• Contents
• Dynamic Quantization Description
• Model Architecture
• Dataset
• Environment Requirements
• Script Description
• Eval Process
  • Usage
  • Launch
  • Result
• ModelZoo Homepage

Dynamic Quantization Description

To conduct the low-bit quantization for each image individually, we develop a dynamic quantization scheme for exploring their optimal bit-widths. Experimental results show that our method can be easily embedded with mainstream quantization frameworks and boost their performance.

Paper：Zhenhua Liu, Yunhe Wang, Kai Han, Siwei Ma and Wen Gao. "Instance-Aware Dynamic Neural Network Quantization", CVPR 2022.

Model Architecture

A bit-controller is employed to generate the bit-width of each layer for different samples and the bit-controller is jointly optimized with the main network. You can find the details in the paper.

Dataset

Dataset used: ImageNet2012

• Dataset size 224*224 colorful images in 1000 classes
  • Train: 1,281,167 images
  • Test: 50,000 images
• Data format: jpeg
  • Note: Data will be processed in dataset.py

Environment Requirements

• Hardware（Ascend/GPU/CPU）
  • Prepare hardware environment with Ascend/GPU/CPU processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Script description

DynamicQuant
├── src
    └── dataset.py # dataset loader
    └── gumbelsoftmax.py # implementation of gumbel softmax
    └── quant.py # dynamic quantization
    └── resnet.py # resnet network
├── eval.py # inference entry
├── readme.md # Readme

Eval Process

Usage

After installing MindSpore via the official website, you can start evaluation as follows:

Launch

python eval.py --dataset_path [DATASET]

Result

result: {'acc': 0.6901} ckpt= ./resnet18_dq.ckpt

Checkpoint can be downloaded at https://download.mindspore.cn/model_zoo/research/cv/DynamicQuant/.

ModelZoo Homepage

Please check the official homepage.