV 2021

Memory-Efficient Multi-Level In-Situ Generation (MLG) By Jiaqi Gu, Hanqing Zhu, Chenghao Feng, Mingjie Liu, Zixuan Jiang, Ray T. Chen and David Z. Pan

Jiaqi Gu 2 Jan 04, 2022
Official Pytorch implementation for AAAI2021 paper (RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning)

RSPNet Official Pytorch implementation for AAAI2021 paper "RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning" [Suppleme

35 Jun 24, 2022
Editing a Conditional Radiance Field

Editing Conditional Radiance Fields Project | Paper | Video | Demo Editing Conditional Radiance Fields Steven Liu, Xiuming Zhang, Zhoutong Zhang, Rich

Steven Liu 216 Dec 30, 2022
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

Table of Content Introduction Getting Started Datasets Installation Experiments Training & Testing Pretrained models Texture fine-tuning Demo Toward R

VinAI Research 42 Dec 05, 2022
exponential adaptive pooling for PyTorch

AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling Abstract Pooling layers are essential building blocks of Convolutional Ne

Alexandros Stergiou 55 Jan 04, 2023
Mall-Customers-Segmentation - Customer Segmentation Using K-Means Clustering

Overview Customer Segmentation is one the most important applications of unsupervised learning. Using clustering techniques, companies can identify th

NelakurthiSudheer 2 Jan 03, 2022
Code for "PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds", CVPR 2021

PV-RAFT This repository contains the PyTorch implementation for paper "PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clou

Yi Wei 43 Dec 05, 2022