PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Last update: Dec 19, 2022

Related tags

Deep Learning SAQ

Overview

Sharpness-aware Quantization for Deep Neural Networks

Recent Update

2021.11.23: We release the source code of SAQ.

Setup the environments

Clone the repository locally:

git clone https://github.com/zhuang-group/SAQ

Install pytorch 1.8+, tensorboard and prettytable

conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip install tensorboard
pip install prettytable

Data preparation

ImageNet

Download the ImageNet 2012 dataset from here, and prepare the dataset based on this script.
Change the dataset path in link_imagenet.py and link the ImageNet-100 by

python link_imagenet.py

CIFAR-100

Download the CIFAR-100 dataset from here.

After downloading ImageNet and CIFAR-100, the file structure should look like:

dataset
├── imagenet
    ├── train
    │   ├── class1
    │   │   ├── img1.jpeg
    │   │   ├── img2.jpeg
    │   │   └── ...
    │   ├── class2
    │   │   ├── img3.jpeg
    │   │   └── ...
    │   └── ...
    └── val
        ├── class1
        │   ├── img4.jpeg
        │   ├── img5.jpeg
        │   └── ...
        ├── class2
        │   ├── img6.jpeg
        │   └── ...
        └── ...
├── cifar100
    ├── cifar-100-python
    │   ├── meta
    │   ├── test
    │   ├── train
    │   └── ...
    └── ...

Training

Fixed-precision quantization

Download the pre-trained full-precision models from the model zoo.
Train low-precision models.

To train low-precision ResNet-20 on CIFAR-100, run:

sh script/train_qsam_cifar_r20.sh

To train low-precision ResNet-18 on ImageNet, run:

sh script/train_qsam_imagenet_r18.sh

Mixed-precision quantization

Download the pre-trained full-precision models from the model zoo.
Train the configuration generator.

To train the configuration generator of ResNet-20 on CIFAR-100, run:

sh script/train_generator_cifar_r20.sh

To train the configuration generator on ImageNet, run:

sh script/train_generator_imagenet_r18.sh

After training the configuration generator, run following commands to fine-tune the resulting models with the obtained bitwidth configurations on CIFAR-100 and ImageNet.

sh script/finetune_cifar_r20.sh

sh script/finetune_imagenet_r18.sh

Results on CIFAR-100

Network	Method	Bitwidth	BOPs (M)	Top-1 Acc. (%)	Top-5 Acc. (%)
ResNet-20	SAQ	4	674.6	68.7	91.2
ResNet-20	SAMQ	MP	659.3	68.7	91.2
ResNet-20	SAQ	3	392.1	67.7	90.8
ResNet-20	SAMQ	MP	374.4	68.6	91.2
MobileNetV2	SAQ	4	1508.9	75.6	93.7
MobileNetV2	SAMQ	MP	1482.1	75.5	93.6
MobileNetV2	SAQ	3	877.1	74.4	93.2
MobileNetV2	SAMQ	MP	869.5	75.5	93.7

Results on ImageNet

Network	Method	Bitwidth	BOPs (G)	Top-1 Acc. (%)	Top-5 Acc. (%)
ResNet-18	SAQ	4	34.7	71.3	90.0
ResNet-18	SAMQ	MP	33.7	71.4	89.9
ResNet-18	SAQ	2	14.4	67.1	87.3
MobileNetV2	SAQ	4	5.3	70.2	89.4
MobileNetV2	SAMQ	MP	5.3	70.3	89.4

License

This repository is released under the Apache 2.0 license as found in the LICENSE file.

Acknowledgement

This repository has adopted codes from SAM, ASAM and ESAM, we thank the authors for their open-sourced code.

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Comments

Quantize_first_last_layer

Hi! I noticed that in your code, you set bits_weights=8 and bits_activations=32 for first layer as default, it's not what is claimed in your paper " For the first and last layers of all quantized models, we quantize both weights and activations to 8-bit. " And I see an accuracy drop if I adjust the bits_activations to 8 for the first layer, could u please explain what is the reason? Thanks!

opened by mmmiiinnnggg 0
代码问题请求帮助

你好，带佬的代码写的很好，有部分代码不太懂，想请教一下， parser.add_argument( "--arch_bits", type=lambda s: [float(item) for item in s.split(",")] if len(s) != 0 else "", default=" ", help="bits configuration of each layer",

if len(args.arch_bits) != 0: if args.wa_same_bit: set_wae_bits(model, args.arch_bits) elif args.search_w_bit: set_w_bits(model, args.arch_bits) else: set_bits(model, args.arch_bits) show_bits(model) logger.info("Set arch bits to: {}".format(args.arch_bits)) logger.info(model) 这个arch_bits主要是做什么的呢，卡在这里有段时间了

opened by LKAMING97 0

PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Related tags

Overview

Sharpness-aware Quantization for Deep Neural Networks

Recent Update

Setup the environments

Data preparation

ImageNet

CIFAR-100

Training

Fixed-precision quantization

Mixed-precision quantization

Results on CIFAR-100

Results on ImageNet

License

Acknowledgement

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Comments

Quantize_first_last_layer

代码问题请求帮助

Releases(v0.1.1)

v0.1.1(Nov 23, 2021)

v0.1(Nov 23, 2021)

Owner

Zhuang AI Group

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