Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

Related tags

Deep Learningmachine-learningdeep-learningneural-networkmodel-compression
Overview

Iterative Training: Finding Binary Weight Deep Neural Networks with Layer Binarization

This repository contains the source code for the paper (link will be posted).

Requirements

  • GPU
  • Python 3
  • PyTorch 1.9
    • Earlier version may work, but untested.
  • pip install -r requirements.txt
  • If running ResNet-21 or ImageNet experiments, first download and prepare the ImageNet 2012 dataset with bin/imagenet_prep.sh script.

Running

For non-ImageNet experiments, the main python file is main.py. To see its arguments:

python main.py --help

Running for the first time can take a little longer due to automatic downloading of the MNIST and Cifar-10 dataset from the Internet.

For ImageNet experiments, the main python files are main_imagenet_float.py and main_imagenet_binary.py. Too see their arguments:

python main_imagenet_float.py --help

and

python main_imagenet_binary.py --help

The ImageNet dataset must be already downloaded and prepared. Please see the requirements section for details.

Scripts

The main python file has many options. The following scripts runs training with hyper-parameters given in the paper. Output includes a run-log text file and tensorboard files. These files are saved to ./logs and reused for subsequent runs.

300-100-10

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/mnist/300/sensitivity/layer.sh sensitivity forward 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/mnist/300/sensitivity/layer.sh sensitivity 231 0.1 0
# Layer 3. Learning rate 0.1.
./scripts/mnist/300/sensitivity/layer.sh sensitivity reverse 0.1 0

Output files and run-log are written to ./logs/mnist/val/sensitivity/.

Hyperparam search

For floating-point training:

# Learning rate 0.1.
./scripts/mnist/300/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1.
./scripts/mnist/300/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam forward 0.1 0
# Reverse order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam reverse 0.1 0
# 1, 3, 2 order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam 132 0.1 0
# 2, 1, 3 order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam 213 0.1 0
# 2, 3, 1 order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam 231 0.1 0
# 3, 1, 2 order. Learning rate 0.1.
./scripts/mnist/300/val/layer.sh hyperparam 312 0.1 0

Output files and run-log are written to ./logs/mnist/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full forward 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full reverse 0.1 316 0
# 1, 3, 2 order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full 132 0.1 316 0
# 2, 1, 3 order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full 213 0.1 316 0
# 2, 3, 1 order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full 231 0.1 316 0
# 3, 1, 2 order. Learning rate 0.1. Seed 316.
./scripts/mnist/300/run/layer.sh full 312 0.1 316 0

Output files and run-log are written to ./logs/mnist/run/full/.

784-100-10

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/mnist/784/sensitivity/layer.sh sensitivity forward 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/mnist/784/sensitivity/layer.sh sensitivity 231 0.1 0
# Layer 3. Learning rate 0.1.
./scripts/mnist/784/sensitivity/layer.sh sensitivity reverse 0.1 0

Output files and run-log are written to ./logs/mnist/val/sensitivity/.

Hyperparam search

For floating-point training:

# Learning rate 0.1.
./scripts/mnist/784/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1.
./scripts/mnist/784/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam forward 0.1 0
# Reverse order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam reverse 0.1 0
# 1, 3, 2 order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam 132 0.1 0
# 2, 1, 3 order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam 213 0.1 0
# 2, 3, 1 order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam 231 0.1 0
# 3, 1, 2 order. Learning rate 0.1.
./scripts/mnist/784/val/layer.sh hyperparam 312 0.1 0

Output files and run-log are written to ./logs/mnist/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full forward 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full reverse 0.1 316 0
# 1, 3, 2 order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full 132 0.1 316 0
# 2, 1, 3 order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full 213 0.1 316 0
# 2, 3, 1 order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full 231 0.1 316 0
# 3, 1, 2 order. Learning rate 0.1. Seed 316.
./scripts/mnist/784/run/layer.sh full 312 0.1 316 0

Output files and run-log are written to ./logs/mnist/run/full/.

Vgg-5

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/cifar10/vgg5/sensitivity/layer.sh sensitivity 1 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/cifar10/vgg5/sensitivity/layer.sh sensitivity 2 0.1 0
# Layer 5. Learning rate 0.1.
./scripts/cifar10/vgg5/sensitivity/layer.sh sensitivity 5 0.1 0

Output files and run-log are written to ./logs/cifar10/val/sensitivity/.

Hyperparam Search

For floating-point training:

# Learning rate 0.1.
./scripts/cifar10/vgg5/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1.
./scripts/cifar10/vgg5/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam forward 0.1 0
# Ascend order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam ascend 0.1 0
# Reverse order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam reverse 0.1 0
# Descend order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam descend 0.1 0
# Random order. Learning rate 0.1.
./scripts/cifar10/vgg5/val/layer.sh hyperparam random 0.1 0

Output files and run-log are written to ./logs/cifar10/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full forward 0.1 316 0
# Ascend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full ascend 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full reverse 0.1 316 0
# Descend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full descend 0.1 316 0
# Random order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg5/run/layer.sh full random 0.1 316 0

Output files and run-log are written to ./logs/cifar10/run/full/.

Vgg-9

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/cifar10/vgg9/sensitivity/layer.sh sensitivity 1 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/cifar10/vgg9/sensitivity/layer.sh sensitivity 2 0.1 0
# Layer 5. Learning rate 0.1.
./scripts/cifar10/vgg9/sensitivity/layer.sh sensitivity 5 0.1 0

Output files and run-log are written to ./logs/cifar10/val/sensitivity/.

Hyperparam Search

For floating-point training:

# Learning rate 0.1.
./scripts/cifar10/vgg9/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1.
./scripts/cifar10/vgg9/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam forward 0.1 0
# Ascend order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam ascend 0.1 0
# Reverse order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam reverse 0.1 0
# Descend order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam descend 0.1 0
# Random order. Learning rate 0.1.
./scripts/cifar10/vgg9/val/layer.sh hyperparam random 0.1 0

Output files and run-log are written to ./logs/cifar10/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full forward 0.1 316 0
# Ascend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full ascend 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full reverse 0.1 316 0
# Descend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full descend 0.1 316 0
# Random order. Learning rate 0.1. Seed 316.
./scripts/cifar10/vgg9/run/layer.sh full random 0.1 316 0

Output files and run-log are written to ./logs/cifar10/run/full/.

ResNet-20

Sensitivity Pre-training

# Layer 1. Learning rate 0.1.
./scripts/cifar10/resnet20/sensitivity/layer.sh sensitivity 1 0.1 0
# Layer 2. Learning rate 0.1.
./scripts/cifar10/resnet20/sensitivity/layer.sh sensitivity 2 0.1 0
# ...
# Layer 20. Learning rate 0.1.
./scripts/cifar10/resnet20/sensitivity/layer.sh sensitivity 20 0.1 0

Output files and run-log are written to ./logs/cifar10/val/sensitivity/.

Hyperparam Search

For floating-point training:

# Learning rate 0.1
./scripts/cifar10/resnet20/val/float.sh hyperparam 0.1 0

For full binary training:

# Learning rate 0.1
./scripts/cifar10/resnet20/val/binary.sh hyperparam 0.1 0

For iterative training:

# Forward order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam forward 0.1 0
# Ascend order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam ascend 0.1 0
# Reverse order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam reverse 0.1 0
# Descend order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam descend 0.1 0
# Random order. Learning rate 0.1
./scripts/cifar10/resnet20/val/layer.sh hyperparam random 0.1 0

Output files and run-log are written to ./logs/cifar10/val/hyperparam/.

Full Training

For floating-point training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/float.sh full 0.1 316 0

For full binary training:

# Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/binary.sh full 0.1 316 0

For iterative training:

# Forward order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full forward 0.1 316 0
# Ascend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full ascend 0.1 316 0
# Reverse order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full reverse 0.1 316 0
# Descend order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full descend 0.1 316 0
# Random order. Learning rate 0.1. Seed 316.
./scripts/cifar10/resnet20/run/layer.sh full random 0.1 316 0

Output files and run-log are written to ./logs/cifar10/run/full/.

ResNet-21

To run experiments for ResNet-21, first download and prepare the ImageNet dataset. See the requirements section at the beginning of this readme. We assume the dataset is prepared and is at ./imagenet.

Sensitivity Pre-training

# Layer 1. Learning rate 0.01.
./scripts/imagenet/layer.sh sensitivity ./imagenet 20 "[20]" 20 1 0.01
# Layer 2. Learning rate 0.01.
./scripts/imagenet/layer.sh sensitivity ./imagenet 20 "[20]" 20 2 0.01
# Layer 21. Learning rate 0.01.
./scripts/imagenet/layer.sh sensitivity ./imagenet 20 "[20]" 20 21 0.01

Output files and run-log are written to ./logs/imagenet/sensitivity/.

Full Training

For floating-point training:

# Learning rate 0.01.
./scripts/imagenet/float.sh full ./imagenet 67 "[42,57]" 0.01

For full binary training:

# Learning rate 0.01.
./scripts/imagenet/binary.sh full ./imagenet 67 "[42,57]" 0.01

For layer-by-layer training:

# Forward order
./scripts/imagenet/layer.sh full ./imagenet 67 "[42,57]" 2 forward 0.01
# Ascending order
./scripts/imagenet/layer.sh full ./imagenet 67 "[42,57]" 2 ascend 0.01

For all scripts, output files and run-log are written to ./logs/imagenet/full/.

License

See LICENSE

Contributing

See the contributing guide for details of how to participate in development of the module.

Owner
Rakuten Group, Inc.
Rakuten Group, Inc.
GitHub Repository
Range Image-based LiDAR Localization for Autonomous Vehicles Using Mesh Maps

Range Image-based 3D LiDAR Localization This repo contains the code for our ICRA2021 paper: Range Image-based LiDAR Localization for Autonomous Vehicl

Photogrammetry & Robotics Bonn 208 Dec 15, 2022
Code for GNMR in ICDE 2021

GNMR Code for GNMR in ICDE 2021 Please unzip data files in Datasets/MultiInt-ML10M first. Run labcode_preSamp.py (with graph sampling) for ECommerce-c

7 Oct 27, 2022
[CVPR 2022] Deep Equilibrium Optical Flow Estimation

Deep Equilibrium Optical Flow Estimation This is the official repo for the paper Deep Equilibrium Optical Flow Estimation (CVPR 2022), by Shaojie Bai*

CMU Locus Lab 136 Dec 18, 2022
Current state of supervised and unsupervised depth completion methods

Awesome Depth Completion Table of Contents About Sparse-to-Dense Depth Completion Current State of Depth Completion Unsupervised VOID Benchmark Superv

224 Dec 28, 2022
Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated Learning

Federated_Learning This repo provides a federated learning framework that allows to carry out backdoor attacks under varying conditions. This is a ker

Arçelik ARGE Açık Kaynak Yazılım Organizasyonu 0 Nov 30, 2021
Lightweight mmm - Lightweight (Bayesian) Media Mix Model

Lightweight (Bayesian) Media Mix Model This is not an official Google product. L

Google 342 Jan 03, 2023
Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph".

multilingual-mrc-isdg Code for the AAAI 2022 paper "Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-Sentence Dependency Graph". This r

Liyan 5 Dec 07, 2022
an implementation of softmax splatting for differentiable forward warping using PyTorch

softmax-splatting This is a reference implementation of the softmax splatting operator, which has been proposed in Softmax Splatting for Video Frame I

Simon Niklaus 338 Dec 28, 2022
[ECCV 2020] Reimplementation of 3DDFAv2, including face mesh, head pose, landmarks, and more.

Stable Head Pose Estimation and Landmark Regression via 3D Dense Face Reconstruction Reimplementation of (ECCV 2020) Towards Fast, Accurate and Stable

Remilia Scarlet 221 Dec 30, 2022
scalingscattering

Scaling The Scattering Transform : Deep Hybrid Networks This repository contains the experiments found in the paper: https://arxiv.org/abs/1703.08961

Edouard Oyallon 78 Dec 21, 2022
Tensorflow implementation of soft-attention mechanism for video caption generation.

SA-tensorflow Tensorflow implementation of soft-attention mechanism for video caption generation. An example of soft-attention mechanism. The attentio

Paul Chen 153 Nov 14, 2022
Fast and simple implementation of RL algorithms, designed to run fully on GPU.

RSL RL Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's I

Robotic Systems Lab - Legged Robotics at ETH Zürich 68 Dec 29, 2022
Official repository of ICCV21 paper "Viewpoint Invariant Dense Matching for Visual Geolocalization"

Viewpoint Invariant Dense Matching for Visual Geolocalization: PyTorch implementation This is the implementation of the ICCV21 paper: G Berton, C. Mas

Gabriele Berton 44 Jan 03, 2023
Course materials for Fall 2021 "CIS6930 Topics in Computing for Data Science" at New College of Florida

Fall 2021 CIS6930 Topics in Computing for Data Science This repository hosts course materials used for a 13-week course "CIS6930 Topics in Computing f

Yoshi Suhara 101 Nov 30, 2022
Face Mask Detection System built with OpenCV, TensorFlow using Computer Vision concepts

Face mask detection Face Mask Detection System built with OpenCV, TensorFlow using Computer Vision concepts in order to detect face masks in static im

Vaibhav Shukla 1 Oct 27, 2021
Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker

Deploying PyTorch Model to Production with FastAPI in CUDA-supported Docker A example FastAPI PyTorch Model deploy with nvidia/cuda base docker. Model

Ming 68 Jan 04, 2023
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

Pytorch Pedestrian Attribute Recognition: A strong PyTorch baseline of pedestrian attribute recognition and multi-label classification.

Jian 79 Dec 18, 2022
ppo_pytorch_cpp - an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 09, 2022
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting Recent progress in neural forecasting instigated significant improvements in the

Cristian Challu 82 Jan 04, 2023
SmoothGrad implementation in PyTorch

SmoothGrad implementation in PyTorch PyTorch implementation of SmoothGrad: removing noise by adding noise. Vanilla Gradients SmoothGrad Guided backpro

SSKH 143 Jan 05, 2023