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.

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

Related tags

Overview

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

Requirements

Running

Scripts

300-100-10

Sensitivity Pre-training

Hyperparam search

Full Training

784-100-10

Sensitivity Pre-training

Hyperparam search

Full Training

Vgg-5

Sensitivity Pre-training

Hyperparam Search

Full Training

Vgg-9

Sensitivity Pre-training

Hyperparam Search

Full Training

ResNet-20

Sensitivity Pre-training

Hyperparam Search

Full Training

ResNet-21

Sensitivity Pre-training

Full Training

License

Contributing

Owner

Rakuten Group, Inc.

Pytorch implementation of our paper under review — Lottery Jackpots Exist in Pre-trained Models

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

SEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)

A modular PyTorch library for optical flow estimation using neural networks

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

Python implementation of ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images, AAAI2022.

A PyTorch implementation of Implicit Q-Learning

Implementation of the 😇 Attention layer from the paper, Scaling Local Self-Attention For Parameter Efficient Visual Backbones

ICCV2021 Expert-Goal Trajectory Prediction

Tracking Progress in Question Answering over Knowledge Graphs

MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.

Differential fuzzing for the masses!

一套完整的微博舆情分析流程代码，包括微博爬虫、LDA主题分析和情感分析。

A highly efficient, fast, powerful and light-weight anime downloader and streamer for your favorite anime.

Model Quantization Benchmark

A PyTorch implementation of "DGC-Net: Dense Geometric Correspondence Network"

Code for How To Create A Fully Automated AI Based Trading System With Python

VOLO: Vision Outlooker for Visual Recognition

Build Graph Nets in Tensorflow

Soft actor-critic is a deep reinforcement learning framework for training maximum entropy policies in continuous domains.