OpenAL

OpenAL is an Active Learning (AL) framework for classification.

The AL is classified into two approaches by samples of unlabeled data. The AL that assumes the unlabeled data contains only in-distribution data is called Standard AL. If the unlabeled data includes not only in-distribution but also out-of-distribution, it is called Open-set AL. This framework covers Standard AL and Open-set AL. So, we named our framework OpenAL.

We hope that AL research can advance further through this framework.

Environments

We build environments based on the docker image nvcr.io/nvidia/pytorch:22.12-py3.

python==3.8.10
torch==1.14.0a0+410ce96
torchvision==0.15.0a0
accelerate==0.18.0
wandb
torchvision==0.15.0a0
omegaconf
timm==0.9.2
seaborn==0.12.2
torchlars==0.1.2
ftfy==6.1.3
open-clip-torch==2.24.0
finch-clust==0.1.9

Query Strategies

AL	Type	Method	Class Name	Paper
None-AL	-	Random Sampling	RandomSampling	-
Standard AL	Uncertainty	Least Confidence	LeastConfidence	IJCNN 2014 - paper
Standard AL	Uncertainty	Margin Sampling	MarginSampling	IJCNN 2014 - paper
Standard AL	Uncertainty	Entropy	EntropySampling	IJCNN 2014 - paper
Standard AL	Uncertainty	VarRatio	VarRatio	ICMLW 2020 - paper
Standard AL	Uncertainty	MeanSTD	MeanSTD	CVPRW 2016 - paper
Standard AL	Uncertainty	Learning Loss	LearningLossAL	CVPR 2019 - paper, unofficial
Standard AL	Uncertainty	AlphaMix	AlphaMixSampling	CVPR 2022 - paper, official
Standard AL	Uncertainty	BALD	BALD	arXiv 2011.12 - paper, unofficial
Standard AL	Hybrid	BADGE	BADGE	NeurIPS 2019 - paper, official
Standard AL	Diversity	K-Center Greedy	KCenterGreedy	CVPR 2018 - paper, official
Standard AL	Diversity	K-Center Greedy + Class Balanced	KCenterGreedyCB	WACV 2022 - paper, official
Open-set AL	Contrastive Learning	CCAL	CCAL	ICCV 2021 - paper, official
Open-set AL	Contrastive Learning	MQNet	MQNet	NeurIPS 2022 - paper, official
Open-set AL	OOD Detector	LfOSA	LfOSA	CVPR 2022 - paper, official
Open-set AL	OOD Detector	EOAL	EOAL	AAAI 2024 - paper, official
Open-set AL	VLM	CLIPNAL	CLIPNAL	arXiv 2024.8 - paper, official

CLIPN checkpoint

CLIPNAL uses a CLIPN checkpoint shared from CLIPN repository. The checkpoint can download in here.

Configuration for Experiments

All configuration files are in ./configs.
You can modify the config files to run your experiment settings.

./configs
├── default_setting.yaml
├── openset_al
│ ├── ccal.yaml
│ ├── clipnal.yaml
│ ├── eoal.yaml
│ ├── lfosa.yaml
│ └── mqnet.yaml
├── ssl
│ ├── csi.yaml
│ └── simclr.yaml
└── standard_al
├── badge.yaml
├── bald.yaml
├── entropy_sampling.yaml
├── featmix_sampling.yaml
├── kcenter_greedy_cb.yaml
├── kcenter_greedy.yaml
├── learning_loss.yaml
├── least_confidence.yaml
├── margin_sampling.yaml
├── meanstd_sampling.yaml
├── random_sampling.yaml
└── varratio_sampling.yaml

How to Use

• You can use our framework in colab. This code is a tutorial on how to use the query strategy for Standard AL and the CLIPNAL method, which is one of the Open AL methods.

Standard AL

fromquery_strategiesimportcreate_query_strategymodel=# classifiertrainset=# training data set with labeled and unlabeled samplestransform=# transforms for extracting featuressampler_name=# SubsetRandomSampler or SubsetWeightedRandomSampleris_labeled=# bool type 1-d array (N,). N is the number of samples. True is labeled samples and False is unlabeled samples. n_query=# the number of samples for annotationn_subset=# sampling size for unlabeled databatch_size=# batch sizenum_workers=# number of workersstrategy=create_query_strategy(
strategy_name=# strategy name, model=model,
dataset=trainset, transform=transform,
sampler_name=sampler_name,
is_labeled=is_labeled, n_query=n_query, n_subset=n_subset,
batch_size=batch_size, num_workers=num_workers
)
# select query using the trained model on labeled samplesquery_idx=strategy.query(model)
strategy.update(query_idx=query_idx)

Open-set AL

fromquery_strategiesimportcreate_query_strategymodel=# classifiertrainset=# training data set with labeled and unlabeled samplestransform=# transforms for extracting featuressampler_name=# SubsetRandomSampler or SubsetWeightedRandomSampleris_labeled=# bool type 1-d array (N,). N is the number of samples. True is labeled ID samples and False is unlabeled samples. n_query=# the number of samples for annotationn_subset=# sampling size for unlabeled databatch_size=# batch sizenum_workers=# number of workers# select strategy openset_params= {
'is_openset' : # if unlabeled data contains OOD samples, True, or False'is_unlabeled' : # bool type 1-d array (N,). N is the number of samples. True is unlabeled samples and False is labeled ID and OOD samples.'is_ood' : # bool type 1-d array (N,). N is the number of samples. True is OOD samples and False is unlabeled and ID samples.'id_classes' : # ID class names'savedir' : # save directory'seed' : # seed
}
strategy=create_query_strategy(
strategy_name=# strategy name, model=model,
dataset=trainset, transform=transform,
sampler_name=sampler_name,
is_labeled=is_labeled, n_query=n_query, n_subset=n_subset,
batch_size=batch_size, num_workers=num_workers,
**openset_params
)
# select query using trained model on labeled samplesquery_idx=strategy.query(model)
id_query_idx=strategy.update(query_idx=query_idx)

How to Run

Supervised Learning with full train dataset

python main.py \
default_cfg=./configs/default_setting.yaml \
DATASET.name=$dataname \
DEFAULT.savedir=$savedir

Standard AL

python main.py \
default_cfg=./configs/default_setting.yaml \
strategy_cfg=./configs/standard_al/$strategy_name.yaml \
DATASET.name=$dataname \
AL.n_start=$n_start \
AL.n_query=$n_query \
AL.n_end=$n_end \
DEFAULT.savedir=$savedir

Open-set AL

python main.py \
default_cfg=./configs/default_setting.yaml \
openset_cfg=./configs/openset_al/$strategy_name.yaml \
DATASET.name=$dataname \
AL.ood_ratio=$ood_ratio \
AL.id_ratio=$id_ratio \
AL.n_start=$n_start \
AL.n_query=$n_query \
AL.n_end=$n_end \
DEFAULT.savedir=$savedir