DGMS

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #llm #golang #android #docker #macos #windows #awesome #awesome-list #pytorch

(★ 11)

[ICML 2022 Spotlight] Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks

DGMS 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Differentiable Gaussian Mixture Weight Sharing Network Quantization, ICML 2022 Spotlight
> [**Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks**](https://proceedings.mlr.press/v162/dong22a.html), ICML 2022<br>
> [Runpei Dong](https://runpeidong.com/)\*, [Zhanhong Tan](https://www.zhanhongtan.com/)\*, [Mengdi Wu](), [Linfeng Zhang](https://scholar.google.com.hk/citations?user=AK9VF30AAAAJ&hl=en), and [Kaisheng Ma](http://group.iiis.tsinghua.edu.cn/~maks/leader.html) <br>

Created by [Runpei Dong](https://runpeidong.com/)\*, [Zhanhong Tan](https://www.zhanhongtan.com/)\*, [Mengdi Wu](https://scholar.google.com.hk/citations?user=F9EN5zgAAAAJ&hl=en&oi=sra), [Linfeng Zhang](http://group.iiis.tsinghua.edu.cn/~maks/linfeng/index.html), and [Kaisheng Ma](http://group.iiis.tsinghua.edu.cn/~maks/leader.html).

[PMLR](https://proceedings.mlr.press/v162/dong22a.html) | [arXiv](https://arxiv.org/abs/2112.15139) | [Models](https://drive.google.com/drive/folders/1rQJLAbP8gb5ZIUyIjEVHof0euyhsVGu4?usp=sharing)

This repository contains the code release of the paper **Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks** (ICML 2022).

## Installation

Our code works with Python 3.8.3. we recommend to use [Anaconda](https://www.anaconda.com/), and you can install the dependencies by running:

```shell
$ python3 -m venv env
$ source env/bin/activate
(env) $ python3 -m pip install -r requirements.txt
```
## How to Run

The main procedures are written in script `main.py`, please run the following command for instructions:

```shell
$ python main.py -h
```

### Datasets

Before running the code, you can specify the path for datasets in `config.py`, or you can specify it by `--train-dir` and `--val-dir`.

### Training on ImageNet

We have provided a simple SHELL script to train a 4-bit `ResNet-18` with `DGMS`. Run:

```shell
$ sh tools/train_imgnet.sh
```

### Inference on ImageNet

To inference compressed models on ImageNet, you only need to follow 2 steps:

* **Step-1**: Download the checkpoints released on [Google Drive](https://drive.google.com/drive/folders/1rQJLAbP8gb5ZIUyIjEVHof0euyhsVGu4?usp=sharing).

* **Step-2**: Run the inference SHELL script we provide:

```shell
  $ sh tools/validation.sh
  ```

## Q-SIMD

The [TVM](https://github.com/apache/tvm) based Q-SIMD codes can be download from [Google Drive](https://drive.google.com/file/d/1hGeXXdHetGKZKSd4dp7xTSRxjWXgPkjc/view?usp=sharing).

## Citation

If you find our work useful in your research, please consider citing:

```tex
@inproceedings{dong2021finding,
  title={Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks},
  author={Dong, Runpei and Tan, Zhanhong and Wu, Mengdi and Zhang, Linfeng and Ma, Kaisheng},
  booktitle={Proceedings of the International Conference on Machine Learning (ICML)},
  year={2022}
}
```

## License

DGMS is released under the Apache 2.0 license.  See the [LICENSE](./LICENSE) file for more details.

DGMS

DGMS 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

DGMS

DGMS 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README