Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation. In CVPR 2022.

Overview

Nonuniform-to-Uniform Quantization

This repository contains the training code of N2UQ introduced in our CVPR 2022 paper: "Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation"

In this study, we propose a quantization method that can learn the non-uniform input thresholds to maintain the strong representation ability of nonuniform methods, while output uniform quantized levels to be hardware-friendly and efficient as the uniform quantization for model inference.

To train the quantized network with learnable input thresholds, we introduce a generalized straight-through estimator (G-STE) for intractable backward derivative calculation w.r.t. threshold parameters.

The formula for N2UQ is simply as follows,

Forward pass:

Backward pass:

Moreover, we proposed L1 norm based entropy preserving weight regularization for weight quantization.

Citation

If you find our code useful for your research, please consider citing:

@inproceedings{liu2022nonuniform,
  title={Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation},
  author={Liu, Zechun and Cheng, Kwang-Ting and Huang, Dong and Xing, Eric and Shen, Zhiqiang},
  journal={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Run

1. Requirements:

  • python 3.6, pytorch 1.7.1, torchvision 0.8.2
  • gdown

2. Data:

  • Download ImageNet dataset

3. Pretrained Models:

  • pip install gdown # gdown will automatically download the models
  • If gdown doesn't work, you may need to manually download the pretrained models and put them in the correponding ./models/ folder.

4. Steps to run:

(1) For ResNet architectures:

  • Change directory to ./resnet/
  • Run bash run.sh architecture n_bits quantize_downsampling
  • E.g., bash run.sh resnet18 2 0 for quantize resnet18 to 2-bit without quantizing downsampling layers

(2) For MobileNet architectures:

  • Change directory to ./mobilenetv2/
  • Run bash run.sh

Models

1. ResNet

Network Methods W2/A2 W3/A3 W4/A4
ResNet-18
PACT 64.4 68.1 69.2
DoReFa-Net 64.7 67.5 68.1
LSQ 67.6 70.2 71.1
N2UQ 69.4 Model-Res18-2bit 71.9 Model-Res18-3bit 72.9 Model-Res18-4bit
N2UQ * 69.7 Model-Res18-2bit 72.1 Model-Res18-3bit 73.1 Model-Res18-4bit
ResNet-34
LSQ 71.6 73.4 74.1
N2UQ 73.3 Model-Res34-2bit 75.2 Model-Res34-3bit 76.0 Model-Res34-4bit
N2UQ * 73.4 Model-Res34-2bit 75.3 Model-Res34-3bit 76.1 Model-Res34-4bit
ResNet-50
PACT 64.4 68.1 69.2
LSQ 67.6 70.2 71.1
N2UQ 75.8 Model-Res50-2bit 77.5 Model-Res50-3bit 78.0 Model-Res50-4bit
N2UQ * 76.4 Model-Res50-2bit 77.6 Model-Res50-3bit 78.0 Model-Res50-4bit

Note that N2UQ without * denotes quantizing all the convolutional layers except the first input convolutional layer.

N2UQ with * denotes quantizing all the convolutional layers except the first input convolutional layer and three downsampling layers.

W2/A2, W3/A3, W4/A4 denote the cases where the weights and activations are both quantized to 2 bits, 3 bits, and 4 bits, respectively.

2. MobileNet

Network Methods W4/A4
MobileNet-V2 N2UQ 72.1 Model-MBV2-4bit

Contact

Zechun Liu, HKUST (zliubq at connect.ust.hk)

Owner
Zechun Liu
Ph.D student in HKUST and visiting scholar in CMU
Zechun Liu
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style [NeurIPS 2021] Official code to reproduce the results and data p

Yash Sharma 27 Sep 19, 2022
Code for CPM-2 Pre-Train

CPM-2 Pre-Train Pre-train CPM-2 此分支为110亿非 MoE 模型的预训练代码,MoE 模型的预训练代码请切换到 moe 分支 CPM-2技术报告请参考link。 0 模型下载 请在智源资源下载页面进行申请,文件介绍如下: 文件名 描述 参数大小 100000.tar

Tsinghua AI 136 Dec 28, 2022
A bunch of random PyTorch models using PyTorch's C++ frontend

PyTorch Deep Learning Models using the C++ frontend Gettting started Clone the repo 1. https://github.com/mrdvince/pytorchcpp 2. cd fashionmnist or

Vince 0 Jul 13, 2021
CowHerd is a partially-observed reinforcement learning environment

CowHerd is a partially-observed reinforcement learning environment, where the player walks around an area and is rewarded for milking cows. The cows try to escape and the player can place fences to h

Danijar Hafner 6 Mar 06, 2022
RoMa: A lightweight library to deal with 3D rotations in PyTorch.

RoMa: A lightweight library to deal with 3D rotations in PyTorch. RoMa (which stands for Rotation Manipulation) provides differentiable mappings betwe

NAVER 90 Dec 27, 2022
R-Drop: Regularized Dropout for Neural Networks

R-Drop: Regularized Dropout for Neural Networks R-drop is a simple yet very effective regularization method built upon dropout, by minimizing the bidi

756 Dec 27, 2022
💡 Learnergy is a Python library for energy-based machine learning models.

Learnergy: Energy-based Machine Learners Welcome to Learnergy. Did you ever reach a bottleneck in your computational experiments? Are you tired of imp

Gustavo Rosa 57 Nov 17, 2022
PyTorch code for training MM-DistillNet for multimodal knowledge distillation

There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge MM-DistillNet is a

51 Dec 20, 2022
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral)

DSA^2 F: Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion (CVPR'2021, Oral) This repo is the official imp

如今我已剑指天涯 46 Dec 21, 2022
Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization

CoMatch: Semi-supervised Learning with Contrastive Graph Regularization (Salesforce Research) This is a PyTorch implementation of the CoMatch paper [B

Salesforce 107 Dec 14, 2022
Reference models and tools for Cloud TPUs.

Cloud TPUs This repository is a collection of reference models and tools used with Cloud TPUs. The fastest way to get started training a model on a Cl

5k Jan 05, 2023
High-Resolution Image Synthesis with Latent Diffusion Models

Latent Diffusion Models arXiv | BibTeX High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach*, Andreas Blattmann*, Dominik Lorenz

CompVis Heidelberg 5.6k Dec 30, 2022
Read and write layered TIFF ImageSourceData and ImageResources tags

Read and write layered TIFF ImageSourceData and ImageResources tags Psdtags is a Python library to read and write the Adobe Photoshop(r) specific Imag

Christoph Gohlke 4 Feb 05, 2022
UnpNet - Rethinking 3-D LiDAR Point Cloud Segmentation(IEEE TNNLS)

UnpNet Citation Please cite the following paper if you use this repository in your reseach. @article {PMID:34914599, Title = {Rethinking 3-D LiDAR Po

Shijie Li 4 Jul 15, 2022
Implementation of the ALPHAMEPOL algorithm, presented in Unsupervised Reinforcement Learning in Multiple Environments.

ALPHAMEPOL This repository contains the implementation of the ALPHAMEPOL algorithm, presented in Unsupervised Reinforcement Learning in Multiple Envir

3 Dec 23, 2021
The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc).

Will Thompson 166 Jan 04, 2023
A practical ML pipeline for data labeling with experiment tracking using DVC.

Auto Label Pipeline A practical ML pipeline for data labeling with experiment tracking using DVC Goals: Demonstrate reproducible ML Use DVC to build a

Todd Cook 4 Mar 08, 2022
Exploit ILP to learn symmetry breaking constraints of ASP programs.

ILP Symmetry Breaking Overview This project aims to exploit inductive logic programming to lift symmetry breaking constraints of ASP programs. Given a

Research Group Production Systems 1 Apr 13, 2022
Binary Passage Retriever (BPR) - an efficient passage retriever for open-domain question answering

BPR Binary Passage Retriever (BPR) is an efficient neural retrieval model for open-domain question answering. BPR integrates a learning-to-hash techni

Studio Ousia 147 Dec 07, 2022
DISTIL: Deep dIverSified inTeractIve Learning.

DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.

decile-team 110 Dec 06, 2022