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}
}

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