A Closer Look at Structured Pruning for Neural Network Compression

Overview

A Closer Look at Structured Pruning for Neural Network Compression

Code used to reproduce experiments in https://arxiv.org/abs/1810.04622.

To prune, we fill our networks with custom MaskBlocks, which are manipulated using Pruner in funcs.py. There will certainly be a better way to do this, but we leave this as an exercise to someone who can code much better than we can.

Setup

This is best done in a clean conda environment:

conda create -n prunes python=3.6
conda activate prunes
conda install pytorch torchvision -c pytorch

Repository layout

-train.py: contains all of the code for training large models from scratch and for training pruned models from scratch
-prune.py: contains the code for pruning trained models
-funcs.py: contains useful pruning functions and any functions we used commonly

CIFAR Experiments

First, you will need some initial models.

To train a WRN-40-2:

python train.py --net='res' --depth=40 --width=2.0 --data_loc= --save_file='res'

The default arguments of train.py are suitable for training WRNs. The following trains a DenseNet-BC-100 (k=12) with its default hyperparameters:

python train.py --net='dense' --depth=100 --data_loc= --save_file='dense' --no_epochs 300 -b 64 --epoch_step '[150,225]' --weight_decay 0.0001 --lr_decay_ratio 0.1

These will automatically save checkpoints to the checkpoints folder.

Pruning

Once training is finished, we can prune our networks using prune.py (defaults are set to WRN pruning, so extra arguments are needed for DenseNets)

python prune.py --net='res'   --data_loc= --base_model='res' --save_file='res_fisher'
python prune.py --net='res'   --data_loc= --l1_prune=True --base_model='res' --save_file='res_l1'

python prune.py --net='dense' --depth 100 --data_loc= --base_model='dense' --save_file='dense_fisher' --learning_rate 1e-3 --weight_decay 1e-4 --batch_size 64 --no_epochs 2600
python prune.py --net='dense' --depth 100 --data_loc= --l1_prune=True --base_model='dense' --save_file='dense_l1'  --learning_rate 1e-3 --weight_decay 1e-4 --batch_size 64  --no_epochs 2600

Note that the default is to perform Fisher pruning, so you don't need to pass a flag to use it.
Once finished, we can train the pruned models from scratch, e.g.:

python train.py --data_loc= --net='res' --base_file='res_fisher__prunes' --deploy --mask=1 --save_file='res_fisher__prunes_scratch'

Each model can then be evaluated using:

python train.py --deploy --eval --data_loc= --net='res' --mask=1 --base_file='res_fisher__prunes'

Training Reduced models

This can be done by varying the input arguments to train.py. To reduce depth or width of a WRN, change the corresponding option:

python train.py --net='res' --depth= --width= --data_loc= --save_file='res_reduced'

To add bottlenecks, use the following:

python train.py --net='res' --depth=40 --width=2.0 --data_loc= --save_file='res_bottle' --bottle --bottle_mult 

With DenseNets you can modify the depth or growth, or use --bottle --bottle_mult as above.

Acknowledgements

Jack Turner wrote the L1 stuff, and some other stuff for that matter.

Code has been liberally borrowed from many a repo, including, but not limited to:

https://github.com/xternalz/WideResNet-pytorch
https://github.com/bamos/densenet.pytorch
https://github.com/kuangliu/pytorch-cifar
https://github.com/ShichenLiu/CondenseNet

Citing this work

If you would like to cite this work, please use the following bibtex entry:

@article{crowley2018pruning,
  title={A Closer Look at Structured Pruning for Neural Network Compression},
  author={Crowley, Elliot J and Turner, Jack and Storkey, Amos and O'Boyle, Michael},
  journal={arXiv preprint arXiv:1810.04622},
  year={2018},
  }
Owner
Bayesian and Neural Systems Group
Machine learning research group @ University of Edinburgh
Bayesian and Neural Systems Group
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

LSTM Neural Network for Time Series Prediction LSTM built using the Keras Python package to predict time series steps and sequences. Includes sine wav

Jakob Aungiers 4.1k Jan 02, 2023
E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation

E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation E2EC: An End-to-End Contour-based Method for High-Quality H

zhangtao 146 Dec 29, 2022
A curated list and survey of awesome Vision Transformers.

English | 简体中文 A curated list and survey of awesome Vision Transformers. You can use mind mapping software to open the mind mapping source file. You c

OpenMMLab 281 Dec 21, 2022
Space-invaders - Simple Game created using Python & PyGame, as my Beginner Python Project

Space Invaders This is a simple SPACE INVADER game create using PYGAME whihc hav

Gaurav Pandey 2 Jan 08, 2022
transfer attack; adversarial examples; black-box attack; unrestricted Adversarial Attacks on ImageNet; CVPR2021 天池黑盒竞赛

transfer_adv CVPR-2021 AIC-VI: unrestricted Adversarial Attacks on ImageNet CVPR2021 安全AI挑战者计划第六期赛道2:ImageNet无限制对抗攻击 介绍 : 深度神经网络已经在各种视觉识别问题上取得了最先进的性能。

25 Dec 08, 2022
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation

PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation Winner method of the ICCV-2021 SemKITTI-DVPS Challenge. [arxiv] [

Yuan Haobo 38 Jan 03, 2023
COVID-Net Open Source Initiative

The COVID-Net models provided here are intended to be used as reference models that can be built upon and enhanced as new data becomes available

Linda Wang 1.1k Dec 26, 2022
A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.

2021: A Year Full of Amazing AI papers- A Review 📌 A curated list of the latest breakthroughs in AI by release date with a clear video explanation, l

Louis-François Bouchard 2.9k Dec 31, 2022
An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine

A Gentle Introduction to Satellite Image Processing Welcome to this introductory course on Satellite Image Analysis! Satellite imagery has become a pr

Edward Oughton 32 Jan 03, 2023
🗣️ Microsoft Edge TTS for Home Assistant, no need for app_key

Microsoft Edge TTS for Home Assistant This component is based on the TTS service of Microsoft Edge browser, no need to apply for app_key. Install Down

152 Dec 31, 2022
PyTorch Implementation of Google Brain's WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis

WaveGrad2 - PyTorch Implementation PyTorch Implementation of Google Brain's WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis. Status (202

Keon Lee 59 Dec 06, 2022
Official PyTorch implementation of BlobGAN: Spatially Disentangled Scene Representations

BlobGAN: Spatially Disentangled Scene Representations Official PyTorch Implementation Paper | Project Page | Video | Interactive Demo BlobGAN.mp4 This

148 Dec 29, 2022
Denoising Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models This repo contains code for DDPM training. Based on Denoising Diffusion Probabilistic Models, Improved Denois

Alexander Markov 7 Dec 15, 2022
Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment.

(ACMMM 2021 Oral) SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment This repository shows two tasks: Face landmark detection and Fac

BoomStar 51 Dec 13, 2022
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection

SAGA Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection Please refer to the Jupyter notebook (Example.ipynb) for an example of using t

9 Dec 28, 2022
Neural Motion Learner With Python

Neural Motion Learner Introduction This work is to extract skeletal structure from volumetric observations and to learn motion dynamics from the detec

Jinseok Bae 14 Nov 28, 2022
Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

Geotrend 79 Dec 28, 2022
A semantic segmentation toolbox based on PyTorch

Introduction vedaseg is an open source semantic segmentation toolbox based on PyTorch. Features Modular Design We decompose the semantic segmentation

407 Dec 15, 2022
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery Ava Soleimany*, Alexander Amini*, Samuel Goldman*, Daniela Rus, Sangee

Alexander Amini 75 Dec 15, 2022
AI-generated-characters for Learning and Wellbeing

AI-generated-characters for Learning and Wellbeing Click here for the full project page. This repository contains the source code for the paper AI-gen

MIT Media Lab 214 Jan 01, 2023