Skip to content

P
PaddleHub

PaddlePaddle / PaddleHub
大约 2 年 前同步成功

通知 285
Star 12117
Fork 2091
P
PaddleHub
未验证 提交 1696dad9 编写于 作者: K KP 提交者： GitHub
浏览文件
操作

Merge branch 'develop' into add_photopen_module

上级 a2db134e c66be094
展开全部 隐藏空白更改
内联 并排
Showing with 18126 addition and 7025 deletion
.pre-commit-config.yaml
浏览文件 @ 1696dad9
...@@ -30,3 +30,9 @@ ...@@ -30,3 +30,9 @@
- --show-source - --show-source
- --statistics - --statistics
files: \.py$ files: \.py$
- repo: https://github.com/asottile/reorder_python_imports
rev: v2.4.0
hooks:
- id: reorder-python-imports
exclude: (?=third_party).*(\.py)$
README.md
浏览文件 @ 1696dad9
...@@ -4,7 +4,7 @@ English | [简体中文](README_ch.md) ...@@ -4,7 +4,7 @@ English | [简体中文](README_ch.md)
<img src="./docs/imgs/paddlehub_logo.jpg" align="middle"> <img src="./docs/imgs/paddlehub_logo.jpg" align="middle">
<p align="center"> <p align="center">
<div align="center"> <div align="center">
<h3> <a href=#QuickStart> QuickStart </a> | <a href="https://paddlehub.readthedocs.io/en/release-v2.1"> Tutorial </a> | <a href="https://www.paddlepaddle.org.cn/hublist"> Models List </a> | <a href="https://www.paddlepaddle.org.cn/hub"> Demos </a> </h3> <h3> <a href=#QuickStart> QuickStart </a> | <a href="https://paddlehub.readthedocs.io/en/release-v2.1"> Tutorial </a> | <a href="./modules"> Models List </a> | <a href="https://www.paddlepaddle.org.cn/hub"> Demos </a> </h3>
</div> </div>
------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------
...@@ -28,7 +28,7 @@ English | [简体中文](README_ch.md) ...@@ -28,7 +28,7 @@ English | [简体中文](README_ch.md)
## Introduction and Features ## Introduction and Features
- **PaddleHub** aims to provide developers with rich, high-quality, and directly usable pre-trained models. - **PaddleHub** aims to provide developers with rich, high-quality, and directly usable pre-trained models.
- **Abundant Pre-trained Models**: 300+ pre-trained models cover the 5 major categories, including Image, Text, Audio, Video, and Industrial application. All of them are free for download and offline usage. - **Abundant Pre-trained Models**: 360+ pre-trained models cover the 5 major categories, including Image, Text, Audio, Video, and Industrial application. All of them are free for download and offline usage.
- **No Need for Deep Learning Background**: you can use AI models quickly and enjoy the dividends of the artificial intelligence era. - **No Need for Deep Learning Background**: you can use AI models quickly and enjoy the dividends of the artificial intelligence era.
- **Quick Model Prediction**: model prediction can be realized through a few lines of scripts to quickly experience the model effect. - **Quick Model Prediction**: model prediction can be realized through a few lines of scripts to quickly experience the model effect.
- **Model As Service**: one-line command to build deep learning model API service deployment capabilities. - **Model As Service**: one-line command to build deep learning model API service deployment capabilities.
...@@ -36,6 +36,7 @@ English | [简体中文](README_ch.md) ...@@ -36,6 +36,7 @@ English | [简体中文](README_ch.md)
- **Cross-platform**: support Linux, Windows, MacOS and other operating systems. - **Cross-platform**: support Linux, Windows, MacOS and other operating systems.
### Recent updates ### Recent updates
- **2021.12.22**，The v2.2.0 version is released. [1]More than 100 new models released，including dialog, speech, segmentation, OCR, text processing, GANs, and many other categories. The total number of pre-trained models reaches [**【360】**](https://www.paddlepaddle.org.cn/hublist). [2]Add an [indexed file](./modules/README.md) including useful information of pretrained models supported by PaddleHub. [3]Refactor README of pretrained models.
- **2021.05.12:** Add an open-domain dialogue system, i.e., [plato-mini](https://www.paddlepaddle.org.cn/hubdetail?name=plato-mini&en_category=TextGeneration), to make it easy to build a chatbot in wechat with the help of the wechaty, [See Demo](https://github.com/KPatr1ck/paddlehub-wechaty-demo) - **2021.05.12:** Add an open-domain dialogue system, i.e., [plato-mini](https://www.paddlepaddle.org.cn/hubdetail?name=plato-mini&en_category=TextGeneration), to make it easy to build a chatbot in wechat with the help of the wechaty, [See Demo](https://github.com/KPatr1ck/paddlehub-wechaty-demo)
- **2021.04.27:** The v2.1.0 version is released. [1] Add supports for five new models, including two high-precision semantic segmentation models based on VOC dataset and three voice classification models. [2] Enforce the transfer learning capabilities for image semantic segmentation, text semantic matching and voice classification on related datasets. [3] Add the export function APIs for two kinds of model formats, i.,e, ONNX and PaddleInference. [4] Add the support for [BentoML](https://github.com/bentoml/BentoML/), which is a cloud native framework for serving deployment. Users can easily serve pre-trained models from PaddleHub by following the [Tutorial notebooks](https://github.com/PaddlePaddle/PaddleHub/blob/release/v2.1/demo/serving/bentoml/cloud-native-model-serving-with-bentoml.ipynb). Also, see this announcement and [Release note](https://github.com/bentoml/BentoML/releases/tag/v0.12.1) from BentoML. (Many thanks to @[parano](https://github.com/parano) @[cqvu](https://github.com/cqvu) @[deehrlic](https://github.com/deehrlic) for contributing this feature in PaddleHub). [5] The total number of pre-trained models reaches **【300】**. - **2021.04.27:** The v2.1.0 version is released. [1] Add supports for five new models, including two high-precision semantic segmentation models based on VOC dataset and three voice classification models. [2] Enforce the transfer learning capabilities for image semantic segmentation, text semantic matching and voice classification on related datasets. [3] Add the export function APIs for two kinds of model formats, i.,e, ONNX and PaddleInference. [4] Add the support for [BentoML](https://github.com/bentoml/BentoML/), which is a cloud native framework for serving deployment. Users can easily serve pre-trained models from PaddleHub by following the [Tutorial notebooks](https://github.com/PaddlePaddle/PaddleHub/blob/release/v2.1/demo/serving/bentoml/cloud-native-model-serving-with-bentoml.ipynb). Also, see this announcement and [Release note](https://github.com/bentoml/BentoML/releases/tag/v0.12.1) from BentoML. (Many thanks to @[parano](https://github.com/parano) @[cqvu](https://github.com/cqvu) @[deehrlic](https://github.com/deehrlic) for contributing this feature in PaddleHub). [5] The total number of pre-trained models reaches **【300】**.
- **2021.02.18:** The v2.0.0 version is released, making model development and debugging easier, and the finetune task is more flexible and easy to use.The ability to transfer learning for visual tasks is fully upgraded, supporting various tasks such as image classification, image coloring, and style transfer; Transformer models such as BERT, ERNIE, and RoBERTa are upgraded to dynamic graphs, supporting Fine-Tune capabilities for text classification and sequence labeling; Optimize the Serving capability, support multi-card prediction, automatic load balancing, and greatly improve performance; the new automatic data enhancement capability Auto Augment can efficiently search for data enhancement strategy combinations suitable for data sets. 61 new word vector models were added, including 51 Chinese models and 10 English models; add 4 image segmentation models, 2 depth models, 7 image generation models, and 3 text generation models, the total number of pre-trained models reaches **【274】**. - **2021.02.18:** The v2.0.0 version is released, making model development and debugging easier, and the finetune task is more flexible and easy to use.The ability to transfer learning for visual tasks is fully upgraded, supporting various tasks such as image classification, image coloring, and style transfer; Transformer models such as BERT, ERNIE, and RoBERTa are upgraded to dynamic graphs, supporting Fine-Tune capabilities for text classification and sequence labeling; Optimize the Serving capability, support multi-card prediction, automatic load balancing, and greatly improve performance; the new automatic data enhancement capability Auto Augment can efficiently search for data enhancement strategy combinations suitable for data sets. 61 new word vector models were added, including 51 Chinese models and 10 English models; add 4 image segmentation models, 2 depth models, 7 image generation models, and 3 text generation models, the total number of pre-trained models reaches **【274】**.
...@@ -44,8 +45,8 @@ English | [简体中文](README_ch.md) ...@@ -44,8 +45,8 @@ English | [简体中文](README_ch.md)
## Visualization Demo [[More]](./docs/docs_en/visualization.md) ## Visualization Demo [[More]](./docs/docs_en/visualization.md) [[ModelList]](./modules)
### **Computer Vision (161 models)** ### **[Computer Vision (212 models)](./modules#Image)**
<div align="center"> <div align="center">
<img src="./docs/imgs/Readme_Related/Image_all.gif" width = "530" height = "400" /> <img src="./docs/imgs/Readme_Related/Image_all.gif" width = "530" height = "400" />
</div> </div>
...@@ -53,7 +54,7 @@ English | [简体中文](README_ch.md) ...@@ -53,7 +54,7 @@ English | [简体中文](README_ch.md)
- Many thanks to CopyRight@[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)、[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)、[PaddleGAN](https://github.com/PaddlePaddle/PaddleGAN)、[AnimeGAN](https://github.com/TachibanaYoshino/AnimeGANv2)、[openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)、[PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg)、[Zhengxia Zou](https://github.com/jiupinjia/SkyAR)、[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) for the pre-trained models, you can try to train your models with them. - Many thanks to CopyRight@[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)、[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)、[PaddleGAN](https://github.com/PaddlePaddle/PaddleGAN)、[AnimeGAN](https://github.com/TachibanaYoshino/AnimeGANv2)、[openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)、[PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg)、[Zhengxia Zou](https://github.com/jiupinjia/SkyAR)、[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) for the pre-trained models, you can try to train your models with them.
### **Natural Language Processing (129 models)** ### **[Natural Language Processing (130 models)](./modules#Text)**
<div align="center"> <div align="center">
<img src="./docs/imgs/Readme_Related/Text_all.gif" width = "640" height = "240" /> <img src="./docs/imgs/Readme_Related/Text_all.gif" width = "640" height = "240" />
</div> </div>
...@@ -62,9 +63,37 @@ English | [简体中文](README_ch.md) ...@@ -62,9 +63,37 @@ English | [简体中文](README_ch.md)
### Speech (3 models) ### [Speech (15 models)](./modules#Audio)
- ASR speech recognition algorithm, multiple algorithms are available.
- The speech recognition effect is as follows:
<div align="center">
<table>
<thead>
<tr>
<th width=250> Input Audio </th>
<th width=550> Recognition Result </th>
</tr>
</thead>
<tbody>
<tr>
<td align = "center">
<a href="https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav" rel="nofollow">
<img align="center" src="./docs/imgs/Readme_Related/audio_icon.png" width=250 ></a><br>
</td>
<td >I knocked at the door on the ancient side of the building.</td>
</tr>
<tr>
<td align = "center">
<a href="https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav" rel="nofollow">
<img align="center" src="./docs/imgs/Readme_Related/audio_icon.png" width=250></a><br>
</td>
<td>我认为跑步最重要的就是给我带来了身体健康。</td>
</tr>
</tbody>
</table>
</div>
- TTS speech synthesis algorithm, multiple algorithms are available. - TTS speech synthesis algorithm, multiple algorithms are available.
- Many thanks to CopyRight@[Parakeet](https://github.com/PaddlePaddle/Parakeet) for the pre-trained models, you can try to train your models with Parakeet.
- Input: `Life was like a box of chocolates, you never know what you're gonna get.` - Input: `Life was like a box of chocolates, you never know what you're gonna get.`
- The synthesis effect is as follows: - The synthesis effect is as follows:
<div align="center"> <div align="center">
...@@ -95,7 +124,9 @@ English | [简体中文](README_ch.md) ...@@ -95,7 +124,9 @@ English | [简体中文](README_ch.md)
</table> </table>
</div> </div>
### Video (8 models) - Many thanks to CopyRight@[PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech) for the pre-trained models, you can try to train your models with PaddleSpeech.
### [Video (8 models)](./modules#Video)
- Short video classification trained via large-scale video datasets, supports 3000+ tag types prediction for short Form Videos. - Short video classification trained via large-scale video datasets, supports 3000+ tag types prediction for short Form Videos.
- Many thanks to CopyRight@[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo) for the pre-trained model, you can try to train your models with PaddleVideo. - Many thanks to CopyRight@[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo) for the pre-trained model, you can try to train your models with PaddleVideo.
- `Example: Input a short video of swimming, the algorithm can output the result of "swimming"` - `Example: Input a short video of swimming, the algorithm can output the result of "swimming"`
...@@ -231,3 +262,4 @@ We welcome you to contribute code to PaddleHub, and thank you for your feedback. ...@@ -231,3 +262,4 @@ We welcome you to contribute code to PaddleHub, and thank you for your feedback.
* Many thanks to [zl1271](https://github.com/zl1271) for fixing serving docs typo * Many thanks to [zl1271](https://github.com/zl1271) for fixing serving docs typo
* Many thanks to [AK391](https://github.com/AK391) for adding the webdemo of UGATIT and deoldify models in Hugging Face spaces * Many thanks to [AK391](https://github.com/AK391) for adding the webdemo of UGATIT and deoldify models in Hugging Face spaces
* Many thanks to [itegel](https://github.com/itegel) for fixing quick start docs typo * Many thanks to [itegel](https://github.com/itegel) for fixing quick start docs typo
* Many thanks to [AK391](https://github.com/AK391) for adding the webdemo of Photo2Cartoon model in Hugging Face spaces
README_ch.md
浏览文件 @ 1696dad9
...@@ -4,7 +4,7 @@ ...@@ -4,7 +4,7 @@
<img src="./docs/imgs/paddlehub_logo.jpg" align="middle"> <img src="./docs/imgs/paddlehub_logo.jpg" align="middle">
<p align="center"> <p align="center">
<div align="center"> <div align="center">
<h3> <a href=#QuickStart> 快速开始 </a> | <a href="https://paddlehub.readthedocs.io/zh_CN/release-v2.1//"> 教程文档 </a> | <a href="https://www.paddlepaddle.org.cn/hublist"> 模型搜索 </a> | <a href="https://www.paddlepaddle.org.cn/hub"> 演示Demo </a> <h3> <a href=#QuickStart> 快速开始 </a> | <a href="https://paddlehub.readthedocs.io/zh_CN/release-v2.1//"> 教程文档 </a> | <a href="./modules/README_ch.md"> 模型库 </a> | <a href="https://www.paddlepaddle.org.cn/hub"> 演示Demo </a>
</h3> </h3>
</div> </div>
...@@ -30,7 +30,7 @@ ...@@ -30,7 +30,7 @@
## 简介与特性 ## 简介与特性
- PaddleHub旨在为开发者提供丰富的、高质量的、直接可用的预训练模型 - PaddleHub旨在为开发者提供丰富的、高质量的、直接可用的预训练模型
- **【模型种类丰富】**: 涵盖CV、NLP、Audio、Video、工业应用主流五大品类的 300+ 预训练模型，全部开源下载，离线可运行 - **【模型种类丰富】**: 涵盖CV、NLP、Audio、Video、工业应用主流五大品类的 **360+** 预训练模型，全部开源下载，离线可运行
- **【超低使用门槛】**：无需深度学习背景、无需数据与训练过程，可快速使用AI模型 - **【超低使用门槛】**：无需深度学习背景、无需数据与训练过程，可快速使用AI模型
- **【一键模型快速预测】**：通过一行命令行或者极简的Python API实现模型调用，可快速体验模型效果 - **【一键模型快速预测】**：通过一行命令行或者极简的Python API实现模型调用，可快速体验模型效果
- **【一键模型转服务化】**：一行命令，搭建深度学习模型API服务化部署能力 - **【一键模型转服务化】**：一行命令，搭建深度学习模型API服务化部署能力
...@@ -38,6 +38,7 @@ ...@@ -38,6 +38,7 @@
- **【跨平台兼容性】**：可运行于Linux、Windows、MacOS等多种操作系统 - **【跨平台兼容性】**：可运行于Linux、Windows、MacOS等多种操作系统
## 近期更新 ## 近期更新
- **2021.12.22**，发布v2.2.0版本。【1】新增100+高质量模型，涵盖对话、语音处理、语义分割、文字识别、文本处理、图像生成等多个领域，预训练模型总量达到[**【360+】**](https://www.paddlepaddle.org.cn/hublist)；【2】新增模型[检索列表](./modules/README_ch.md)，包含模型名称、网络、数据集和使用场景等信息，快速定位用户所需的模型；【3】模型文档排版优化，呈现数据集、指标、模型大小等更多实用信息。
- **2021.05.12**，新增轻量级中文对话模型[plato-mini](https://www.paddlepaddle.org.cn/hubdetail?name=plato-mini&en_category=TextGeneration)，可以配合使用wechaty实现微信闲聊机器人，[参考demo](https://github.com/KPatr1ck/paddlehub-wechaty-demo) - **2021.05.12**，新增轻量级中文对话模型[plato-mini](https://www.paddlepaddle.org.cn/hubdetail?name=plato-mini&en_category=TextGeneration)，可以配合使用wechaty实现微信闲聊机器人，[参考demo](https://github.com/KPatr1ck/paddlehub-wechaty-demo)
- **2021.04.27**，发布v2.1.0版本。【1】新增基于VOC数据集的高精度语义分割模型2个，语音分类模型3个。【2】新增图像语义分割、文本语义匹配、语音分类等相关任务的Fine-Tune能力以及相关任务数据集;完善部署能力：【3】新增ONNX和PaddleInference等模型格式的导出功能。【4】新增[BentoML](https://github.com/bentoml/BentoML) 云原生服务化部署能力，可以支持统一的多框架模型管理和模型部署的工作流，[详细教程](https://github.com/PaddlePaddle/PaddleHub/blob/release/v2.1/demo/serving/bentoml/cloud-native-model-serving-with-bentoml.ipynb). 更多内容可以参考BentoML 最新 v0.12.1 [Releasenote](https://github.com/bentoml/BentoML/releases/tag/v0.12.1).（感谢@[parano](https://github.com/parano) @[cqvu](https://github.com/cqvu) @[deehrlic](https://github.com/deehrlic)）的贡献与支持。【5】预训练模型总量达到[**【300】**](https://www.paddlepaddle.org.cn/hublist)个。 - **2021.04.27**，发布v2.1.0版本。【1】新增基于VOC数据集的高精度语义分割模型2个，语音分类模型3个。【2】新增图像语义分割、文本语义匹配、语音分类等相关任务的Fine-Tune能力以及相关任务数据集;完善部署能力：【3】新增ONNX和PaddleInference等模型格式的导出功能。【4】新增[BentoML](https://github.com/bentoml/BentoML) 云原生服务化部署能力，可以支持统一的多框架模型管理和模型部署的工作流，[详细教程](https://github.com/PaddlePaddle/PaddleHub/blob/release/v2.1/demo/serving/bentoml/cloud-native-model-serving-with-bentoml.ipynb). 更多内容可以参考BentoML 最新 v0.12.1 [Releasenote](https://github.com/bentoml/BentoML/releases/tag/v0.12.1).（感谢@[parano](https://github.com/parano) @[cqvu](https://github.com/cqvu) @[deehrlic](https://github.com/deehrlic)）的贡献与支持。【5】预训练模型总量达到[**【300】**](https://www.paddlepaddle.org.cn/hublist)个。
- **2021.02.18**，发布v2.0.0版本，【1】模型开发调试更简单，finetune接口更加灵活易用。视觉类任务迁移学习能力全面升级，支持[图像分类](./demo/image_classification/README.md)、[图像着色](./demo/colorization/README.md)、[风格迁移](./demo/style_transfer/README.md)等多种任务；BERT、ERNIE、RoBERTa等Transformer类模型升级至动态图，支持[文本分类](./demo/text_classification/README.md)、[序列标注](./demo/sequence_labeling/README.md)的Fine-Tune能力；【2】优化服务化部署Serving能力，支持多卡预测、自动负载均衡，性能大幅度提升；【3】新增自动数据增强能力[Auto Augment](./demo/autoaug/README.md)，能高效地搜索适合数据集的数据增强策略组合。【4】新增[词向量模型](./modules/text/embedding)61个，其中包含中文模型51个，英文模型10个；新增[图像分割](./modules/thirdparty/image/semantic_segmentation)模型4个、[深度模型](./modules/thirdparty/image/depth_estimation)2个、[图像生成](./modules/thirdparty/image/Image_gan/style_transfer)模型7个、[文本生成](./modules/thirdparty/text/text_generation)模型3个。【5】预训练模型总量达到[**【274】**](https://www.paddlepaddle.org.cn/hublist) 个。 - **2021.02.18**，发布v2.0.0版本，【1】模型开发调试更简单，finetune接口更加灵活易用。视觉类任务迁移学习能力全面升级，支持[图像分类](./demo/image_classification/README.md)、[图像着色](./demo/colorization/README.md)、[风格迁移](./demo/style_transfer/README.md)等多种任务；BERT、ERNIE、RoBERTa等Transformer类模型升级至动态图，支持[文本分类](./demo/text_classification/README.md)、[序列标注](./demo/sequence_labeling/README.md)的Fine-Tune能力；【2】优化服务化部署Serving能力，支持多卡预测、自动负载均衡，性能大幅度提升；【3】新增自动数据增强能力[Auto Augment](./demo/autoaug/README.md)，能高效地搜索适合数据集的数据增强策略组合。【4】新增[词向量模型](./modules/text/embedding)61个，其中包含中文模型51个，英文模型10个；新增[图像分割](./modules/thirdparty/image/semantic_segmentation)模型4个、[深度模型](./modules/thirdparty/image/depth_estimation)2个、[图像生成](./modules/thirdparty/image/Image_gan/style_transfer)模型7个、[文本生成](./modules/thirdparty/text/text_generation)模型3个。【5】预训练模型总量达到[**【274】**](https://www.paddlepaddle.org.cn/hublist) 个。
...@@ -47,9 +48,9 @@ ...@@ -47,9 +48,9 @@
## **精品模型效果展示[【更多】](./docs/docs_ch/visualization.md)** ## **精品模型效果展示[【更多】](./docs/docs_ch/visualization.md)[【模型库】](./modules/README_ch.md)**
### **图像类（161个）** ### **[图像类（212个）](./modules/README_ch.md#图像)**
- 包括图像分类、人脸检测、口罩检测、车辆检测、人脸/人体/手部关键点检测、人像分割、80+语言文本识别、图像超分/上色/动漫化等 - 包括图像分类、人脸检测、口罩检测、车辆检测、人脸/人体/手部关键点检测、人像分割、80+语言文本识别、图像超分/上色/动漫化等
<div align="center"> <div align="center">
<img src="./docs/imgs/Readme_Related/Image_all.gif" width = "530" height = "400" /> <img src="./docs/imgs/Readme_Related/Image_all.gif" width = "530" height = "400" />
...@@ -58,7 +59,7 @@ ...@@ -58,7 +59,7 @@
- 感谢CopyRight@[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)、[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)、[PaddleGAN](https://github.com/PaddlePaddle/PaddleGAN)、[AnimeGAN](https://github.com/TachibanaYoshino/AnimeGANv2)、[openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)、[PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg)、[Zhengxia Zou](https://github.com/jiupinjia/SkyAR)、[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 提供相关预训练模型，训练能力开放，欢迎体验。 - 感谢CopyRight@[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)、[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)、[PaddleGAN](https://github.com/PaddlePaddle/PaddleGAN)、[AnimeGAN](https://github.com/TachibanaYoshino/AnimeGANv2)、[openpose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)、[PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg)、[Zhengxia Zou](https://github.com/jiupinjia/SkyAR)、[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 提供相关预训练模型，训练能力开放，欢迎体验。
### **文本类（129个）** ### **[文本类（130个）](./modules/README_ch.md#文本)**
- 包括中文分词、词性标注与命名实体识别、句法分析、AI写诗/对联/情话/藏头诗、中文的评论情感分析、中文色情文本审核等 - 包括中文分词、词性标注与命名实体识别、句法分析、AI写诗/对联/情话/藏头诗、中文的评论情感分析、中文色情文本审核等
<div align="center"> <div align="center">
<img src="./docs/imgs/Readme_Related/Text_all.gif" width = "640" height = "240" /> <img src="./docs/imgs/Readme_Related/Text_all.gif" width = "640" height = "240" />
...@@ -67,9 +68,37 @@ ...@@ -67,9 +68,37 @@
- 感谢CopyRight@[ERNIE](https://github.com/PaddlePaddle/ERNIE)、[LAC](https://github.com/baidu/LAC)、[DDParser](https://github.com/baidu/DDParser)提供相关预训练模型，训练能力开放，欢迎体验。 - 感谢CopyRight@[ERNIE](https://github.com/PaddlePaddle/ERNIE)、[LAC](https://github.com/baidu/LAC)、[DDParser](https://github.com/baidu/DDParser)提供相关预训练模型，训练能力开放，欢迎体验。
### **语音类（3个）** ### **[语音类（15个）](./modules/README_ch.md#语音)**
- ASR语音识别算法，多种算法可选
- 语音识别效果如下:
<div align="center">
<table>
<thead>
<tr>
<th width=250> Input Audio </th>
<th width=550> Recognition Result </th>
</tr>
</thead>
<tbody>
<tr>
<td align = "center">
<a href="https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav" rel="nofollow">
<img align="center" src="./docs/imgs/Readme_Related/audio_icon.png" width=250 ></a><br>
</td>
<td >I knocked at the door on the ancient side of the building.</td>
</tr>
<tr>
<td align = "center">
<a href="https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav" rel="nofollow">
<img align="center" src="./docs/imgs/Readme_Related/audio_icon.png" width=250></a><br>
</td>
<td>我认为跑步最重要的就是给我带来了身体健康。</td>
</tr>
</tbody>
</table>
</div>
- TTS语音合成算法，多种算法可选 - TTS语音合成算法，多种算法可选
- 感谢CopyRight@[Parakeet](https://github.com/PaddlePaddle/Parakeet)提供预训练模型，训练能力开放，欢迎体验。
- 输入：`Life was like a box of chocolates, you never know what you're gonna get.` - 输入：`Life was like a box of chocolates, you never know what you're gonna get.`
- 合成效果如下: - 合成效果如下:
<div align="center"> <div align="center">
...@@ -100,7 +129,9 @@ ...@@ -100,7 +129,9 @@
</table> </table>
</div> </div>
### **视频类（8个）** - 感谢CopyRight@[PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech)提供预训练模型，训练能力开放，欢迎体验。
### **[视频类（8个）](./modules/README_ch.md#视频)**
- 包含短视频分类，支持3000+标签种类，可输出TOP-K标签，多种算法可选。 - 包含短视频分类，支持3000+标签种类，可输出TOP-K标签，多种算法可选。
- 感谢CopyRight@[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo)提供预训练模型，训练能力开放，欢迎体验。 - 感谢CopyRight@[PaddleVideo](https://github.com/PaddlePaddle/PaddleVideo)提供预训练模型，训练能力开放，欢迎体验。
- `举例：输入一段游泳的短视频，算法可以输出"游泳"结果` - `举例：输入一段游泳的短视频，算法可以输出"游泳"结果`
...@@ -247,3 +278,4 @@ print(results) ...@@ -247,3 +278,4 @@ print(results)
* 非常感谢[zl1271](https://github.com/zl1271)修复了serving文档中的错别字 * 非常感谢[zl1271](https://github.com/zl1271)修复了serving文档中的错别字
* 非常感谢[AK391](https://github.com/AK391)在Hugging Face spaces中添加了UGATIT和deoldify模型的web demo * 非常感谢[AK391](https://github.com/AK391)在Hugging Face spaces中添加了UGATIT和deoldify模型的web demo
* 非常感谢[itegel](https://github.com/itegel)修复了快速开始文档中的错别字 * 非常感谢[itegel](https://github.com/itegel)修复了快速开始文档中的错别字
* 非常感谢[AK391](https://github.com/AK391)在Hugging Face spaces中添加了Photo2Cartoon模型的web demo
docs/docs_en/visualization.md
浏览文件 @ 1696dad9
...@@ -50,6 +50,8 @@ ...@@ -50,6 +50,8 @@
**UGATIT Selfie2anime Huggingface Web Demo**: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/U-GAT-IT-selfie2anime) **UGATIT Selfie2anime Huggingface Web Demo**: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/U-GAT-IT-selfie2anime)
**Photo2Cartoon Huggingface Web Demo**: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/photo2cartoon)
### Object Detection ### Object Detection
- Pedestrian detection, vehicle detection, and more industrial-grade ultra-large-scale pretrained models are provided. - Pedestrian detection, vehicle detection, and more industrial-grade ultra-large-scale pretrained models are provided.
......
modules/README.md
浏览文件 @ 1696dad9
此差异已折叠。
modules/README_ch.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/audio/asr/u2_conformer_aishell/README.md
浏览文件 @ 1696dad9
...@@ -3,7 +3,7 @@ ...@@ -3,7 +3,7 @@
|模型名称|u2_conformer_aishell| |模型名称|u2_conformer_aishell|
| :--- | :---: | | :--- | :---: |
|类别|语音-语音识别| |类别|语音-语音识别|
|网络|DeepSpeech2| |网络|Conformer|
|数据集|AISHELL-1| |数据集|AISHELL-1|
|是否支持Fine-tuning|否| |是否支持Fine-tuning|否|
|模型大小|284MB| |模型大小|284MB|
......
modules/audio/asr/u2_conformer_librispeech/README.md
浏览文件 @ 1696dad9
...@@ -3,7 +3,7 @@ ...@@ -3,7 +3,7 @@
|模型名称|u2_conformer_librispeech| |模型名称|u2_conformer_librispeech|
| :--- | :---: | | :--- | :---: |
|类别|语音-语音识别| |类别|语音-语音识别|
|网络|DeepSpeech2| |网络|Conformer|
|数据集|LibriSpeech| |数据集|LibriSpeech|
|是否支持Fine-tuning|否| |是否支持Fine-tuning|否|
|模型大小|191MB| |模型大小|191MB|
......
modules/audio/asr/u2_conformer_wenetspeech/README.md 0 → 100644
浏览文件 @ 1696dad9
# u2_conformer_wenetspeech
|模型名称|u2_conformer_wenetspeech|
| :--- | :---: |
|类别|语音-语音识别|
|网络|Conformer|
|数据集|WenetSpeech|
|是否支持Fine-tuning|否|
|模型大小|494MB|
|最新更新日期|2021-12-10|
|数据指标|中文CER 0.087 |
## 一、模型基本信息
### 模型介绍
U2 Conformer模型是一种适用于英文和中文的end-to-end语音识别模型。u2_conformer_wenetspeech采用了conformer的encoder和transformer的decoder的模型结构，并且使用了ctc-prefix beam search的方式进行一遍打分，再利用attention decoder进行二次打分的方式进行解码来得到最终结果。
u2_conformer_wenetspeech在中文普通话开源语音数据集[WenetSpeech](https://wenet-e2e.github.io/WenetSpeech/)进行了预训练，该模型在其DEV测试集上的CER指标是0.087。
<p align="center">
<img src="https://paddlehub.bj.bcebos.com/paddlehub-img/conformer.png" hspace='10'/> <br />
</p>
<p align="center">
<img src="https://paddlehub.bj.bcebos.com/paddlehub-img/u2_conformer.png" hspace='10'/> <br />
</p>
更多详情请参考:
- [Unified Streaming and Non-streaming Two-pass End-to-end Model for Speech Recognition](https://arxiv.org/abs/2012.05481)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://arxiv.org/abs/2005.08100)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://arxiv.org/abs/2110.03370)
## 二、安装
- ### 1、系统依赖
- libsndfile
- Linux
```shell
$ sudo apt-get install libsndfile
or
$ sudo yum install libsndfile
```
- MacOs
```
$ brew install libsndfile
```
- ### 2、环境依赖
- paddlepaddle >= 2.2.0
- paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
- ### 3、安装
- ```shell
$ hub install u2_conformer_wenetspeech
```
- 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
| [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
## 三、模型API预测
- ### 1、预测代码示例
```python
import paddlehub as hub
# 采样率为16k，格式为wav的中文语音音频
wav_file = '/PATH/TO/AUDIO'
model = hub.Module(
name='u2_conformer_wenetspeech',
version='1.0.0')
text = model.speech_recognize(wav_file)
print(text)
```
- ### 2、API
- ```python
def check_audio(audio_file)
```
- 检查输入音频格式和采样率是否满足为16000，如果不满足，则重新采样至16000并将新的音频文件保存至相同目录。
- **参数**
- `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav`
- ```python
def speech_recognize(
audio_file,
device='cpu',
)
```
- 将输入的音频识别成文字
- **参数**
- `audio_file`：本地音频文件(*.wav)的路径，如`/path/to/input.wav`
- `device`：预测时使用的设备，默认为`cpu`，如需使用gpu预测，请设置为`gpu`。
- **返回**
- `text`：str类型，返回输入音频的识别文字结果。
## 四、服务部署
- PaddleHub Serving可以部署一个在线的语音识别服务。
- ### 第一步：启动PaddleHub Serving
- ```shell
$ hub serving start -m u2_conformer_wenetspeech
```
- 这样就完成了一个语音识别服务化API的部署，默认端口号为8866。
- **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA_VISIBLE_DEVICES环境变量，否则不用设置。
- ### 第二步：发送预测请求
- 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果
- ```python
import requests
import json
# 需要识别的音频的存放路径，确保部署服务的机器可访问
file = '/path/to/input.wav'
# 以key的方式指定text传入预测方法的时的参数，此例中为"audio_file"
data = {"audio_file": file}
# 发送post请求，content-type类型应指定json方式，url中的ip地址需改为对应机器的ip
url = "http://127.0.0.1:8866/predict/u2_conformer_wenetspeech"
# 指定post请求的headers为application/json方式
headers = {"Content-Type": "application/json"}
r = requests.post(url=url, headers=headers, data=json.dumps(data))
print(r.json())
```
## 五、更新历史
* 1.0.0
初始发布
```shell
$ hub install u2_conformer_wenetspeech
```
modules/audio/asr/u2_conformer_wenetspeech/module.py 0 → 100644
浏览文件 @ 1696dad9
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import paddle
from paddleaudio import load, save_wav
from paddlespeech.cli import ASRExecutor
from paddlehub.module.module import moduleinfo, serving
from paddlehub.utils.log import logger
@moduleinfo(
name="u2_conformer_wenetspeech", version="1.0.0", summary="", author="Wenet", author_email="", type="audio/asr")
class U2Conformer(paddle.nn.Layer):
def __init__(self):
super(U2Conformer, self).__init__()
self.asr_executor = ASRExecutor()
self.asr_kw_args = {
'model': 'conformer_wenetspeech',
'lang': 'zh',
'sample_rate': 16000,
'config': None, # Set `config` and `ckpt_path` to None to use pretrained model.
'ckpt_path': None,
}
@staticmethod
def check_audio(audio_file):
assert audio_file.endswith('.wav'), 'Input file must be a wave file `*.wav`.'
sig, sample_rate = load(audio_file)
if sample_rate != 16000:
sig, _ = load(audio_file, 16000)
audio_file_16k = audio_file[:audio_file.rindex('.')] + '_16k.wav'
logger.info('Resampling to 16000 sample rate to new audio file: {}'.format(audio_file_16k))
save_wav(sig, 16000, audio_file_16k)
return audio_file_16k
else:
return audio_file
@serving
def speech_recognize(self, audio_file, device='cpu'):
assert os.path.isfile(audio_file), 'File not exists: {}'.format(audio_file)
audio_file = self.check_audio(audio_file)
text = self.asr_executor(audio_file=audio_file, device=device, **self.asr_kw_args)
return text
modules/audio/tts/deepvoice3_ljspeech/README.md
浏览文件 @ 1696dad9
## 概述 # deepvoice3_ljspeech
|模型名称|deepvoice3_ljspeech|
| :--- | :---: |
|类别|语音-语音合成|
|网络|DeepVoice3|
|数据集|LJSpeech-1.1|
|是否支持Fine-tuning|否|
|模型大小|58MB|
|最新更新日期|2020-10-27|
|数据指标|-|
## 一、模型基本信息
### 模型介绍
Deep Voice 3是百度研究院2017年发布的端到端的TTS模型（论文录用于ICLR 2018）。它是一个基于卷积神经网络和注意力机制的seq2seq模型,由于不包含循环神经网络，它可以并行训练，远快于基于循环神经网络的模型。Deep Voice 3可以学习到多个说话人的特征，也支持搭配多种声码器使用。deepvoice3_ljspeech是基于ljspeech英文语音数据集预训练得到的英文TTS模型，仅支持预测。 Deep Voice 3是百度研究院2017年发布的端到端的TTS模型（论文录用于ICLR 2018）。它是一个基于卷积神经网络和注意力机制的seq2seq模型,由于不包含循环神经网络，它可以并行训练，远快于基于循环神经网络的模型。Deep Voice 3可以学习到多个说话人的特征，也支持搭配多种声码器使用。deepvoice3_ljspeech是基于ljspeech英文语音数据集预训练得到的英文TTS模型，仅支持预测。
<p align="center"> <p align="center">
<img src="https://github.com/PaddlePaddle/Parakeet/blob/develop/examples/deepvoice3/images/model_architecture.png" hspace='10'/> <br /> <img src="https://raw.githubusercontent.com/PaddlePaddle/Parakeet/release/v0.1/examples/deepvoice3/images/model_architecture.png" hspace='10'/> <br/>
</p> </p>
更多详情参考论文[Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://arxiv.org/abs/1710.07654) 更多详情参考论文[Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning](https://arxiv.org/abs/1710.07654)
## 命令行预测
```shell ## 二、安装
$ hub run deepvoice3_ljspeech --input_text='Simple as this proposition is, it is necessary to be stated' --use_gpu True --vocoder griffin-lim
```
## API - ### 1、系统依赖
```python 对于Ubuntu用户，请执行：
def synthesize(texts, use_gpu=False, vocoder="griffin-lim"): ```
``` sudo apt-get install libsndfile1
```
对于Centos用户，请执行：
```
sudo yum install libsndfile
```
预测API，由输入文本合成对应音频波形。 - ### 2、环境依赖
**参数** - 2.0.0 > paddlepaddle >= 1.8.2
* texts (list\[str\]): 待预测文本； - 2.0.0 > paddlehub >= 1.7.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
* use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA\_VISIBLE\_DEVICES环境变量**；
* vocoder: 指定声码器，可选 "griffin-lim"或"waveflow"
**返回** - ### 3、安装
* wavs (list): 语音合成结果列表，列表中每一个元素为对应输入文本的音频波形，可使用`soundfile.write`进一步处理或保存。 - ```shell
* sample\_rate (int): 合成音频的采样率。 $ hub install deepvoice3_ljspeech
```
- 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
| [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
**代码示例**
```python ## 三、模型API预测
import paddlehub as hub
import soundfile as sf
# Load deepvoice3_ljspeech module. - ### 1、命令行预测
module = hub.Module(name="deepvoice3_ljspeech")
# Predict sentiment label - ```shell
test_texts = ['Simple as this proposition is, it is necessary to be stated', $ hub run deepvoice3_ljspeech --input_text='Simple as this proposition is, it is necessary to be stated' --use_gpu True --vocoder griffin-lim
'Parakeet stands for Paddle PARAllel text-to-speech toolkit'] ```
wavs, sample_rate = module.synthesize(texts=test_texts) - 通过命令行方式实现语音合成模型的调用，更多请见[PaddleHub命令行指令](https://github.com/shinichiye/PaddleHub/blob/release/v2.1/docs/docs_ch/tutorial/cmd_usage.rst)
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 服务部署 - ### 2、预测代码示例
PaddleHub Serving 可以部署在线服务。 - ```python
import paddlehub as hub
import soundfile as sf
### 第一步：启动PaddleHub Serving # Load deepvoice3_ljspeech module.
module = hub.Module(name="deepvoice3_ljspeech")
运行启动命令： # Predict sentiment label
```shell test_texts = ['Simple as this proposition is, it is necessary to be stated',
$ hub serving start -m deepvoice3_ljspeech 'Parakeet stands for Paddle PARAllel text-to-speech toolkit']
``` wavs, sample_rate = module.synthesize(texts=test_texts)
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
这样就完成了一个服务化API的部署，默认端口号为8866。 - ### 3、API
**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。 - ```python
def synthesize(texts, use_gpu=False, vocoder="griffin-lim"):
```
### 第二步：发送预测请求 - 预测API，由输入文本合成对应音频波形。
配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 - **参数**
- texts (list\[str\]): 待预测文本；
- use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA\_VISIBLE\_DEVICES环境变量**；
- vocoder: 指定声码器，可选 "griffin-lim"或"waveflow"
```python - **返回**
import requests - wavs (list): 语音合成结果列表，列表中每一个元素为对应输入文本的音频波形，可使用`soundfile.write`进一步处理或保存。
import json - sample\_rate (int): 合成音频的采样率。
import soundfile as sf
# 发送HTTP请求 ## 四、服务部署
data = {'texts':['Simple as this proposition is, it is necessary to be stated', - PaddleHub Serving可以部署一个在线语音合成服务，可以将此接口用于在线web应用。
'Parakeet stands for Paddle PARAllel text-to-speech toolkit'],
'use_gpu':False}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/deepvoice3_ljspeech"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
# 保存结果 - ### 第一步：启动PaddleHub Serving
result = r.json()["results"]
wavs = result["wavs"]
sample_rate = result["sample_rate"]
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 查看代码 - 运行启动命令
- ```shell
$ hub serving start -m deepvoice3_ljspeech
```
- 这样就完成了服务化API的部署，默认端口号为8866。
- **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。
https://github.com/PaddlePaddle/Parakeet - ### 第二步：发送预测请求
### 依赖 - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果
paddlepaddle >= 1.8.2 - ```python
import requests
import json
paddlehub >= 1.7.0 import soundfile as sf
**NOTE:** 除了python依赖外还必须安装libsndfile库 # 发送HTTP请求
对于Ubuntu用户，请执行： data = {'texts':['Simple as this proposition is, it is necessary to be stated',
``` 'Parakeet stands for Paddle PARAllel text-to-speech toolkit'],
sudo apt-get install libsndfile1 'use_gpu':False}
``` headers = {"Content-type": "application/json"}
对于Centos用户，请执行： url = "http://127.0.0.1:8866/predict/deepvoice3_ljspeech"
``` r = requests.post(url=url, headers=headers, data=json.dumps(data))
sudo yum install libsndfile
```
## 更新历史 # 保存结果
result = r.json()["results"]
wavs = result["wavs"]
sample_rate = result["sample_rate"]
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 五、更新历史
* 1.0.0 * 1.0.0
初始发布 初始发布
```shell
$ hub install deepvoice3_ljspeech
```
modules/audio/tts/fastspeech_ljspeech/README.md
浏览文件 @ 1696dad9
## 概述 # fastspeech_ljspeech
|模型名称|fastspeech_ljspeech|
| :--- | :---: |
|类别|语音-语音合成|
|网络|FastSpeech|
|数据集|LJSpeech-1.1|
|是否支持Fine-tuning|否|
|模型大小|320MB|
|最新更新日期|2020-10-27|
|数据指标|-|
## 一、模型基本信息
### 模型介绍
FastSpeech是基于Transformer的前馈神经网络，作者从encoder-decoder结构的teacher model中提取attention对角线来做发音持续时间预测，即使用长度调节器对文本序列进行扩展来匹配目标梅尔频谱的长度，以便并行生成梅尔频谱。该模型基本上消除了复杂情况下的跳词和重复的问题，并且可以平滑地调整语音速度，更重要的是，该模型大幅度提升了梅尔频谱的生成速度。fastspeech_ljspeech是基于ljspeech英文语音数据集预训练得到的英文TTS模型，仅支持预测。 FastSpeech是基于Transformer的前馈神经网络，作者从encoder-decoder结构的teacher model中提取attention对角线来做发音持续时间预测，即使用长度调节器对文本序列进行扩展来匹配目标梅尔频谱的长度，以便并行生成梅尔频谱。该模型基本上消除了复杂情况下的跳词和重复的问题，并且可以平滑地调整语音速度，更重要的是，该模型大幅度提升了梅尔频谱的生成速度。fastspeech_ljspeech是基于ljspeech英文语音数据集预训练得到的英文TTS模型，仅支持预测。
<p align="center"> <p align="center">
<img src="https://github.com/PaddlePaddle/Parakeet/blob/develop/examples/fastspeech/images/model_architecture.png" hspace='10'/> <br /> <img src="https://raw.githubusercontent.com/PaddlePaddle/Parakeet/release/v0.1/examples/fastspeech/images/model_architecture.png" hspace='10'/> <br/>
</p> </p>
更多详情参考论文[FastSpeech: Fast, Robust and Controllable Text to Speech](https://arxiv.org/abs/1905.09263) 更多详情参考论文[FastSpeech: Fast, Robust and Controllable Text to Speech](https://arxiv.org/abs/1905.09263)
## 命令行预测
```shell ## 二、安装
$ hub run fastspeech_ljspeech --input_text='Simple as this proposition is, it is necessary to be stated' --use_gpu True --vocoder griffin-lim
```
## API - ### 1、系统依赖
```python 对于Ubuntu用户，请执行：
def synthesize(texts, use_gpu=False, speed=1.0, vocoder="griffin-lim"): ```
``` sudo apt-get install libsndfile1
```
对于Centos用户，请执行：
```
sudo yum install libsndfile
```
预测API，由输入文本合成对应音频波形。 - ### 2、环境依赖
**参数** - 2.0.0 > paddlepaddle >= 1.8.2
* texts (list\[str\]): 待预测文本； - 2.0.0 > paddlehub >= 1.7.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
* use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA\_VISIBLE\_DEVICES环境变量**；
* speed(float): 音频速度，1.0表示以原速输出。
* vocoder: 指定声码器，可选 "griffin-lim"或"waveflow"
**返回** - ### 3、安装
* wavs (list): 语音合成结果列表，列表中每一个元素为对应输入文本的音频波形，可使用`soundfile.write`进一步处理或保存。 - ```shell
* sample\_rate (int): 合成音频的采样率。 $ hub install fastspeech_ljspeech
```
- 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
| [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
**代码示例**
```python ## 三、模型API预测
import paddlehub as hub
import soundfile as sf
# Load fastspeech_ljspeech module. - ### 1、命令行预测
module = hub.Module(name="fastspeech_ljspeech")
# Predict sentiment label - ```shell
test_texts = ['Simple as this proposition is, it is necessary to be stated', $ hub run fastspeech_ljspeech --input_text='Simple as this proposition is, it is necessary to be stated' --use_gpu True --vocoder griffin-lim
'Parakeet stands for Paddle PARAllel text-to-speech toolkit'] ```
wavs, sample_rate = module.synthesize(texts=test_texts) - 通过命令行方式实现语音合成模型的调用，更多请见[PaddleHub命令行指令](https://github.com/shinichiye/PaddleHub/blob/release/v2.1/docs/docs_ch/tutorial/cmd_usage.rst)
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 服务部署 - ### 2、预测代码示例
PaddleHub Serving 可以部署在线服务。 - ```python
import paddlehub as hub
import soundfile as sf
### 第一步：启动PaddleHub Serving # Load fastspeech_ljspeech module.
module = hub.Module(name="fastspeech_ljspeech")
运行启动命令： # Predict sentiment label
```shell test_texts = ['Simple as this proposition is, it is necessary to be stated',
$ hub serving start -m fastspeech_ljspeech 'Parakeet stands for Paddle PARAllel text-to-speech toolkit']
``` wavs, sample_rate = module.synthesize(texts=test_texts)
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
这样就完成了一个服务化API的部署，默认端口号为8866。 - ### 3、API
**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。 - ```python
def synthesize(texts, use_gpu=False, speed=1.0, vocoder="griffin-lim"):
```
### 第二步：发送预测请求 - 预测API，由输入文本合成对应音频波形。
配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 - **参数**
- texts (list\[str\]): 待预测文本；
- use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA\_VISIBLE\_DEVICES环境变量**；
- speed(float): 音频速度，1.0表示以原速输出。
- vocoder: 指定声码器，可选 "griffin-lim"或"waveflow"
```python - **返回**
import requests - wavs (list): 语音合成结果列表，列表中每一个元素为对应输入文本的音频波形，可使用`soundfile.write`进一步处理或保存。
import json - sample\_rate (int): 合成音频的采样率。
import soundfile as sf
# 发送HTTP请求 ## 四、服务部署
data = {'texts':['Simple as this proposition is, it is necessary to be stated', - PaddleHub Serving可以部署一个在线语音合成服务，可以将此接口用于在线web应用。
'Parakeet stands for Paddle PARAllel text-to-speech toolkit'],
'use_gpu':False}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/fastspeech_ljspeech"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
# 保存结果 - ### 第一步：启动PaddleHub Serving
result = r.json()["results"]
wavs = result["wavs"]
sample_rate = result["sample_rate"]
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 查看代码 - 运行启动命令
- ```shell
$ hub serving start -m fastspeech_ljspeech
```
- 这样就完成了服务化API的部署，默认端口号为8866。
- **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。
https://github.com/PaddlePaddle/Parakeet - ### 第二步：发送预测请求
### 依赖 - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果
paddlepaddle >= 1.8.2 - ```python
import requests
import json
paddlehub >= 1.7.0 import soundfile as sf
**NOTE:** 除了python依赖外还必须安装libsndfile库 # 发送HTTP请求
对于Ubuntu用户，请执行： data = {'texts':['Simple as this proposition is, it is necessary to be stated',
``` 'Parakeet stands for Paddle PARAllel text-to-speech toolkit'],
sudo apt-get install libsndfile1 'use_gpu':False}
``` headers = {"Content-type": "application/json"}
对于Centos用户，请执行： url = "http://127.0.0.1:8866/predict/fastspeech_ljspeech"
``` r = requests.post(url=url, headers=headers, data=json.dumps(data))
sudo yum install libsndfile
```
## 更新历史 # 保存结果
result = r.json()["results"]
wavs = result["wavs"]
sample_rate = result["sample_rate"]
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 五、更新历史
* 1.0.0 * 1.0.0
初始发布 初始发布
```shell
$ hub install fastspeech_ljspeech
```
modules/audio/tts/transformer_tts_ljspeech/README.md
浏览文件 @ 1696dad9
## 概述 # transformer_tts_ljspeech
|模型名称|transformer_tts_ljspeech|
| :--- | :---: |
|类别|语音-语音合成|
|网络|Transformer|
|数据集|LJSpeech-1.1|
|是否支持Fine-tuning|否|
|模型大小|54MB|
|最新更新日期|2020-10-27|
|数据指标|-|
## 一、模型基本信息
### 模型介绍
TansformerTTS 是使用了 Transformer 结构的端到端语音合成模型，对 Transformer 和 Tacotron2 进行了融合，取得了令人满意的效果。因为删除了 RNN 的循环连接，可并行的提供 decoder 的输入，进行并行训练，大大提升了模型的训练速度。transformer_tts_ljspeech是基于ljspeech英文语音数据集预训练得到的英文TTS模型，仅支持预测。 TansformerTTS 是使用了 Transformer 结构的端到端语音合成模型，对 Transformer 和 Tacotron2 进行了融合，取得了令人满意的效果。因为删除了 RNN 的循环连接，可并行的提供 decoder 的输入，进行并行训练，大大提升了模型的训练速度。transformer_tts_ljspeech是基于ljspeech英文语音数据集预训练得到的英文TTS模型，仅支持预测。
<p align="center"> <p align="center">
<img src="https://github.com/PaddlePaddle/Parakeet/blob/develop/examples/transformer_tts/images/model_architecture.jpg" hspace='10'/> <br /> <img src="https://raw.githubusercontent.com/PaddlePaddle/Parakeet/release/v0.1/examples/transformer_tts/images/model_architecture.jpg" hspace='10'/> <br/>
</p> </p>
更多详情参考论文[Neural Speech Synthesis with Transformer Network](https://arxiv.org/abs/1809.08895) 更多详情参考论文[Neural Speech Synthesis with Transformer Network](https://arxiv.org/abs/1809.08895)
## 命令行预测
```shell ## 二、安装
$ hub run transformer_tts_ljspeech --input_text="Life was like a box of chocolates, you never know what you're gonna get." --use_gpu True --vocoder griffin-lim
``` - ### 1、系统依赖
## API 对于Ubuntu用户，请执行：
```
sudo apt-get install libsndfile1
```
对于Centos用户，请执行：
```
sudo yum install libsndfile
```
```python - ### 2、环境依赖
def synthesize(texts, use_gpu=False, vocoder="griffin-lim"):
```
预测API，由输入文本合成对应音频波形。 - 2.0.0 > paddlepaddle >= 1.8.2
**参数** - 2.0.0 > paddlehub >= 1.7.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
* texts (list\[str\]): 待预测文本； - ### 3、安装
* use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA\_VISIBLE\_DEVICES环境变量**；
* vocoder: 指定声码器，可选 "griffin-lim"或"waveflow"
**返回** - ```shell
$ hub install transformer_tts_ljspeech
```
- 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
| [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
* wavs (list): 语音合成结果列表，列表中每一个元素为对应输入文本的音频波形，可使用`soundfile.write`进一步处理或保存。
* sample\_rate (int): 合成音频的采样率。
**代码示例** ## 三、模型API预测
```python - ### 1、命令行预测
import paddlehub as hub
import soundfile as sf
# Load transformer_tts_ljspeech module. - ```shell
module = hub.Module(name="transformer_tts_ljspeech") $ hub run transformer_tts_ljspeech --input_text="Life was like a box of chocolates, you never know what you're gonna get." --use_gpu True --vocoder griffin-lim
```
- 通过命令行方式实现语音合成模型的调用，更多请见[PaddleHub命令行指令](https://github.com/shinichiye/PaddleHub/blob/release/v2.1/docs/docs_ch/tutorial/cmd_usage.rst)
# Predict sentiment label - ### 2、预测代码示例
test_texts = ["Life was like a box of chocolates, you never know what you're gonna get."]
wavs, sample_rate = module.synthesize(texts=test_texts, use_gpu=True, vocoder="waveflow")
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 服务部署 - ```python
import paddlehub as hub
import soundfile as sf
PaddleHub Serving 可以部署在线服务。 # Load transformer_tts_ljspeech module.
module = hub.Module(name="transformer_tts_ljspeech")
### 第一步：启动PaddleHub Serving # Predict sentiment label
test_texts = ["Life was like a box of chocolates, you never know what you're gonna get."]
wavs, sample_rate = module.synthesize(texts=test_texts, use_gpu=True, vocoder="waveflow")
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
运行启动命令： - ### 3、API
```shell
$ hub serving start -m transformer_tts_ljspeech
```
这样就完成了一个服务化API的部署，默认端口号为8866。 - ```python
def synthesize(texts, use_gpu=False, vocoder="griffin-lim"):
```
**NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。 - 预测API，由输入文本合成对应音频波形。
### 第二步：发送预测请求 - **参数**
- texts (list\[str\]): 待预测文本；
- use\_gpu (bool): 是否使用 GPU；**若使用GPU，请先设置CUDA\_VISIBLE\_DEVICES环境变量**；
- vocoder: 指定声码器，可选 "griffin-lim"或"waveflow"
配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果 - **返回**
- wavs (list): 语音合成结果列表，列表中每一个元素为对应输入文本的音频波形，可使用`soundfile.write`进一步处理或保存。
- sample\_rate (int): 合成音频的采样率。
```python
import requests
import json
import soundfile as sf ## 四、服务部署
# 发送HTTP请求 - PaddleHub Serving可以部署一个在线语音合成服务，可以将此接口用于在线web应用。
data = {'texts':['Simple as this proposition is, it is necessary to be stated', - ### 第一步：启动PaddleHub Serving
'Parakeet stands for Paddle PARAllel text-to-speech toolkit'],
'use_gpu':False}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/transformer_tts_ljspeech"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
# 保存结果 - 运行启动命令
result = r.json()["results"]
wavs = result["wavs"]
sample_rate = result["sample_rate"]
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 查看代码 - ```shell
$ hub serving start -m transformer_tts_ljspeech
```
- 这样就完成了服务化API的部署，默认端口号为8866。
- **NOTE:** 如使用GPU预测，则需要在启动服务之前，请设置CUDA\_VISIBLE\_DEVICES环境变量，否则不用设置。
https://github.com/PaddlePaddle/Parakeet - ### 第二步：发送预测请求
### 依赖 - 配置好服务端，以下数行代码即可实现发送预测请求，获取预测结果
paddlepaddle >= 1.8.2 - ```python
import requests
import json
paddlehub >= 1.7.0 import soundfile as sf
**NOTE:** 除了python依赖外还必须安装libsndfile库 # 发送HTTP请求
对于Ubuntu用户，请执行： data = {'texts':['Simple as this proposition is, it is necessary to be stated',
``` 'Parakeet stands for Paddle PARAllel text-to-speech toolkit'],
sudo apt-get install libsndfile1 'use_gpu':False}
``` headers = {"Content-type": "application/json"}
对于Centos用户，请执行： url = "http://127.0.0.1:8866/predict/transformer_tts_ljspeech"
``` r = requests.post(url=url, headers=headers, data=json.dumps(data))
sudo yum install libsndfile
```
## 更新历史 # 保存结果
result = r.json()["results"]
wavs = result["wavs"]
sample_rate = result["sample_rate"]
for index, wav in enumerate(wavs):
sf.write(f"{index}.wav", wav, sample_rate)
```
## 五、更新历史
* 1.0.0 * 1.0.0
初始发布 初始发布
```shell
$ hub install transformer_tts_ljspeech
```
modules/audio/voice_cloning/ge2e_fastspeech2_pwgan/README.md 0 → 100644
浏览文件 @ 1696dad9
# ge2e_fastspeech2_pwgan
|模型名称|ge2e_fastspeech2_pwgan|
| :--- | :---: |
|类别|语音-声音克隆|
|网络|FastSpeech2|
|数据集|AISHELL-3|
|是否支持Fine-tuning|否|
|模型大小|462MB|
|最新更新日期|2021-12-17|
|数据指标|-|
## 一、模型基本信息
### 模型介绍
声音克隆是指使用特定的音色，结合文字的读音合成音频，使得合成后的音频具有目标说话人的特征，从而达到克隆的目的。
在训练语音克隆模型时，目标音色作为Speaker Encoder的输入，模型会提取这段语音的说话人特征（音色）作为Speaker Embedding。接着，在训练模型重新合成此类音色的语音时，除了输入的目标文本外，说话人的特征也将成为额外条件加入模型的训练。
在预测时，选取一段新的目标音色作为Speaker Encoder的输入，并提取其说话人特征，最终实现输入为一段文本和一段目标音色，模型生成目标音色说出此段文本的语音片段。
![](https://ai-studio-static-online.cdn.bcebos.com/982ab955b87244d3bae3b003aff8e28d9ec159ff0d6246a79757339076dfe7d4)
`ge2e_fastspeech2_pwgan`是一个支持中文的语音克隆模型，分别使用了LSTMSpeakerEncoder、FastSpeech2和PWGan模型分别用于语音特征提取、目标音频特征合成和语音波形转换。
关于模型的详请可参考[PaddleSpeech](https://github.com/PaddlePaddle/PaddleSpeech)。
## 二、安装
- ### 1、环境依赖
- paddlepaddle >= 2.2.0
- paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
- ### 2、安装
- ```shell
$ hub install ge2e_fastspeech2_pwgan
```
- 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
| [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
## 三、模型API预测
- ### 1、预测代码示例
- ```python
import paddlehub as hub
model = hub.Module(name='ge2e_fastspeech2_pwgan', output_dir='./', speaker_audio='/data/man.wav') # 指定目标音色音频文件
texts = [
'语音的表现形式在未来将变得越来越重要$',
'今天的天气怎么样$', ]
wavs = model.generate(texts, use_gpu=True)
for text, wav in zip(texts, wavs):
print('='*30)
print(f'Text: {text}')
print(f'Wav: {wav}')
```
- ### 2、API
- ```python
def __init__(speaker_audio: str = None,
output_dir: str = './')
```
- 初始化module，可配置模型的目标音色的音频文件和输出的路径。
- **参数**
- `speaker_audio`(str): 目标说话人语音音频文件(*.wav)的路径，默认为None(使用默认的女声作为目标音色)。
- `output_dir`(str): 合成音频的输出文件，默认为当前目录。
- ```python
def get_speaker_embedding()
```
- 获取模型的目标说话人特征。
- **返回**
- `results`(numpy.ndarray): 长度为256的numpy数组，代表目标说话人的特征。
- ```python
def set_speaker_embedding(speaker_audio: str)
```
- 设置模型的目标说话人特征。
- **参数**
- `speaker_audio`(str): 必填，目标说话人语音音频文件(*.wav)的路径。
- ```python
def generate(data: Union[str, List[str]], use_gpu: bool = False):
```
- 根据输入文字，合成目标说话人的语音音频文件。
- **参数**
- `data`(Union[str, List[str]]): 必填，目标音频的内容文本列表，目前只支持中文，不支持添加标点符号。
- `use_gpu`(bool): 是否使用gpu执行计算，默认为False。
## 四、更新历史
* 1.0.0
初始发布。
```shell
$ hub install ge2e_fastspeech2_pwgan
```
modules/audio/voice_cloning/ge2e_fastspeech2_pwgan/module.py 0 → 100644
浏览文件 @ 1696dad9
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List, Union
import numpy as np
import paddle
import soundfile as sf
import yaml
from yacs.config import CfgNode
from paddlehub.env import MODULE_HOME
from paddlehub.module.module import moduleinfo, serving
from paddlehub.utils.log import logger
from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Inference
from paddlespeech.t2s.models.parallel_wavegan import PWGGenerator
from paddlespeech.t2s.models.parallel_wavegan import PWGInference
from paddlespeech.t2s.modules.normalizer import ZScore
from paddlespeech.vector.exps.ge2e.audio_processor import SpeakerVerificationPreprocessor
from paddlespeech.vector.models.lstm_speaker_encoder import LSTMSpeakerEncoder
@moduleinfo(
name="ge2e_fastspeech2_pwgan",
version="1.0.0",
summary="",
author="paddlepaddle",
author_email="",
type="audio/voice_cloning",
)
class VoiceCloner(paddle.nn.Layer):
def __init__(self, speaker_audio: str = None, output_dir: str = './'):
super(VoiceCloner, self).__init__()
speaker_encoder_ckpt = os.path.join(MODULE_HOME, 'ge2e_fastspeech2_pwgan', 'assets',
'ge2e_ckpt_0.3/step-3000000.pdparams')
synthesizer_res_dir = os.path.join(MODULE_HOME, 'ge2e_fastspeech2_pwgan', 'assets',
'fastspeech2_nosil_aishell3_vc1_ckpt_0.5')
vocoder_res_dir = os.path.join(MODULE_HOME, 'ge2e_fastspeech2_pwgan', 'assets', 'pwg_aishell3_ckpt_0.5')
# Speaker encoder
self.speaker_processor = SpeakerVerificationPreprocessor(
sampling_rate=16000,
audio_norm_target_dBFS=-30,
vad_window_length=30,
vad_moving_average_width=8,
vad_max_silence_length=6,
mel_window_length=25,
mel_window_step=10,
n_mels=40,
partial_n_frames=160,
min_pad_coverage=0.75,
partial_overlap_ratio=0.5)
self.speaker_encoder = LSTMSpeakerEncoder(n_mels=40, num_layers=3, hidden_size=256, output_size=256)
self.speaker_encoder.set_state_dict(paddle.load(speaker_encoder_ckpt))
self.speaker_encoder.eval()
# Voice synthesizer
with open(os.path.join(synthesizer_res_dir, 'default.yaml'), 'r') as f:
fastspeech2_config = CfgNode(yaml.safe_load(f))
with open(os.path.join(synthesizer_res_dir, 'phone_id_map.txt'), 'r') as f:
phn_id = [line.strip().split() for line in f.readlines()]
model = FastSpeech2(idim=len(phn_id), odim=fastspeech2_config.n_mels, **fastspeech2_config["model"])
model.set_state_dict(paddle.load(os.path.join(synthesizer_res_dir, 'snapshot_iter_96400.pdz'))["main_params"])
model.eval()
stat = np.load(os.path.join(synthesizer_res_dir, 'speech_stats.npy'))
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
fastspeech2_normalizer = ZScore(mu, std)
self.sample_rate = fastspeech2_config.fs
self.fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model)
self.fastspeech2_inference.eval()
# Vocoder
with open(os.path.join(vocoder_res_dir, 'default.yaml')) as f:
pwg_config = CfgNode(yaml.safe_load(f))
vocoder = PWGGenerator(**pwg_config["generator_params"])
vocoder.set_state_dict(
paddle.load(os.path.join(vocoder_res_dir, 'snapshot_iter_1000000.pdz'))["generator_params"])
vocoder.remove_weight_norm()
vocoder.eval()
stat = np.load(os.path.join(vocoder_res_dir, 'feats_stats.npy'))
mu, std = stat
mu = paddle.to_tensor(mu)
std = paddle.to_tensor(std)
pwg_normalizer = ZScore(mu, std)
self.pwg_inference = PWGInference(pwg_normalizer, vocoder)
self.pwg_inference.eval()
# Text frontend
self.frontend = Frontend(phone_vocab_path=os.path.join(synthesizer_res_dir, 'phone_id_map.txt'))
# Speaking embedding
self._speaker_embedding = None
if speaker_audio is None or not os.path.isfile(speaker_audio):
speaker_audio = os.path.join(MODULE_HOME, 'ge2e_fastspeech2_pwgan', 'assets', 'voice_cloning.wav')
logger.warning(f'Due to no speaker audio is specified, speaker encoder will use defult '
f'waveform({speaker_audio}) to extract speaker embedding. You can use '
'"set_speaker_embedding()" method to reset a speaker audio for voice cloning.')
self.set_speaker_embedding(speaker_audio)
self.output_dir = os.path.abspath(output_dir)
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
def get_speaker_embedding(self):
return self._speaker_embedding.numpy()
@paddle.no_grad()
def set_speaker_embedding(self, speaker_audio: str):
assert os.path.exists(speaker_audio), f'Speaker audio file: {speaker_audio} does not exists.'
mel_sequences = self.speaker_processor.extract_mel_partials(
self.speaker_processor.preprocess_wav(speaker_audio))
self._speaker_embedding = self.speaker_encoder.embed_utterance(paddle.to_tensor(mel_sequences))
logger.info(f'Speaker embedding has been set from file: {speaker_audio}')
@paddle.no_grad()
def generate(self, data: Union[str, List[str]], use_gpu: bool = False):
assert self._speaker_embedding is not None, f'Set speaker embedding before voice cloning.'
if isinstance(data, str):
data = [data]
elif isinstance(data, list):
assert len(data) > 0 and isinstance(data[0],
str) and len(data[0]) > 0, f'Input data should be str of List[str].'
else:
raise Exception(f'Input data should be str of List[str].')
paddle.set_device('gpu') if use_gpu else paddle.set_device('cpu')
files = []
for idx, text in enumerate(data):
phone_ids = self.frontend.get_input_ids(text, merge_sentences=True)["phone_ids"][0]
wav = self.pwg_inference(self.fastspeech2_inference(phone_ids, spk_emb=self._speaker_embedding))
output_wav = os.path.join(self.output_dir, f'{idx+1}.wav')
sf.write(output_wav, wav.numpy(), samplerate=self.sample_rate)
files.append(output_wav)
return files
modules/audio/voice_cloning/lstm_tacotron2/README.md
浏览文件 @ 1696dad9
```shell # lstm_tacotron2
$ hub install lstm_tacotron2==1.0.0
``` |模型名称|lstm_tacotron2|
| :--- | :---: |
|类别|语音-语音合成|
|网络|LSTM、Tacotron2、WaveFlow|
|数据集|AISHELL-3|
|是否支持Fine-tuning|否|
|模型大小|327MB|
|最新更新日期|2021-06-15|
|数据指标|-|
## 一、模型基本信息
## 概述 ### 模型介绍
声音克隆是指使用特定的音色，结合文字的读音合成音频，使得合成后的音频具有目标说话人的特征，从而达到克隆的目的。 声音克隆是指使用特定的音色，结合文字的读音合成音频，使得合成后的音频具有目标说话人的特征，从而达到克隆的目的。
...@@ -10,93 +20,107 @@ $ hub install lstm_tacotron2==1.0.0 ...@@ -10,93 +20,107 @@ $ hub install lstm_tacotron2==1.0.0
在预测时，选取一段新的目标音色作为Speaker Encoder的输入，并提取其说话人特征，最终实现输入为一段文本和一段目标音色，模型生成目标音色说出此段文本的语音片段。 在预测时，选取一段新的目标音色作为Speaker Encoder的输入，并提取其说话人特征，最终实现输入为一段文本和一段目标音色，模型生成目标音色说出此段文本的语音片段。
![](https://ai-studio-static-online.cdn.bcebos.com/982ab955b87244d3bae3b003aff8e28d9ec159ff0d6246a79757339076dfe7d4) <p align="center">
<img src="https://ai-studio-static-online.cdn.bcebos.com/982ab955b87244d3bae3b003aff8e28d9ec159ff0d6246a79757339076dfe7d4" hspace='10'/> <br/>
</p>
`lstm_tacotron2`是一个支持中文的语音克隆模型，分别使用了LSTMSpeakerEncoder、Tacotron2和WaveFlow模型分别用于语音特征提取、目标音频特征合成和语音波形转换。 `lstm_tacotron2`是一个支持中文的语音克隆模型，分别使用了LSTMSpeakerEncoder、Tacotron2和WaveFlow模型分别用于语音特征提取、目标音频特征合成和语音波形转换。
关于模型的详请可参考[Parakeet](https://github.com/PaddlePaddle/Parakeet/tree/release/v0.3/parakeet/models)。 更多详情请参考:
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://arxiv.org/pdf/1806.04558.pdf)
- [Parakeet](https://github.com/PaddlePaddle/Parakeet/tree/release/v0.3/parakeet/models)
## API ## 二、安装
```python - ### 1、环境依赖
def __init__(speaker_audio: str = None,
output_dir: str = './')
```
初始化module，可配置模型的目标音色的音频文件和输出的路径。
**参数** - paddlepaddle >= 2.0.0
- `speaker_audio`(str): 目标说话人语音音频文件(*.wav)的路径，默认为None(使用默认的女声作为目标音色)。
- `output_dir`(str): 合成音频的输出文件，默认为当前目录。
- paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
```python - ### 2、安装
def get_speaker_embedding()
```
获取模型的目标说话人特征。
**返回** - ```shell
* `results`(numpy.ndarray): 长度为256的numpy数组，代表目标说话人的特征。 $ hub install lstm_tacotron2
```
- 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
| [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
```python
def set_speaker_embedding(speaker_audio: str)
```
设置模型的目标说话人特征。
**参数** ## 三、模型API预测
- `speaker_audio`(str): 必填，目标说话人语音音频文件(*.wav)的路径。
```python - ### 1、预测代码示例
def generate(data: List[str], batch_size: int = 1, use_gpu: bool = False):
```
根据输入文字，合成目标说话人的语音音频文件。
**参数** - ```python
- `data`(List[str]): 必填，目标音频的内容文本列表，目前只支持中文，不支持添加标点符号。 import paddlehub as hub
- `batch_size`(int): 可选，模型合成语音时的batch_size，默认为1。
- `use_gpu`(bool): 是否使用gpu执行计算，默认为False。
model = hub.Module(name='lstm_tacotron2', output_dir='/data', speaker_audio='/data/man.wav') # 指定目标音色音频文件
texts = [
'语音的表现形式在未来将变得越来越重要$',
'今天的天气怎么样$', ]
wavs = model.generate(texts, use_gpu=True)
**代码示例** for text, wav in zip(texts, wavs):
print('='*30)
print(f'Text: {text}')
print(f'Wav: {wav}')
```
```
==============================
Text: 语音的表现形式在未来将变得越来越重要$
Wav: /data/1.wav
==============================
Text: 今天的天气怎么样$
Wav: /data/2.wav
```
```python - ### 2、API
import paddlehub as hub
model = hub.Module(name='lstm_tacotron2', output_dir='./', speaker_audio='/data/man.wav') # 指定目标音色音频文件 - ```python
texts = [ def __init__(speaker_audio: str = None,
'语音的表现形式在未来将变得越来越重要$', output_dir: str = './')
'今天的天气怎么样$', ] ```
wavs = model.generate(texts, use_gpu=True) - 初始化module，可配置模型的目标音色的音频文件和输出的路径。
for text, wav in zip(texts, wavs): - **参数**
print('='*30) - `speaker_audio`(str): 目标说话人语音音频文件(*.wav)的路径，默认为None(使用默认的女声作为目标音色)。
print(f'Text: {text}') - `output_dir`(str): 合成音频的输出文件，默认为当前目录。
print(f'Wav: {wav}')
```
输出
```
==============================
Text: 语音的表现形式在未来将变得越来越重要$
Wav: /data/1.wav
==============================
Text: 今天的天气怎么样$
Wav: /data/2.wav
```
- ```python
def get_speaker_embedding()
```
- 获取模型的目标说话人特征。
- **返回**
- `results`(numpy.ndarray): 长度为256的numpy数组，代表目标说话人的特征。
## 查看代码 - ```python
def set_speaker_embedding(speaker_audio: str)
```
- 设置模型的目标说话人特征。
https://github.com/PaddlePaddle/Parakeet - **参数**
- `speaker_audio`(str): 必填，目标说话人语音音频文件(*.wav)的路径。
## 依赖 - ```python
def generate(data: List[str], batch_size: int = 1, use_gpu: bool = False):
```
- 根据输入文字，合成目标说话人的语音音频文件。
paddlepaddle >= 2.0.0 - **参数**
- `data`(List[str]): 必填，目标音频的内容文本列表，目前只支持中文，不支持添加标点符号。
- `batch_size`(int): 可选，模型合成语音时的batch_size，默认为1。
- `use_gpu`(bool): 是否使用gpu执行计算，默认为False。
paddlehub >= 2.1.0
## 更新历史 ## 四、更新历史
* 1.0.0 * 1.0.0
初始发布 初始发布
```shell
$ hub install lstm_tacotron2==1.0.0
```
modules/image/Image_editing/colorization/deoldify/README.md
浏览文件 @ 1696dad9
...@@ -53,14 +53,14 @@ ...@@ -53,14 +53,14 @@
## 三、模型API预测 ## 三、模型API预测
- ### 1、代码示例 - ### 1、预测代码示例
```python - ```python
import paddlehub as hub import paddlehub as hub
model = hub.Module(name='deoldify') model = hub.Module(name='deoldify')
model.predict('/PATH/TO/IMAGE/OR/VIDEO') model.predict('/PATH/TO/IMAGE/OR/VIDEO')
``` ```
- ### 2、API - ### 2、API
......
modules/image/Image_editing/colorization/deoldify/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# deoldify
| Module Name |deoldify|
| :--- | :---: |
|Category|Image editing|
|Network |NoGAN|
|Dataset|ILSVRC 2012|
|Fine-tuning supported or not |No|
|Module Size |834MB|
|Data indicators|-|
|Latest update date |2021-04-13|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/130886749-668dfa38-42ed-4a09-8d4a-b18af0475375.jpg" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130886685-76221736-839a-46a2-8415-e5e0dd3b345e.png" width = "450" height = "300" hspace='10'/>
</p>
- ### Module Introduction
- Deoldify is a color rendering model for images and videos, which can restore color for black and white photos and videos.
- For more information, please refer to: [deoldify](https://github.com/jantic/DeOldify)
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- NOTE: This Module relies on ffmpeg, Please install ffmpeg before using this Module.
```shell
$ conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge
```
- ### 2、Installation
- ```shell
$ hub install deoldify
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Prediction Code Example
- ```python
import paddlehub as hub
model = hub.Module(name='deoldify')
model.predict('/PATH/TO/IMAGE/OR/VIDEO')
```
- ### 2、API
- ```python
def predict(self, input):
```
- Prediction API.
- **Parameter**
- input (str): Image path.
- **Return**
- If input is image path, the output is：
- pred_img(np.ndarray): image data, ndarray.shape is in the format [H, W, C], BGR.
- out_path(str): save path of images.
- If input is video path, the output is ：
- frame_pattern_combined(str): save path of frames from output video.
- vid_out_path(str): save path of output video.
- ```python
def run_image(self, img):
```
- Prediction API for image.
- **Parameter**
- img (str｜np.ndarray): Image data, str or ndarray. ndarray.shape is in the format [H, W, C], BGR.
- **Return**
- pred_img(np.ndarray): Ndarray.shape is in the format [H, W, C], BGR.
- ```python
def run_video(self, video):
```
- Prediction API for video.
- **Parameter**
- video(str): Video path.
- **Return**
- frame_pattern_combined(str): Save path of frames from output video.
- vid_out_path(str): Save path of output video.
## IV. Server Deployment
- PaddleHub Serving can deploy an online service of coloring old photos or videos.
- ### Step 1: Start PaddleHub Serving
- Run the startup command:
- ```shell
$ hub serving start -m deoldify
```
- The servitization API is now deployed and the default port number is 8866.
- **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
- ### Step 2: Send a predictive request
- With a configured server, use the following lines of code to send the prediction request and obtain the result.
- ```python
import requests
import json
import base64
import cv2
import numpy as np
def cv2_to_base64(image):
data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(data.tostring()).decode('utf8')
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
# Send an HTTP request
org_im = cv2.imread('/PATH/TO/ORIGIN/IMAGE')
data = {'images':cv2_to_base64(org_im)}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/deoldify"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
img = base64_to_cv2(r.json()["results"])
cv2.imwrite('/PATH/TO/SAVE/IMAGE', img)
```
## V. Release Note
- 1.0.0
First release
- 1.0.1
Adapt to paddlehub2.0
modules/image/Image_editing/colorization/photo_restoration/README.md
浏览文件 @ 1696dad9
...@@ -51,17 +51,17 @@ ...@@ -51,17 +51,17 @@
## 三、模型API预测 ## 三、模型API预测
- ### 1、代码示例 - ### 1、预测代码示例
```python - ```python
import cv2 import cv2
import paddlehub as hub import paddlehub as hub
model = hub.Module(name='photo_restoration', visualization=True) model = hub.Module(name='photo_restoration', visualization=True)
im = cv2.imread('/PATH/TO/IMAGE') im = cv2.imread('/PATH/TO/IMAGE')
res = model.run_image(im) res = model.run_image(im)
``` ```
- ### 2、API - ### 2、API
......
modules/image/Image_editing/colorization/photo_restoration/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# photo_restoration
|Module Name|photo_restoration|
| :--- | :---: |
|Category|Image editing|
|Network|deoldify and realsr|
|Fine-tuning supported or not|No|
|Module Size |64MB+834MB|
|Data indicators|-|
|Latest update date|2021-08-19|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/130897828-d0c86b81-63d1-4e9a-8095-bc000b8c7ca8.jpg" width = "260" height = "400" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130897762-5c9fa711-62bc-4067-8d44-f8feff8c574c.png" width = "260" height = "400" hspace='10'/>
</p>
- ### Module Introduction
- Photo_restoration can restore old photos. It mainly consists of two parts: coloring and super-resolution. The coloring model is deoldify
, and super resolution model is realsr. Therefore, when using this model, please install deoldify and realsr in advance.
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- NOTE: This Module relies on ffmpeg, Please install ffmpeg before using this Module.
```shell
$ conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge
```
- ### 2、Installation
- ```shell
$ hub install photo_restoration
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Prediction Code Example
- ```python
import cv2
import paddlehub as hub
model = hub.Module(name='photo_restoration', visualization=True)
im = cv2.imread('/PATH/TO/IMAGE')
res = model.run_image(im)
```
- ### 2、API
- ```python
def run_image(self,
input,
model_select= ['Colorization', 'SuperResolution'],
save_path = 'photo_restoration'):
```
- Predicition API, produce repaired photos.
- **Parameter**
- input (numpy.ndarray｜str): Image data，numpy.ndarray or str. ndarray.shape is in the format [H, W, C], BGR.
- model_select (list\[str\]): Mode selection，\['Colorization'\] only colorize the input image， \['SuperResolution'\] only increase the image resolution；
default is \['Colorization', 'SuperResolution'\]。
- save_path (str): Save path, default is 'photo_restoration'.
- **Return**
- output (numpy.ndarray): Restoration result，ndarray.shape is in the format [H, W, C], BGR.
## IV. Server Deployment
- PaddleHub Serving can deploy an online service of photo restoration.
- ### Step 1: Start PaddleHub Serving
- Run the startup command:
- ```shell
$ hub serving start -m photo_restoration
```
- The servitization API is now deployed and the default port number is 8866.
- **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
- ### Step 2: Send a predictive request
- With a configured server, use the following lines of code to send the prediction request and obtain the result
- ```python
import requests
import json
import base64
import cv2
import numpy as np
def cv2_to_base64(image):
data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(data.tostring()).decode('utf8')
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
# Send an HTTP request
org_im = cv2.imread('PATH/TO/IMAGE')
data = {'images':cv2_to_base64(org_im), 'model_select': ['Colorization', 'SuperResolution']}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/photo_restoration"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
img = base64_to_cv2(r.json()["results"])
cv2.imwrite('PATH/TO/SAVE/IMAGE', img)
```
## V. Release Note
- 1.0.0
First release
- 1.0.1
Adapt to paddlehub2.0
modules/image/Image_editing/colorization/user_guided_colorization/README.md
浏览文件 @ 1696dad9
...@@ -22,7 +22,7 @@ ...@@ -22,7 +22,7 @@
<img src="https://user-images.githubusercontent.com/35907364/136653401-6644bd46-d280-4c15-8d48-680b7eb152cb.png" width = "300" height = "450" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/136648959-40493c9c-08ec-46cd-a2a2-5e2038dcbfa7.png" width = "300" height = "450" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/136653401-6644bd46-d280-4c15-8d48-680b7eb152cb.png" width = "300" height = "450" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/136648959-40493c9c-08ec-46cd-a2a2-5e2038dcbfa7.png" width = "300" height = "450" hspace='10'/>
</p> </p>
- user_guided_colorization 是基于''Real-Time User-Guided Image Colorization with Learned Deep Priors"的着色模型，该模型利用预先提供的着色块对图像进行着色。 - user_guided_colorization 是基于"Real-Time User-Guided Image Colorization with Learned Deep Priors"的着色模型，该模型利用预先提供的着色块对图像进行着色。
## 二、安装 ## 二、安装
......
modules/image/Image_editing/colorization/user_guided_colorization/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# user_guided_colorization
|Module Name|user_guided_colorization|
| :--- | :---: |
|Category |Image editing|
|Network| Local and Global Hints Network |
|Dataset|ILSVRC 2012|
|Fine-tuning supported or notFine-tuning|Yes|
|Module Size|131MB|
|Data indicators|-|
|Latest update date |2021-02-26|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/136653401-6644bd46-d280-4c15-8d48-680b7eb152cb.png" width = "300" height = "450" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/136648959-40493c9c-08ec-46cd-a2a2-5e2038dcbfa7.png" width = "300" height = "450" hspace='10'/>
</p>
- ### Module Introduction
- User_guided_colorization is a colorization model based on "Real-Time User-Guided Image Colorization with Learned Deep Priors"，this model uses pre-supplied coloring blocks to color the gray image.
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- ### 2、Installation
- ```shell
$ hub install user_guided_colorization
```
- In case of any problems during installation, please refer to: [Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Command line Prediction
```shell
$ hub run user_guided_colorization --input_path "/PATH/TO/IMAGE"
```
- If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
- ### 2、Prediction Code Example
```python
import paddle
import paddlehub as hub
if __name__ == '__main__':
model = hub.Module(name='user_guided_colorization')
model.set_config(prob=0.1)
result = model.predict(images=['/PATH/TO/IMAGE'])
```
- ### 3.Fine-tune and Encapsulation
- After completing the installation of PaddlePaddle and PaddleHub, you can start using the user_guided_colorization model to fine-tune datasets such as [Canvas](../../docs/reference/datasets.md#class-hubdatasetsCanvas) by executing `python train.py`.
- Steps:
- Step1: Define the data preprocessing method
- ```python
import paddlehub.vision.transforms as T
transform = T.Compose([T.Resize((256, 256), interpolation='NEAREST'),
T.RandomPaddingCrop(crop_size=176),
T.RGB2LAB()], to_rgb=True)
```
- `transforms`: The data enhancement module defines lots of data preprocessing methods. Users can replace the data preprocessing methods according to their needs.
- Step2: Download the dataset
- ```python
from paddlehub.datasets import Canvas
color_set = Canvas(transform=transform, mode='train')
```
* `transforms`: Data preprocessing methods.
* `mode`: Select the data mode, the options are `train`, `test`, `val`. Default is `train`.
* `hub.datasets.Canvas()`: The dataset will be automatically downloaded from the network and decompressed to the `$HOME/.paddlehub/dataset` directory under the user directory.
- Step3: Load the pre-trained model
- ```python
model = hub.Module(name='user_guided_colorization', load_checkpoint=None)
model.set_config(classification=True, prob=1)
```
* `name`: Model name.
* `load_checkpoint`: Whether to load the self-trained model, if it is None, load the provided parameters.
* `classification`: The model is trained by two mode. At the beginning, `classification` is set to True, which is used for shallow network training. In the later stage of training, set `classification` to False, which is used to train the output layer of the network.
* `prob`: The probability that a priori color block is not added to each input image, the default is 1, that is, no prior color block is added. For example, when `prob` is set to 0.9, the probability that there are two a priori color blocks on a picture is(1-0.9)*(1-0.9)*0.9=0.009.
- Step4: Optimization strategy
```python
optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())
trainer = Trainer(model, optimizer, checkpoint_dir='img_colorization_ckpt_cls_1')
trainer.train(color_set, epochs=201, batch_size=25, eval_dataset=color_set, log_interval=10, save_interval=10)
```
- Run configuration
- `Trainer` mainly control the training of Fine-tune, including the following controllable parameters:
* `model`: Optimized model.
* `optimizer`: Optimizer selection.
* `use_vdl`: Whether to use vdl to visualize the training process.
* `checkpoint_dir`: The storage address of the model parameters.
* `compare_metrics`: The measurement index of the optimal model.
- `trainer.train` mainly control the specific training process, including the following controllable parameters:
* `train_dataset`: Training dataset.
* `epochs`: Epochs of training process.
* `batch_size`: Batch size.
* `num_workers`: Number of workers.
* `eval_dataset`: Validation dataset.
* `log_interval`:The interval for printing logs.
* `save_interval`: The interval for saving model parameters.
- Model prediction
- When Fine-tune is completed, the model with the best performance on the verification set will be saved in the `${CHECKPOINT_DIR}/best_model` directory. We use this model to make predictions. The `predict.py` script is as follows:
- ```python
import paddle
import paddlehub as hub
if __name__ == '__main__':
model = hub.Module(name='user_guided_colorization', load_checkpoint='/PATH/TO/CHECKPOINT')
model.set_config(prob=0.1)
result = model.predict(images=['/PATH/TO/IMAGE'])
```
- **NOTE:** If you want to get the oil painting style, please download the parameter file [Canvas colorization](https://paddlehub.bj.bcebos.com/dygraph/models/canvas_rc.pdparams)
## IV. Server Deployment
- PaddleHub Serving can deploy an online service of colorization.
- ### Step 1: Start PaddleHub Serving
- Run the startup command:
- ```shell
$ hub serving start -m user_guided_colorization
```
- The servitization API is now deployed and the default port number is 8866.
- **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
- ### Step 2: Send a predictive request
- With a configured server, use the following lines of code to send the prediction request and obtain the result
- ```python
import requests
import json
import cv2
import base64
import numpy as np
def cv2_to_base64(image):
data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(data.tostring()).decode('utf8')
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
# Send an HTTP request
org_im = cv2.imread('/PATH/TO/IMAGE')
data = {'images':[cv2_to_base64(org_im)]}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/user_guided_colorization"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
data = base64_to_cv2(r.json()["results"]['data'][0]['fake_reg'])
cv2.imwrite('color.png', data)
```
## V. Release Note
* 1.0.0
First release
modules/image/Image_editing/super_resolution/dcscn/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# dcscn
|Module Name|dcscn|
| :--- | :---: |
|Category |Image editing|
|Network|dcscn|
|Dataset|DIV2k|
|Fine-tuning supported or not|No|
|Module Size|260KB|
|Data indicators|PSNR37.63|
|Data indicators |2021-02-26|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/133558583-0b7049db-ed1f-4a16-8676-f2141fcb3dee.png" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130899031-a6f8c58a-5cb7-4105-b990-8cca5ae15368.png" width = "450" height = "300" hspace='10'/>
</p>
- ### Module Introduction
- DCSCN is a super resolution model based on 'Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network'. The model uses residual structure and skip connections to extract local and global features. It uses a parallel 1*1 convolutional network to learn detailed features to improve model performance. This model provides super resolution result with scale factor x2.
- For more information, please refer to: [dcscn](https://github.com/jiny2001/dcscn-super-resolution)
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- ### 2、Installation
- ```shell
$ hub install dcscn
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Command line Prediction
- ```
$ hub run dcscn --input_path "/PATH/TO/IMAGE"
```
- If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
- ### 2、Prediction Code Example
- ```python
import cv2
import paddlehub as hub
sr_model = hub.Module(name='dcscn')
im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
res = sr_model.reconstruct(images=[im], visualization=True)
print(res[0]['data'])
sr_model.save_inference_model()
```
- ### 3、API
- ```python
def reconstruct(self,
images=None,
paths=None,
use_gpu=False,
visualization=False,
output_dir="dcscn_output")
```
- Prediction API.
- **Parameter**
* images (list\[numpy.ndarray\]): Image data，ndarray.shape is in the format \[H, W, C\]，BGR.
* paths (list\[str\]): image path.
* use\_gpu (bool): Use GPU or not. **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**.
* visualization (bool): Whether to save the recognition results as picture files.
* output\_dir (str): Save path of images, "dcscn_output" by default.
- **Return**
* res (list\[dict\]): The list of model results, where each element is dict and each field is:
* save\_path (str, optional): Save path of the result, save_path is '' if no image is saved.
* data (numpy.ndarray): Result of super resolution.
- ```python
def save_inference_model(self,
dirname='dcscn_save_model',
model_filename=None,
params_filename=None,
combined=False)
```
- Save the model to the specified path.
- **Parameters**
* dirname: Save path.
* model\_filename: Model file name，defalt is \_\_model\_\_
* params\_filename: Parameter file name，defalt is \_\_params\_\_(Only takes effect when `combined` is True)
* combined: Whether to save the parameters to a unified file.
## IV. Server Deployment
- PaddleHub Serving can deploy an online service of super resolution.
- ### Step 1: Start PaddleHub Serving
- Run the startup command:
- ```shell
$ hub serving start -m dcscn
```
- The servitization API is now deployed and the default port number is 8866.
- **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
- ### Step 2: Send a predictive request
- With a configured server, use the following lines of code to send the prediction request and obtain the result
- ```python
import requests
import json
import base64
import cv2
import numpy as np
def cv2_to_base64(image):
data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(data.tostring()).decode('utf8')
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
org_im = cv2.imread('/PATH/TO/IMAGE')
data = {'images':[cv2_to_base64(org_im)]}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/dcscn"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
sr = np.expand_dims(cv2.cvtColor(base64_to_cv2(r.json()["results"][0]['data']), cv2.COLOR_BGR2GRAY), axis=2)
shape =sr.shape
org_im = cv2.cvtColor(org_im, cv2.COLOR_BGR2YUV)
uv = cv2.resize(org_im[...,1:], (shape[1], shape[0]), interpolation=cv2.INTER_CUBIC)
combine_im = cv2.cvtColor(np.concatenate((sr, uv), axis=2), cv2.COLOR_YUV2BGR)
cv2.imwrite('dcscn_X2.png', combine_im)
print("save image as dcscn_X2.png")
```
## V. Release Note
- 1.0.0
First release
modules/image/Image_editing/super_resolution/falsr_a/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# falsr_a
|Module Name|falsr_a|
| :--- | :---: |
|Category |Image editing|
|Network |falsr_a|
|Dataset|DIV2k|
|Fine-tuning supported or not|No|
|Module Size |8.9MB|
|Data indicators|PSNR37.82|
|Latest update date|2021-02-26|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/133558583-0b7049db-ed1f-4a16-8676-f2141fcb3dee.png" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130899031-a6f8c58a-5cb7-4105-b990-8cca5ae15368.png" width = "450" height = "300" hspace='10'/>
</p>
- ### Module Introduction
- Falsr_a is a lightweight super-resolution model based on "Accurate and Lightweight Super-Resolution with Neural Architecture Search". The model uses a multi-objective approach to deal with the over-segmentation problem, and uses an elastic search strategy based on a hybrid controller to improve the performance of the model. This model provides super resolution result with scale factor x2.
- For more information, please refer to: [falsr_a](https://github.com/xiaomi-automl/FALSR)
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- ### 2、Installation
- ```shell
$ hub install falsr_a
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Command line Prediction
- ```
$ hub run falsr_a --input_path "/PATH/TO/IMAGE"
```
- If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
- ### 2、Prediction Code Example
- ```python
import cv2
import paddlehub as hub
sr_model = hub.Module(name='falsr_a')
im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
res = sr_model.reconstruct(images=[im], visualization=True)
print(res[0]['data'])
sr_model.save_inference_model()
```
- ### 3、API
- ```python
def reconstruct(self,
images=None,
paths=None,
use_gpu=False,
visualization=False,
output_dir="falsr_a_output")
```
- Prediction API.
- **Parameter**
* images (list\[numpy.ndarray\]): image data，ndarray.shape is in the format \[H, W, C\]，BGR.
* paths (list\[str\]): image path.
* use\_gpu (bool): use GPU or not. **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**.
* visualization (bool): Whether to save the recognition results as picture files.
* output\_dir (str): save path of images, "dcscn_output" by default.
- **Return**
* res (list\[dict\]): The list of model results, where each element is dict and each field is:
* save\_path (str, optional): Save path of the result, save_path is '' if no image is saved.
* data (numpy.ndarray): result of super resolution.
- ```python
def save_inference_model(self,
dirname='falsr_a_save_model',
model_filename=None,
params_filename=None,
combined=False)
```
- Save the model to the specified path.
- **Parameters**
* dirname: Save path.
* model\_filename: model file name，defalt is \_\_model\_\_
* params\_filename: parameter file name，defalt is \_\_params\_\_(Only takes effect when `combined` is True)
* combined: Whether to save the parameters to a unified file.
## IV. Server Deployment
- PaddleHub Serving can deploy an online service of super resolution.
- ### Step 1: Start PaddleHub Serving
- Run the startup command:
- ```shell
$ hub serving start -m falsr_a
```
- The servitization API is now deployed and the default port number is 8866.
- **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
- ### Step 2: Send a predictive request
- With a configured server, use the following lines of code to send the prediction request and obtain the result
- ```python
import requests
import json
import base64
import cv2
import numpy as np
def cv2_to_base64(image):
data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(data.tostring()).decode('utf8')
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
org_im = cv2.imread('/PATH/TO/IMAGE')
data = {'images':[cv2_to_base64(org_im)]}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/falsr_a"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
sr = base64_to_cv2(r.json()["results"][0]['data'])
cv2.imwrite('falsr_a_X2.png', sr)
print("save image as falsr_a_X2.png")
```
## V. Release Note
- 1.0.0
First release
modules/image/Image_editing/super_resolution/falsr_b/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# falsr_b
|Module Name|falsr_b|
| :--- | :---: |
|Category |Image editing|
|Network |falsr_b|
|Dataset|DIV2k|
|Fine-tuning supported or not|No|
|Module Size |4MB|
|Data indicators|PSNR37.61|
|Latest update date|2021-02-26|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/133558583-0b7049db-ed1f-4a16-8676-f2141fcb3dee.png" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130899031-a6f8c58a-5cb7-4105-b990-8cca5ae15368.png" width = "450" height = "300" hspace='10'/>
</p>
- ### Module Introduction
- Falsr_b is a lightweight super-resolution model based on "Accurate and Lightweight Super-Resolution with Neural Architecture Search". The model uses a multi-objective approach to deal with the over-segmentation problem, and uses an elastic search strategy based on a hybrid controller to improve the performance of the model. This model provides super resolution result with scale factor x2.
- For more information, please refer to:[falsr_b](https://github.com/xiaomi-automl/FALSR)
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- ### 2、Installation
- ```shell
$ hub install falsr_b
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Command line Prediction
- ```
$ hub run falsr_b --input_path "/PATH/TO/IMAGE"
```
- If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
- ### 2、Prediction Code Example
```python
import cv2
import paddlehub as hub
sr_model = hub.Module(name='falsr_b')
im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
res = sr_model.reconstruct(images=[im], visualization=True)
print(res[0]['data'])
sr_model.save_inference_model()
```
- ### 3、API
- ```python
def reconstruct(self,
images=None,
paths=None,
use_gpu=False,
visualization=False,
output_dir="falsr_b_output")
```
- Prediction API.
- **Parameter**
* images (list\[numpy.ndarray\]): Image data，ndarray.shape is in the format \[H, W, C\]，BGR.
* paths (list\[str\]): Image path.
* use\_gpu (bool): Use GPU or not. **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**.
* visualization (bool): Whether to save the recognition results as picture files.
* output\_dir (str): Save path of images, "dcscn_output" by default.
- **Return**
* res (list\[dict\]): The list of model results, where each element is dict and each field is:
* save\_path (str, optional): Save path of the result, save_path is '' if no image is saved.
* data (numpy.ndarray): Result of super resolution.
- ```python
def save_inference_model(self,
dirname='falsr_b_save_model',
model_filename=None,
params_filename=None,
combined=False)
```
- Save the model to the specified path.
- **Parameters**
* dirname: Save path.
* model\_filename: Model file name，defalt is \_\_model\_\_
* params\_filename: Parameter file name，defalt is \_\_params\_\_(Only takes effect when `combined` is True)
* combined: Whether to save the parameters to a unified file.
## IV. Server Deployment
- PaddleHub Serving can deploy an online service of super resolution.
- ### Step 1: Start PaddleHub Serving
- Run the startup command:
- ```shell
$ hub serving start -m falsr_b
```
- The servitization API is now deployed and the default port number is 8866.
- **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
- ### Step 2: Send a predictive request
- With a configured server, use the following lines of code to send the prediction request and obtain the result
- ```python
import requests
import json
import base64
import cv2
import numpy as np
def cv2_to_base64(image):
data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(data.tostring()).decode('utf8')
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
org_im = cv2.imread('/PATH/TO/IMAGE')
data = {'images':[cv2_to_base64(org_im)]}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/falsr_b"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
sr = base64_to_cv2(r.json()["results"][0]['data'])
cv2.imwrite('falsr_b_X2.png', sr)
print("save image as falsr_b_X2.png")
```
## V. Release Note
- 1.0.0
First release
modules/image/Image_editing/super_resolution/falsr_c/README.md
浏览文件 @ 1696dad9
...@@ -51,7 +51,7 @@ ...@@ -51,7 +51,7 @@
- ``` - ```
$ hub run falsr_c --input_path "/PATH/TO/IMAGE" $ hub run falsr_c --input_path "/PATH/TO/IMAGE"
``` ```
- ### 代码示例 - ### 2、预测代码示例
```python ```python
import cv2 import cv2
...@@ -65,7 +65,7 @@ ...@@ -65,7 +65,7 @@
sr_model.save_inference_model() sr_model.save_inference_model()
``` ```
- ### 2、API - ### 3、API
- ```python - ```python
def reconstruct(self, def reconstruct(self,
......
modules/image/Image_editing/super_resolution/falsr_c/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# falsr_c
|Module Name|falsr_c|
| :--- | :---: |
|Category |Image editing|
|Network |falsr_c|
|Dataset|DIV2k|
|Fine-tuning supported or not|No|
|Module Size |4.4MB|
|Data indicators|PSNR37.66|
|Latest update date|2021-02-26|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/133558583-0b7049db-ed1f-4a16-8676-f2141fcb3dee.png" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130899031-a6f8c58a-5cb7-4105-b990-8cca5ae15368.png" width = "450" height = "300" hspace='10'/>
</p>
- ### Module Introduction
- Falsr_c is a lightweight super-resolution model based on "Accurate and Lightweight Super-Resolution with Neural Architecture Search". The model uses a multi-objective approach to deal with the over-segmentation problem, and uses an elastic search strategy based on a hybrid controller to improve the performance of the model. This model provides super resolution result with scale factor x2.
- For more information, please refer to:[falsr_c](https://github.com/xiaomi-automl/FALSR)
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- ### 2、Installation
- ```shell
$ hub install falsr_c
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Command line Prediction
- ```
$ hub run falsr_c --input_path "/PATH/TO/IMAGE"
```
- If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
- ### 2、Prediction Code Example
```python
import cv2
import paddlehub as hub
sr_model = hub.Module(name='falsr_c')
im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
res = sr_model.reconstruct(images=[im], visualization=True)
print(res[0]['data'])
sr_model.save_inference_model()
```
- ### 3、API
- ```python
def reconstruct(self,
images=None,
paths=None,
use_gpu=False,
visualization=False,
output_dir="falsr_c_output")
```
- Prediction API.
- **Parameter**
* images (list\[numpy.ndarray\]): Image data，ndarray.shape is in the format \[H, W, C\]，BGR.
* paths (list\[str\]): Image path.
* use\_gpu (bool): Use GPU or not. **set the CUDA_VISIBLE_DEVICES environment variable first if you are using GPU**.
* visualization (bool): Whether to save the recognition results as picture files.
* output\_dir (str): Save path of images, "dcscn_output" by default.
- **Return**
* res (list\[dict\]): The list of model results, where each element is dict and each field is:
* save\_path (str, optional): Save path of the result, save_path is '' if no image is saved.
* data (numpy.ndarray): Result of super resolution.
- ```python
def save_inference_model(self,
dirname='falsr_c_save_model',
model_filename=None,
params_filename=None,
combined=False)
```
- Save the model to the specified path.
- **Parameters**
* dirname: Save path.
* model\_filename: Model file name，defalt is \_\_model\_\_
* params\_filename: Parameter file name，defalt is \_\_params\_\_(Only takes effect when `combined` is True)
* combined: Whether to save the parameters to a unified file.
## IV. Server Deployment
- PaddleHub Serving can deploy an online service of super resolution.
- ### Step 1: Start PaddleHub Serving
- Run the startup command:
- ```shell
$ hub serving start -m falsr_c
```
- The servitization API is now deployed and the default port number is 8866.
- **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
- ### Step 2: Send a predictive request
- With a configured server, use the following lines of code to send the prediction request and obtain the result
- ```python
import requests
import json
import base64
import cv2
import numpy as np
def cv2_to_base64(image):
data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(data.tostring()).decode('utf8')
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
org_im = cv2.imread('/PATH/TO/IMAGE')
data = {'images':[cv2_to_base64(org_im)]}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/falsr_c"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
sr = base64_to_cv2(r.json()["results"][0]['data'])
cv2.imwrite('falsr_c_X2.png', sr)
print("save image as falsr_c_X2.png")
```
## V. Release Note
- 1.0.0
First release
modules/image/Image_editing/super_resolution/realsr/README.md
浏览文件 @ 1696dad9
...@@ -57,7 +57,7 @@ ...@@ -57,7 +57,7 @@
## 三、模型API预测 ## 三、模型API预测
- ### 1、代码示例 - ### 1、预测代码示例
```python ```python
import paddlehub as hub import paddlehub as hub
......
modules/image/Image_editing/super_resolution/realsr/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# realsr
|Module Name |reasr|
| :--- | :---: |
|Category |Image editing|
|Network|LP-KPN|
|Dataset |RealSR dataset|
|Fine-tuning supported or not|No|
|Module Size |64MB|
|Latest update date|2021-02-26|
|Data indicators |PSNR29.05|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/133558583-0b7049db-ed1f-4a16-8676-f2141fcb3dee.png" width = "450" height = "300" hspace='10'/> <img src="https://user-images.githubusercontent.com/35907364/130789888-a0d4f78e-acd6-44c1-9570-7390e90ae8dc.png" width = "450" height = "300" hspace='10'/>
</p>
- ### Module Introduction
- Realsr is a super resolution model for image and video based on "Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Mode". This model provides super resolution result with scale factor x4.
- For more information, please refer to: [realsr](https://github.com/csjcai/RealSR)
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 2.0.0
- paddlehub >= 2.0.0
- **NOTE**: This Module relies on ffmpeg, Please install ffmpeg before using this Module.
```shell
$ conda install x264=='1!152.20180717' ffmpeg=4.0.2 -c conda-forge
```
- ### 2、Installation
- ```shell
$ hub install realsr
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Prediction Code Example
- ```python
import paddlehub as hub
model = hub.Module(name='realsr')
model.predict('/PATH/TO/IMAGE/OR/VIDEO')
```
- ### 2、API
- ```python
def predict(self, input):
```
- Prediction API.
- **Parameter**
- input (str): image path.
- **Return**
- If input is image path, the output is：
- pred_img(np.ndarray): image data, ndarray.shape is in the format [H, W, C], BGR.
- out_path(str): save path of images.
- If input is video path, the output is ：
- frame_pattern_combined(str): save path of frames from output video.
- vid_out_path(str): save path of output video.
- ```python
def run_image(self, img):
```
- Prediction API for images.
- **Parameter**
- img (str｜np.ndarray): Image data, str or ndarray. ndarray.shape is in the format [H, W, C], BGR.
- **Return**
- pred_img(np.ndarray): Prediction result, ndarray.shape is in the format [H, W, C], BGR.
- ```python
def run_video(self, video):
```
- Prediction API for video.
- **Parameter**
- video(str): Video path.
- **Return**
- frame_pattern_combined(str): Save path of frames from output video.
- vid_out_path(str): Save path of output video.
## IV. Server Deployment
- PaddleHub Serving can deploy an online service of image super resolution.
- ### Step 1: Start PaddleHub Serving
- Run the startup command:
- ```shell
$ hub serving start -m realsr
```
- The servitization API is now deployed and the default port number is 8866.
- **NOTE:** If GPU is used for prediction, set CUDA_VISIBLE_DEVICES environment variable before the service, otherwise it need not be set.
- ### Step 2: Send a predictive request
- With a configured server, use the following lines of code to send the prediction request and obtain the result
- ```python
import requests
import json
import base64
import cv2
import numpy as np
def cv2_to_base64(image):
data = cv2.imencode('.jpg', image)[1]
return base64.b64encode(data.tostring()).decode('utf8')
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
org_im = cv2.imread('/PATH/TO/IMAGE')
data = {'images':cv2_to_base64(org_im)}
headers = {"Content-type": "application/json"}
url = "http://127.0.0.1:8866/predict/realsr"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
img = base64_to_cv2(r.json()["results"])
cv2.imwrite('/PATH/TO/SAVE/IMAGE', img)
```
## V. Release Note
- 1.0.0
First release
* 1.0.1
Support paddlehub2.0
modules/image/Image_gan/attgan_celeba/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# attgan_celeba
|Module Name|attgan_celeba|
| :--- | :---: |
|Category |image generation|
|Network |AttGAN|
|Dataset|Celeba|
|Fine-tuning supported or not |No|
|Module Size |167MB|
|Latest update date|2021-02-26|
|Data indicators |-|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/137855667-43c5c40c-28f5-45d8-accc-028e185b988f.JPG" width=1200><br/>
The image attributes are: original image, Bald, Bangs, Black_Hair, Blond_Hair, Brown_Hair, Bushy_Eyebrows, Eyeglasses, Gender, Mouth_Slightly_Open, Mustache, No_Beard, Pale_Skin, Aged<br/>
</p>
- ### Module Introduction
- AttGAN is a Generative Adversarial Network, which uses classification loss and reconstruction loss to train the network. The PaddleHub Module is trained one Celeba dataset and currently supports attributes of "Bald", "Bangs", "Black_Hair", "Blond_Hair", "Brown_Hair", "Bushy_Eyebrows", "Eyeglasses", "Gender", "Mouth_Slightly_Open", "Mustache", "No_Beard", "Pale_Skin", "Aged".
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 1.5.2
- paddlehub >= 1.0.0 | [How to install PaddleHub](../../../../docs/docs_en/get_start/installation.rst)
- ### 2、Installation
- ```shell
$ hub install attgan_celeba==1.0.0
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md).
## III. Module API Prediction
- ### 1、Command line Prediction
- ```shell
$ hub run attgan_celeba --image "/PATH/TO/IMAGE" --style "target_attribute"
```
- **Parameters**
- image: Input image path.
- style: Specify the attributes to be converted. The options are "Bald", "Bangs", "Black_Hair", "Blond_Hair", "Brown_Hair", "Bushy_Eyebrows", "Eyeglasses", "Gender", "Mouth_Slightly_Open", "Mustache", "No_Beard", "Pale_Skin", "Aged". You can choose one of the options.
- If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
- ### 2、Prediction Code Example
- ```python
import paddlehub as hub
attgan = hub.Module(name="attgan_celeba")
test_img_path = ["/PATH/TO/IMAGE"]
trans_attr = ["Bangs"]
# set input dict
input_dict = {"image": test_img_path, "style": trans_attr}
# execute predict and print the result
results = attgan.generate(data=input_dict)
print(results)
```
- ### 3、API
- ```python
def generate(data)
```
- Style transfer API.
- **Parameter**
- data(list[dict]): Each element in the list is dict and each field is:
- image (list\[str\])： Each element in the list is the path of the image to be converted.
- style (list\[str\])： Each element in the list is a string, fill in the face attributes to be converted.
- **Return**
- res (list\[str\]): Save path of the result.
## IV. Release Note
- 1.0.0
First release
modules/image/Image_gan/cyclegan_cityscapes/README_en.md 0 → 100644
浏览文件 @ 1696dad9
# cyclegan_cityscapes
|Module Name|cyclegan_cityscapes|
| :--- | :---: |
|Category |Image generation|
|Network |CycleGAN|
|Dataset|Cityscapes|
|Fine-tuning supported or not |No|
|Module Size |33MB|
|Latest update date |2021-02-26|
|Data indicators|-|
## I. Basic Information
- ### Application Effect Display
- Sample results:
<p align="center">
<img src="https://user-images.githubusercontent.com/35907364/137839740-4be4cf40-816f-401e-a73f-6cda037041dd.png" width = "450" height = "300" hspace='10'/>
<br />
Input image
<br />
<img src="https://user-images.githubusercontent.com/35907364/137839777-89fc705b-f0d7-4a93-94e2-76c0d3c5a0b0.png" width = "450" height = "300" hspace='10'/>
<br />
Output image
<br />
</p>
- ### Module Introduction
- CycleGAN belongs to Generative Adversarial Networks(GANs). Unlike traditional GANs that can only generate pictures in one direction, CycleGAN can simultaneously complete the style transfer of two domains. The PaddleHub Module is trained by Cityscapes dataset, and supports the conversion from real images to semantic segmentation results, and also supports conversion from semantic segmentation results to real images.
## II. Installation
- ### 1、Environmental Dependence
- paddlepaddle >= 1.4.0
- paddlehub >= 1.1.0
- ### 2、Installation
- ```shell
$ hub install cyclegan_cityscapes==1.0.0
```
- In case of any problems during installation, please refer to:[Windows_Quickstart](../../../../docs/docs_en/get_start/windows_quickstart.md)
| [Linux_Quickstart](../../../../docs/docs_en/get_start/linux_quickstart.md) | [Mac_Quickstart](../../../../docs/docs_en/get_start/mac_quickstart.md)
## III. Module API Prediction
- ### 1、Command line Prediction
- ```shell
$ hub run cyclegan_cityscapes --input_path "/PATH/TO/IMAGE"
```
- **Parameters**
- input_path: image path
- If you want to call the Hub module through the command line, please refer to: [PaddleHub Command Line Instruction](../../../../docs/docs_en/tutorial/cmd_usage.rst)
- ### 2、Prediction Code Example
- ```python
import paddlehub as hub
cyclegan = hub.Module(name="cyclegan_cityscapes")
test_img_path = "/PATH/TO/IMAGE"
# set input dict
input_dict = {"image": [test_img_path]}
# execute predict and print the result
results = cyclegan.generate(data=input_dict)
print(results)
```
- ### 3、API
- ```python
def generate(data)
```
- Style transfer API.
- **Parameters**
- data(list[dict]): Each element in the list is dict and each field is:
- image (list\[str\])： Image path.
- **Return**
- res (list\[str\]): The list of style transfer results, where each element is dict and each field is:
- origin: Original input path.
- generated: Save path of images.
## IV. Release Note
* 1.0.0
First release
modules/image/Image_gan/gan/first_order_motion/README.md 0 → 100644
浏览文件 @ 1696dad9
# first_order_motion
|模型名称|first_order_motion|
| :--- | :---: |
|类别|图像 - 图像生成|
|网络|S3FD|
|数据集|-|
|是否支持Fine-tuning|否|
|模型大小|343MB|
|最新更新日期|2021-12-24|
|数据指标|-|
## 一、模型基本信息
- ### 应用效果展示
- 样例结果示例：
<p align="center">
<img src="https://user-images.githubusercontent.com/22424850/147347145-1a7e84b6-2853-4490-8eaf-caf9cfdca79b.png" width = "40%" hspace='10'/>
<br />
输入图像
<br />
<img src="https://user-images.githubusercontent.com/22424850/147347151-d6c5690b-00cd-433f-b82b-3f8bb90bc7bd.gif" width = "40%" hspace='10'/>
<br />
输入视频
<br />
<img src="https://user-images.githubusercontent.com/22424850/147348127-52eb3f26-9b2c-49d5-a4a2-20a31f159802.gif" width = "40%" hspace='10'/>
<br />
输出视频
<br />
</p>
- ### 模型介绍
- First Order Motion的任务是图像动画/Image Animation，即输入为一张源图片和一个驱动视频，源图片中的人物则会做出驱动视频中的动作。
## 二、安装
- ### 1、环境依赖
- paddlepaddle >= 2.1.0
- paddlehub >= 2.1.0 | [如何安装PaddleHub](../../../../docs/docs_ch/get_start/installation.rst)
- ### 2、安装
- ```shell
$ hub install first_order_motion
```
- 如您安装时遇到问题，可参考：[零基础windows安装](../../../../docs/docs_ch/get_start/windows_quickstart.md)
| [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
## 三、模型API预测
- ### 1、命令行预测
- ```shell
$ hub run first_order_motion --source_image "/PATH/TO/IMAGE" --driving_video "/PATH/TO/VIDEO" --use_gpu
```
- 通过命令行方式实现视频驱动生成模型的调用，更多请见 [PaddleHub命令行指令](../../../../docs/docs_ch/tutorial/cmd_usage.rst)
- ### 2、预测代码示例
- ```python
import paddlehub as hub
module = hub.Module(name="first_order_motion")
module.generate(source_image="/PATH/TO/IMAGE", driving_video="/PATH/TO/VIDEO", ratio=0.4, image_size=256, output_dir='./motion_driving_result/', filename='result.mp4', use_gpu=False)
```
- ### 3、API
- ```python
generate(self, source_image=None, driving_video=None, ratio=0.4, image_size=256, output_dir='./motion_driving_result/', filename='result.mp4', use_gpu=False)
```
- 视频驱动生成API。
- **参数**
- source_image (str): 原始图片，支持单人图片和多人图片，视频中人物的表情动作将迁移到该原始图片中的人物上。
- driving_video (str): 驱动视频，视频中人物的表情动作作为待迁移的对象。
- ratio (float): 贴回驱动生成的人脸区域占原图的比例, 用户需要根据生成的效果调整该参数，尤其对于多人脸距离比较近的情况下需要调整改参数, 默认为0.4，调整范围是[0.4, 0.5]。
- image_size (int): 图片人脸大小，默认为256，可设置为512。
- output\_dir (str): 结果保存的文件夹名； <br/>
- filename (str): 结果保存的文件名。
- use\_gpu (bool): 是否使用 GPU；<br/>
## 四、更新历史
* 1.0.0
初始发布
- ```shell
$ hub install first_order_motion==1.0.0
```
modules/image/Image_gan/gan/first_order_motion/model.py 0 → 100644
浏览文件 @ 1696dad9
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
import os
import sys
import math
import pickle
import yaml
import imageio
import numpy as np
from tqdm import tqdm
from scipy.spatial import ConvexHull
import cv2
import paddle
from ppgan.utils.download import get_path_from_url
from ppgan.utils.animate import normalize_kp
from ppgan.modules.keypoint_detector import KPDetector
from ppgan.models.generators.occlusion_aware import OcclusionAwareGenerator
from ppgan.faceutils import face_detection
class FirstOrderPredictor:
def __init__(self,
weight_path=None,
config=None,
image_size=256,
relative=True,
adapt_scale=False,
find_best_frame=False,
best_frame=None,
face_detector='sfd',
multi_person=False,
face_enhancement=True,
batch_size=1,
mobile_net=False):
if config is not None and isinstance(config, str):
with open(config) as f:
self.cfg = yaml.load(f, Loader=yaml.SafeLoader)
elif isinstance(config, dict):
self.cfg = config
elif config is None:
self.cfg = {
'model': {
'common_params': {
'num_kp': 10,
'num_channels': 3,
'estimate_jacobian': True
},
'generator': {
'kp_detector_cfg': {
'temperature': 0.1,
'block_expansion': 32,
'max_features': 1024,
'scale_factor': 0.25,
'num_blocks': 5
},
'generator_cfg': {
'block_expansion': 64,
'max_features': 512,
'num_down_blocks': 2,
'num_bottleneck_blocks': 6,
'estimate_occlusion_map': True,
'dense_motion_params': {
'block_expansion': 64,
'max_features': 1024,
'num_blocks': 5,
'scale_factor': 0.25
}
}
}
}
}
self.image_size = image_size
if weight_path is None:
if mobile_net:
vox_cpk_weight_url = 'https://paddlegan.bj.bcebos.com/applications/first_order_model/vox-mobile.pdparams'
else:
if self.image_size == 512:
vox_cpk_weight_url = 'https://paddlegan.bj.bcebos.com/applications/first_order_model/vox-cpk-512.pdparams'
else:
vox_cpk_weight_url = 'https://paddlegan.bj.bcebos.com/applications/first_order_model/vox-cpk.pdparams'
weight_path = get_path_from_url(vox_cpk_weight_url)
self.weight_path = weight_path
self.relative = relative
self.adapt_scale = adapt_scale
self.find_best_frame = find_best_frame
self.best_frame = best_frame
self.face_detector = face_detector
self.generator, self.kp_detector = self.load_checkpoints(self.cfg, self.weight_path)
self.multi_person = multi_person
self.face_enhancement = face_enhancement
self.batch_size = batch_size
if face_enhancement:
from ppgan.faceutils.face_enhancement import FaceEnhancement
self.faceenhancer = FaceEnhancement(batch_size=batch_size)
def read_img(self, path):
img = imageio.imread(path)
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
# som images have 4 channels
if img.shape[2] > 3:
img = img[:, :, :3]
return img
def run(self, source_image, driving_video, ratio, image_size, output_dir, filename):
self.ratio = ratio
self.image_size = image_size
self.output = output_dir
self.filename = filename
if not os.path.exists(output_dir):
os.makedirs(output_dir)
def get_prediction(face_image):
if self.find_best_frame or self.best_frame is not None:
i = self.best_frame if self.best_frame is not None else self.find_best_frame_func(
source_image, driving_video)
print("Best frame: " + str(i))
driving_forward = driving_video[i:]
driving_backward = driving_video[:(i + 1)][::-1]
predictions_forward = self.make_animation(
face_image,
driving_forward,
self.generator,
self.kp_detector,
relative=self.relative,
adapt_movement_scale=self.adapt_scale)
predictions_backward = self.make_animation(
face_image,
driving_backward,
self.generator,
self.kp_detector,
relative=self.relative,
adapt_movement_scale=self.adapt_scale)
predictions = predictions_backward[::-1] + predictions_forward[1:]
else:
predictions = self.make_animation(
face_image,
driving_video,
self.generator,
self.kp_detector,
relative=self.relative,
adapt_movement_scale=self.adapt_scale)
return predictions
source_image = self.read_img(source_image)
reader = imageio.get_reader(driving_video)
fps = reader.get_meta_data()['fps']
driving_video = []
try:
for im in reader:
driving_video.append(im)
except RuntimeError:
print("Read driving video error!")
pass
reader.close()
driving_video = [cv2.resize(frame, (self.image_size, self.image_size)) / 255.0 for frame in driving_video]
results = []
bboxes = self.extract_bbox(source_image.copy())
print(str(len(bboxes)) + " persons have been detected")
# for multi person
for rec in bboxes:
face_image = source_image.copy()[rec[1]:rec[3], rec[0]:rec[2]]
face_image = cv2.resize(face_image, (self.image_size, self.image_size)) / 255.0
predictions = get_prediction(face_image)
results.append({'rec': rec, 'predict': [predictions[i] for i in range(predictions.shape[0])]})
if len(bboxes) == 1 or not self.multi_person:
break
out_frame = []
for i in range(len(driving_video)):
frame = source_image.copy()
for result in results:
x1, y1, x2, y2, _ = result['rec']
h = y2 - y1
w = x2 - x1
out = result['predict'][i]
out = cv2.resize(out.astype(np.uint8), (x2 - x1, y2 - y1))
if len(results) == 1:
frame[y1:y2, x1:x2] = out
break
else:
patch = np.zeros(frame.shape).astype('uint8')
patch[y1:y2, x1:x2] = out
mask = np.zeros(frame.shape[:2]).astype('uint8')
cx = int((x1 + x2) / 2)
cy = int((y1 + y2) / 2)
cv2.circle(mask, (cx, cy), math.ceil(h * self.ratio), (255, 255, 255), -1, 8, 0)
frame = cv2.copyTo(patch, mask, frame)
out_frame.append(frame)
imageio.mimsave(os.path.join(self.output, self.filename), [frame for frame in out_frame], fps=fps)
def load_checkpoints(self, config, checkpoint_path):
generator = OcclusionAwareGenerator(
**config['model']['generator']['generator_cfg'], **config['model']['common_params'], inference=True)
kp_detector = KPDetector(**config['model']['generator']['kp_detector_cfg'], **config['model']['common_params'])
checkpoint = paddle.load(self.weight_path)
generator.set_state_dict(checkpoint['generator'])
kp_detector.set_state_dict(checkpoint['kp_detector'])
generator.eval()
kp_detector.eval()
return generator, kp_detector
def make_animation(self,
source_image,
driving_video,
generator,
kp_detector,
relative=True,
adapt_movement_scale=True):
with paddle.no_grad():
predictions = []
source = paddle.to_tensor(source_image[np.newaxis].astype(np.float32)).transpose([0, 3, 1, 2])
driving = paddle.to_tensor(np.array(driving_video).astype(np.float32)).transpose([0, 3, 1, 2])
kp_source = kp_detector(source)
kp_driving_initial = kp_detector(driving[0:1])
kp_source_batch = {}
kp_source_batch["value"] = paddle.tile(kp_source["value"], repeat_times=[self.batch_size, 1, 1])
kp_source_batch["jacobian"] = paddle.tile(kp_source["jacobian"], repeat_times=[self.batch_size, 1, 1, 1])
source = paddle.tile(source, repeat_times=[self.batch_size, 1, 1, 1])
begin_idx = 0
for frame_idx in tqdm(range(int(np.ceil(float(driving.shape[0]) / self.batch_size)))):
frame_num = min(self.batch_size, driving.shape[0] - begin_idx)
driving_frame = driving[begin_idx:begin_idx + frame_num]
kp_driving = kp_detector(driving_frame)
kp_source_img = {}
kp_source_img["value"] = kp_source_batch["value"][0:frame_num]
kp_source_img["jacobian"] = kp_source_batch["jacobian"][0:frame_num]
kp_norm = normalize_kp(
kp_source=kp_source,
kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial,
use_relative_movement=relative,
use_relative_jacobian=relative,
adapt_movement_scale=adapt_movement_scale)
out = generator(source[0:frame_num], kp_source=kp_source_img, kp_driving=kp_norm)
img = np.transpose(out['prediction'].numpy(), [0, 2, 3, 1]) * 255.0
if self.face_enhancement:
img = self.faceenhancer.enhance_from_batch(img)
predictions.append(img)
begin_idx += frame_num
return np.concatenate(predictions)
def find_best_frame_func(self, source, driving):
import face_alignment
def normalize_kp(kp):
kp = kp - kp.mean(axis=0, keepdims=True)
area = ConvexHull(kp[:, :2]).volume
area = np.sqrt(area)
kp[:, :2] = kp[:, :2] / area
return kp
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True)
kp_source = fa.get_landmarks(255 * source)[0]
kp_source = normalize_kp(kp_source)
norm = float('inf')
frame_num = 0
for i, image in tqdm(enumerate(driving)):
kp_driving = fa.get_landmarks(255 * image)[0]
kp_driving = normalize_kp(kp_driving)
new_norm = (np.abs(kp_source - kp_driving)**2).sum()
if new_norm < norm:
norm = new_norm
frame_num = i
return frame_num
def extract_bbox(self, image):
detector = face_detection.FaceAlignment(
face_detection.LandmarksType._2D, flip_input=False, face_detector=self.face_detector)
frame = [image]
predictions = detector.get_detections_for_image(np.array(frame))
person_num = len(predictions)
if person_num == 0:
return np.array([])
results = []
face_boxs = []
h, w, _ = image.shape
for rect in predictions:
bh = rect[3] - rect[1]
bw = rect[2] - rect[0]
cy = rect[1] + int(bh / 2)
cx = rect[0] + int(bw / 2)
margin = max(bh, bw)
y1 = max(0, cy - margin)
x1 = max(0, cx - int(0.8 * margin))
y2 = min(h, cy + margin)
x2 = min(w, cx + int(0.8 * margin))
area = (y2 - y1) * (x2 - x1)
results.append([x1, y1, x2, y2, area])
# if a person has more than one bbox, keep the largest one
# maybe greedy will be better?
sorted(results, key=lambda area: area[4], reverse=True)
results_box = [results[0]]
for i in range(1, person_num):
num = len(results_box)
add_person = True
for j in range(num):
pre_person = results_box[j]
iou = self.IOU(pre_person[0], pre_person[1], pre_person[2], pre_person[3], pre_person[4], results[i][0],
results[i][1], results[i][2], results[i][3], results[i][4])
if iou > 0.5:
add_person = False
break
if add_person:
results_box.append(results[i])
boxes = np.array(results_box)
return boxes
def IOU(self, ax1, ay1, ax2, ay2, sa, bx1, by1, bx2, by2, sb):
#sa = abs((ax2 - ax1) * (ay2 - ay1))
#sb = abs((bx2 - bx1) * (by2 - by1))
x1, y1 = max(ax1, bx1), max(ay1, by1)
x2, y2 = min(ax2, bx2), min(ay2, by2)
w = x2 - x1
h = y2 - y1
if w < 0 or h < 0:
return 0.0
else:
return 1.0 * w * h / (sa + sb - w * h)
modules/image/Image_gan/gan/first_order_motion/module.py 0 → 100644
浏览文件 @ 1696dad9
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import argparse
import copy
import paddle
import paddlehub as hub
from paddlehub.module.module import moduleinfo, runnable, serving
import numpy as np
import cv2
from skimage.io import imread
from skimage.transform import rescale, resize
from .model import FirstOrderPredictor
@moduleinfo(
name="first_order_motion", type="CV/gan", author="paddlepaddle", author_email="", summary="", version="1.0.0")
class FirstOrderMotion:
def __init__(self):
self.pretrained_model = os.path.join(self.directory, "vox-cpk.pdparams")
self.network = FirstOrderPredictor(weight_path=self.pretrained_model, face_enhancement=True)
def generate(self,
source_image=None,
driving_video=None,
ratio=0.4,
image_size=256,
output_dir='./motion_driving_result/',
filename='result.mp4',
use_gpu=False):
'''
source_image (str): path to image<br/>
driving_video (str) : path to driving_video<br/>
ratio: margin ratio
image_size: size of image
output_dir: the dir to save the results
filename: filename to save the results
use_gpu: if True, use gpu to perform the computation, otherwise cpu.
'''
paddle.disable_static()
place = 'gpu:0' if use_gpu else 'cpu'
place = paddle.set_device(place)
if source_image == None or driving_video == None:
print('No image or driving video provided. Please input an image and a driving video.')
return
self.network.run(source_image, driving_video, ratio, image_size, output_dir, filename)
@runnable
def run_cmd(self, argvs: list):
"""
Run as a command.
"""
self.parser = argparse.ArgumentParser(
description="Run the {} module.".format(self.name),
prog='hub run {}'.format(self.name),
usage='%(prog)s',
add_help=True)
self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
self.arg_config_group = self.parser.add_argument_group(
title="Config options", description="Run configuration for controlling module behavior, not required.")
self.add_module_config_arg()
self.add_module_input_arg()
self.args = self.parser.parse_args(argvs)
self.generate(
source_image=self.args.source_image,
driving_video=self.args.driving_video,
ratio=self.args.ratio,
image_size=self.args.image_size,
output_dir=self.args.output_dir,
use_gpu=self.args.use_gpu)
return
def add_module_config_arg(self):
"""
Add the command config options.
"""
self.arg_config_group.add_argument('--use_gpu', action='store_true', help="use GPU or not")
self.arg_config_group.add_argument(
'--output_dir', type=str, default='motion_driving_result', help='output directory for saving result.')
self.arg_config_group.add_argument("--filename", default='result.mp4', help="filename to output")
def add_module_input_arg(self):
"""
Add the command input options.
"""
self.arg_input_group.add_argument("--source_image", type=str, help="path to source image")
self.arg_input_group.add_argument("--driving_video", type=str, help="path to driving video")
self.arg_input_group.add_argument("--ratio", dest="ratio", type=float, default=0.4, help="margin ratio")
self.arg_input_group.add_argument(
"--image_size", dest="image_size", type=int, default=256, help="size of image")
modules/image/Image_gan/gan/pixel2style2pixel/README.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/Image_gan/gan/pixel2style2pixel/model.py 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/Image_gan/gan/pixel2style2pixel/module.py 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/Image_gan/gan/pixel2style2pixel/util.py 0 → 100644
浏览文件 @ 1696dad9
import base64
import cv2
import numpy as np
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
modules/image/Image_gan/gan/styleganv2_editing/model.py 0 → 100644
浏览文件 @ 1696dad9
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import cv2
import numpy as np
import paddle
from ppgan.utils.download import get_path_from_url
from .basemodel import StyleGANv2Predictor
model_cfgs = {
'ffhq-config-f': {
'direction_urls': 'https://paddlegan.bj.bcebos.com/models/stylegan2-ffhq-config-f-directions.pdparams'
}
}
def make_image(tensor):
return (((tensor.detach() + 1) / 2 * 255).clip(min=0, max=255).transpose((0, 2, 3, 1)).numpy().astype('uint8'))
class StyleGANv2EditingPredictor(StyleGANv2Predictor):
def __init__(self, model_type=None, direction_path=None, **kwargs):
super().__init__(model_type=model_type, **kwargs)
if direction_path is None and model_type is not None:
assert model_type in model_cfgs, f'There is not any pretrained direction file for {model_type} model.'
direction_path = get_path_from_url(model_cfgs[model_type]['direction_urls'])
self.directions = paddle.load(direction_path)
@paddle.no_grad()
def run(self, latent, direction, offset):
latent = paddle.to_tensor(latent).unsqueeze(0).astype('float32')
direction = self.directions[direction].unsqueeze(0).astype('float32')
latent_n = paddle.concat([latent, latent + offset * direction], 0)
generator = self.generator
img_gen, _ = generator([latent_n], input_is_latent=True, randomize_noise=False)
imgs = make_image(img_gen)
src_img = imgs[0]
dst_img = imgs[1]
dst_latent = (latent + offset * direction)[0].numpy().astype('float32')
return src_img, dst_img, dst_latent
modules/image/Image_gan/gan/styleganv2_editing/util.py 0 → 100644
浏览文件 @ 1696dad9
import base64
import cv2
import numpy as np
def base64_to_cv2(b64str):
data = base64.b64decode(b64str.encode('utf8'))
data = np.fromstring(data, np.uint8)
data = cv2.imdecode(data, cv2.IMREAD_COLOR)
return data
modules/image/Image_gan/gan/wav2lip/README.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/Image_gan/gan/wav2lip/model.py 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/Image_gan/gan/wav2lip/module.py 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/Image_gan/stargan_celeba/README_en.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/Image_gan/stgan_celeba/README_en.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/classification/resnet50_vd_animals/README.md
浏览文件 @ 1696dad9
...@@ -44,7 +44,7 @@ ...@@ -44,7 +44,7 @@
hub run resnet50_vd_animals --input_path "/PATH/TO/IMAGE" hub run resnet50_vd_animals --input_path "/PATH/TO/IMAGE"
``` ```
- ### 2、代码示例 - ### 2、预测代码示例
- ```python - ```python
import paddlehub as hub import paddlehub as hub
......
modules/image/semantic_segmentation/U2Net/README.md
浏览文件 @ 1696dad9
...@@ -43,7 +43,7 @@ ...@@ -43,7 +43,7 @@
| [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md) | [零基础Linux安装](../../../../docs/docs_ch/get_start/linux_quickstart.md) | [零基础MacOS安装](../../../../docs/docs_ch/get_start/mac_quickstart.md)
## 三、模型API预测 ## 三、模型API预测
- ### 1、代码示例 - ### 1、预测代码示例
```python ```python
import cv2 import cv2
......
modules/image/semantic_segmentation/U2Net/README_en.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/semantic_segmentation/U2Netp/README_en.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/image/semantic_segmentation/ace2p/README_en.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/text/text_correction/ernie-csc/README.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/text/text_correction/ernie-csc/module.py 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/text/text_correction/ernie-csc/requirements.txt 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/text/text_generation/ernie_gen/README_en.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/text/text_to_knowledge/nptag/README.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/text/text_to_knowledge/nptag/module.py 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/text/text_to_knowledge/nptag/requirements.txt 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/video/Video_editing/SkyAR/README_en.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/video/classification/tsm_kinetics400/README.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
modules/video/classification/tsn_kinetics400/README.md 0 → 100644
浏览文件 @ 1696dad9
此差异已折叠。
paddlehub/__init__.py
浏览文件 @ 1696dad9
此差异已折叠。
paddlehub/server/server.py
浏览文件 @ 1696dad9
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或