QAT(quantize aware training) for classification with MQBench

Last update: Nov 18, 2022

Overview

MQBench Quantization Aware Training with PyTorch

I am using MQBench(Model Quantization Benchmark)(http://mqbench.tech/) to quantize the model for deployment.

MQBench is a benchmark and framework for evluating the quantization algorithms under real world hardware deployments.

Prerequisites

Python 3.7+
PyTorch 1.8.1+

Install MQBench Lib

Before run this repository, you should install MQBench:

git clone https://github.com/ModelTC/MQBench.git
cd MQBench
python setup.py build
python setup.py install

Training Fp32 Model

# Start training fp32 model with: 
# model_name can be ResNet18, MobileNet, ...
python main.py model_name

# You can manually config the training with: 
python main.py --resume --lr=0.01

Training Quantize Model

# Start training quantize model with: 
# model_name can be ResNet18, MobileNet, ...
python main.py model_name --quantize

# You can manually config the training with: 
python main.py --resume --parallel DP --BackendType Tensorrt --quantize
python -m torch.distributed.launch main.py --local_rank 0 --parallel DDP --resume  --BackendType Tensorrt --quantize

Fp32 Accuracy

Model	Acc.
VGG16	92.64%
ResNet18	93.02%
ResNet50	93.62%
ResNet101	93.75%
RegNetX_200MF	94.24%
RegNetY_400MF	94.29%
MobileNetV2	94.43%
ResNeXt29(32x4d)	94.73%
ResNeXt29(2x64d)	94.82%
SimpleDLA	94.89%
DenseNet121	95.04%
PreActResNet18	95.11%
DPN92	95.16%
DLA	95.47%

QAT(quantize aware training) for classification with MQBench

Related tags

Overview

MQBench Quantization Aware Training with PyTorch

Prerequisites

Install MQBench Lib

Training Fp32 Model

Training Quantize Model

Fp32 Accuracy

Owner

Ling Zhang

This tool uses Deep Learning to help you draw and write with your hand and webcam.

The official codes of our CVPR2022 paper: A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

LoFTR:Detector-Free Local Feature Matching with Transformers CVPR 2021

[NeurIPS 2021] Garment4D: Garment Reconstruction from Point Cloud Sequences

Online Multi-Granularity Distillation for GAN Compression (ICCV2021)

Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

This repository contains demos I made with the Transformers library by HuggingFace.

A curated list and survey of awesome Vision Transformers.

PixelPyramids: Exact Inference Models from Lossless Image Pyramids (ICCV 2021)

Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

Image reconstruction done with untrained neural networks.

Nodule Generation Algorithm Baseline and template code for node21 generation track

Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors, CVPR 2021

Predicting 10 different clothing types using Xception pre-trained model.

Unofficial implementation of Google "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization" in PyTorch

A Dying Light 2 (DL2) PAKFile Utility for Modders and Mod Makers.

IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.

Pytorch implementation of CoCon: A Self-Supervised Approach for Controlled Text Generation

This is the code used in the paper "Entity Embeddings of Categorical Variables".

A code implementation of AC-GC: Activation Compression with Guaranteed Convergence, in NeurIPS 2021.