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README.md
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Build Docs](https://github.com/zhanghang1989/PyTorch-Encoding/workflows/Build%20Docs/badge.svg)](https://github.com/zhanghang1989/PyTorch-Encoding/actions)
[![Unit Test](https://github.com/zhanghang1989/PyTorch-Encoding/workflows/Unit%20Test/badge.svg)](https://github.com/zhanghang1989/PyTorch-Encoding/actions)
# <div align="center"><strong>PyTorch-Encoding</strong></div>
## 简介
PyTorch-Encoding 实现了一种更快速、内存效率更高的RNN-T 损失计算方法。
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/semantic-segmentation-on-ade20k)](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k?p=resnest-split-attention-networks)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/semantic-segmentation-on-pascal-context)](https://paperswithcode.com/sota/semantic-segmentation-on-pascal-context?p=resnest-split-attention-networks)
## 安装
组件支持组合
# PyTorch-Encoding
| PyTorch版本 | fastpt版本 |PyTorch-Encoding版本 | DTK版本 | Python版本 | 推荐编译方式 |
| ----------- | ----------- | ----------- | ------------------------ | -----------------| ------------ |
| 2.5.1 | 2.1.0 |master | >= 25.04 | 3.8、3.10、3.11 | fastpt不转码 |
| 2.4.1 | 2.0.1 |master | >= 25.04 | 3.8、3.10、3.11 | fastpt不转码 |
| 其他 | 其他 | 其他 | 其他 | 3.8、3.10、3.11 | hip转码 |
created by [Hang Zhang](http://hangzh.com/)
+ pytorch版本大于2.4.1 && dtk版本大于25.04 推荐使用fastpt不转码编译。
## [Documentation](http://hangzh.com/PyTorch-Encoding/)
- Please visit the [**Docs**](http://hangzh.com/PyTorch-Encoding/) for detail instructions of installation and usage.
### 1、使用pip方式安装
PyTorch-Encoding whl包下载目录：[光和开发者社区](https://download.sourcefind.cn:65024/4/main)，选择对应的pytorch版本和python版本下载对应PyTorch-Encoding的whl包
```shell
pip install torch* (下载torch的whl包)
pip install fastpt* --no-deps (下载fastpt的whl包)
source /usr/local/bin/fastpt -E
pip install torch_encoding* (下载的PyTorch-Encoding的whl包)
```
### 2、使用源码编译方式安装
- Please visit the [link](http://hangzh.com/PyTorch-Encoding/model_zoo/imagenet.html) to image classification models.
#### 编译环境准备
提供基于fastpt不转码编译：
- Please visit the [link](http://hangzh.com/PyTorch-Encoding/model_zoo/segmentation.html) to semantic segmentation models.
1. 基于光源pytorch基础镜像环境：镜像下载地址：[光合开发者社区](https://sourcefind.cn/#/image/dcu/pytorch)，根据pytorch、python、dtk及系统下载对应的镜像版本。
## Citations
2. 基于现有python环境：安装pytorch，fastpt whl包下载目录：[光合开发者社区](https://sourcefind.cn/#/image/dcu/pytorch)，根据python、dtk版本,下载对应pytorch的whl包。安装命令如下：
```shell
pip install torch* (下载torch的whl包)
pip install fastpt* --no-deps (下载fastpt的whl包, 安装顺序，先安装torch，后安装fastpt)
pip install pytest
pip install wheel
```
**ResNeSt: Split-Attention Networks** [[arXiv]](https://arxiv.org/abs/2004.08955)
[Hang Zhang](http://hangzh.com/), Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li and Alex Smola
#### 源码编译安装
- 代码下载
```shell
git clone http://developer.sourcefind.cn/codes/OpenDAS/PyTorch-Encoding.git # 根据编译需要切换分支
```
@article{zhang2020resnest,
title={ResNeSt: Split-Attention Networks},
author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
journal={arXiv preprint},
year={2020}
}
- 提供2种源码编译方式（进入PyTorch-Encoding目录）：
```
1. 设置不转码编译环境变量
source /usr/local/bin/fastpt -C
**Context Encoding for Semantic Segmentation** [[arXiv]](https://arxiv.org/pdf/1803.08904.pdf)
[Hang Zhang](http://hangzh.com/), [Kristin Dana](http://eceweb1.rutgers.edu/vision/dana.html), [Jianping Shi](http://shijianping.me/), [Zhongyue Zhang](http://zhongyuezhang.com/), [Xiaogang Wang](http://www.ee.cuhk.edu.hk/~xgwang/), [Ambrish Tyagi](https://scholar.google.com/citations?user=GaSWCoUAAAAJ&hl=en), [Amit Agrawal](http://www.amitkagrawal.com/)
```
@InProceedings{Zhang_2018_CVPR,
author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
title = {Context Encoding for Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}
2. 编译whl包并安装
python3 setup.py bdist_wheel
pip install dist/torch_encoding* --no-deps
3. 源码编译安装
python3 setup.py install --no-deps
```
#### 注意事项
+ 若使用pip install下载安装过慢，可添加pypi清华源：-i https://pypi.tuna.tsinghua.edu.cn/simple/
+ ROCM_PATH为dtk的路径，默认为/opt/dtk
+ 在pytorch2.5.1环境下编译需要支持c++17语法，打开setup.py文件，把文件中的 -std=c++14 修改为 -std=c++17
**Deep TEN: Texture Encoding Network** [[arXiv]](https://arxiv.org/pdf/1612.02844.pdf)
[Hang Zhang](http://hangzh.com/), [Jia Xue](http://jiaxueweb.com/), [Kristin Dana](http://eceweb1.rutgers.edu/vision/dana.html)
## 验证
```
@InProceedings{Zhang_2017_CVPR,
author = {Zhang, Hang and Xue, Jia and Dana, Kristin},
title = {Deep TEN: Texture Encoding Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}
python3
Python 3.10.12 (main, May 27 2025, 17:12:29) [GCC 11.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import encoding
>>> encoding.__version__
'1.2.2b20250928'
>>>
```
版本号与官方版本同步，查询该软件的版本号，例如0.3；
## Known Issue
- 无
## 参考资料
- [README_ORIGIN](README_ORIGIN.md)
- [https://github.com/princeton-vl/PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)
README_ORIGIN.md 0 → 100644
View file @ b2b3c0ba
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Build Docs](https://github.com/zhanghang1989/PyTorch-Encoding/workflows/Build%20Docs/badge.svg)](https://github.com/zhanghang1989/PyTorch-Encoding/actions)
[![Unit Test](https://github.com/zhanghang1989/PyTorch-Encoding/workflows/Unit%20Test/badge.svg)](https://github.com/zhanghang1989/PyTorch-Encoding/actions)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/semantic-segmentation-on-ade20k)](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k?p=resnest-split-attention-networks)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/resnest-split-attention-networks/semantic-segmentation-on-pascal-context)](https://paperswithcode.com/sota/semantic-segmentation-on-pascal-context?p=resnest-split-attention-networks)
# PyTorch-Encoding
created by [Hang Zhang](http://hangzh.com/)
## [Documentation](http://hangzh.com/PyTorch-Encoding/)
- Please visit the [**Docs**](http://hangzh.com/PyTorch-Encoding/) for detail instructions of installation and usage.
- Please visit the [link](http://hangzh.com/PyTorch-Encoding/model_zoo/imagenet.html) to image classification models.
- Please visit the [link](http://hangzh.com/PyTorch-Encoding/model_zoo/segmentation.html) to semantic segmentation models.
## Citations
**ResNeSt: Split-Attention Networks** [[arXiv]](https://arxiv.org/abs/2004.08955)
[Hang Zhang](http://hangzh.com/), Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li and Alex Smola
```
@article{zhang2020resnest,
title={ResNeSt: Split-Attention Networks},
author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
journal={arXiv preprint},
year={2020}
}
```
**Context Encoding for Semantic Segmentation** [[arXiv]](https://arxiv.org/pdf/1803.08904.pdf)
[Hang Zhang](http://hangzh.com/), [Kristin Dana](http://eceweb1.rutgers.edu/vision/dana.html), [Jianping Shi](http://shijianping.me/), [Zhongyue Zhang](http://zhongyuezhang.com/), [Xiaogang Wang](http://www.ee.cuhk.edu.hk/~xgwang/), [Ambrish Tyagi](https://scholar.google.com/citations?user=GaSWCoUAAAAJ&hl=en), [Amit Agrawal](http://www.amitkagrawal.com/)
```
@InProceedings{Zhang_2018_CVPR,
author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
title = {Context Encoding for Semantic Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}
```
**Deep TEN: Texture Encoding Network** [[arXiv]](https://arxiv.org/pdf/1612.02844.pdf)
[Hang Zhang](http://hangzh.com/), [Jia Xue](http://jiaxueweb.com/), [Kristin Dana](http://eceweb1.rutgers.edu/vision/dana.html)
```
@InProceedings{Zhang_2017_CVPR,
author = {Zhang, Hang and Xue, Jia and Dana, Kristin},
title = {Deep TEN: Texture Encoding Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}
```
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