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PaddleOCR

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README.md
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PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
## Notice
PaddleOCR supports both dynamic graph and static graph programming paradigm
- Dynamic graph: dygraph branch (default), **supported by paddle 2.0.0 ([installation](./doc/doc_en/installation_en.md))**
- Static graph: develop branch
**Recent updates**
- PaddleOCR R&D team would like to share the released tools with developers, at 20:15 pm on August 4th, [Live Address](https://live.bilibili.com/21689802).
- PaddleOCR R&D team would like to share the key points of PP-OCRv2, at 20:15 pm on September 8th, [Live Address](https://live.bilibili.com/21689802).
- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
- 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files).
- 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized.
- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
- [more](./doc/doc_en/update_en.md)
## Features
- PPOCR series of high-quality pre-trained models, comparable to commercial effects
- Ultra lightweight ppocr_mobile series models: detection (3.0M) + direction classifier (1.4M) + recognition (5.0M) = 9.4M
- General ppocr_server series models: detection (47.1M) + direction classifier (1.4M) + recognition (94.9M) = 143.4M
- PP-OCR series of high-quality pre-trained models, comparable to commercial effects
- Ultra lightweight PP-OCRv2 series models: detection (3.1M) + direction classifier (1.4M) + recognition 8.5M) = 13.0M
- Ultra lightweight PP-OCR mobile series models: detection (3.0M) + direction classifier (1.4M) + recognition (5.0M) = 9.4M
- General PP-OCR server series models: detection (47.1M) + direction classifier (1.4M) + recognition (94.9M) = 143.4M
- Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition
- Support multi-language recognition: Korean, Japanese, German, French
- Rich toolkits related to the OCR areas
......@@ -88,16 +82,16 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
<a name="Supported-Chinese-model-list"></a>
## PP-OCR 2.0 series model list（Update on Dec 15）
**Note** : Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
## PP-OCR series model list（Update on September 8th）
| Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model |
| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| Chinese and English ultra-lightweight OCR model (9.4M) | ch_ppocr_mobile_v2.0_xx | Mobile & server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| Chinese and English general OCR model (143.4M) | ch_ppocr_server_v2.0_xx | Server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_traingit.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
| Chinese and English ultra-lightweight PP-OCRv2 model（11.6M） | ch_PP-OCRv2_xx |Mobile&Server|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/ch/ch_PP-OCRv2_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)|
| Chinese and English ultra-lightweight PP-OCR model (9.4M) | ch_ppocr_mobile_v2.0_xx | Mobile & server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| Chinese and English general PP-OCR model (143.4M) | ch_ppocr_server_v2.0_xx | Server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_traingit.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
For more model downloads (including multiple languages), please refer to [PP-OCR v2.0 series model downloads](./doc/doc_en/models_list_en.md).
For more model downloads (including multiple languages), please refer to [PP-OCR series model downloads](./doc/doc_en/models_list_en.md).
For a new language request, please refer to [Guideline for new language_requests](#language_requests).
......@@ -109,13 +103,12 @@ For a new language request, please refer to [Guideline for new language_requests
- [PP-OCR Model and Configuration](./doc/doc_en/models_and_config_en.md)
- [PP-OCR Model Download](./doc/doc_en/models_list_en.md)
- [Yml Configuration](./doc/doc_en/config_en.md)
- [Python Inference](./doc/doc_en/inference_en.md)
- [Python Inference for PP-OCR Model Library](./doc/doc_en/inference_ppocr_en.md)
- [PP-OCR Training](./doc/doc_en/training_en.md)
- [Text Detection](./doc/doc_en/detection_en.md)
- [Text Recognition](./doc/doc_en/recognition_en.md)
- [Direction Classification](./doc/doc_en/angle_class_en.md)
- Inference and Deployment
- [Python Inference](./doc/doc_en/inference_en.md)
- [C++ Inference](./deploy/cpp_infer/readme_en.md)
- [Serving](./deploy/pdserving/README.md)
- [Mobile](./deploy/lite/readme_en.md)
......@@ -126,6 +119,7 @@ For a new language request, please refer to [Guideline for new language_requests
- Academic Circles
- [Two-stage Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [PGNet Algorithm](./doc/doc_en/algorithm_overview_en.md)
- [Python Inference](./doc/doc_en/inference_en.md)
- Data Annotation and Synthesis
- [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md)
- [Data Synthesis Tool: Style-Text](./StyleText/README.md)
......@@ -143,17 +137,18 @@ For a new language request, please refer to [Guideline for new language_requests
- [License](#LICENSE)
- [Contribution](#CONTRIBUTION)
<a name="PP-OCRv2"></a>
## PP-OCRv2 Pipeline
<div align="center">
<img src="./doc/ppocrv2_framework.jpg" width="800">
</div>
<a name="PP-OCR-Pipeline"></a>
[1] PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module (as shown in the green box above). The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941).
## PP-OCR Pipeline
[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (arXiv link is coming soon).
<div align="center">
<img src="./doc/ppocr_framework.png" width="800">
</div>
PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection[2], detection frame correction and CRNN text recognition[7]. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner [8] and PACT quantization [9] is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
## Visualization [more](./doc/doc_en/visualization_en.md)
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README_ch.md
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## 简介
PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力使用者训练出更好的模型，并应用落地。
## 注意
PaddleOCR同时支持动态图与静态图两种编程范式
- 动态图版本：release/2.3（默认分支，开发分支为dygraph分支），需将paddle版本升级至2.0.0或以上版本（[快速安装](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/installation.md)）
- 静态图版本：develop分支
**近期更新**
- PaddleOCR研发团队对最新发版内容技术深入解读，9月8日晚上20:15，[直播地址](https://live.bilibili.com/21689802)。
- 2021.9.7 发布PaddleOCR v2.3，发布PP-OCRv2算法，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
- 2021.8.3 发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
- 2021.6.29 [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/FAQ.md)新增5个高频问题，总数248个，每周一都会更新，欢迎大家持续关注。
- 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/pgnet.md)开源，[多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/multi_languages.md)支持种类增加到80+。
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## 特性
- PPOCR系列高质量预训练模型，准确的识别效果
- 超轻量ppocrv2系列：检测（3.1M）+ 识别（8.5M）= 11.6M
- 超轻量ppocr_mobile移动端系列：检测（3.0M）+方向分类器（1.4M）+ 识别（5.0M）= 9.4M
- 通用ppocr_server系列：检测（47.1M）+方向分类器（1.4M）+ 识别（94.9M）= 143.4M
- PP-OCR系列高质量预训练模型，准确的识别效果
- 超轻量PP-OCRv2系列：检测（3.1M）+ 方向分类器（1.4M）+ 识别（8.5M）= 13.0M
- 超轻量PP-OCR mobile移动端系列：检测（3.0M）+方向分类器（1.4M）+ 识别（5.0M）= 9.4M
- 通用PPOCR server系列：检测（47.1M）+方向分类器（1.4M）+ 识别（94.9M）= 143.4M
- 支持中英文数字组合识别、竖排文本识别、长文本识别
- 支持多语言识别：韩语、日语、德语、法语
- 丰富易用的OCR相关工具组件
- 半自动数据标注工具PPOCRLabel：支持快速高效的数据标注
- 数据合成工具Style-Text：批量合成大量与目标场景类似的图像
- 文档分析能力PP-Structure：版面分析与表格识别
- 支持用户自定义训练，提供丰富的预测推理部署方案
- 支持PIP快速安装使用
- 可运行于Linux、Windows、MacOS等多种系统
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<img src="doc/imgs_results/ch_ppocr_mobile_v2.0/00018069.jpg" width="800">
</div>
上图是通用ppocr_server模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
上图是通用PP-OCR server模型效果展示，更多效果图请见[效果展示页面](./doc/doc_ch/visualization.md)。
<a name="欢迎加入PaddleOCR技术交流群"></a>
## 欢迎加入PaddleOCR技术交流群
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- 代码体验：从[快速安装](./doc/doc_ch/quickstart.md) 开始
<a name="模型下载"></a>
## PP-OCR 2.0系列模型列表（更新中）
**说明** ：2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md)的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
## PP-OCR系列模型列表（更新中）
| 模型简介 | 模型名称 |推荐场景 | 检测模型 | 方向分类器 | 识别模型 |
| ------------ | --------------- | ----------------|---- | ---------- | -------- |
| 中英文超轻量OCR模型（9.4M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| 中英文通用OCR模型（143.4M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
| 中英文超轻量PP-OCRv2模型（13.0M） | ch_PP-OCRv2_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/chinese/ch_PP-OCRv2_det_distill_train.tar)| [推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)|
| 中英文超轻量PP-OCR mobile模型（9.4M） | ch_ppocr_mobile_v2.0_xx |移动端&服务器端|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
| 中英文通用PP-OCR server模型（143.4M） |ch_ppocr_server_v2.0_xx|服务器端 |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
更多模型下载（包括多语言），可以参考[PP-OCR v2.0 系列模型下载](./doc/doc_ch/models_list.md)
更多模型下载（包括多语言），可以参考[PP-OCR 系列模型下载](./doc/doc_ch/models_list.md)
## 文档教程
- [运行环境准备](./doc/doc_ch/environment.md)
......@@ -100,13 +95,12 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- [PP-OCR模型与配置文件](./doc/doc_ch/models_and_config.md)
- [PP-OCR模型下载](./doc/doc_ch/models_list.md)
- [配置文件内容与生成](./doc/doc_ch/config.md)
- [模型库快速使用](./doc/doc_ch/inference.md)
- [PP-OCR模型库快速推理](./doc/doc_ch/inference_ppocr.md)
- [PP-OCR模型训练](./doc/doc_ch/training.md)
- [文本检测](./doc/doc_ch/detection.md)
- [文本识别](./doc/doc_ch/recognition.md)
- [方向分类器](./doc/doc_ch/angle_class.md)
- PP-OCR模型推理部署
- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
- [基于C++预测引擎推理](./deploy/cpp_infer/readme.md)
- [服务化部署](./deploy/pdserving/README_CN.md)
- [端侧部署](./deploy/lite/readme.md)
......@@ -122,6 +116,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- OCR学术圈
- [两阶段模型介绍与下载](./doc/doc_ch/algorithm_overview.md)
- [端到端PGNet算法](./doc/doc_ch/pgnet.md)
- [基于Python脚本预测引擎推理](./doc/doc_ch/inference.md)
- 数据集
- [通用中英文OCR数据集](./doc/doc_ch/datasets.md)
- [手写中文OCR数据集](./doc/doc_ch/handwritten_datasets.md)
......@@ -129,8 +124,8 @@ PaddleOCR同时支持动态图与静态图两种编程范式
- [效果展示](#效果展示)
- FAQ
- [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用32个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战110个问题](./doc/doc_ch/FAQ.md)
- [【理论篇】OCR通用50个问题](./doc/doc_ch/FAQ.md)
- [【实战篇】PaddleOCR实战183个问题](./doc/doc_ch/FAQ.md)
- [技术交流群](#欢迎加入PaddleOCR技术交流群)
- [参考文献](./doc/doc_ch/reference.md)
- [许可证书](#许可证书)
......@@ -147,15 +142,13 @@ PaddleOCR同时支持动态图与静态图两种编程范式
[1] PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身(如绿框所示)，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941
[2] PP-OCRv2在PP—OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCR技术方案（arxiv链接生成中）。
[2] PP-OCRv2在PP-OCR的基础上，进一步在5个方面重点优化，检测模型采用CML协同互学习知识蒸馏策略和CopyPaste数据增广策略；识别模型采用LCNet轻量级骨干网络、UDML 改进知识蒸馏策略和Enhanced CTC loss损失函数改进（如上图红框所示），进一步在推理速度和预测效果上取得明显提升。更多细节请参考PP-OCR技术方案（arxiv链接生成中）。
<a name="效果展示"></a>
## 效果展示 [more](./doc/doc_ch/visualization.md)
- 中文模型
<div align="center">
<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00015504.jpg" width="800">
<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00056221.jpg" width="800">
<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/rotate_00052204.jpg" width="800">
</div>
......
configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml → configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml
浏览文件 @ 3db92cf3
......@@ -8,7 +8,7 @@ Global:
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/ch_ppocr_mobile_v2.1_det_distill_train/best_accuracy
pretrained_model: ./pretrain_models/ch_PP-OCRv2_det_distill_train/best_accuracy
checkpoints:
save_inference_dir:
use_visualdl: False
......
configs/det/det_mv3_db.yml
浏览文件 @ 3db92cf3
......@@ -128,4 +128,4 @@ Eval:
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 8
use_shared_memory: False
\ No newline at end of file
use_shared_memory: False
configs/det/det_r50_vd_db.yml
浏览文件 @ 3db92cf3
......@@ -98,7 +98,7 @@ Train:
shuffle: True
drop_last: False
batch_size_per_card: 16
num_workers: 8
num_workers: 4
Eval:
dataset:
......@@ -125,4 +125,4 @@ Eval:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 8
\ No newline at end of file
num_workers: 8
configs/rec/rec_r31_sar.yml 0 → 100644
浏览文件 @ 3db92cf3
Global:
use_gpu: true
epoch_num: 5
log_smooth_window: 20
print_batch_step: 20
save_model_dir: ./sar_rec
save_epoch_step: 1
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img:
# for data or label process
character_dict_path: ppocr/utils/dict90.txt
character_type: EN_symbol
max_text_length: 30
infer_mode: False
use_space_char: False
rm_symbol: True
save_res_path: ./output/rec/predicts_sar.txt
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Piecewise
decay_epochs: [3, 4]
values: [0.001, 0.0001, 0.00001]
regularizer:
name: 'L2'
factor: 0
Architecture:
model_type: rec
algorithm: SAR
Transform:
Backbone:
name: ResNet31
Head:
name: SARHead
Loss:
name: SARLoss
PostProcess:
name: SARLabelDecode
Metric:
name: RecMetric
Train:
dataset:
name: SimpleDataSet
label_file_list: ['./train_data/train_list.txt']
data_dir: ./train_data/
ratio_list: 1.0
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SARLabelEncode: # Class handling label
- SARRecResizeImg:
image_shape: [3, 48, 48, 160] # h:48 w:[48,160]
width_downsample_ratio: 0.25
- KeepKeys:
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 64
drop_last: True
num_workers: 8
use_shared_memory: False
Eval:
dataset:
name: LMDBDataSet
data_dir: ./eval_data/evaluation/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- SARLabelEncode: # Class handling label
- SARRecResizeImg:
image_shape: [3, 48, 48, 160]
width_downsample_ratio: 0.25
- KeepKeys:
keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 64
num_workers: 4
use_shared_memory: False
deploy/cpp_infer/docs/vs2019_build_withgpu_config.png 0 → 100644
浏览文件 @ 3db92cf3
因为 它太大了无法显示 image diff 。你可以改为 查看blob。
deploy/cpp_infer/docs/windows_vs2019_build.md
浏览文件 @ 3db92cf3
......@@ -5,20 +5,20 @@ PaddleOCR在Windows 平台下基于`Visual Studio 2019 Community` 进行了测
## 前置条件
* Visual Studio 2019
* CUDA 9.0 / CUDA 10.0，cudnn 7+ （仅在使用GPU版本的预测库时需要）
* CUDA 10.2，cudnn 7+ （仅在使用GPU版本的预测库时需要）
* CMake 3.0+
请确保系统已经安装好上述基本软件，我们使用的是`VS2019`的社区版。
**下面所有示例以工作目录为 `D:\projects`演示**。
### Step1: 下载PaddlePaddle C++ 预测库 fluid_inference
### Step1: 下载PaddlePaddle C++ 预测库 paddle_inference
PaddlePaddle C++ 预测库针对不同的`CPU`和`CUDA`版本提供了不同的预编译版本，请根据实际情况下载: [C++预测库下载列表](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#windows)
解压后`D:\projects\fluid_inference`目录包含内容为：
解压后`D:\projects\paddle_inference`目录包含内容为：
```
fluid_inference
paddle_inference
├── paddle # paddle核心库和头文件
|
├── third_party # 第三方依赖库和头文件
......@@ -46,13 +46,13 @@ fluid_inference
![step2.2](https://paddleseg.bj.bcebos.com/inference/vs2019_step3.png)
3. 点击：`项目`->`cpp_inference_demo的CMake设置`
3. 点击：`项目`->`CMake设置`
![step3](https://paddleseg.bj.bcebos.com/inference/vs2019_step4.png)
4. 点击`浏览`，分别设置编译选项指定`CUDA`、`CUDNN_LIB`、`OpenCV`、`Paddle预测库`的路径
4. 分别设置编译选项指定`CUDA`、`CUDNN_LIB`、`OpenCV`、`Paddle预测库`的路径
三个编译参数的含义说明如下（带`*`表示仅在使用**GPU版本**预测库时指定, 其中CUDA库版本尽量对齐，**使用9.0、10.0版本，不使用9.2、10.1等版本CUDA库**）：
三个编译参数的含义说明如下（带`*`表示仅在使用**GPU版本**预测库时指定, 其中CUDA库版本尽量对齐）：
| 参数名 | 含义 |
| ---- | ---- |
......@@ -67,6 +67,11 @@ fluid_inference
![step4](https://paddleseg.bj.bcebos.com/inference/vs2019_step5.png)
下面给出with GPU的配置示例：
![step5](./vs2019_build_withgpu_config.png)
**注意：**
CMAKE_BACKWARDS的版本要根据平台安装cmake的版本进行设置。
**设置完成后**, 点击上图中`保存并生成CMake缓存以加载变量`。
5. 点击`生成`->`全部生成`
......@@ -74,24 +79,34 @@ fluid_inference
![step6](https://paddleseg.bj.bcebos.com/inference/vs2019_step6.png)
### Step4: 预测及可视化
### Step4: 预测
上述`Visual Studio 2019`编译产出的可执行文件在`out\build\x64-Release`目录下，打开`cmd`，并切换到该目录：
上述`Visual Studio 2019`编译产出的可执行文件在`out\build\x64-Release\Release`目录下，打开`cmd`，并切换到`D:\projects\PaddleOCR\deploy\cpp_infer\`：
```
cd D:\projects\PaddleOCR\deploy\cpp_infer\out\build\x64-Release
cd D:\projects\PaddleOCR\deploy\cpp_infer
```
可执行文件`ocr_system.exe`即为样例的预测程序，其主要使用方法如下
可执行文件`ppocr.exe`即为样例的预测程序，其主要使用方法如下，更多使用方法可以参考[说明文档](../readme.md)`运行demo`部分。
```shell
#预测图片 `D:\projects\PaddleOCR\doc\imgs\10.jpg`
.\ocr_system.exe D:\projects\PaddleOCR\deploy\cpp_infer\tools\config.txt D:\projects\PaddleOCR\doc\imgs\10.jpg
#识别中文图片 `D:\projects\PaddleOCR\doc\imgs_words\ch\`
.\out\build\x64-Release\Release\ppocr.exe rec --rec_model_dir=D:\projects\PaddleOCR\ch_ppocr_mobile_v2.0_rec_infer --image_dir=D:\projects\PaddleOCR\doc\imgs_words\ch\
#识别英文图片 'D:\projects\PaddleOCR\doc\imgs_words\en\'
.\out\build\x64-Release\Release\ppocr.exe rec --rec_model_dir=D:\projects\PaddleOCR\inference\rec_mv3crnn --image_dir=D:\projects\PaddleOCR\doc\imgs_words\en\ --char_list_file=D:\projects\PaddleOCR\ppocr\utils\dict\en_dict.txt
```
第一个参数为配置文件路径，第二个参数为需要预测的图片路径。
第一个参数为配置文件路径，第二个参数为需要预测的图片路径，第三个参数为配置文本识别的字典。
### 注意
### FQA
* 在Windows下的终端中执行文件exe时，可能会发生乱码的现象，此时需要在终端中输入`CHCP 65001`，将终端的编码方式由GBK编码(默认)改为UTF-8编码，更加具体的解释可以参考这篇博客：[https://blog.csdn.net/qq_35038153/article/details/78430359](https://blog.csdn.net/qq_35038153/article/details/78430359)。
* 编译时，如果报错`错误：C1083 无法打开包括文件:"dirent.h":No such file or directory`，可以参考该[文档](https://blog.csdn.net/Dora_blank/article/details/117740837#41_C1083_direnthNo_such_file_or_directory_54)，新建`dirent.h`文件，并添加到`VC++`的包含目录中。
* 编译时，如果报错`错误：C1083 无法打开包括文件:"dirent.h":No such file or directory`，可以参考该[文档](https://blog.csdn.net/Dora_blank/article/details/117740837#41_C1083_direnthNo_such_file_or_directory_54)，新建`dirent.h`文件，并添加到`utility.cpp`的头文件引用中。同时修改`utility.cpp`70行：`lstat`改成`stat`。
* 编译时，如果报错`Autolog未定义`，新建`autolog.h`文件，内容为：[autolog.h](https://github.com/LDOUBLEV/AutoLog/blob/main/auto_log/autolog.h)，并添加到`main.cpp`的头文件引用中，再次编译。
* 运行时，如果弹窗报错找不到`paddle_inference.dll`或者`openblas.dll`，在`D:\projects\paddle_inference`预测库内找到这两个文件，复制到`D:\projects\PaddleOCR\deploy\cpp_infer\out\build\x64-Release\Release`目录下。不用重新编译，再次运行即可。
* 运行时，弹窗报错提示`应用程序无法正常启动(0xc0000142)`，并且`cmd`窗口内提示`You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found.`，把tensort目录下的lib里面的所有dll文件复制到release目录下，再次运行即可。
deploy/cpp_infer/src/main.cpp
浏览文件 @ 3db92cf3
......@@ -44,7 +44,7 @@ DEFINE_int32(cpu_threads, 10, "Num of threads with CPU.");
DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU.");
DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8");
DEFINE_bool(benchmark, true, "Whether use benchmark.");
DEFINE_bool(benchmark, false, "Whether use benchmark.");
DEFINE_string(save_log_path, "./log_output/", "Save benchmark log path.");
// detection related
DEFINE_string(image_dir, "", "Dir of input image.");
......@@ -127,9 +127,15 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
int main_rec(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info = {0, 0, 0};
std::string char_list_file = FLAGS_char_list_file;
if (FLAGS_benchmark)
char_list_file = FLAGS_char_list_file.substr(6);
cout << "label file: " << char_list_file << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_char_list_file,
FLAGS_enable_mkldnn, char_list_file,
FLAGS_use_tensorrt, FLAGS_precision);
for (int i = 0; i < cv_all_img_names.size(); ++i) {
......@@ -148,12 +154,28 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
time_info[1] += rec_times[1];
time_info[2] += rec_times[2];
}
if (FLAGS_benchmark) {
AutoLogger autolog("ocr_rec",
FLAGS_use_gpu,
FLAGS_use_tensorrt,
FLAGS_enable_mkldnn,
FLAGS_cpu_threads,
1,
"dynamic",
FLAGS_precision,
time_info,
cv_all_img_names.size());
autolog.report();
}
return 0;
}
int main_system(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info_det = {0, 0, 0};
std::vector<double> time_info_rec = {0, 0, 0};
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
......@@ -169,17 +191,20 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
FLAGS_use_tensorrt, FLAGS_precision);
}
std::string char_list_file = FLAGS_char_list_file;
if (FLAGS_benchmark)
char_list_file = FLAGS_char_list_file.substr(6);
cout << "label file: " << char_list_file << endl;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_threads,
FLAGS_enable_mkldnn, FLAGS_char_list_file,
FLAGS_enable_mkldnn, char_list_file,
FLAGS_use_tensorrt, FLAGS_precision);
auto start = std::chrono::system_clock::now();
for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[i];
cv::Mat srcimg = cv::imread(FLAGS_image_dir, cv::IMREAD_COLOR);
cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
exit(1);
......@@ -189,7 +214,10 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
std::vector<double> rec_times;
det.Run(srcimg, boxes, &det_times);
time_info_det[0] += det_times[0];
time_info_det[1] += det_times[1];
time_info_det[2] += det_times[2];
cv::Mat crop_img;
for (int j = 0; j < boxes.size(); j++) {
crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
......@@ -198,18 +226,36 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
crop_img = cls->Run(crop_img);
}
rec.Run(crop_img, &rec_times);
time_info_rec[0] += rec_times[0];
time_info_rec[1] += rec_times[1];
time_info_rec[2] += rec_times[2];
}
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
std::cout << "Cost "
<< double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den
<< "s" << std::endl;
}
if (FLAGS_benchmark) {
AutoLogger autolog_det("ocr_det",
FLAGS_use_gpu,
FLAGS_use_tensorrt,
FLAGS_enable_mkldnn,
FLAGS_cpu_threads,
1,
"dynamic",
FLAGS_precision,
time_info_det,
cv_all_img_names.size());
AutoLogger autolog_rec("ocr_rec",
FLAGS_use_gpu,
FLAGS_use_tensorrt,
FLAGS_enable_mkldnn,
FLAGS_cpu_threads,
1,
"dynamic",
FLAGS_precision,
time_info_rec,
cv_all_img_names.size());
autolog_det.report();
std::cout << endl;
autolog_rec.report();
}
return 0;
}
......
deploy/pdserving/web_service_det.py 0 → 100644
浏览文件 @ 3db92cf3
# 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.
from paddle_serving_server.web_service import WebService, Op
import logging
import numpy as np
import cv2
import base64
# from paddle_serving_app.reader import OCRReader
from ocr_reader import OCRReader, DetResizeForTest
from paddle_serving_app.reader import Sequential, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
_LOGGER = logging.getLogger()
class DetOp(Op):
def init_op(self):
self.det_preprocess = Sequential([
DetResizeForTest(), Div(255),
Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose(
(2, 0, 1))
])
self.filter_func = FilterBoxes(10, 10)
self.post_func = DBPostProcess({
"thresh": 0.3,
"box_thresh": 0.5,
"max_candidates": 1000,
"unclip_ratio": 1.5,
"min_size": 3
})
def preprocess(self, input_dicts, data_id, log_id):
(_, input_dict), = input_dicts.items()
data = base64.b64decode(input_dict["image"].encode('utf8'))
self.raw_im = data
data = np.fromstring(data, np.uint8)
# Note: class variables(self.var) can only be used in process op mode
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
self.ori_h, self.ori_w, _ = im.shape
det_img = self.det_preprocess(im)
_, self.new_h, self.new_w = det_img.shape
return {"x": det_img[np.newaxis, :].copy()}, False, None, ""
def postprocess(self, input_dicts, fetch_dict, log_id):
det_out = fetch_dict["save_infer_model/scale_0.tmp_1"]
ratio_list = [
float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w
]
dt_boxes_list = self.post_func(det_out, [ratio_list])
dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w])
out_dict = {"dt_boxes": str(dt_boxes)}
return out_dict, None, ""
class OcrService(WebService):
def get_pipeline_response(self, read_op):
det_op = DetOp(name="det", input_ops=[read_op])
return det_op
uci_service = OcrService(name="ocr")
uci_service.prepare_pipeline_config("config.yml")
uci_service.run_service()
deploy/pdserving/web_service_rec.py 0 → 100644
浏览文件 @ 3db92cf3
# 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.
from paddle_serving_server.web_service import WebService, Op
import logging
import numpy as np
import cv2
import base64
# from paddle_serving_app.reader import OCRReader
from ocr_reader import OCRReader, DetResizeForTest
from paddle_serving_app.reader import Sequential, ResizeByFactor
from paddle_serving_app.reader import Div, Normalize, Transpose
_LOGGER = logging.getLogger()
class RecOp(Op):
def init_op(self):
self.ocr_reader = OCRReader(
char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
def preprocess(self, input_dicts, data_id, log_id):
(_, input_dict), = input_dicts.items()
raw_im = base64.b64decode(input_dict["image"].encode('utf8'))
data = np.fromstring(raw_im, np.uint8)
im = cv2.imdecode(data, cv2.IMREAD_COLOR)
feed_list = []
max_wh_ratio = 0
## Many mini-batchs, the type of feed_data is list.
max_batch_size = 6 # len(dt_boxes)
# If max_batch_size is 0, skipping predict stage
if max_batch_size == 0:
return {}, True, None, ""
boxes_size = max_batch_size
rem = boxes_size % max_batch_size
h, w = im.shape[0:2]
wh_ratio = w * 1.0 / h
max_wh_ratio = max(max_wh_ratio, wh_ratio)
_, w, h = self.ocr_reader.resize_norm_img(im, max_wh_ratio).shape
norm_img = self.ocr_reader.resize_norm_img(im, max_batch_size)
norm_img = norm_img[np.newaxis, :]
feed = {"x": norm_img.copy()}
feed_list.append(feed)
return feed_list, False, None, ""
def postprocess(self, input_dicts, fetch_data, log_id):
res_list = []
if isinstance(fetch_data, dict):
if len(fetch_data) > 0:
rec_batch_res = self.ocr_reader.postprocess(
fetch_data, with_score=True)
for res in rec_batch_res:
res_list.append(res[0])
elif isinstance(fetch_data, list):
for one_batch in fetch_data:
one_batch_res = self.ocr_reader.postprocess(
one_batch, with_score=True)
for res in one_batch_res:
res_list.append(res[0])
res = {"res": str(res_list)}
return res, None, ""
class OcrService(WebService):
def get_pipeline_response(self, read_op):
rec_op = RecOp(name="rec", input_ops=[read_op])
return rec_op
uci_service = OcrService(name="ocr")
uci_service.prepare_pipeline_config("config.yml")
uci_service.run_service()
doc/doc_ch/FAQ.md
浏览文件 @ 3db92cf3
此差异已折叠。
doc/doc_ch/algorithm_overview.md
浏览文件 @ 3db92cf3
......@@ -45,6 +45,7 @@ PaddleOCR基于动态图开源的文本识别算法列表：
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
- [x] NRTR([paper](https://arxiv.org/abs/1806.00926v2))
- [x] SAR([paper](https://arxiv.org/abs/1811.00751v2))
参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
......@@ -60,6 +61,6 @@ PaddleOCR基于动态图开源的文本识别算法列表：
|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar) |
|NRTR|NRTR_MTB| 84.3% | rec_mtb_nrtr | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar) |
|SAR|Resnet31| 87.2% | rec_r31_sar | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
PaddleOCR文本识别算法的训练和使用请参考文档教程中[模型训练/评估中的文本识别部分](./recognition.md)。
doc/doc_ch/detection.md
浏览文件 @ 3db92cf3
......@@ -19,15 +19,16 @@
<a name="11-----"></a>
## 1.1 数据准备
icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到，首次下载需注册。
icdar2015 TextLocalization数据集是文本检测的数据集，包含1000张训练图像和500张测试图像。
icdar2015数据集可以从[官网](https://rrc.cvc.uab.es/?ch=4&com=downloads)下载到，首次下载需注册。
注册完成登陆后，下载下图中红色框标出的部分，其中， `Training Set Images`下载的内容保存为`icdar_c4_train_imgs`文件夹下，`Test Set Images` 下载的内容保存为`ch4_test_images`文件夹下
<p align="center">
<img src="./doc/datasets/ic15_location_download.png" align="middle" width = "600"/>
<img src="../datasets/ic15_location_download.png" align="middle" width = "700"/>
<p align="center">
将下载到的数据集解压到工作目录下，假设解压在 PaddleOCR/train_data/ 下。另外，PaddleOCR将零散的标注文件整理成单独的标注文件
将下载到的数据集解压到工作目录下，假设解压在 PaddleOCR/train_data/下。另外，PaddleOCR将零散的标注文件整理成单独的标注文件
，您可以通过wget的方式进行下载。
```shell
# 在PaddleOCR路径下
......
doc/doc_ch/environment.md
浏览文件 @ 3db92cf3
# 运行环境准备
Windows和Mac用户推荐使用Anaconda搭建Python环境，Linux用户建议使用docker搭建PyThon环境。
如果对于Python环境熟悉的用户可以直接跳到第2步安装PaddlePaddle。
* [1. Python环境搭建](#1)
+ [1.1 Windows](#1.1)
......@@ -63,9 +66,9 @@
```
<img src="../install/windows/conda_list_env.png" alt="create environment" width="600" align="center"/>
以上anaconda环境和python环境安装完毕
......@@ -80,9 +83,9 @@
- 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境
- Anaconda下载：
- 地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
<img src="../install/mac/anaconda_start.png" alt="anaconda download" width="800" align="center"/>
- 选择最下方的`Anaconda3-2021.05-MacOSX-x86_64.pkg`下载
- 下载完成后，双击.pkg文件进入图形界面
- 按默认设置即可，安装需要花费一段时间
......@@ -177,7 +180,7 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker
- 说明：使用paddlepaddle需要先安装python环境，这里我们选择python集成环境Anaconda工具包
- Anaconda是1个常用的python包管理程序
- 安装完Anaconda后，可以安装python环境，以及numpy等所需的工具包环境
- **下载Anaconda**：
- 下载地址：https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
......@@ -185,22 +188,22 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker
- 选择适合您操作系统的版本
- 可在终端输入`uname -m`查询系统所用的指令集
- 下载法1：本地下载，再将安装包传到linux服务器上
- 下载法2：直接使用linux命令行下载
```shell
# 首先安装wget
sudo apt-get install wget # Ubuntu
sudo yum install wget # CentOS
```
```shell
# 然后使用wget从清华源上下载
# 如要下载Anaconda3-2021.05-Linux-x86_64.sh，则下载命令如下：
wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.05-Linux-x86_64.sh
# 若您要下载其他版本，需要将最后1个/后的文件名改成您希望下载的版本
```
......@@ -210,7 +213,7 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker
- 若您下载的是其它版本，则将该命令的文件名替换为您下载的文件名
- 按照安装提示安装即可
- 查看许可时可输入q来退出
- **将conda加入环境变量**
- 加入环境变量是为了让系统能识别conda命令，若您在安装时已将conda加入环境变量path，则可跳过本步
......@@ -277,13 +280,13 @@ Linux用户可选择Anaconda或Docker两种方式运行。如果你熟悉Docker
# 激活paddle_env环境
conda activate paddle_env
```
以上anaconda环境和python环境安装完毕
#### 1.3.2 Docker环境配置
**注意：第一次使用这个镜像，会自动下载该镜像，请耐心等待。**
**注意：第一次使用这个镜像，会自动下载该镜像，请耐心等待。您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。**
```bash
# 切换到工作目录下
......@@ -297,8 +300,6 @@ sudo docker run --name ppocr -v $PWD:/paddle --network=host -it paddlepaddle/pad
如果使用CUDA10，请运行以下命令创建容器，设置docker容器共享内存shm-size为64G，建议设置32G以上
sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。
# ctrl+P+Q可退出docker 容器，重新进入docker 容器使用如下命令
sudo docker container exec -it ppocr /bin/bash
```
......
doc/doc_ch/knowledge_distillation.md
浏览文件 @ 3db92cf3
......@@ -39,7 +39,7 @@ PaddleOCR中集成了知识蒸馏的算法，具体地，有以下几个主要
### 2.1 识别配置文件解析
配置文件在[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)。
配置文件在[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)。
#### 2.1.1 模型结构
......
doc/doc_ch/models_and_config.md
浏览文件 @ 3db92cf3
......@@ -2,7 +2,7 @@
# PP-OCR模型与配置文件
PP-OCR模型与配置文件一章主要补充一些OCR模型的基本概念、配置文件的内容与作用以便对模型后续的参数调整和训练中拥有更好的体验。
本节包含三个部分，首先在[PP-OCR模型下载](./models_list.md)中解释PP-OCR模型的类型概念，并提供所有模型的下载链接。然后在[配置文件内容与生成](./config.md)中详细说明调整PP-OCR模型所需的参数。最后的[模型库快速使用](./inference.md)是对第一节PP-OCR模型库使用方法的介绍，可以通过Python推理引擎快速利用丰富的模型库模型获得测试结果。
本章包含三个部分，首先在[PP-OCR模型下载](./models_list.md)中解释PP-OCR模型的类型概念，并提供所有模型的下载链接。然后在[配置文件内容与生成](./config.md)中详细说明调整PP-OCR模型所需的参数。最后的[模型库快速使用](./inference_ppocr.md)是对第一节PP-OCR模型库使用方法的介绍，可以通过Python推理引擎快速利用丰富的模型库模型获得测试结果。
------
......
doc/doc_ch/models_list.md
浏览文件 @ 3db92cf3
## OCR模型列表（V2.0，2021年1月20日更新）
## OCR模型列表（V2.1，2021年9月6日更新）
> **说明**
> 1. 2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md) 的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
> 2. 本文档提供的是PPOCR自研模型列表，更多基于公开数据集的算法介绍与预训练模型可以参考：[算法概览文档](./algorithm_overview.md)。
> 1. 2.1版模型相比2.0版模型，2.1的模型在模型精度上做了提升
> 2. 2.0版模型和[1.1版模型](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md) 的主要区别在于动态图训练vs.静态图训练，模型性能上无明显差距。
> 3. 本文档提供的是PPOCR自研模型列表，更多基于公开数据集的算法介绍与预训练模型可以参考：[算法概览文档](./algorithm_overview.md)。
- [一、文本检测模型](#文本检测模型)
......@@ -32,8 +33,8 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.1_det|slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_det_lite_train_cml_v2.1.yml](../../configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml)| 3M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_slim_quant_infer.tar)|
|ch_ppocr_mobile_v2.1_det|原始超轻量模型，支持中英文、多语种文本检测|[ch_det_lite_train_cml_v2.1.ym](../../configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_distill_train.tar)|
|ch_PP-OCRv2_det_slim|slim量化+蒸馏版超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml)| 3M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
|ch_PP-OCRv2_det|原始超轻量模型，支持中英文、多语种文本检测|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
|ch_ppocr_mobile_slim_v2.0_det|slim裁剪版超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)| 2.6M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
|ch_ppocr_mobile_v2.0_det|原始超轻量模型，支持中英文、多语种文本检测|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
|ch_ppocr_server_v2.0_det|通用模型，支持中英文、多语种文本检测，比超轻量模型更大，但效果更好|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
......@@ -47,8 +48,8 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|模型名称|模型简介|配置文件|推理模型大小|下载地址|
| --- | --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.1_rec|slim量化版超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)| 9M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_quant_train.tar) |
|ch_ppocr_mobile_v2.1_rec|原始超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)|8.5M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_train.tar) |
|ch_PP-OCRv2_rec_slim|slim量化版超轻量模型，支持中英文、数字识别|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
|ch_PP-OCRv2_rec|原始超轻量模型，支持中英文、数字识别|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)|8.5M|[推理模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|slim裁剪量化版超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|ch_ppocr_mobile_v2.0_rec|原始超轻量模型，支持中英文、数字识别|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
|ch_ppocr_server_v2.0_rec|通用模型，支持中英文、数字识别|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [预训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
......@@ -92,12 +93,13 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
|ch_ppocr_mobile_slim_v2.0_cls|slim量化版模型，对检测到的文本行文字角度分类|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)| 2.1M |[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_slim_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) |
|ch_ppocr_mobile_v2.0_cls|原始分类器模型，对检测到的文本行文字角度分类|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
<a name="Paddle-Lite模型"></a>
### 四、Paddle-Lite 模型
|模型版本|模型简介|模型大小|检测模型|文本方向分类模型|识别模型|Paddle-Lite版本|
|---|---|---|---|---|---|---|
|V2.1|ppocr_v2.1蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_infer_opt.nb)|v2.9|
|V2.1(slim)|ppocr_v2.1蒸馏版超轻量中文OCR移动端模型|4.9M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_opt.nb)|v2.9|
|PP-OCRv2|蒸馏版超轻量中文OCR移动端模型|11M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
|PP-OCRv2(slim)|蒸馏版超轻量中文OCR移动端模型|4.9M|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_opt.nb)|v2.9|
|V2.0|ppocr_v2.0超轻量中文OCR移动端模型|7.8M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
|V2.0(slim)|ppocr_v2.0超轻量中文OCR移动端模型|3.3M|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[下载地址](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
doc/doc_ch/quickstart.md
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......@@ -2,7 +2,7 @@
- [PaddleOCR快速开始](#paddleocr)
+ [1. 安装PaddleOCR whl包](#1)
* [2. 便捷使用](#2)
+ [2.1 命令行使用](#21)
......@@ -90,7 +90,7 @@ cd /path/to/ppocr_img
```
更多whl包使用可参考[whl包文档](./whl.md)
如需使用2.0模型，请指定参数`--version 2.0`，paddleocr默认使用2.1模型。更多whl包使用可参考[whl包文档](./whl.md)
<a name="212"></a>
......@@ -166,7 +166,7 @@ paddleocr --image_dir=./table/1.png --type=structure
```
/output/table/1/
└─ res.txt
└─ res.txt
└─ [454, 360, 824, 658].xlsx 表格识别结果
└─ [16, 2, 828, 305].jpg 被裁剪出的图片区域
└─ [17, 361, 404, 711].xlsx 表格识别结果
......@@ -183,7 +183,7 @@ paddleocr --image_dir=./table/1.png --type=structure
大部分参数和paddleocr whl包保持一致，见 [whl包文档](./whl.md)
<a name="22"></a>
### 2.2 Python脚本使用
......@@ -232,6 +232,7 @@ im_show.save('result.jpg')
<img src="../imgs_results/whl/11_det_rec.jpg" width="800">
</div>
<a name="222"></a>
#### 2.2.2 版面分析
```python
......
doc/doc_ch/recognition.md
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......@@ -7,15 +7,13 @@
- [1.2 数据下载](#数据下载)
- [1.3 字典](#字典)
- [1.4 支持空格](#支持空格)
- [2 启动训练](#启动训练)
- [2.1 数据增强](#数据增强)
- [2.2 通用模型训练](#通用模型训练)
- [2.3 多语言模型训练](#多语言模型训练)
- [3 评估](#评估)
- [4 预测](#预测)
- [5 转Inference模型测试](#Inference)
<a name="数据准备"></a>
......@@ -88,7 +86,10 @@ train_data/rec/train/word_002.jpg 用科技让复杂的世界更简单
若您本地没有数据集，可以在官网下载 [ICDAR2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ，下载 benchmark 所需的lmdb格式数据集。
如果希望复现SAR的论文指标，需要下载[SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg), 提取码：627x。此外，真实数据集icdar2013, icdar2015, cocotext, IIIT5也作为训练数据的一部分。具体数据细节可以参考论文SAR。
如果你使用的是icdar2015的公开数据集，PaddleOCR 提供了一份用于训练 ICDAR2015 数据集的标签文件，通过以下方式下载：
```
# 训练集标签
wget -P ./train_data/ic15_data https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
......@@ -232,6 +233,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
| rec_mtb_nrtr.yml | NRTR | nrtr_mtb | None | transformer encoder | transformer decoder |
| rec_r31_sar.yml | SAR | ResNet31 | None | LSTM encoder | LSTM decoder |
训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
......@@ -424,3 +426,39 @@ python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v
infer_img: doc/imgs_words/ch/word_1.jpg
result: ('韩国小馆', 0.997218)
```
<a name="Inference"></a>
## 5. 转Inference模型测试
识别模型转inference模型与检测的方式相同，如下：
```
# -c 后面设置训练算法的yml配置文件
# -o 配置可选参数
# Global.pretrained_model 参数设置待转换的训练模型地址，不用添加文件后缀 .pdmodel，.pdopt或.pdparams。
# Global.save_inference_dir参数设置转换的模型将保存的地址。
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
```
**注意：**如果您是在自己的数据集上训练的模型，并且调整了中文字符的字典文件，请注意修改配置文件中的`character_dict_path`是否是所需要的字典文件。
转换成功后，在目录下有三个文件：
```
/inference/rec_crnn/
├── inference.pdiparams # 识别inference模型的参数文件
├── inference.pdiparams.info # 识别inference模型的参数信息，可忽略
└── inference.pdmodel # 识别inference模型的program文件
```
- 自定义模型推理
如果训练时修改了文本的字典，在使用inference模型预测时，需要通过`--rec_char_dict_path`指定使用的字典路径，并且设置 `rec_char_type=ch`
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path"
```
doc/doc_ch/update.md
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# 更新
- 2021.9.7 发布PaddleOCR v2.3，发布[PP-OCRv2](#PP-OCRv2)，CPU推理速度相比于PP-OCR server提升220%；效果相比于PP-OCR mobile 提升7%。
- 2021.8.3 发布PaddleOCR v2.2，新增文档结构分析[PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README_ch.md)工具包，支持版面分析与表格识别（含Excel导出）。
- 2021.6.29 [FAQ](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/FAQ.md)新增5个高频问题，总数248个，每周一都会更新，欢迎大家持续关注。
- 2021.4.8 release 2.1版本，新增AAAI 2021论文[端到端识别算法PGNet](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/pgnet.md)开源，[多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/doc/doc_ch/multi_languages.md)支持种类增加到80+。
- 2020.12.15 更新数据合成工具[Style-Text](../../StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
- 2020.12.07 [FAQ](../../doc/doc_ch/FAQ.md)新增5个高频问题，总数124个，并且计划以后每周一都会更新，欢迎大家持续关注。
- 2020.11.25 更新半自动标注工具[PPOCRLabel](../../PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
......
doc/doc_ch/visualization.md
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# 效果展示
<a name="超轻量PP-OCRv2效果展示"></a>
## 超轻量PP-OCRv2效果展示
<img src="../imgs_results/PP-OCRv2/PP-OCRv2-pic001.jpg" width="800">
<img src="../imgs_results/PP-OCRv2/PP-OCRv2-pic002.jpg" width="800">
<img src="../imgs_results/PP-OCRv2/PP-OCRv2-pic003.jpg" width="800">
<a name="超轻量ppocr_server_2.0效果展示"></a>
## 通用ppocr_server_2.0 效果展示
## 通用PP-OCR server 效果展示
<div align="center">
<img src="../imgs_results/ch_ppocr_mobile_v2.0/00006737.jpg" width="800">
......@@ -10,8 +16,6 @@
<img src="../imgs_results/ch_ppocr_mobile_v2.0/00018069.jpg" width="800">
<img src="../imgs_results/ch_ppocr_mobile_v2.0/00056221.jpg" width="800">
<img src="../imgs_results/ch_ppocr_mobile_v2.0/00057937.jpg" width="800">
<img src="../imgs_results/ch_ppocr_mobile_v2.0/00059985.jpg" width="800">
<img src="../imgs_results/ch_ppocr_mobile_v2.0/00111002.jpg" width="800">
<img src="../imgs_results/ch_ppocr_mobile_v2.0/00077949.jpg" width="800">
<img src="../imgs_results/ch_ppocr_mobile_v2.0/00207393.jpg" width="800">
</div>
......
doc/doc_en/algorithm_overview_en.md
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......@@ -47,6 +47,7 @@ PaddleOCR open-source text recognition algorithms list:
- [x] RARE([paper](https://arxiv.org/abs/1603.03915v1))[12]
- [x] SRN([paper](https://arxiv.org/abs/2003.12294))[5]
- [x] NRTR([paper](https://arxiv.org/abs/1806.00926v2))
- [x] SAR([paper](https://arxiv.org/abs/1811.00751v2))
Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:
......@@ -62,5 +63,6 @@ Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation r
|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_att_v2.0_train.tar)|
|SRN|Resnet50_vd_fpn| 88.52% | rec_r50fpn_vd_none_srn |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
|NRTR|NRTR_MTB| 84.3% | rec_mtb_nrtr | [Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar) |
|SAR|Resnet31| 87.2% | rec_r31_sar | [Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
Please refer to the document for training guide and use of PaddleOCR text recognition algorithms [Text recognition model training/evaluation/prediction](./recognition_en.md)
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......@@ -18,13 +18,14 @@
This section uses the icdar2015 dataset as an example to introduce the training, evaluation, and testing of the detection model in PaddleOCR.
## 1.1 DATA PREPARATION
The icdar2015 dataset can be obtained from [official website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Registration is required for downloading.
The icdar2015 dataset contains train set which has 1000 images obtained with wearable cameras and test set which has 500 images obtained with wearable cameras. The icdar2015 can be obtained from [official website](https://rrc.cvc.uab.es/?ch=4&com=downloads). Registration is required for downloading.
After registering and logging in, download the part marked in the red box in the figure below. And, the content downloaded by `Training Set Images` should be saved as the folder `icdar_c4_train_imgs`, and the content downloaded by `Test Set Images` is saved as the folder `ch4_test_images`
<p align="center">
<img src="./doc/datasets/ic15_location_download.png" align="middle" width = "600"/>
<img src="../datasets/ic15_location_download.png" align="middle" width = "700"/>
<p align="center">
Decompress the downloaded dataset to the working directory, assuming it is decompressed under PaddleOCR/train_data/. In addition, PaddleOCR organizes many scattered annotation files into two separate annotation files for train and test respectively, which can be downloaded by wget:
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doc/doc_en/inference_ppocr_en.md
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# Reasoning based on Python prediction engine
# Python Inference for PP-OCR Model Library
This article introduces the use of the Python inference engine for the PP-OCR model library. The content is in order of text detection, text recognition, direction classifier and the prediction method of the three in series on the CPU and GPU.
- [TEXT DETECTION MODEL INFERENCE](#DETECTION_MODEL_INFERENCE)
- [Text Detection Model Inference](#DETECTION_MODEL_INFERENCE)
- [TEXT RECOGNITION MODEL INFERENCE](#RECOGNITION_MODEL_INFERENCE)
- [1. LIGHTWEIGHT CHINESE MODEL](#LIGHTWEIGHT_RECOGNITION)
- [2. MULTILINGUAL MODEL INFERENCE](MULTILINGUAL_MODEL_INFERENCE)
- [Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
- [1. Lightweight Chinese Recognition Model Inference](#LIGHTWEIGHT_RECOGNITION)
- [2. Multilingaul Model Inference](#MULTILINGUAL_MODEL_INFERENCE)
- [ANGLE CLASSIFICATION MODEL INFERENCE](#ANGLE_CLASS_MODEL_INFERENCE)
- [Angle Classification Model Inference](#ANGLE_CLASS_MODEL_INFERENCE)
- [TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION](#CONCATENATION)
- [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
<a name="DETECTION_MODEL_INFERENCE"></a>
## TEXT DETECTION MODEL INFERENCE
## Text Detection Model Inference
The default configuration is based on the inference setting of the DB text detection model. For lightweight Chinese detection model inference, you can execute the following commands:
......@@ -52,11 +52,11 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di
<a name="RECOGNITION_MODEL_INFERENCE"></a>
## TEXT RECOGNITION MODEL INFERENCE
## Text Recognition Model Inference
<a name="LIGHTWEIGHT_RECOGNITION"></a>
### 1. LIGHTWEIGHT CHINESE TEXT RECOGNITION MODEL REFERENCE
### 1. Lightweight Chinese Recognition Model Inference
For lightweight Chinese recognition model inference, you can execute the following commands:
......@@ -77,7 +77,7 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
<a name="MULTILINGUAL_MODEL_INFERENCE"></a>
### 2. MULTILINGAUL MODEL INFERENCE
### 2. Multilingaul Model Inference
If you need to predict other language models, when using inference model prediction, you need to specify the dictionary path used by `--rec_char_dict_path`. At the same time, in order to get the correct visualization results,
You need to specify the visual font path through `--vis_font_path`. There are small language fonts provided by default under the `doc/fonts` path, such as Korean recognition:
......@@ -94,7 +94,7 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
<a name="ANGLE_CLASS_MODEL_INFERENCE"></a>
## ANGLE CLASSIFICATION MODEL INFERENCE
## Angle Classification Model Inference
For angle classification model inference, you can execute the following commands:
......@@ -114,7 +114,7 @@ After executing the command, the prediction results (classification angle and sc
```
<a name="CONCATENATION"></a>
## TEXT DETECTION ANGLE CLASSIFICATION AND RECOGNITION INFERENCE CONCATENATION
## Text Detection Angle Classification and Recognition Inference Concatenation
When performing prediction, you need to specify the path of a single image or a folder of images through the parameter `image_dir`, the parameter `det_model_dir` specifies the path to detect the inference model, the parameter `cls_model_dir` specifies the path to angle classification inference model and the parameter `rec_model_dir` specifies the path to identify the inference model. The parameter `use_angle_cls` is used to control whether to enable the angle classification model. The parameter `use_mp` specifies whether to use multi-process to infer `total_process_num` specifies process number when using multi-process. The parameter . The visualized recognition results are saved to the `./inference_results` folder by default.
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doc/doc_en/models_and_config_en.md
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# PP-OCR Model and Configuration
The chapter on PP-OCR model and configuration file mainly adds some basic concepts of OCR model and the content and role of configuration file to have a better experience in the subsequent parameter adjustment and training of the model.
This chapter contains three parts. Firstly, [PP-OCR Model Download](. /models_list_en.md) explains the concept of PP-OCR model types and provides links to download all models. Then in [Yml Configuration](. /config_en.md) details the parameters needed to fine-tune the PP-OCR models. The final [Python Inference for PP-OCR Model Library](. /inference_ppocr_en.md) is an introduction to the use of the PP-OCR model library in the first section, which can quickly utilize the rich model library models to obtain test results through the Python inference engine.
------
Let's first understand some basic concepts.
- [INTRODUCTION ABOUT OCR](#introduction-about-ocr)
* [BASIC CONCEPTS OF OCR DETECTION MODEL](#basic-concepts-of-ocr-detection-model)
* [Basic concepts of OCR recognition model](#basic-concepts-of-ocr-recognition-model)
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## OCR model list（V2.0, updated on 2021.1.20）
## OCR model list（V2.1, updated on 2021.9.6）
> **Note**
> 1. Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
> 2. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to [algorithm overview tutorial](./algorithm_overview_en.md).
> 1. Compared with the model v2.0, the 2.1 version of the detection model has a improvement in accuracy, and the 2.1 version of the recognition model is optimized in accuracy and CPU speed.
> 2. Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
> 3. All models in this tutorial are all ppocr-series models, for more introduction of algorithms and models based on public dataset, you can refer to [algorithm overview tutorial](./algorithm_overview_en.md).
- [1. Text Detection Model](#Detection)
- [2. Text Recognition Model](#Recognition)
......@@ -28,8 +29,8 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.1_det|slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_lite_train_cml_v2.1.yml](../../configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml)| 3M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_slim_quant_infer.tar)|
|ch_ppocr_mobile_v2.1_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_lite_train_cml_v2.1.ym](../../configs/det/ch_ppocr_v2.1/ch_det_lite_train_cml_v2.1.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_distill_train.tar)|
|ch_PP-OCRv2_det_slim|slim quantization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml)| 3M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_quant_infer.tar)|
|ch_PP-OCRv2_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)|
|ch_ppocr_mobile_slim_v2.0_det|Slim pruned lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|2.6M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_infer.tar)|
|ch_ppocr_mobile_v2.0_det|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_det_mv3_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml)|3M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|
|ch_ppocr_server_v2.0_det|General model, which is larger than the lightweight model, but achieved better performance|[ch_det_res18_db_v2.0.yml](../../configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml)|47M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)|
......@@ -42,8 +43,8 @@ Relationship of the above models is as follows.
|model name|description|config|model size|download|
| --- | --- | --- | --- | --- |
|ch_ppocr_mobile_slim_v2.1_rec|Slim qunatization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_quant_train.tar) |
|ch_ppocr_mobile_v2.1_rec|Original lightweight model, supporting Chinese, English, multilingual text detection|[rec_chinese_lite_train_distillation_v2.1.yml](../../configs/rec/ch_ppocr_v2.1/rec_chinese_lite_train_distillation_v2.1.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_train.tar) |
|ch_PP-OCRv2_rec_slim|Slim qunatization with distillation lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)| 9M |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_quant_train.tar) |
|ch_PP-OCRv2_rec|Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_rec.yml](../../configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml)|8.5M|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar) |
|ch_ppocr_mobile_slim_v2.0_rec|Slim pruned and quantized lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)| 6M | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_slim_train.tar) |
|ch_ppocr_mobile_v2.0_rec|Original lightweight model, supporting Chinese, English and number recognition|[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)|5.2M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
|ch_ppocr_server_v2.0_rec|General model, supporting Chinese, English and number recognition|[rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml)|94.8M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
......@@ -92,7 +93,7 @@ For more supported languages, please refer to : [Multi-language model](./multi_l
### 4. Paddle-Lite Model
|Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
|---|---|---|---|---|---|---|
|V2.1|ppocr_v2.1 extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_infer_opt.nb)|v2.9|
|V2.1(slim)|extra-lightweight chinese OCR optimized model|4.9M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/chinese/ch_ppocr_mobile_v2.1_rec_slim_opt.nb)|v2.9|
|PP-OCRv2|extra-lightweight chinese OCR optimized model|11M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer_opt.nb)|v2.9|
|PP-OCRv2(slim)|extra-lightweight chinese OCR optimized model|4.9M|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_slim_opt.nb)|v2.9|
|V2.0|ppocr_v2.0 extra-lightweight chinese OCR optimized model|7.8M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_opt.nb)|v2.9|
|V2.0(slim)|ppovr_v2.0 extra-lightweight chinese OCR optimized model|3.3M|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_det_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_cls_slim_opt.nb)|[download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/lite/ch_ppocr_mobile_v2.0_rec_slim_opt.nb)|v2.9|
doc/doc_en/quickstart_en.md
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......@@ -95,7 +95,7 @@ If you do not use the provided test image, you can replace the following `--imag
['PAIN', 0.990372]
```
More whl package usage can be found in [whl package](./whl_en.md)
If you need to use the 2.0 model, please specify the parameter `--version 2.0`, paddleocr uses the 2.1 model by default. More whl package usage can be found in [whl package](./whl_en.md)
<a name="212-multi-language-model"></a>
#### 2.1.2 Multi-language Model
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doc/doc_en/recognition_en.md
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......@@ -15,6 +15,7 @@
- [4 PREDICTION](#PREDICTION)
- [4.1 Training engine prediction](#Training_engine_prediction)
- [5 CONVERT TO INFERENCE MODEL](#Inference)
<a name="DATA_PREPARATION"></a>
## 1 DATA PREPARATION
......@@ -91,6 +92,8 @@ Similar to the training set, the test set also needs to be provided a folder con
If you do not have a dataset locally, you can download it on the official website [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads).
Also refer to [DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here) ，download the lmdb format dataset required for benchmark
If you want to reproduce the paper SAR, you need to download extra dataset [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg), extraction code: 627x. Besides, icdar2013, icdar2015, cocotext, IIIT5k datasets are also used to train. For specific details, please refer to the paper SAR.
PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:
```
......@@ -235,6 +238,8 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
| rec_r34_vd_tps_bilstm_att.yml | CRNN | Resnet34_vd | TPS | BiLSTM | att |
| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
| rec_mtb_nrtr.yml | NRTR | nrtr_mtb | None | transformer encoder | transformer decoder |
| rec_r31_sar.yml | SAR | ResNet31 | None | LSTM encoder | LSTM decoder |
For training Chinese data, it is recommended to use
[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
......@@ -361,6 +366,7 @@ Eval:
```
<a name="EVALUATION"></a>
## 3 EVALUATION
The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
......@@ -432,3 +438,40 @@ Get the prediction result of the input image:
infer_img: doc/imgs_words/ch/word_1.jpg
result: ('韩国小馆', 0.997218)
```
<a name="Inference"></a>
## 5 CONVERT TO INFERENCE MODEL
The recognition model is converted to the inference model in the same way as the detection, as follows:
```
# -c Set the training algorithm yml configuration file
# -o Set optional parameters
# Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
# Global.save_inference_dir Set the address where the converted model will be saved.
python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy Global.save_inference_dir=./inference/rec_crnn/
```
If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
After the conversion is successful, there are three files in the model save directory:
```
inference/det_db/
├── inference.pdiparams # The parameter file of recognition inference model
├── inference.pdiparams.info # The parameter information of recognition inference model, which can be ignored
└── inference.pdmodel # The program file of recognition model
```
- Text recognition model Inference using custom characters dictionary
If the text dictionary is modified during training, when using the inference model to predict, you need to specify the dictionary path used by `--rec_char_dict_path`, and set `rec_char_type=ch`
```
python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./your inference model" --rec_image_shape="3, 32, 100" --rec_char_type="ch" --rec_char_dict_path="your text dict path"
```
doc/doc_en/update_en.md
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# RECENT UPDATES
- 2021.9.7 release PaddleOCR v2.3, [PP-OCRv2](#PP-OCRv2) is proposed. The CPU inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
- 2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., [PP-Structure](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.2/ppstructure/README.md), support layout analysis and table recognition (One-key to export chart images to Excel files).
- 2021.4.8 release end-to-end text recognition algorithm [PGNet](https://www.aaai.org/AAAI21Papers/AAAI-2885.WangP.pdf) which is published in AAAI 2021. Find tutorial [here](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/pgnet_en.md)；release multi language recognition [models](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md), support more than 80 languages recognition; especically, the performance of [English recognition model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/models_list_en.md#English) is Optimized.
- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](../../StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](../../PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
......
doc/doc_en/visualization_en.md
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# Visualization
<a name="PP-OCRv2"></a>
## PP-OCRv2
<img src="../imgs_results/PP-OCRv2/PP-OCRv2-pic001.jpg" width="800">
<img src="../imgs_results/PP-OCRv2/PP-OCRv2-pic002.jpg" width="800">
<img src="../imgs_results/PP-OCRv2/PP-OCRv2-pic003.jpg" width="800">
<a name="ppocr_server_2.0"></a>
## ch_ppocr_server_2.0
......
paddleocr.py
浏览文件 @ 3db92cf3
......@@ -33,104 +33,141 @@ from tools.infer.utility import draw_ocr, str2bool
from ppstructure.utility import init_args, draw_structure_result
from ppstructure.predict_system import OCRSystem, save_structure_res
__all__ = ['PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result', 'save_structure_res','download_with_progressbar']
model_urls = {
'det': {
'ch':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
'en':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
'structure': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
__all__ = [
'PaddleOCR', 'PPStructure', 'draw_ocr', 'draw_structure_result',
'save_structure_res', 'download_with_progressbar'
]
SUPPORT_DET_MODEL = ['DB']
VERSION = '2.2.1'
SUPPORT_REC_MODEL = ['CRNN']
BASE_DIR = os.path.expanduser("~/.paddleocr/")
DEFAULT_MODEL_VERSION = '2.0'
MODEL_URLS = {
'2.1': {
'det': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar',
},
},
'rec': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
}
}
},
'rec': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
'2.0': {
'det': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar',
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_ppocr_mobile_v2.0_det_infer.tar',
},
'structure': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_det_infer.tar'
}
},
'en': {
'url':
'rec': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/ppocr_keys_v1.txt'
},
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/en_dict.txt'
},
'french': {
'url':
'dict_path': './ppocr/utils/en_dict.txt'
},
'french': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/french_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/french_dict.txt'
},
'german': {
'url':
'dict_path': './ppocr/utils/dict/french_dict.txt'
},
'german': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/german_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/german_dict.txt'
},
'korean': {
'url':
'dict_path': './ppocr/utils/dict/german_dict.txt'
},
'korean': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
'dict_path': './ppocr/utils/dict/korean_dict.txt'
},
'japan': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/japan_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'dict_path': './ppocr/utils/dict/japan_dict.txt'
},
'chinese_cht': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/chinese_cht_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'dict_path': './ppocr/utils/dict/chinese_cht_dict.txt'
},
'ta': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
'dict_path': './ppocr/utils/dict/ta_dict.txt'
},
'te': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/te_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
'dict_path': './ppocr/utils/dict/te_dict.txt'
},
'ka': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ka_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
'dict_path': './ppocr/utils/dict/ka_dict.txt'
},
'latin': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/latin_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
'dict_path': './ppocr/utils/dict/latin_dict.txt'
},
'arabic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/arabic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
'dict_path': './ppocr/utils/dict/arabic_dict.txt'
},
'cyrillic': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/cyrillic_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
'dict_path': './ppocr/utils/dict/cyrillic_dict.txt'
},
'devanagari': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/devanagari_ppocr_mobile_v2.0_rec_infer.tar',
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
'dict_path': './ppocr/utils/dict/devanagari_dict.txt'
},
'structure': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
'dict_path': 'ppocr/utils/dict/table_dict.txt'
}
},
'cls': {
'ch': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
}
},
'structure': {
'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_rec_infer.tar',
'dict_path': 'ppocr/utils/dict/table_dict.txt'
'table': {
'en': {
'url':
'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
}
}
},
'cls': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
'table': {
'url': 'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_structure_infer.tar',
'dict_path': 'ppocr/utils/dict/table_structure_dict.txt'
}
}
SUPPORT_DET_MODEL = ['DB']
VERSION = '2.2.0.1'
SUPPORT_REC_MODEL = ['CRNN']
BASE_DIR = os.path.expanduser("~/.paddleocr/")
def parse_args(mMain=True):
import argparse
......@@ -140,6 +177,7 @@ def parse_args(mMain=True):
parser.add_argument("--det", type=str2bool, default=True)
parser.add_argument("--rec", type=str2bool, default=True)
parser.add_argument("--type", type=str, default='ocr')
parser.add_argument("--version", type=str, default='2.1')
for action in parser._actions:
if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
......@@ -155,19 +193,19 @@ def parse_args(mMain=True):
def parse_lang(lang):
latin_lang = [
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga',
'hr', 'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms',
'mt', 'nl', 'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk',
'sl', 'sq', 'sv', 'sw', 'tl', 'tr', 'uz', 'vi'
'af', 'az', 'bs', 'cs', 'cy', 'da', 'de', 'es', 'et', 'fr', 'ga', 'hr',
'hu', 'id', 'is', 'it', 'ku', 'la', 'lt', 'lv', 'mi', 'ms', 'mt', 'nl',
'no', 'oc', 'pi', 'pl', 'pt', 'ro', 'rs_latin', 'sk', 'sl', 'sq', 'sv',
'sw', 'tl', 'tr', 'uz', 'vi'
]
arabic_lang = ['ar', 'fa', 'ug', 'ur']
cyrillic_lang = [
'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd',
'ava', 'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
'ru', 'rs_cyrillic', 'be', 'bg', 'uk', 'mn', 'abq', 'ady', 'kbd', 'ava',
'dar', 'inh', 'che', 'lbe', 'lez', 'tab'
]
devanagari_lang = [
'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new',
'gom', 'sa', 'bgc'
'hi', 'mr', 'ne', 'bh', 'mai', 'ang', 'bho', 'mah', 'sck', 'new', 'gom',
'sa', 'bgc'
]
if lang in latin_lang:
lang = "latin"
......@@ -177,9 +215,9 @@ def parse_lang(lang):
lang = "cyrillic"
elif lang in devanagari_lang:
lang = "devanagari"
assert lang in model_urls[
assert lang in MODEL_URLS[DEFAULT_MODEL_VERSION][
'rec'], 'param lang must in {}, but got {}'.format(
model_urls['rec'].keys(), lang)
MODEL_URLS[DEFAULT_MODEL_VERSION]['rec'].keys(), lang)
if lang == "ch":
det_lang = "ch"
elif lang == 'structure':
......@@ -189,6 +227,35 @@ def parse_lang(lang):
return lang, det_lang
def get_model_config(version, model_type, lang):
if version not in MODEL_URLS:
logger.warning('version {} not in {}, use version {} instead'.format(
version, MODEL_URLS.keys(), DEFAULT_MODEL_VERSION))
version = DEFAULT_MODEL_VERSION
if model_type not in MODEL_URLS[version]:
if model_type in MODEL_URLS[DEFAULT_MODEL_VERSION]:
logger.warning(
'version {} not support {} models, use version {} instead'.
format(version, model_type, DEFAULT_MODEL_VERSION))
version = DEFAULT_MODEL_VERSION
else:
logger.error('{} models is not support, we only support {}'.format(
model_type, MODEL_URLS[DEFAULT_MODEL_VERSION].keys()))
sys.exit(-1)
if lang not in MODEL_URLS[version][model_type]:
if lang in MODEL_URLS[DEFAULT_MODEL_VERSION][model_type]:
logger.warning('lang {} is not support in {}, use {} instead'.
format(lang, version, DEFAULT_MODEL_VERSION))
version = DEFAULT_MODEL_VERSION
else:
logger.error(
'lang {} is not support, we only support {} for {} models'.
format(lang, MODEL_URLS[DEFAULT_MODEL_VERSION][model_type].keys(
), model_type))
sys.exit(-1)
return MODEL_URLS[version][model_type][lang]
class PaddleOCR(predict_system.TextSystem):
def __init__(self, **kwargs):
"""
......@@ -204,15 +271,21 @@ class PaddleOCR(predict_system.TextSystem):
lang, det_lang = parse_lang(params.lang)
# init model dir
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
model_urls['det'][det_lang])
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
model_urls['rec'][lang]['url'])
params.cls_model_dir, cls_url = confirm_model_dir_url(params.cls_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
model_urls['cls'])
det_model_config = get_model_config(params.version, 'det', det_lang)
params.det_model_dir, det_url = confirm_model_dir_url(
params.det_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
det_model_config['url'])
rec_model_config = get_model_config(params.version, 'rec', lang)
params.rec_model_dir, rec_url = confirm_model_dir_url(
params.rec_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
rec_model_config['url'])
cls_model_config = get_model_config(params.version, 'cls', 'ch')
params.cls_model_dir, cls_url = confirm_model_dir_url(
params.cls_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'cls'),
cls_model_config['url'])
# download model
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
......@@ -226,7 +299,8 @@ class PaddleOCR(predict_system.TextSystem):
sys.exit(0)
if params.rec_char_dict_path is None:
params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
params.rec_char_dict_path = str(
Path(__file__).parent / rec_model_config['dict_path'])
print(params)
# init det_model and rec_model
......@@ -293,24 +367,32 @@ class PPStructure(OCRSystem):
lang, det_lang = parse_lang(params.lang)
# init model dir
params.det_model_dir, det_url = confirm_model_dir_url(params.det_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
model_urls['det'][det_lang])
params.rec_model_dir, rec_url = confirm_model_dir_url(params.rec_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
model_urls['rec'][lang]['url'])
params.table_model_dir, table_url = confirm_model_dir_url(params.table_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),
model_urls['table']['url'])
det_model_config = get_model_config(params.version, 'det', det_lang)
params.det_model_dir, det_url = confirm_model_dir_url(
params.det_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'det', det_lang),
det_model_config['url'])
rec_model_config = get_model_config(params.version, 'rec', lang)
params.rec_model_dir, rec_url = confirm_model_dir_url(
params.rec_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'rec', lang),
rec_model_config['url'])
table_model_config = get_model_config(params.version, 'table', 'en')
params.table_model_dir, table_url = confirm_model_dir_url(
params.table_model_dir,
os.path.join(BASE_DIR, VERSION, 'ocr', 'table'),
table_model_config['url'])
# download model
maybe_download(params.det_model_dir, det_url)
maybe_download(params.rec_model_dir, rec_url)
maybe_download(params.table_model_dir, table_url)
if params.rec_char_dict_path is None:
params.rec_char_dict_path = str(Path(__file__).parent / model_urls['rec'][lang]['dict_path'])
params.rec_char_dict_path = str(
Path(__file__).parent / rec_model_config['dict_path'])
if params.table_char_dict_path is None:
params.table_char_dict_path = str(Path(__file__).parent / model_urls['table']['dict_path'])
params.table_char_dict_path = str(
Path(__file__).parent / table_model_config['dict_path'])
print(params)
super().__init__(params)
......@@ -374,4 +456,3 @@ def main():
for item in result:
item.pop('img')
logger.info(item)
ppocr/data/imaug/__init__.py
浏览文件 @ 3db92cf3
......@@ -21,7 +21,7 @@ from .make_border_map import MakeBorderMap
from .make_shrink_map import MakeShrinkMap
from .random_crop_data import EastRandomCropData, PSERandomCrop
from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg, NRTRRecResizeImg
from .rec_img_aug import RecAug, RecResizeImg, ClsResizeImg, SRNRecResizeImg, NRTRRecResizeImg, SARRecResizeImg
from .randaugment import RandAugment
from .copy_paste import CopyPaste
from .operators import *
......
ppocr/data/imaug/label_ops.py
浏览文件 @ 3db92cf3
......@@ -549,3 +549,49 @@ class TableLabelEncode(object):
assert False, "Unsupport type %s in char_or_elem" \
% char_or_elem
return idx
class SARLabelEncode(BaseRecLabelEncode):
""" Convert between text-label and text-index """
def __init__(self,
max_text_length,
character_dict_path=None,
character_type='ch',
use_space_char=False,
**kwargs):
super(SARLabelEncode,
self).__init__(max_text_length, character_dict_path,
character_type, use_space_char)
def add_special_char(self, dict_character):
beg_end_str = "<BOS/EOS>"
unknown_str = "<UKN>"
padding_str = "<PAD>"
dict_character = dict_character + [unknown_str]
self.unknown_idx = len(dict_character) - 1
dict_character = dict_character + [beg_end_str]
self.start_idx = len(dict_character) - 1
self.end_idx = len(dict_character) - 1
dict_character = dict_character + [padding_str]
self.padding_idx = len(dict_character) - 1
return dict_character
def __call__(self, data):
text = data['label']
text = self.encode(text)
if text is None:
return None
if len(text) >= self.max_text_len - 1:
return None
data['length'] = np.array(len(text))
target = [self.start_idx] + text + [self.end_idx]
padded_text = [self.padding_idx for _ in range(self.max_text_len)]
padded_text[:len(target)] = target
data['label'] = np.array(padded_text)
return data
def get_ignored_tokens(self):
return [self.padding_idx]
ppocr/data/imaug/operators.py
浏览文件 @ 3db92cf3
......@@ -113,7 +113,7 @@ class NormalizeImage(object):
assert isinstance(img,
np.ndarray), "invalid input 'img' in NormalizeImage"
data['image'] = (
img.astype('float32') * self.scale - self.mean) / self.std
img.astype('float32') * self.scale - self.mean) / self.std
return data
......@@ -144,6 +144,34 @@ class KeepKeys(object):
return data_list
class Resize(object):
def __init__(self, size=(640, 640), **kwargs):
self.size = size
def resize_image(self, img):
resize_h, resize_w = self.size
ori_h, ori_w = img.shape[:2] # (h, w, c)
ratio_h = float(resize_h) / ori_h
ratio_w = float(resize_w) / ori_w
img = cv2.resize(img, (int(resize_w), int(resize_h)))
return img, [ratio_h, ratio_w]
def __call__(self, data):
img = data['image']
text_polys = data['polys']
img_resize, [ratio_h, ratio_w] = self.resize_image(img)
new_boxes = []
for box in text_polys:
new_box = []
for cord in box:
new_box.append([cord[0] * ratio_w, cord[1] * ratio_h])
new_boxes.append(new_box)
data['image'] = img_resize
data['polys'] = np.array(new_boxes, dtype=np.float32)
return data
class DetResizeForTest(object):
def __init__(self, **kwargs):
super(DetResizeForTest, self).__init__()
......@@ -215,7 +243,7 @@ class DetResizeForTest(object):
else:
ratio = 1.
elif self.limit_type == 'resize_long':
ratio = float(limit_side_len) / max(h,w)
ratio = float(limit_side_len) / max(h, w)
else:
raise Exception('not support limit type, image ')
resize_h = int(h * ratio)
......
ppocr/data/imaug/rec_img_aug.py
浏览文件 @ 3db92cf3
......@@ -102,6 +102,56 @@ class SRNRecResizeImg(object):
return data
class SARRecResizeImg(object):
def __init__(self, image_shape, width_downsample_ratio=0.25, **kwargs):
self.image_shape = image_shape
self.width_downsample_ratio = width_downsample_ratio
def __call__(self, data):
img = data['image']
norm_img, resize_shape, pad_shape, valid_ratio = resize_norm_img_sar(img, self.image_shape, self.width_downsample_ratio)
data['image'] = norm_img
data['resized_shape'] = resize_shape
data['pad_shape'] = pad_shape
data['valid_ratio'] = valid_ratio
return data
def resize_norm_img_sar(img, image_shape, width_downsample_ratio=0.25):
imgC, imgH, imgW_min, imgW_max = image_shape
h = img.shape[0]
w = img.shape[1]
valid_ratio = 1.0
# make sure new_width is an integral multiple of width_divisor.
width_divisor = int(1 / width_downsample_ratio)
# resize
ratio = w / float(h)
resize_w = math.ceil(imgH * ratio)
if resize_w % width_divisor != 0:
resize_w = round(resize_w / width_divisor) * width_divisor
if imgW_min is not None:
resize_w = max(imgW_min, resize_w)
if imgW_max is not None:
valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
resize_w = min(imgW_max, resize_w)
resized_image = cv2.resize(img, (resize_w, imgH))
resized_image = resized_image.astype('float32')
# norm
if image_shape[0] == 1:
resized_image = resized_image / 255
resized_image = resized_image[np.newaxis, :]
else:
resized_image = resized_image.transpose((2, 0, 1)) / 255
resized_image -= 0.5
resized_image /= 0.5
resize_shape = resized_image.shape
padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
padding_im[:, :, 0:resize_w] = resized_image
pad_shape = padding_im.shape
return padding_im, resize_shape, pad_shape, valid_ratio
def resize_norm_img(img, image_shape):
imgC, imgH, imgW = image_shape
h = img.shape[0]
......
ppocr/losses/__init__.py
浏览文件 @ 3db92cf3
......@@ -26,6 +26,7 @@ from .rec_ctc_loss import CTCLoss
from .rec_att_loss import AttentionLoss
from .rec_srn_loss import SRNLoss
from .rec_nrtr_loss import NRTRLoss
from .rec_sar_loss import SARLoss
# cls loss
from .cls_loss import ClsLoss
......@@ -44,7 +45,7 @@ from .table_att_loss import TableAttentionLoss
def build_loss(config):
support_dict = [
'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss', 'TableAttentionLoss'
'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss', 'TableAttentionLoss', 'SARLoss'
]
config = copy.deepcopy(config)
......
ppocr/losses/rec_sar_loss.py 0 → 100644
浏览文件 @ 3db92cf3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import nn
class SARLoss(nn.Layer):
def __init__(self, **kwargs):
super(SARLoss, self).__init__()
self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="mean", ignore_index=96)
def forward(self, predicts, batch):
predict = predicts[:, :-1, :] # ignore last index of outputs to be in same seq_len with targets
label = batch[1].astype("int64")[:, 1:] # ignore first index of target in loss calculation
batch_size, num_steps, num_classes = predict.shape[0], predict.shape[
1], predict.shape[2]
assert len(label.shape) == len(list(predict.shape)) - 1, \
"The target's shape and inputs's shape is [N, d] and [N, num_steps]"
inputs = paddle.reshape(predict, [-1, num_classes])
targets = paddle.reshape(label, [-1])
loss = self.loss_func(inputs, targets)
return {'loss': loss}
ppocr/modeling/backbones/__init__.py
浏览文件 @ 3db92cf3
......@@ -27,8 +27,9 @@ def build_backbone(config, model_type):
from .rec_resnet_fpn import ResNetFPN
from .rec_mv1_enhance import MobileNetV1Enhance
from .rec_nrtr_mtb import MTB
from .rec_resnet_31 import ResNet31
support_dict = [
'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB'
'MobileNetV1Enhance', 'MobileNetV3', 'ResNet', 'ResNetFPN', 'MTB', "ResNet31"
]
elif model_type == "e2e":
from .e2e_resnet_vd_pg import ResNet
......
ppocr/modeling/backbones/rec_resnet_31.py 0 → 100644
浏览文件 @ 3db92cf3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
import numpy as np
__all__ = ["ResNet31"]
def conv3x3(in_channel, out_channel, stride=1):
return nn.Conv2D(
in_channel,
out_channel,
kernel_size=3,
stride=stride,
padding=1,
bias_attr=False
)
class BasicBlock(nn.Layer):
expansion = 1
def __init__(self, in_channels, channels, stride=1, downsample=False):
super().__init__()
self.conv1 = conv3x3(in_channels, channels, stride)
self.bn1 = nn.BatchNorm2D(channels)
self.relu = nn.ReLU()
self.conv2 = conv3x3(channels, channels)
self.bn2 = nn.BatchNorm2D(channels)
self.downsample = downsample
if downsample:
self.downsample = nn.Sequential(
nn.Conv2D(in_channels, channels * self.expansion, 1, stride, bias_attr=False),
nn.BatchNorm2D(channels * self.expansion),
)
else:
self.downsample = nn.Sequential()
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet31(nn.Layer):
'''
Args:
in_channels (int): Number of channels of input image tensor.
layers (list[int]): List of BasicBlock number for each stage.
channels (list[int]): List of out_channels of Conv2d layer.
out_indices (None | Sequence[int]): Indices of output stages.
last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
'''
def __init__(self,
in_channels=3,
layers=[1, 2, 5, 3],
channels=[64, 128, 256, 256, 512, 512, 512],
out_indices=None,
last_stage_pool=False):
super(ResNet31, self).__init__()
assert isinstance(in_channels, int)
assert isinstance(last_stage_pool, bool)
self.out_indices = out_indices
self.last_stage_pool = last_stage_pool
# conv 1 (Conv Conv)
self.conv1_1 = nn.Conv2D(in_channels, channels[0], kernel_size=3, stride=1, padding=1)
self.bn1_1 = nn.BatchNorm2D(channels[0])
self.relu1_1 = nn.ReLU()
self.conv1_2 = nn.Conv2D(channels[0], channels[1], kernel_size=3, stride=1, padding=1)
self.bn1_2 = nn.BatchNorm2D(channels[1])
self.relu1_2 = nn.ReLU()
# conv 2 (Max-pooling, Residual block, Conv)
self.pool2 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True)
self.block2 = self._make_layer(channels[1], channels[2], layers[0])
self.conv2 = nn.Conv2D(channels[2], channels[2], kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2D(channels[2])
self.relu2 = nn.ReLU()
# conv 3 (Max-pooling, Residual block, Conv)
self.pool3 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True)
self.block3 = self._make_layer(channels[2], channels[3], layers[1])
self.conv3 = nn.Conv2D(channels[3], channels[3], kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2D(channels[3])
self.relu3 = nn.ReLU()
# conv 4 (Max-pooling, Residual block, Conv)
self.pool4 = nn.MaxPool2D(kernel_size=(2, 1), stride=(2, 1), padding=0, ceil_mode=True)
self.block4 = self._make_layer(channels[3], channels[4], layers[2])
self.conv4 = nn.Conv2D(channels[4], channels[4], kernel_size=3, stride=1, padding=1)
self.bn4 = nn.BatchNorm2D(channels[4])
self.relu4 = nn.ReLU()
# conv 5 ((Max-pooling), Residual block, Conv)
self.pool5 = None
if self.last_stage_pool:
self.pool5 = nn.MaxPool2D(kernel_size=2, stride=2, padding=0, ceil_mode=True)
self.block5 = self._make_layer(channels[4], channels[5], layers[3])
self.conv5 = nn.Conv2D(channels[5], channels[5], kernel_size=3, stride=1, padding=1)
self.bn5 = nn.BatchNorm2D(channels[5])
self.relu5 = nn.ReLU()
self.out_channels = channels[-1]
def _make_layer(self, input_channels, output_channels, blocks):
layers = []
for _ in range(blocks):
downsample = None
if input_channels != output_channels:
downsample = nn.Sequential(
nn.Conv2D(
input_channels,
output_channels,
kernel_size=1,
stride=1,
bias_attr=False),
nn.BatchNorm2D(output_channels),
)
layers.append(BasicBlock(input_channels, output_channels, downsample=downsample))
input_channels = output_channels
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1_1(x)
x = self.bn1_1(x)
x = self.relu1_1(x)
x = self.conv1_2(x)
x = self.bn1_2(x)
x = self.relu1_2(x)
outs = []
for i in range(4):
layer_index = i + 2
pool_layer = getattr(self, f'pool{layer_index}')
block_layer = getattr(self, f'block{layer_index}')
conv_layer = getattr(self, f'conv{layer_index}')
bn_layer = getattr(self, f'bn{layer_index}')
relu_layer = getattr(self, f'relu{layer_index}')
if pool_layer is not None:
x = pool_layer(x)
x = block_layer(x)
x = conv_layer(x)
x = bn_layer(x)
x= relu_layer(x)
outs.append(x)
if self.out_indices is not None:
return tuple([outs[i] for i in self.out_indices])
return x
ppocr/modeling/heads/__init__.py
浏览文件 @ 3db92cf3
......@@ -27,12 +27,13 @@ def build_head(config):
from .rec_att_head import AttentionHead
from .rec_srn_head import SRNHead
from .rec_nrtr_head import Transformer
from .rec_sar_head import SARHead
# cls head
from .cls_head import ClsHead
support_dict = [
'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead'
'SRNHead', 'PGHead', 'Transformer', 'TableAttentionHead', 'SARHead'
]
#table head
......
ppocr/modeling/heads/rec_sar_head.py 0 → 100644
浏览文件 @ 3db92cf3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
class SAREncoder(nn.Layer):
"""
Args:
enc_bi_rnn (bool): If True, use bidirectional RNN in encoder.
enc_drop_rnn (float): Dropout probability of RNN layer in encoder.
enc_gru (bool): If True, use GRU, else LSTM in encoder.
d_model (int): Dim of channels from backbone.
d_enc (int): Dim of encoder RNN layer.
mask (bool): If True, mask padding in RNN sequence.
"""
def __init__(self,
enc_bi_rnn=False,
enc_drop_rnn=0.1,
enc_gru=False,
d_model=512,
d_enc=512,
mask=True,
**kwargs):
super().__init__()
assert isinstance(enc_bi_rnn, bool)
assert isinstance(enc_drop_rnn, (int, float))
assert 0 <= enc_drop_rnn < 1.0
assert isinstance(enc_gru, bool)
assert isinstance(d_model, int)
assert isinstance(d_enc, int)
assert isinstance(mask, bool)
self.enc_bi_rnn = enc_bi_rnn
self.enc_drop_rnn = enc_drop_rnn
self.mask = mask
# LSTM Encoder
if enc_bi_rnn:
direction = 'bidirectional'
else:
direction = 'forward'
kwargs = dict(
input_size=d_model,
hidden_size=d_enc,
num_layers=2,
time_major=False,
dropout=enc_drop_rnn,
direction=direction)
if enc_gru:
self.rnn_encoder = nn.GRU(**kwargs)
else:
self.rnn_encoder = nn.LSTM(**kwargs)
# global feature transformation
encoder_rnn_out_size = d_enc * (int(enc_bi_rnn) + 1)
self.linear = nn.Linear(encoder_rnn_out_size, encoder_rnn_out_size)
def forward(self, feat, img_metas=None):
if img_metas is not None:
assert len(img_metas[0]) == feat.shape[0]
valid_ratios = None
if img_metas is not None and self.mask:
valid_ratios = img_metas[-1]
h_feat = feat.shape[2] # bsz c h w
feat_v = F.max_pool2d(
feat, kernel_size=(h_feat, 1), stride=1, padding=0)
feat_v = feat_v.squeeze(2) # bsz * C * W
feat_v = paddle.transpose(feat_v, perm=[0, 2, 1]) # bsz * W * C
holistic_feat = self.rnn_encoder(feat_v)[0] # bsz * T * C
if valid_ratios is not None:
valid_hf = []
T = holistic_feat.shape[1]
for i, valid_ratio in enumerate(valid_ratios):
valid_step = min(T, math.ceil(T * valid_ratio)) - 1
valid_hf.append(holistic_feat[i, valid_step, :])
valid_hf = paddle.stack(valid_hf, axis=0)
else:
valid_hf = holistic_feat[:, -1, :] # bsz * C
holistic_feat = self.linear(valid_hf) # bsz * C
return holistic_feat
class BaseDecoder(nn.Layer):
def __init__(self, **kwargs):
super().__init__()
def forward_train(self, feat, out_enc, targets, img_metas):
raise NotImplementedError
def forward_test(self, feat, out_enc, img_metas):
raise NotImplementedError
def forward(self,
feat,
out_enc,
label=None,
img_metas=None,
train_mode=True):
self.train_mode = train_mode
if train_mode:
return self.forward_train(feat, out_enc, label, img_metas)
return self.forward_test(feat, out_enc, img_metas)
class ParallelSARDecoder(BaseDecoder):
"""
Args:
out_channels (int): Output class number.
enc_bi_rnn (bool): If True, use bidirectional RNN in encoder.
dec_bi_rnn (bool): If True, use bidirectional RNN in decoder.
dec_drop_rnn (float): Dropout of RNN layer in decoder.
dec_gru (bool): If True, use GRU, else LSTM in decoder.
d_model (int): Dim of channels from backbone.
d_enc (int): Dim of encoder RNN layer.
d_k (int): Dim of channels of attention module.
pred_dropout (float): Dropout probability of prediction layer.
max_seq_len (int): Maximum sequence length for decoding.
mask (bool): If True, mask padding in feature map.
start_idx (int): Index of start token.
padding_idx (int): Index of padding token.
pred_concat (bool): If True, concat glimpse feature from
attention with holistic feature and hidden state.
"""
def __init__(
self,
out_channels, # 90 + unknown + start + padding
enc_bi_rnn=False,
dec_bi_rnn=False,
dec_drop_rnn=0.0,
dec_gru=False,
d_model=512,
d_enc=512,
d_k=64,
pred_dropout=0.1,
max_text_length=30,
mask=True,
pred_concat=True,
**kwargs):
super().__init__()
self.num_classes = out_channels
self.enc_bi_rnn = enc_bi_rnn
self.d_k = d_k
self.start_idx = out_channels - 2
self.padding_idx = out_channels - 1
self.max_seq_len = max_text_length
self.mask = mask
self.pred_concat = pred_concat
encoder_rnn_out_size = d_enc * (int(enc_bi_rnn) + 1)
decoder_rnn_out_size = encoder_rnn_out_size * (int(dec_bi_rnn) + 1)
# 2D attention layer
self.conv1x1_1 = nn.Linear(decoder_rnn_out_size, d_k)
self.conv3x3_1 = nn.Conv2D(
d_model, d_k, kernel_size=3, stride=1, padding=1)
self.conv1x1_2 = nn.Linear(d_k, 1)
# Decoder RNN layer
if dec_bi_rnn:
direction = 'bidirectional'
else:
direction = 'forward'
kwargs = dict(
input_size=encoder_rnn_out_size,
hidden_size=encoder_rnn_out_size,
num_layers=2,
time_major=False,
dropout=dec_drop_rnn,
direction=direction)
if dec_gru:
self.rnn_decoder = nn.GRU(**kwargs)
else:
self.rnn_decoder = nn.LSTM(**kwargs)
# Decoder input embedding
self.embedding = nn.Embedding(
self.num_classes,
encoder_rnn_out_size,
padding_idx=self.padding_idx)
# Prediction layer
self.pred_dropout = nn.Dropout(pred_dropout)
pred_num_classes = self.num_classes - 1
if pred_concat:
fc_in_channel = decoder_rnn_out_size + d_model + d_enc
else:
fc_in_channel = d_model
self.prediction = nn.Linear(fc_in_channel, pred_num_classes)
def _2d_attention(self,
decoder_input,
feat,
holistic_feat,
valid_ratios=None):
y = self.rnn_decoder(decoder_input)[0]
# y: bsz * (seq_len + 1) * hidden_size
attn_query = self.conv1x1_1(y) # bsz * (seq_len + 1) * attn_size
bsz, seq_len, attn_size = attn_query.shape
attn_query = paddle.unsqueeze(attn_query, axis=[3, 4])
# (bsz, seq_len + 1, attn_size, 1, 1)
attn_key = self.conv3x3_1(feat)
# bsz * attn_size * h * w
attn_key = attn_key.unsqueeze(1)
# bsz * 1 * attn_size * h * w
attn_weight = paddle.tanh(paddle.add(attn_key, attn_query))
# bsz * (seq_len + 1) * attn_size * h * w
attn_weight = paddle.transpose(attn_weight, perm=[0, 1, 3, 4, 2])
# bsz * (seq_len + 1) * h * w * attn_size
attn_weight = self.conv1x1_2(attn_weight)
# bsz * (seq_len + 1) * h * w * 1
bsz, T, h, w, c = attn_weight.shape
assert c == 1
if valid_ratios is not None:
# cal mask of attention weight
for i, valid_ratio in enumerate(valid_ratios):
valid_width = min(w, math.ceil(w * valid_ratio))
attn_weight[i, :, :, valid_width:, :] = float('-inf')
attn_weight = paddle.reshape(attn_weight, [bsz, T, -1])
attn_weight = F.softmax(attn_weight, axis=-1)
attn_weight = paddle.reshape(attn_weight, [bsz, T, h, w, c])
attn_weight = paddle.transpose(attn_weight, perm=[0, 1, 4, 2, 3])
# attn_weight: bsz * T * c * h * w
# feat: bsz * c * h * w
attn_feat = paddle.sum(paddle.multiply(feat.unsqueeze(1), attn_weight),
(3, 4),
keepdim=False)
# bsz * (seq_len + 1) * C
# Linear transformation
if self.pred_concat:
hf_c = holistic_feat.shape[-1]
holistic_feat = paddle.expand(
holistic_feat, shape=[bsz, seq_len, hf_c])
y = self.prediction(paddle.concat((y, attn_feat, holistic_feat), 2))
else:
y = self.prediction(attn_feat)
# bsz * (seq_len + 1) * num_classes
if self.train_mode:
y = self.pred_dropout(y)
return y
def forward_train(self, feat, out_enc, label, img_metas):
'''
img_metas: [label, valid_ratio]
'''
if img_metas is not None:
assert len(img_metas[0]) == feat.shape[0]
valid_ratios = None
if img_metas is not None and self.mask:
valid_ratios = img_metas[-1]
label = label.cuda()
lab_embedding = self.embedding(label)
# bsz * seq_len * emb_dim
out_enc = out_enc.unsqueeze(1)
# bsz * 1 * emb_dim
in_dec = paddle.concat((out_enc, lab_embedding), axis=1)
# bsz * (seq_len + 1) * C
out_dec = self._2d_attention(
in_dec, feat, out_enc, valid_ratios=valid_ratios)
# bsz * (seq_len + 1) * num_classes
return out_dec[:, 1:, :] # bsz * seq_len * num_classes
def forward_test(self, feat, out_enc, img_metas):
if img_metas is not None:
assert len(img_metas[0]) == feat.shape[0]
valid_ratios = None
if img_metas is not None and self.mask:
valid_ratios = img_metas[-1]
seq_len = self.max_seq_len
bsz = feat.shape[0]
start_token = paddle.full(
(bsz, ), fill_value=self.start_idx, dtype='int64')
# bsz
start_token = self.embedding(start_token)
# bsz * emb_dim
emb_dim = start_token.shape[1]
start_token = start_token.unsqueeze(1)
start_token = paddle.expand(start_token, shape=[bsz, seq_len, emb_dim])
# bsz * seq_len * emb_dim
out_enc = out_enc.unsqueeze(1)
# bsz * 1 * emb_dim
decoder_input = paddle.concat((out_enc, start_token), axis=1)
# bsz * (seq_len + 1) * emb_dim
outputs = []
for i in range(1, seq_len + 1):
decoder_output = self._2d_attention(
decoder_input, feat, out_enc, valid_ratios=valid_ratios)
char_output = decoder_output[:, i, :] # bsz * num_classes
char_output = F.softmax(char_output, -1)
outputs.append(char_output)
max_idx = paddle.argmax(char_output, axis=1, keepdim=False)
char_embedding = self.embedding(max_idx) # bsz * emb_dim
if i < seq_len:
decoder_input[:, i + 1, :] = char_embedding
outputs = paddle.stack(outputs, 1) # bsz * seq_len * num_classes
return outputs
class SARHead(nn.Layer):
def __init__(self,
out_channels,
enc_bi_rnn=False,
enc_drop_rnn=0.1,
enc_gru=False,
dec_bi_rnn=False,
dec_drop_rnn=0.0,
dec_gru=False,
d_k=512,
pred_dropout=0.1,
max_text_length=30,
pred_concat=True,
**kwargs):
super(SARHead, self).__init__()
# encoder module
self.encoder = SAREncoder(
enc_bi_rnn=enc_bi_rnn, enc_drop_rnn=enc_drop_rnn, enc_gru=enc_gru)
# decoder module
self.decoder = ParallelSARDecoder(
out_channels=out_channels,
enc_bi_rnn=enc_bi_rnn,
dec_bi_rnn=dec_bi_rnn,
dec_drop_rnn=dec_drop_rnn,
dec_gru=dec_gru,
d_k=d_k,
pred_dropout=pred_dropout,
max_text_length=max_text_length,
pred_concat=pred_concat)
def forward(self, feat, targets=None):
'''
img_metas: [label, valid_ratio]
'''
holistic_feat = self.encoder(feat, targets) # bsz c
if self.training:
label = targets[0] # label
label = paddle.to_tensor(label, dtype='int64')
final_out = self.decoder(
feat, holistic_feat, label, img_metas=targets)
if not self.training:
final_out = self.decoder(
feat,
holistic_feat,
label=None,
img_metas=targets,
train_mode=False)
# (bsz, seq_len, num_classes)
return final_out
ppocr/postprocess/__init__.py
浏览文件 @ 3db92cf3
......@@ -25,7 +25,7 @@ from .db_postprocess import DBPostProcess, DistillationDBPostProcess
from .east_postprocess import EASTPostProcess
from .sast_postprocess import SASTPostProcess
from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, NRTRLabelDecode, \
TableLabelDecode
TableLabelDecode, SARLabelDecode
from .cls_postprocess import ClsPostProcess
from .pg_postprocess import PGPostProcess
......@@ -33,7 +33,8 @@ def build_post_process(config, global_config=None):
support_dict = [
'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
'DistillationCTCLabelDecode', 'NRTRLabelDecode', 'TableLabelDecode', 'DistillationDBPostProcess'
'DistillationCTCLabelDecode', 'TableLabelDecode',
'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode'
]
config = copy.deepcopy(config)
......
ppocr/postprocess/rec_postprocess.py
浏览文件 @ 3db92cf3
......@@ -15,6 +15,7 @@ import numpy as np
import string
import paddle
from paddle.nn import functional as F
import re
class BaseRecLabelDecode(object):
......@@ -165,21 +166,21 @@ class NRTRLabelDecode(BaseRecLabelDecode):
use_space_char=True,
**kwargs):
super(NRTRLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
character_type, use_space_char)
def __call__(self, preds, label=None, *args, **kwargs):
if preds.dtype == paddle.int64:
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
if preds[0][0]==2:
preds_idx = preds[:,1:]
if preds[0][0] == 2:
preds_idx = preds[:, 1:]
else:
preds_idx = preds
text = self.decode(preds_idx)
if label is None:
return text
label = self.decode(label[:,1:])
label = self.decode(label[:, 1:])
else:
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
......@@ -188,13 +189,13 @@ class NRTRLabelDecode(BaseRecLabelDecode):
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label[:,1:])
label = self.decode(label[:, 1:])
return text, label
def add_special_char(self, dict_character):
dict_character = ['blank','<unk>','<s>','</s>'] + dict_character
dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
return dict_character
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
""" convert text-index into text-label. """
result_list = []
......@@ -203,10 +204,11 @@ class NRTRLabelDecode(BaseRecLabelDecode):
char_list = []
conf_list = []
for idx in range(len(text_index[batch_idx])):
if text_index[batch_idx][idx] == 3: # end
if text_index[batch_idx][idx] == 3: # end
break
try:
char_list.append(self.character[int(text_index[batch_idx][idx])])
char_list.append(self.character[int(text_index[batch_idx][
idx])])
except:
continue
if text_prob is not None:
......@@ -218,7 +220,6 @@ class NRTRLabelDecode(BaseRecLabelDecode):
return result_list
class AttnLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
......@@ -256,7 +257,8 @@ class AttnLabelDecode(BaseRecLabelDecode):
if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
batch_idx][idx]:
continue
char_list.append(self.character[int(text_index[batch_idx][idx])])
char_list.append(self.character[int(text_index[batch_idx][
idx])])
if text_prob is not None:
conf_list.append(text_prob[batch_idx][idx])
else:
......@@ -386,10 +388,9 @@ class SRNLabelDecode(BaseRecLabelDecode):
class TableLabelDecode(object):
""" """
def __init__(self,
character_dict_path,
**kwargs):
list_character, list_elem = self.load_char_elem_dict(character_dict_path)
def __init__(self, character_dict_path, **kwargs):
list_character, list_elem = self.load_char_elem_dict(
character_dict_path)
list_character = self.add_special_char(list_character)
list_elem = self.add_special_char(list_elem)
self.dict_character = {}
......@@ -408,7 +409,8 @@ class TableLabelDecode(object):
list_elem = []
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split("\t")
substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split(
"\t")
character_num = int(substr[0])
elem_num = int(substr[1])
for cno in range(1, 1 + character_num):
......@@ -428,14 +430,14 @@ class TableLabelDecode(object):
def __call__(self, preds):
structure_probs = preds['structure_probs']
loc_preds = preds['loc_preds']
if isinstance(structure_probs,paddle.Tensor):
if isinstance(structure_probs, paddle.Tensor):
structure_probs = structure_probs.numpy()
if isinstance(loc_preds,paddle.Tensor):
if isinstance(loc_preds, paddle.Tensor):
loc_preds = loc_preds.numpy()
structure_idx = structure_probs.argmax(axis=2)
structure_probs = structure_probs.max(axis=2)
structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode(structure_idx,
structure_probs, 'elem')
structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode(
structure_idx, structure_probs, 'elem')
res_html_code_list = []
res_loc_list = []
batch_num = len(structure_str)
......@@ -450,8 +452,13 @@ class TableLabelDecode(object):
res_loc = np.array(res_loc)
res_html_code_list.append(res_html_code)
res_loc_list.append(res_loc)
return {'res_html_code': res_html_code_list, 'res_loc': res_loc_list, 'res_score_list': result_score_list,
'res_elem_idx_list': result_elem_idx_list,'structure_str_list':structure_str}
return {
'res_html_code': res_html_code_list,
'res_loc': res_loc_list,
'res_score_list': result_score_list,
'res_elem_idx_list': result_elem_idx_list,
'structure_str_list': structure_str
}
def decode(self, text_index, structure_probs, char_or_elem):
"""convert text-label into text-index.
......@@ -516,3 +523,82 @@ class TableLabelDecode(object):
assert False, "Unsupport type %s in char_or_elem" \
% char_or_elem
return idx
class SARLabelDecode(BaseRecLabelDecode):
""" Convert between text-label and text-index """
def __init__(self,
character_dict_path=None,
character_type='ch',
use_space_char=False,
**kwargs):
super(SARLabelDecode, self).__init__(character_dict_path,
character_type, use_space_char)
self.rm_symbol = kwargs.get('rm_symbol', False)
def add_special_char(self, dict_character):
beg_end_str = "<BOS/EOS>"
unknown_str = "<UKN>"
padding_str = "<PAD>"
dict_character = dict_character + [unknown_str]
self.unknown_idx = len(dict_character) - 1
dict_character = dict_character + [beg_end_str]
self.start_idx = len(dict_character) - 1
self.end_idx = len(dict_character) - 1
dict_character = dict_character + [padding_str]
self.padding_idx = len(dict_character) - 1
return dict_character
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
""" convert text-index into text-label. """
result_list = []
ignored_tokens = self.get_ignored_tokens()
batch_size = len(text_index)
for batch_idx in range(batch_size):
char_list = []
conf_list = []
for idx in range(len(text_index[batch_idx])):
if text_index[batch_idx][idx] in ignored_tokens:
continue
if int(text_index[batch_idx][idx]) == int(self.end_idx):
if text_prob is None and idx == 0:
continue
else:
break
if is_remove_duplicate:
# only for predict
if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
batch_idx][idx]:
continue
char_list.append(self.character[int(text_index[batch_idx][
idx])])
if text_prob is not None:
conf_list.append(text_prob[batch_idx][idx])
else:
conf_list.append(1)
text = ''.join(char_list)
if self.rm_symbol:
comp = re.compile('[^A-Z^a-z^0-9^\u4e00-\u9fa5]')
text = text.lower()
text = comp.sub('', text)
result_list.append((text, np.mean(conf_list)))
return result_list
def __call__(self, preds, label=None, *args, **kwargs):
if isinstance(preds, paddle.Tensor):
preds = preds.numpy()
preds_idx = preds.argmax(axis=2)
preds_prob = preds.max(axis=2)
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
if label is None:
return text
label = self.decode(label, is_remove_duplicate=False)
return text, label
def get_ignored_tokens(self):
return [self.padding_idx]
tests/configs/det_mv3_db.yml 0 → 100644
浏览文件 @ 3db92cf3
Global:
use_gpu: false
epoch_num: 5
log_smooth_window: 20
print_batch_step: 1
save_model_dir: ./output/db_mv3/
save_epoch_step: 1200
# evaluation is run every 2000 iterations
eval_batch_step: [0, 400]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Loss:
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam #Momentum
#momentum: 0.9
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- Resize:
# size: [640, 640]
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1
num_workers: 0
use_shared_memory: False
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 0
use_shared_memory: False
tests/configs/det_r50_vd_db.yml 0 → 100644
浏览文件 @ 3db92cf3
Global:
use_gpu: false
epoch_num: 5
log_smooth_window: 20
print_batch_step: 1
save_model_dir: ./output/db_mv3/
save_epoch_step: 1200
# evaluation is run every 2000 iterations
eval_batch_step: [0, 400]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
model_type: det
algorithm: DB
Transform:
Backbone:
name: ResNet #MobileNetV3
layers: 50
Neck:
name: DBFPN
out_channels: 256
Head:
name: DBHead
k: 50
Loss:
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5 #5
beta: 10 #10
ohem_ratio: 3
Optimizer:
name: Adam #Momentum
#momentum: 0.9
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- Resize:
# size: [640, 640]
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1
num_workers: 0
use_shared_memory: False
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 0
use_shared_memory: False
tests/ocr_det_params.txt
浏览文件 @ 3db92cf3
......@@ -13,34 +13,34 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train|pact_train
norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
pact_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
norm_train:tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
pact_train:deploy/slim/quantization/quant.py -c tests/configs/det_mv3_db.yml -o
fpgm_train:deploy/slim/prune/sensitivity_anal.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
eval:tools/eval.py -c tests/configs/det_mv3_db.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/det_mv3_db.yml -o
norm_export:tools/export_model.py -c tests/configs/det_mv3_db.yml -o
quant_export:deploy/slim/quantization/export_model.py -c tests/configs/det_mv3_db.yml -o
fpgm_export:deploy/slim/prune/export_prune_model.py -c tests/configs/det_mv3_db.yml -o
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:null
train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
infer_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
infer_quant:False
inference:tools/infer/predict_det.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--cpu_threads:6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
......@@ -62,6 +62,21 @@ inference:./deploy/cpp_infer/build/ppocr det
--precision:fp32|fp16
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
null:null
--benchmark:True
===========================serving_params===========================
trans_model:-m paddle_serving_client.convert
--dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
--model_filename:inference.pdmodel
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
serving_dir:./deploy/pdserving
web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
op.det.local_service_conf.devices:null|0
op.det.local_service_conf.use_mkldnn:True|False
op.det.local_service_conf.thread_num:1|6
op.det.local_service_conf.use_trt:False|True
op.det.local_service_conf.precision:fp32|fp16|int8
pipline:pipeline_http_client.py --image_dir=../../doc/imgs
tests/ocr_det_server_params.txt
浏览文件 @ 3db92cf3
......@@ -13,7 +13,7 @@ train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
null:null
##
trainer:norm_train|pact_train
norm_train:tools/train.py -c configs/det/det_r50_vd_db.yml -o Global.pretrained_model=""
norm_train:tools/train.py -c tests/configs/det_r50_vd_db.yml -o Global.pretrained_model=""
pact_train:null
fpgm_train:null
distill_train:null
......@@ -21,13 +21,13 @@ null:null
null:null
##
===========================eval_params===========================
eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
eval:tools/eval.py -c tests/configs/det_r50_vd_db.yml -o
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:tools/export_model.py -c configs/det/det_r50_vd_db.yml -o
norm_export:tools/export_model.py -c tests/configs/det_r50_vd_db.yml -o
quant_export:null
fpgm_export:null
distill_export:null
......
tests/ocr_ppocr_mobile_params.txt 0 → 100644
浏览文件 @ 3db92cf3
===========================train_params===========================
model_name:ocr_system
python:python3.7
gpu_list:null
Global.use_gpu:null
Global.auto_cast:null
Global.epoch_num:null
Global.save_model_dir:./output/
Train.loader.batch_size_per_card:null
Global.pretrained_model:null
train_model_name:null
train_infer_img_dir:null
null:null
##
trainer:
norm_train:null
pact_train:null
fpgm_train:null
distill_train:null
null:null
null:null
##
===========================eval_params===========================
eval:null
null:null
##
===========================infer_params===========================
Global.save_inference_dir:./output/
Global.pretrained_model:
norm_export:null
quant_export:null
fpgm_export:null
distill_export:null
export1:null
export2:null
##
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_export:null
infer_quant:False
inference:tools/infer/predict_system.py
--use_gpu:True
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16|int8
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--save_log_path:null
--benchmark:True
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr system
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16
--det_model_dir:
--image_dir:./inference/ch_det_data_50/all-sum-510/
--rec_model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
--benchmark:True
tests/ocr_rec_params.txt
浏览文件 @ 3db92cf3
===========================train_params===========================
model_name:ocr_rec
python:python3.7
gpu_list:0|2,3
gpu_list:0|0,1
Global.use_gpu:True|True
Global.auto_cast:null
Global.epoch_num:lite_train_infer=2|whole_train_infer=300
......@@ -9,7 +9,7 @@ Global.save_model_dir:./output/
Train.loader.batch_size_per_card:lite_train_infer=128|whole_train_infer=128
Global.pretrained_model:null
train_model_name:latest
train_infer_img_dir:./train_data/ic15_data/train
train_infer_img_dir:./inference/rec_inference
null:null
##
trainer:norm_train|pact_train
......@@ -41,7 +41,7 @@ inference:tools/infer/predict_rec.py
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--rec_batch_num:1|6
--use_tensorrt:True|False
--precision:fp32|fp16|int8
--rec_model_dir:
......@@ -49,3 +49,18 @@ inference:tools/infer/predict_rec.py
--save_log_path:./test/output/
--benchmark:True
null:null
===========================cpp_infer_params===========================
use_opencv:True
infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
infer_quant:False
inference:./deploy/cpp_infer/build/ppocr rec
--use_gpu:True|False
--enable_mkldnn:True|False
--cpu_threads:1|6
--rec_batch_num:1
--use_tensorrt:False|True
--precision:fp32|fp16
--rec_model_dir:
--image_dir:./inference/rec_inference/
null:null
--benchmark:True
\ No newline at end of file
tests/prepare.sh
浏览文件 @ 3db92cf3
#!/bin/bash
FILENAME=$1
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer']
# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer', 'serving_infer']
MODE=$2
dataline=$(cat ${FILENAME})
......@@ -40,11 +41,13 @@ if [ ${MODE} = "lite_train_infer" ];then
rm -rf ./train_data/ic15_data
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar # todo change to bcebos
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
wget -nc -P ./deploy/slim/prune https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle
cd ./train_data/ && tar xf icdar2015_lite.tar && tar xf ic15_data.tar
ln -s ./icdar2015_lite ./icdar2015
cd ../
cd ./inference && tar xf rec_inference.tar && cd ../
elif [ ${MODE} = "whole_train_infer" ];then
wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
rm -rf ./train_data/icdar2015
......@@ -61,64 +64,99 @@ elif [ ${MODE} = "whole_infer" ];then
cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
ln -s ./icdar2015_infer ./icdar2015
cd ../
elif [ ${MODE} = "infer" ] || [ ${MODE} = "cpp_infer" ];then
elif [ ${MODE} = "infer" ];then
if [ ${model_name} = "ocr_det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
eval_model_name="ch_ppocr_mobile_v2.0_det_train"
rm -rf ./train_data/icdar2015
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
elif [ ${model_name} = "ocr_server_det" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../
elif [ ${model_name} = "ocr_system" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../
else
rm -rf ./train_data/ic15_data
eval_model_name="ch_ppocr_mobile_v2.0_rec_infer"
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../
fi
elif [ ${MODE} = "cpp_infer" ];then
if [ ${model_name} = "ocr_det" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../
elif [ ${model_name} = "ocr_rec" ]; then
wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
cd ./inference && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf rec_inference.tar && cd ../
elif [ ${model_name} = "ocr_system" ]; then
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ic15_data.tar && cd ../
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../
fi
fi
if [ ${MODE} = "serving_infer" ];then
# prepare serving env
python_name=$(func_parser_value "${lines[2]}")
${python_name} -m pip install install paddle-serving-server-gpu==0.6.1.post101
${python_name} -m pip install paddle_serving_client==0.6.1
${python_name} -m pip install paddle-serving-app==0.6.1
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && cd ../
fi
if [ ${MODE} = "cpp_infer" ];then
cd deploy/cpp_infer
use_opencv=$(func_parser_value "${lines[52]}")
if [ ${use_opencv} = "True" ]; then
echo "################### build opencv ###################"
rm -rf 3.4.7.tar.gz opencv-3.4.7/
wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
tar -xf 3.4.7.tar.gz
if [ -d "opencv-3.4.7/opencv3/" ] && [ $(md5sum opencv-3.4.7.tar.gz | awk -F ' ' '{print $1}') = "faa2b5950f8bee3f03118e600c74746a" ];then
echo "################### build opencv skipped ###################"
else
echo "################### build opencv ###################"
rm -rf opencv-3.4.7.tar.gz opencv-3.4.7/
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/opencv-3.4.7.tar.gz
tar -xf opencv-3.4.7.tar.gz
cd opencv-3.4.7/
install_path=$(pwd)/opencv-3.4.7/opencv3
cd opencv-3.4.7/
install_path=$(pwd)/opencv3
rm -rf build
mkdir build
cd build
rm -rf build
mkdir build
cd build
cmake .. \
-DCMAKE_INSTALL_PREFIX=${install_path} \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DWITH_IPP=OFF \
-DBUILD_IPP_IW=OFF \
-DWITH_LAPACK=OFF \
-DWITH_EIGEN=OFF \
-DCMAKE_INSTALL_LIBDIR=lib64 \
-DWITH_ZLIB=ON \
-DBUILD_ZLIB=ON \
-DWITH_JPEG=ON \
-DBUILD_JPEG=ON \
-DWITH_PNG=ON \
-DBUILD_PNG=ON \
-DWITH_TIFF=ON \
-DBUILD_TIFF=ON
cmake .. \
-DCMAKE_INSTALL_PREFIX=${install_path} \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=OFF \
-DWITH_IPP=OFF \
-DBUILD_IPP_IW=OFF \
-DWITH_LAPACK=OFF \
-DWITH_EIGEN=OFF \
-DCMAKE_INSTALL_LIBDIR=lib64 \
-DWITH_ZLIB=ON \
-DBUILD_ZLIB=ON \
-DWITH_JPEG=ON \
-DBUILD_JPEG=ON \
-DWITH_PNG=ON \
-DBUILD_PNG=ON \
-DWITH_TIFF=ON \
-DBUILD_TIFF=ON
make -j
make install
cd ../
echo "################### build opencv finished ###################"
make -j
make install
cd ../
echo "################### build opencv finished ###################"
fi
fi
......@@ -149,4 +187,4 @@ if [ ${MODE} = "cpp_infer" ];then
make -j
echo "################### build PaddleOCR demo finished ###################"
fi
\ No newline at end of file
fi
tests/readme.md
浏览文件 @ 3db92cf3
......@@ -23,36 +23,46 @@ test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从
```bash
tests/
├── ocr_det_params.txt # 测试OCR检测模型的参数配置文件
├── ocr_rec_params.txt # 测试OCR识别模型的参数配置文件
└── prepare.sh # 完成test.sh运行所需要的数据和模型下载
└── test.sh # 根据
├── ocr_det_params.txt # 测试OCR检测模型的参数配置文件
├── ocr_rec_params.txt # 测试OCR识别模型的参数配置文件
├── ocr_ppocr_mobile_params.txt # 测试OCR检测+识别模型串联的参数配置文件
└── prepare.sh # 完成test.sh运行所需要的数据和模型下载
└── test.sh # 测试主程序
```
# 使用方法
test.sh包含四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
- 模式1 lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```
- 模式1：lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```shell
bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer'
```
- 模式2 whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
```
```
- 模式2：whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
```shell
bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer'
```
```
- 模式3 infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
```
- 模式3：infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
```shell
bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
用法1:
# 用法1:
bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1'
```
```
模式4: whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度
```
- 模式4：whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度；
```shell
bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
```
```
- 模式5：cpp_infer , CE： 验证inference model的c++预测是否走通；
```shell
bash tests/prepare.sh ./tests/ocr_det_params.txt 'cpp_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'cpp_infer'
```
tests/test.sh
浏览文件 @ 3db92cf3
......@@ -144,6 +144,32 @@ benchmark_key=$(func_parser_key "${lines[49]}")
benchmark_value=$(func_parser_value "${lines[49]}")
infer_key1=$(func_parser_key "${lines[50]}")
infer_value1=$(func_parser_value "${lines[50]}")
# parser serving
trans_model_py=$(func_parser_value "${lines[67]}")
infer_model_dir_key=$(func_parser_key "${lines[68]}")
infer_model_dir_value=$(func_parser_value "${lines[68]}")
model_filename_key=$(func_parser_key "${lines[69]}")
model_filename_value=$(func_parser_value "${lines[69]}")
params_filename_key=$(func_parser_key "${lines[70]}")
params_filename_value=$(func_parser_value "${lines[70]}")
serving_server_key=$(func_parser_key "${lines[71]}")
serving_server_value=$(func_parser_value "${lines[71]}")
serving_client_key=$(func_parser_key "${lines[72]}")
serving_client_value=$(func_parser_value "${lines[72]}")
serving_dir_value=$(func_parser_value "${lines[73]}")
web_service_py=$(func_parser_value "${lines[74]}")
web_use_gpu_key=$(func_parser_key "${lines[75]}")
web_use_gpu_list=$(func_parser_value "${lines[75]}")
web_use_mkldnn_key=$(func_parser_key "${lines[76]}")
web_use_mkldnn_list=$(func_parser_value "${lines[76]}")
web_cpu_threads_key=$(func_parser_key "${lines[77]}")
web_cpu_threads_list=$(func_parser_value "${lines[77]}")
web_use_trt_key=$(func_parser_key "${lines[78]}")
web_use_trt_list=$(func_parser_value "${lines[78]}")
web_precision_key=$(func_parser_key "${lines[79]}")
web_precision_list=$(func_parser_value "${lines[79]}")
pipeline_py=$(func_parser_value "${lines[80]}")
if [ ${MODE} = "cpp_infer" ]; then
# parser cpp inference model
......@@ -166,7 +192,8 @@ if [ ${MODE} = "cpp_infer" ]; then
cpp_infer_model_key=$(func_parser_key "${lines[62]}")
cpp_image_dir_key=$(func_parser_key "${lines[63]}")
cpp_infer_img_dir=$(func_parser_value "${lines[63]}")
cpp_save_log_key=$(func_parser_key "${lines[64]}")
cpp_infer_key1=$(func_parser_key "${lines[64]}")
cpp_infer_value1=$(func_parser_value "${lines[64]}")
cpp_benchmark_key=$(func_parser_key "${lines[65]}")
cpp_benchmark_value=$(func_parser_value "${lines[65]}")
fi
......@@ -244,6 +271,81 @@ function func_inference(){
fi
done
}
function func_serving(){
IFS='|'
_python=$1
_script=$2
_model_dir=$3
# pdserving
set_dirname=$(func_set_params "${infer_model_dir_key}" "${infer_model_dir_value}")
set_model_filename=$(func_set_params "${model_filename_key}" "${model_filename_value}")
set_params_filename=$(func_set_params "${params_filename_key}" "${params_filename_value}")
set_serving_server=$(func_set_params "${serving_server_key}" "${serving_server_value}")
set_serving_client=$(func_set_params "${serving_client_key}" "${serving_client_value}")
trans_model_cmd="${python} ${trans_model_py} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_serving_server} ${set_serving_client}"
eval $trans_model_cmd
cd ${serving_dir_value}
echo $PWD
unset https_proxy
unset http_proxy
for use_gpu in ${web_use_gpu_list[*]}; do
echo ${ues_gpu}
if [ ${use_gpu} = "null" ]; then
for use_mkldnn in ${web_use_mkldnn_list[*]}; do
if [ ${use_mkldnn} = "False" ]; then
continue
fi
for threads in ${web_cpu_threads_list[*]}; do
_save_log_path="${_log_path}/server_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
set_cpu_threads=$(func_set_params "${web_cpu_threads_key}" "${threads}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${web_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} &>${_save_log_path} &"
eval $web_service_cmd
sleep 2s
pipeline_cmd="${python} ${pipeline_py}"
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
PID=$!
kill $PID
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
elif [ ${use_gpu} = "0" ]; then
for use_trt in ${web_use_trt_list[*]}; do
for precision in ${web_precision_list[*]}; do
if [[ ${_flag_quant} = "False" ]] && [[ ${precision} =~ "int8" ]]; then
continue
fi
if [[ ${precision} =~ "fp16" || ${precision} =~ "int8" ]] && [ ${use_trt} = "False" ]; then
continue
fi
if [[ ${use_trt} = "Falg_quantse" || ${precision} =~ "int8" ]]; then
continue
fi
_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
set_tensorrt=$(func_set_params "${web_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${web_precision_key}" "${precision}")
web_service_cmd="${python} ${web_service_py} ${web_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} &>${_save_log_path} & "
eval $web_service_cmd
sleep 2s
pipeline_cmd="${python} ${pipeline_py}"
eval $pipeline_cmd
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
status_check $last_status "${pipeline_cmd}" "${status_log}"
PID=$!
kill $PID
sleep 2s
ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
done
done
else
echo "Does not support hardware other than CPU and GPU Currently!"
fi
done
}
function func_cpp_inference(){
IFS='|'
......@@ -267,7 +369,8 @@ function func_cpp_inference(){
set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
set_cpu_threads=$(func_set_params "${cpp_cpu_threads_key}" "${threads}")
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${cpp_use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
......@@ -295,7 +398,8 @@ function func_cpp_inference(){
set_tensorrt=$(func_set_params "${cpp_use_trt_key}" "${use_trt}")
set_precision=$(func_set_params "${cpp_precision_key}" "${precision}")
set_model_dir=$(func_set_params "${cpp_infer_model_key}" "${_model_dir}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
set_infer_params1=$(func_set_params "${cpp_infer_key1}" "${cpp_infer_value1}")
command="${_script} ${cpp_use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
eval $command
last_status=${PIPESTATUS[0]}
eval "cat ${_save_log_path}"
......@@ -332,9 +436,7 @@ if [ ${MODE} = "infer" ]; then
export_cmd="${python} ${norm_export} ${set_export_weight} ${set_save_infer_key}"
eval $export_cmd
status_export=$?
if [ ${status_export} = 0 ];then
status_check $status_export "${export_cmd}" "${status_log}"
fi
status_check $status_export "${export_cmd}" "${status_log}"
else
save_infer_dir=${infer_model}
fi
......@@ -362,6 +464,20 @@ elif [ ${MODE} = "cpp_infer" ]; then
func_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant}
Count=$(($Count + 1))
done
elif [ ${MODE} = "serving_infer" ]; then
GPUID=$3
if [ ${#GPUID} -le 0 ];then
env=" "
else
env="export CUDA_VISIBLE_DEVICES=${GPUID}"
fi
# set CUDA_VISIBLE_DEVICES
eval $env
export Count=0
IFS="|"
#run serving
func_serving "${web_service_cmd}"
else
IFS="|"
......
tools/eval.py
浏览文件 @ 3db92cf3
......@@ -55,6 +55,7 @@ def main():
model = build_model(config['Architecture'])
use_srn = config['Architecture']['algorithm'] == "SRN"
use_sar = config['Architecture']['algorithm'] == "SAR"
if "model_type" in config['Architecture'].keys():
model_type = config['Architecture']['model_type']
else:
......@@ -71,7 +72,7 @@ def main():
# start eval
metric = program.eval(model, valid_dataloader, post_process_class,
eval_class, model_type, use_srn)
eval_class, model_type, use_srn, use_sar)
logger.info('metric eval ***************')
for k, v in metric.items():
logger.info('{}:{}'.format(k, v))
......
tools/infer/predict_e2e.py
浏览文件 @ 3db92cf3
......@@ -141,6 +141,7 @@ if __name__ == "__main__":
img, flag = check_and_read_gif(image_file)
if not flag:
img = cv2.imread(image_file)
img = img[:, :, ::-1]
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
......
tools/infer/predict_system.py
浏览文件 @ 3db92cf3
......@@ -173,6 +173,9 @@ def main(args):
logger.info("The predict total time is {}".format(time.time() - _st))
logger.info("\nThe predict total time is {}".format(total_time))
if args.benchmark:
text_sys.text_detector.autolog.report()
text_sys.text_recognizer.autolog.report()
if __name__ == "__main__":
......
tools/infer/utility.py
浏览文件 @ 3db92cf3
......@@ -236,11 +236,11 @@ def create_predictor(args, mode, logger):
max_input_shape.update(max_pact_shape)
opt_input_shape.update(opt_pact_shape)
elif mode == "rec":
min_input_shape = {"x": [args.rec_batch_num, 3, 32, 10]}
min_input_shape = {"x": [1, 3, 32, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 32, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
elif mode == "cls":
min_input_shape = {"x": [args.rec_batch_num, 3, 48, 10]}
min_input_shape = {"x": [1, 3, 48, 10]}
max_input_shape = {"x": [args.rec_batch_num, 3, 48, 2000]}
opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
else:
......
tools/infer_rec.py
浏览文件 @ 3db92cf3
......@@ -74,6 +74,10 @@ def main():
'image', 'encoder_word_pos', 'gsrm_word_pos',
'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
]
elif config['Architecture']['algorithm'] == "SAR":
op[op_name]['keep_keys'] = [
'image', 'valid_ratio'
]
else:
op[op_name]['keep_keys'] = ['image']
transforms.append(op)
......@@ -106,11 +110,16 @@ def main():
paddle.to_tensor(gsrm_slf_attn_bias1_list),
paddle.to_tensor(gsrm_slf_attn_bias2_list)
]
if config['Architecture']['algorithm'] == "SAR":
valid_ratio = np.expand_dims(batch[-1], axis=0)
img_metas = [paddle.to_tensor(valid_ratio)]
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_tensor(images)
if config['Architecture']['algorithm'] == "SRN":
preds = model(images, others)
elif config['Architecture']['algorithm'] == "SAR":
preds = model(images, img_metas)
else:
preds = model(images)
post_result = post_process_class(preds)
......
tools/program.py
浏览文件 @ 3db92cf3
......@@ -187,7 +187,7 @@ def train(config,
use_srn = config['Architecture']['algorithm'] == "SRN"
use_nrtr = config['Architecture']['algorithm'] == "NRTR"
use_sar = config['Architecture']['algorithm'] == 'SAR'
try:
model_type = config['Architecture']['model_type']
except:
......@@ -215,7 +215,7 @@ def train(config,
images = batch[0]
if use_srn:
model_average = True
if use_srn or model_type == 'table' or use_nrtr:
if use_srn or model_type == 'table' or use_nrtr or use_sar:
preds = model(images, data=batch[1:])
else:
preds = model(images)
......@@ -279,7 +279,8 @@ def train(config,
post_process_class,
eval_class,
model_type,
use_srn=use_srn)
use_srn=use_srn,
use_sar=use_sar)
cur_metric_str = 'cur metric, {}'.format(', '.join(
['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
logger.info(cur_metric_str)
......@@ -351,7 +352,8 @@ def eval(model,
post_process_class,
eval_class,
model_type,
use_srn=False):
use_srn=False,
use_sar=False):
model.eval()
with paddle.no_grad():
total_frame = 0.0
......@@ -364,7 +366,7 @@ def eval(model,
break
images = batch[0]
start = time.time()
if use_srn or model_type == 'table':
if use_srn or model_type == 'table' or use_sar:
preds = model(images, data=batch[1:])
else:
preds = model(images)
......@@ -400,7 +402,7 @@ def preprocess(is_train=False):
alg = config['Architecture']['algorithm']
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn'
'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR'
]
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
......
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