Implementation of "Selection via Proxy: Efficient Data Selection for Deep Learning" from ICLR 2020.

Related tags

Deep Learningselection-via-proxy
Overview

Selection via Proxy: Efficient Data Selection for Deep Learning

This repository contains a refactored implementation of "Selection via Proxy: Efficient Data Selection for Deep Learning" from ICLR 2020.

If you use this code in your research, please use the following BibTeX entry.

@inproceedings{
    coleman2020selection,
    title={Selection via Proxy: Efficient Data Selection for Deep Learning},
    author={Cody Coleman and Christopher Yeh and Stephen Mussmann and Baharan Mirzasoleiman and Peter Bailis and Percy Liang and Jure Leskovec and Matei Zaharia},
    booktitle={International Conference on Learning Representations},
    year={2020},
    url={https://openreview.net/forum?id=HJg2b0VYDr}
}

The original code is also available as a zip file, but lacks documentation, uses outdated packages, and won't be maintained. Please use this repository instead and report issues here.

Setup

Prerequisites

Installation

git clone https://github.com/stanford-futuredata/selection-via-proxy.git
cd selection-via-proxy
pip install -e .

or simply

pip install git+https://github.com/stanford-futuredata/selection-via-proxy.git

Quickstart

Perform active learning on CIFAR10 from the command line:

python -m svp.cifar active

Or from the python interpreter:

from svp.cifar.active import active
active()

"Selection via proxy" happens when --proxy-arch doesn't match --arch:

# ResNet20 selecting data for a ResNet164
python -m svp.cifar active --proxy-arch preact20 --arch preact164

For help, see python -m svp.cifar active --help or active()'s docstrinng.

Example Usage

Below are more examples of the command line interface that cover different datasets (e.g., CIFAR100, ImageNet, Amazon Review Polarity) and commands (e.g., train, coreset).

Basic Training

CIFAR10 and CIFAR100

Preliminaries

None. The CIFAR10 and CIFAR100 datasets will download if they don't exist in ./data/cifar10 and ./data/cifar100 respectively.

Examples
# Train ResNet164 with pre-activation (https://arxiv.org/abs/1603.05027) on CIFAR10.
python -m svp.cifar train --dataset cifar10 --arch preact164

Replace --dataset CIFAR10 with --dataset CIFAR100 to run on CIFAR100 rather than CIFAR10.

# Train ResNet164 with pre-activation (https://arxiv.org/abs/1603.05027) on CIFAR100.
python -m svp.cifar train --dataset cifar100 --arch preact164

The same is true for all the python -m svp.cifar commands below

ImageNet

Preliminaries
  • Download the ImageNet dataset into a directory called imagenet.
  • Extract the images.
# Extract train data.
mkdir train && mv ILSVRC2012_img_train.tar train/ && cd train
tar -xvf ILSVRC2012_img_train.tar && rm -f ILSVRC2012_img_train.tar
find . -name "*.tar" | while read NAME ; do mkdir -p "${NAME%.tar}"; tar -xvf "${NAME}" -C "${NAME%.tar}"; rm -f "${NAME}"; done
# Extract validation data.
cd ../ && mkdir val && mv ILSVRC2012_img_val.tar val/ && cd val && tar -xvf ILSVRC2012_img_val.tar
wget -qO- https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh | bash
  • Replace /path/to/data in all the python -m svp.imagenet commands below with the path to the imagenet directory you created. Note, do not include imagenet in the path; the script will automatically do that.
Examples
# Train ResNet50 (https://arxiv.org/abs/1512.03385).
python -m svp.imagenet train --dataset-dir '/path/to/data' --arch resnet50 --num-workers 20

For convenience, you can use larger batch sizes and scale learning rates according to "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour" with --scale-learning-rates:

# Train ResNet50 with a batch size of 1048 and scaled learning rates accordingly.
python -m svp.imagenet train --dataset-dir '/path/to/data' --arch resnet50 --num-workers 20 \
    --batch-size 1048 --scale-learning-rates

Mixed precision training is also supported using apex. Apex isn't installed during the pip install instructions above, so please follow the installation instructions in the apex repository before running the command below.

# Use mixed precision training to train ResNet50 with a batch size of 1048 and scale learning rates accordingly.
python -m svp.imagenet train --dataset-dir '/path/to/data' --arch resnet50 --num-workers 20 \
    --batch-size 1048 --scale-learning-rates --fp16

Amazon Review Polarity and Full

Preliminaries
tar -xvzf amazon_review_full_csv.tar.gz
tar -xvzf amazon_review_polarity_csv.tar.gz
  • Replace /path/to/data in all the python -m svp.amazon commands below with the path to the root directory you created. Note, do not include amazon_review_full_csv or amazon_review_polarity_csv in the path; the script will automatically do that.
Examples
# Train VDCNN29 (https://arxiv.org/abs/1606.01781) on Amazon Review Polarity.
python -m svp.amazon train --datasets-dir '/path/to/data' --dataset amazon_review_polarity --arch vdcnn29-conv \
    --num-workers 4 --eval-num-workers 8

Replace --dataset amazon_review_polarity with --dataset amazon_review_full to run on Amazon Review Full rather than Amazon Review Polarity.

# Train VDCNN29 (https://arxiv.org/abs/1606.01781) on Amazon Review Full.
python -m svp.amazon train --datasets-dir '/path/to/data' --dataset amazon_review_full --arch vdcnn29-maxpool \
    --num-workers 4 --eval-num-workers 8

The same is true for all the python -m svp.amazon commands below

Active learning

Active learning selects points to label from a large pool of unlabeled data by repeatedly training a model on a small pool of labeled data and selecting additional examples to label based on the model’s uncertainty (e.g., the entropy of predicted class probabilities) or other heuristics. The commands below demonstrate how to perform active learning on CIFAR10, CIFAR100, ImageNet, Amazon Review Polarity and Amazon Review Full with a variety of models and selection methods.

CIFAR10 and CIFAR100

Baseline Approach
# Perform active learning with ResNet164 for both selection and the final predictions.
python -m svp.cifar active --dataset cifar10 --arch preact164 --num-workers 4 \
	--selection-method least_confidence \
	--initial-subset 1000 \
	--round 4000 \
	--round 5000 \
	--round 5000 \
	--round 5000 \
	--round 5000
Selection via Proxy

If the model architectures (arch vs proxy_arch) or the learning rate schedules don't match, "selection via proxy" (SVP) is performed and two separate models are trained. The proxy is used for selecting which examples to label, while the target is only used for evaluating the quality of the selection. By default, the target model (arch) is trained and evaluated after each selection round. To change this behavior set eval_target_at to evaluate at a specific labeling budget(s) or set train_target to False to skip evaluating the target model.

# Perform active learning with ResNet20 for selection and ResNet164 for the final predictions.
python -m svp.cifar active --dataset cifar10 --arch preact164 --num-workers 4 \
	--selection-method least_confidence --proxy-arch preact20 \
	--initial-subset 1000 \
	--round 4000 \
	--round 5000 \
	--round 5000 \
	--round 5000 \
	--round 5000 \
	--eval-target-at 25000

To train the proxy for fewer epochs, use the --proxy-* options as shown below:

# Perform active learning with ResNet20 after only 50 epochs for selection.
python -m svp.cifar active --dataset cifar10 --arch preact164 --num-workers 4 \
	--selection-method least_confidence --proxy-arch preact20 \
	--proxy-learning-rate 0.01 --proxy-epochs 1 \
	--proxy-learning-rate 0.1 --proxy-epochs 45 \
	--proxy-learning-rate 0.01 --proxy-epochs 4 \
	--initial-subset 1000 \
	--round 4000 \
	--round 5000 \
	--round 5000 \
	--round 5000 \
	--round 5000 \
	--eval-target-at 25000

ImageNet

Baseline Approach
# Perform active learning with ResNet50 for both selection and the final predictions.
python -m svp.imagenet active --datasets-dir '/path/to/data' --arch resnet50 --num-workers 20
Selection via Proxy

If the model architectures (arch vs proxy_arch) or the learning rate schedules don't match, "selection via proxy" (SVP) is performed and two separate models are trained. The proxy is used for selecting which examples to label, while the target is only used for evaluating the quality of the selection. By default, the target model (arch) is trained and evaluated after each selection round. To change this behavior set eval_target_at to evaluate at a specific labeling budget(s) or set train_target to False to skip evaluating the target model.

# Perform active learning with ResNet18 for selection and ResNet50 for the final predictions.
python -m svp.imagenet active --datasets-dir '/path/to/data' --arch resnet50 --num-workers 20 \
    --proxy-arch resnet18 --proxy-batch-size 1028 --proxy-scale-learning-rates \
    --eval-target-at 512467

To train the proxy for fewer epochs, use the --proxy-* options as shown below:

# Perform active learning with ResNet18 after only 45 epochs for selection.
python -m svp.imagenet active --datasets-dir '/path/to/data' --arch resnet50 --num-workers 20 \
    --proxy-arch resnet18 --proxy-batch-size 1028 --proxy-scale-learning-rates \
    --eval-target-at 512467 \
    --proxy-learning-rate 0.0167 --proxy-epochs 1 \
    --proxy-learning-rate 0.0333 --proxy-epochs 1 \
    --proxy-learning-rate 0.05 --proxy-epochs 1 \
    --proxy-learning-rate 0.0667 --proxy-epochs 1 \
    --proxy-learning-rate 0.0833 --proxy-epochs 1 \
    --proxy-learning-rate 0.1 --proxy-epochs 25 \
    --proxy-learning-rate 0.01 --proxy-epochs 15

Amazon Review Polarity and Full

Baseline Approach
# Perform active learning with VDCNN29 for both selection and the final predictions.
python -m svp.amazon active --datasets-dir '/path/to/data' --dataset amazon_review_polarity  --num-workers 8 \
    --arch vdcnn29-conv --selection-method least_confidence
Selection via Proxy

If the model architectures (arch vs proxy_arch) or the learning rate schedules don't match, "selection via proxy" (SVP) is performed and two separate models are trained. The proxy is used for selecting which examples to label, while the target is only used for evaluating the quality of the selection. By default, the target model (arch) is trained and evaluated after each selection round. To change this behavior set eval_target_at to evaluate at a specific labeling budget(s) or set train_target to False to skip evaluating the target model. You can evaluate a series of selections later using the precomputed_selection option.

# Perform active learning with VDCNN9 for selection and VDCNN29 for the final predictions.
python -m svp.amazon active --datasets-dir '/path/to/data' --dataset amazon_review_polarity --num-workers 8 \
    --arch vdcnn29-conv --selection-method least_confidence \
    --proxy-arch vdcnn9-maxpool --eval-target-at 1440000

To use fastText as a proxy, Install fastText 0.1.0 and replace /path/to/fastText/fasttext in the python -m svp.amazon fasttext commands below with the path to the fastText binary you created.

# For convenience, save fastText results in a separate directory
mkdir fasttext
# Perform active learning with fastText.
python -m svp.amazon fasttext '/path/to/fastText/fasttext' --run-dir fasttext \
    --datasets-dir '/path/to/data' --dataset amazon_review_polarity --selection-method least_confidence \
    --size 72000 --size 360000 --size 720000 --size 1080000 --size 1440000
# Get the most recent timestamp from the fasttext directory.
fasttext_path="fasttext/$(ls fasttext | sort -nr | head -n 1)"
# Use selected labeled data from fastText to train VDCNN29
python -m svp.amazon active --datasets-dir '/path/to/data' --dataset amazon_review_polarity --num-workers 8 \
    --arch vdcnn29-conv --selection-method least_confidence \
    --precomputed-selection $fasttext_path --eval-target-at 1440000

Core-set Selection

Core-set selection techniques start with a large labeled or unlabeled dataset and aim to find a small subset that accurately approximates the full dataset by selecting representative examples. The commands below demonstrate how to perform core-set selection on CIFAR10, CIFAR100, ImageNet, Amazon Review Polarity and Amazon Review Full with a variety of models and selection methods.

CIFAR10 and CIFAR100

Baseline Approach
# Perform core-set selection with an oracle that uses ResNet164 for both selection and the final predictions.
python -m svp.cifar coreset --dataset cifar10 --arch preact164 --num-workers 4 \
    --subset 25000 --selection-method forgetting_events
Selection via Proxy
# Perform core-set selection with ResNet20 selecting for ResNet164.
python -m svp.cifar coreset --dataset cifar10 --arch preact164 --num-workers 4 \
    --subset 25000 --selection-method forgetting_events \
    --proxy-arch preact20

To train the proxy for fewer epochs, use the --proxy-* options as shown below:

# Perform core-set selection with ResNet20 after only 50 epochs.
python -m svp.cifar coreset --dataset cifar10 --arch preact164 --num-workers 4 \
    --subset 25000 --selection-method forgetting_events \
    --proxy-arch preact20 \
	--proxy-learning-rate 0.01 --proxy-epochs 1 \
	--proxy-learning-rate 0.1 --proxy-epochs 45 \
	--proxy-learning-rate 0.01 --proxy-epochs 4

ImageNet

Baseline Approach
# Perform core-set selection with an oracle that uses ResNet50 for both selection and the final predictions.
python -m svp.imagenet coreset --datasets-dir '/path/to/data' --arch resnet50 --num-workers 20 \
    --subset 768700 --selection-method forgetting_events
Selection via Proxy
# Perform core-set selection with ResNet18 selecting for ResNet50.
python -m svp.imagenet coreset --datasets-dir '/path/to/data' --arch resnet50 --num-workers 20 \
    --subset 768700 --selection-method forgetting_events \
    --proxy-arch resnet18 --proxy-batch-size 1028 --proxy-scale-learning-rates

Amazon Review Polarity and Full

Baseline Approach
# Perform core-set selection with an oracle that uses VDCNN29 for both selection and the final predictions.
python -m svp.amazon coreset --datasets-dir '/path/to/data' --dataset amazon_review_polarity --num-workers 8 \
    --arch vdcnn29-conv --subset 2160000  --selection-method entropy
Selection via Proxy
# Perform core-set selection with VDCNN9 selecting for VDCNN29.
python -m svp.amazon coreset --datasets-dir '/path/to/data' --dataset amazon_review_polarity --num-workers 8 \
    --arch vdcnn29-conv --subset 2160000 --selection-method entropy \
    --proxy-arch vdcnn9-maxpool

To use fastText as a proxy, Install fastText 0.1.0 and replace /path/to/fastText/fasttext in the python -m svp.amazon fasttext commands below with the path to the fastText binary you created.

# For convenience, save fastText results in a separate directory
mkdir fasttext
# Perform core-set selection with fastText.
python -m svp.amazon fasttext '/path/to/fastText/fasttext' --run-dir fasttext \
    --datasets-dir '/path/to/data' --dataset amazon_review_polarity \
    --selection-method entropy --size 3600000 --size 2160000
# Get the most recent timestamp from the fasttext directory.
fasttext_path="fasttext/$(ls fasttext | sort -nr | head -n 1)"
# Use selected labeled data from fastText to train VDCNN29
python -m svp.amazon coreset --datasets-dir '/path/to/data' --dataset amazon_review_polarity --num-workers 8 \
    --arch vdcnn29-conv --precomputed-selection $fasttext_path
Owner
Stanford Future Data Systems
We are a CS research group at Stanford building data-intensive systems
Stanford Future Data Systems
GitHub Repository
ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'

ICCV2021: Code for 'Spatial Uncertainty-Aware Semi-Supervised Crowd Counting'

Yanda Meng 14 May 13, 2022
Fast, differentiable sorting and ranking in PyTorch

Torchsort Fast, differentiable sorting and ranking in PyTorch. Pure PyTorch implementation of Fast Differentiable Sorting and Ranking (Blondel et al.)

Teddy Koker 655 Jan 04, 2023
《Lerning n Intrinsic Grment Spce for Interctive Authoring of Grment Animtion》

Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation Overview This is the demo code for training a motion invariant enco

YuanBo 213 Dec 14, 2022
PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks Recent Update 2021.11.23: We release the source code of SAQ. Setup the environments Clone the re

Zhuang AI Group 30 Dec 19, 2022
Human motion synthesis using Unity3D

Human motion synthesis using Unity3D Prerequisite: Software: amc2bvh.exe, Unity 2017, Blender. Unity: RockVR (Video Capture), scenes, character models

Hao Xu 9 Jun 01, 2022
Use graph-based analysis to re-classify stocks and to improve Markowitz portfolio optimization

Dynamic Stock Industrial Classification Use graph-based analysis to re-classify stocks and experiment different re-classification methodologies to imp

Sheng Yang 10 Dec 05, 2022
Processed, version controlled history of Minecraft's generated data and assets

mcmeta Processed, version controlled history of Minecraft's generated data and assets Repository structure Each of the following branches has a commit

Misode 75 Dec 28, 2022
Feature board for ERPNext

ERPNext Feature Board Feature board for ERPNext Development Prerequisites k3d kubectl helm bench Install K3d Cluster # export K3D_FIX_CGROUPV2=1 # use

Revant Nandgaonkar 16 Nov 09, 2022
This project aims to explore the deployment of Swin-Transformer based on TensorRT, including the test results of FP16 and INT8.

Swin Transformer This project aims to explore the deployment of SwinTransformer based on TensorRT, including the test results of FP16 and INT8. Introd

maggiez 87 Dec 21, 2022
A robust pointcloud registration pipeline based on correlation.

PHASER: A Robust and Correspondence-Free Global Pointcloud Registration Ubuntu 18.04+ROS Melodic: Overview Pointcloud registration using correspondenc

ETHZ ASL 101 Dec 01, 2022
Simple streamlit app to demonstrate HERE Tour Planning

Table of Contents About the Project Built With Getting Started Prerequisites Installation Usage Roadmap Contributing License Acknowledgements About Th

Amol 8 Sep 05, 2022
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)

Contrastive Unpaired Translation (CUT) video (1m) | video (10m) | website | paper We provide our PyTorch implementation of unpaired image-to-image tra

1.7k Dec 27, 2022
ML-Decoder: Scalable and Versatile Classification Head

ML-Decoder: Scalable and Versatile Classification Head Paper Official PyTorch Implementation Tal Ridnik, Gilad Sharir, Avi Ben-Cohen, Emanuel Ben-Baru

189 Jan 04, 2023
"Learning Free Gait Transition for Quadruped Robots vis Phase-Guided Controller"

PhaseGuidedControl The current version is developed based on the old version of RaiSim series, and possibly requires further modification. It will be

X-Mechanics 12 Oct 21, 2022
PoseCamera is python based SDK for human pose estimation through RGB webcam.

PoseCamera PoseCamera is python based SDK for human pose estimation through RGB webcam. Install install posecamera package through pip pip install pos

WonderTree 7 Jul 20, 2021
This repository focus on Image Captioning & Video Captioning & Seq-to-Seq Learning & NLP

Awesome-Visual-Captioning Table of Contents ACL-2021 CVPR-2021 AAAI-2021 ACMMM-2020 NeurIPS-2020 ECCV-2020 CVPR-2020 ACL-2020 AAAI-2020 ACL-2019 NeurI

Ziqi Zhang 362 Jan 03, 2023
Python scripts using the Mediapipe models for Halloween.

Mediapipe-Halloween-Examples Python scripts using the Mediapipe models for Halloween. WHY Mainly for fun. But this repository also includes useful exa

Ibai Gorordo 23 Jan 06, 2023
NAS-FCOS: Fast Neural Architecture Search for Object Detection (CVPR 2020)

NAS-FCOS: Fast Neural Architecture Search for Object Detection This project hosts the train and inference code with pretrained model for implementing

Ning Wang 180 Dec 06, 2022
Link prediction using Multiple Order Local Information (MOLI)

Understanding the network formation pattern for better link prediction Authors: [e

Wu Lab 0 Oct 18, 2021
New AidForBlind - Various Libraries used like OpenCV and other mentioned in Requirements.txt

AidForBlind Recommended PyCharm IDE Various Libraries used like OpenCV and other

Aalhad Chandewar 1 Jan 13, 2022