fix conflict

.github/ISSUE_TEMPLATE/custom.md

+---
+name: Issue template
+about: Issue template for code error.
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+请提供下述完整信息以便快速定位问题/Please provide the following information to quickly locate the problem
+
+- 系统环境/System Environment：
+- 版本号/Version：Paddle：  PaddleOCR： 问题相关组件/Related components：
+- 运行指令/Command Code：
+- 完整报错/Complete Error Message：

MANIFEST.in

@@ -5,5 +5,6 @@ recursive-include ppocr/utils *.txt utility.py logging.py network.py
 recursive-include ppocr/data *.py
 recursive-include ppocr/postprocess *.py
 recursive-include tools/infer *.py
+recursive-include tools __init__.py
 recursive-include ppocr/utils/e2e_utils *.py
 recursive-include ppstructure *.py
\ No newline at end of file

PPOCRLabel/PPOCRLabel.py

@@ -35,6 +35,7 @@ import numpy as np
 
 sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../PaddleOCR')))
 sys.path.append("..")
 
 from paddleocr import PaddleOCR
@@ -113,7 +114,7 @@ class MainWindow(QMainWindow, WindowMixin):
         getStr = lambda strId: self.stringBundle.getString(strId)
 
         self.defaultSaveDir = defaultSaveDir
-        self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=gpu, lang=lang)
+        self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=gpu, lang=lang, show_log=False)
 
         if os.path.exists('./data/paddle.png'):
             result = self.ocr.ocr('./data/paddle.png', cls=True, det=True)
@@ -390,7 +391,7 @@ class MainWindow(QMainWindow, WindowMixin):
                           'Ctrl+J', 'edit', u'Move and edit Boxs', enabled=False)
 
         create = action(getStr('crtBox'), self.createShape,
-                        'w', 'new', getStr('crtBoxDetail'), enabled=False)
+                        'w', 'objects', getStr('crtBoxDetail'), enabled=False)
 
         delete = action(getStr('delBox'), self.deleteSelectedShape,
                         'backspace', 'delete', getStr('delBoxDetail'), enabled=False)
@@ -1388,7 +1389,6 @@ class MainWindow(QMainWindow, WindowMixin):
         for box in self.PPlabel[imgidx]:
             shapes.append((box['transcription'], box['points'], None, None, box['difficult']))
 
-        print(shapes)
         self.loadLabels(shapes)
         self.canvas.verified = False
 

PPOCRLabel/README.md

@@ -8,7 +8,10 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, w
 
 ### Recent Update
 
-- 2021.8.11：
+- 2021.11.17:
+  - Support install and start PPOCRLabel through the whl package (by [d2623587501](https://github.com/d2623587501))
+  - Dataset segmentation: Divide the annotation file into training, verification and testing parts (refer to section 3.5 below, by [MrCuiHao](https://github.com/MrCuiHao))
+- 2021.8.11:
   - New functions: Open the dataset folder, image rotation (Note: Please delete the label box before rotating the image) (by [Wei-JL](https://github.com/Wei-JL))
   - Added shortcut key description (Help-Shortcut Key), repaired the direction shortcut key movement function under batch processing (by [d2623587501](https://github.com/d2623587501))
 - 2021.2.5: New batch processing and undo functions (by [Evezerest](https://github.com/Evezerest)):
@@ -21,14 +24,11 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, w
   - Click to modify the recognition result.(If you can't change the result, please switch to the system default input method, or switch back to the original input method again)
 - 2020.12.18: Support re-recognition of a single label box (by [ninetailskim](https://github.com/ninetailskim) ), perfect shortcut keys.
 
-### TODO:
-- Lock box mode: For the same scene data, the size and position of the locked detection box can be transferred between different pictures.
 
-## Installation
 
-### 1. Environment Preparation
+## 1. Installation and Run
 
-#### **Install PaddlePaddle 2.0**
+### 1.1 Install PaddlePaddle
 
 ```bash
 pip3 install --upgrade pip
@@ -42,61 +42,57 @@ python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
 
 For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
 
-#### **Install PaddleOCR**
+### 1.2 Install and Run PPOCRLabel
 
-```bash
-# Recommend
-git clone https://github.com/PaddlePaddle/PaddleOCR
-
-# If you cannot pull successfully due to network problems, you can also choose to use the code hosting on the cloud:
-
-git clone https://gitee.com/paddlepaddle/PaddleOCR
-
-# Note: The cloud-hosting code may not be able to synchronize the update with this GitHub project in real time. There might be a delay of 3-5 days. Please give priority to the recommended method.
-```
+PPOCRLabel can be started in two ways: whl package and Python script. The whl package form is more convenient to start, and the python script to start is convenient for secondary development.
 
-#### **Install Third-party Libraries**
+#### Windows
 
 ```bash
-cd PaddleOCR
-pip3 install -r requirements.txt
+pip install PPOCRLabel  # install
+PPOCRLabel  # run
 ```
 
-If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows. Please try to download Shapely whl file using http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely.
-
-Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
+> If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows. Please try to download Shapely whl file using http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely.
+>
+> Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
+>
 
-### 2. Install PPOCRLabel
+#### Ubuntu Linux
 
-#### Windows
+```bash
+pip3 install PPOCRLabel
+pip3 install trash-cli
+PPOCRLabel
+```
 
+#### MacOS
 ```bash
-pip install pyqt5
-cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python PPOCRLabel.py
+pip3 install PPOCRLabel
+pip3 install opencv-contrib-python-headless==4.2.0.32
+PPOCRLabel # run
 ```
 
-#### Ubuntu Linux
+#### 1.2.2 Build and Install the Whl Package Locally
 
 ```bash
-pip3 install pyqt5
-pip3 install trash-cli
-cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python3 PPOCRLabel.py
+cd PaddleOCR/PPOCRLabel
+python3 setup.py bdist_wheel 
+pip3 install dist/PPOCRLabel-1.0.0-py2.py3-none-any.whl
 ```
 
-#### MacOS
+#### 1.2.3 Run PPOCRLabel by Python Script
+
 ```bash
-pip3 install pyqt5
-pip3 uninstall opencv-python # Uninstall opencv manually as it conflicts with pyqt
-pip3 install opencv-contrib-python-headless==4.2.0.32 # Install the headless version of opencv
-cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
-python3 PPOCRLabel.py
+cd ./PPOCRLabel  # Switch to the PPOCRLabel directory
+python PPOCRLabel.py --lang ch
 ```
 
-## Usage
 
-### Steps
+
+## 2. Usage
+
+### 2.1 Steps
 
 1. Build and launch using the instructions above.
 
@@ -122,7 +118,7 @@ python3 PPOCRLabel.py
 
 10. Labeling result: the user can export the label result manually through the menu "File - Export Label", while the program will also export automatically if "File - Auto export Label Mode" is selected. The manually checked label will be stored in *Label.txt* under the opened picture folder. Click "File"-"Export Recognition Results" in the menu bar, the recognition training data of such pictures will be saved in the *crop_img* folder, and the recognition label will be saved in *rec_gt.txt*<sup>[4]</sup>.
 
-### Note
+### 2.2 Note
 
 [1] PPOCRLabel uses the opened folder as the project. After opening the image folder, the picture will not be displayed in the dialog. Instead, the pictures under the folder will be directly imported into the program after clicking "Open Dir".
 
@@ -140,9 +136,11 @@ python3 PPOCRLabel.py
 |  rec_gt.txt   | The recognition label file, which can be directly used for PPOCR identification model training, is generated after the user clicks on the menu bar "File"-"Export recognition result". |
 |   crop_img    | The recognition data, generated at the same time with *rec_gt.txt* |
 
-## Explanation
 
-### Shortcut keys
+
+## 3. Explanation
+
+### 3.1 Shortcut keys
 
 | Shortcut keys            | Description                                      |
 | ------------------------ | ------------------------------------------------ |
@@ -162,31 +160,56 @@ python3 PPOCRLabel.py
 | Ctrl--                   | Zoom out                                         |
 | ↑→↓←                     | Move selected box                                |
 
-### Built-in Model
+### 3.2 Built-in Model
 
 - Default model: PPOCRLabel uses the Chinese and English ultra-lightweight OCR model in PaddleOCR by default, supports Chinese, English and number recognition, and multiple language detection.
 
 - Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languages​include French, German, Korean, and Japanese.
   For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md#multilingual-recognition-modelupdating)
 
-- Custom model: The model trained by users can be replaced by modifying PPOCRLabel.py in [PaddleOCR class instantiation](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/PPOCRLabel.py#L110) referring [Custom Model Code](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md#use-custom-model)
+- **Custom Model**: If users want to replace the built-in model with their own inference model, they can follow the [Custom Model Code Usage](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_en/whl_en.md#31-use-by-code) by modifying PPOCRLabel.py for [Instantiation of PaddleOCR class](https://github.com/PaddlePaddle/PaddleOCR/blob/release/ 2.3/PPOCRLabel/PPOCRLabel.py#L116) :
 
-### Export Label Result
+  add parameter `det_model_dir`  in `self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=gpu, lang=lang) `
+
+### 3.3 Export Label Result
 
 PPOCRLabel supports three ways to export Label.txt
 
 - Automatically export: After selecting "File - Auto Export Label Mode", the program will automatically write the annotations into Label.txt every time the user confirms an image. If this option is not turned on, it will be automatically exported after detecting that the user has manually checked 5 images.
+
+  > The automatically export mode is turned off by default
+
 - Manual export: Click "File-Export Marking Results" to manually export the label.
+
 - Close application export
 
 
-### Export Partial Recognition Results
+### 3.4 Export Partial Recognition Results
+
+For some data that are difficult to recognize, the recognition results will not be exported by **unchecking** the corresponding tags in the recognition results checkbox. The unchecked recognition result is saved as `True` in the `difficult` variable in the label file `label.txt`.
+
+> *Note: The status of the checkboxes in the recognition results still needs to be saved manually by clicking Save Button.*
 
-For some data that are difficult to recognize, the recognition results will not be exported by **unchecking** the corresponding tags in the recognition results checkbox.
+### 3.5 Dataset division
 
-*Note: The status of the checkboxes in the recognition results still needs to be saved manually by clicking Save Button.*
+- Enter the following command in the terminal to execute the dataset division script:
 
-### Error message
+  ```
+  cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
+  python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --labelRootPath ../train_data/label --detRootPath ../train_data/det --recRootPath ../train_data/rec
+  ```
+
+  Parameter Description:
+
+  - `trainValTestRatio` is the division ratio of the number of images in the training set, validation set, and test set, set according to your actual situation, the default is `6:2:2`
+
+  - `labelRootPath` is the storage path of the dataset labeled by PPOCRLabel, the default is `../train_data/label`
+
+  - `detRootPath` is the path where the text detection dataset is divided according to the dataset marked by PPOCRLabel. The default is `../train_data/det`
+
+  - `recRootPath` is the path where the character recognition dataset is divided according to the dataset marked by PPOCRLabel. The default is `../train_data/rec`
+
+### 3.6 Error message
 
 - If paddleocr is installed with whl, it has a higher priority than calling PaddleOCR class with paddleocr.py, which may cause an exception if whl package is not updated.
 
@@ -204,6 +227,8 @@ For some data that are difficult to recognize, the recognition results will not
     pip install opencv-contrib-python-headless==4.2.0.32
     ```
 
-### Related
+
+
+### 4. Related
 
 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
\ No newline at end of file

PPOCRLabel/README_ch.md

@@ -2,14 +2,17 @@
 
 # PPOCRLabel
 
-PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置PPOCR模型对数据自动标注和重新识别。使用python3和pyqt5编写，支持矩形框标注和四点标注模式，导出格式可直接用于PPOCR检测和识别模型的训练。
+PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置PP-OCR模型对数据自动标注和重新识别。使用Python3和PyQT5编写，支持矩形框标注和四点标注模式，导出格式可直接用于PaddleOCR检测和识别模型的训练。
 
 <img src="./data/gif/steps.gif" width="100%"/>
 
 #### 近期更新
 
+- 2021.11.17：
+  - 新增支持通过whl包安装和启动PPOCRLabel（by [d2623587501](https://github.com/d2623587501)）
+  - 标注数据集切分：对标注数据进行训练、验证与测试集划分（参考下方3.5节，by [MrCuiHao](https://github.com/MrCuiHao)）
 - 2021.8.11：
-  - 新增功能：打开数据所在文件夹、图像旋转（注意：旋转前的图片上不能存在标记框）（by [Wei-JL](https://github.com/Wei-JL)）
+  - 新增功能：打开数据所在文件夹、右键图像旋转90度（注意：旋转前的图片上不能存在标记框，by [Wei-JL](https://github.com/Wei-JL)）
   - 新增快捷键说明（帮助-快捷键）、修复批处理下的方向快捷键移动功能（by [d2623587501](https://github.com/d2623587501)）
 - 2021.2.5：新增批处理与撤销功能（by [Evezerest](https://github.com/Evezerest))
   - **批处理功能**：按住Ctrl键选择标记框后可批量移动、复制、删除、重新识别。
@@ -21,85 +24,75 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置P
   - 识别结果更改为单击修改。（如果无法修改，请切换为系统自带输入法，或再次切回原输入法）
 - 2020.12.18： 支持对单个标记框进行重新识别（by [ninetailskim](https://github.com/ninetailskim)），完善快捷键。
 
-#### 尽请期待
-
-- 锁定框模式：针对同一场景数据，被锁定的检测框的大小与位置能在不同图片之间传递。
-
 如果您对以上内容感兴趣或对完善工具有不一样的想法，欢迎加入我们的SIG队伍与我们共同开发。可以在[此处](https://github.com/PaddlePaddle/PaddleOCR/issues/1728)完成问卷和前置任务，经过我们确认相关内容后即可正式加入，享受SIG福利，共同为OCR开源事业贡献（特别说明：针对PPOCRLabel的改进也属于PaddleOCR前置任务）
 
 
-## 安装
 
-### 1. 环境搭建
-#### 安装PaddlePaddle
+## 1. 安装与运行
 
+### 1.1 安装PaddlePaddle
 ```bash
 pip3 install --upgrade pip
 
-如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
+# 如果您的机器安装的是CUDA9或CUDA10，请运行以下命令安装
 python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
 
-如果您的机器是CPU，请运行以下命令安装
-
+# 如果您的机器是CPU，请运行以下命令安装
 python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
 ```
 
 更多的版本需求，请参照[安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
 
-#### **安装PaddleOCR**
+### 1.2 安装与运行PPOCRLabel
 
-```bash
-【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
+PPOCRLabel可通过whl包与Python脚本两种方式启动，whl包形式启动更加方便，python脚本启动便于二次开发
 
-如果因为网络问题无法pull成功，也可选择使用码云上的托管：
+#### 1.2.1 通过whl包安装与运行
 
-git clone https://gitee.com/paddlepaddle/PaddleOCR
+##### Windows
 
-注：码云托管代码可能无法实时同步本github项目更新，存在3~5天延时，请优先使用推荐方式。
+```bash
+pip install PPOCRLabel  # 安装
+PPOCRLabel --lang ch  # 运行
 ```
-
-#### 安装第三方库
+> 注意：通过whl包安装PPOCRLabel会自动下载 `paddleocr` whl包，其中shapely依赖可能会出现 `[winRrror 126] 找不到指定模块的问题。` 的错误，建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载并安装
+##### Ubuntu Linux
 
 ```bash
-cd PaddleOCR
-pip3 install -r requirements.txt
+pip3 install PPOCRLabel
+pip3 install trash-cli
+PPOCRLabel --lang ch
 ```
 
-注意，windows环境下，建议从[这里](https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely)下载shapely安装包完成安装， 直接通过pip安装的shapely库可能出现`[winRrror 126] 找不到指定模块的问题`。
-
-### 2. 安装PPOCRLabel
-
-#### Windows
-
+##### MacOS
 ```bash
-pip install pyqt5
-cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
-python PPOCRLabel.py --lang ch
+pip3 install PPOCRLabel
+pip3 install opencv-contrib-python-headless==4.2.0.32 # 如果下载过慢请添加"-i https://mirror.baidu.com/pypi/simple"
+PPOCRLabel --lang ch # 启动
 ```
 
-#### Ubuntu Linux
+#### 1.2.2 本地构建whl包并安装
 
 ```bash
-pip3 install pyqt5
-pip3 install trash-cli
-cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
-python3 PPOCRLabel.py --lang ch
+cd PaddleOCR/PPOCRLabel
+python3 setup.py bdist_wheel 
+pip3 install dist/PPOCRLabel-1.0.0-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple
 ```
 
-#### MacOS
+#### 1.2.3 通过Python脚本运行PPOCRLabel
+
+如果您对PPOCRLabel文件有所更改，通过Python脚本运行会更加方面的看到更改的结果
+
 ```bash
-pip3 install pyqt5
-pip3 uninstall opencv-python # 由于mac版本的opencv与pyqt有冲突，需先手动卸载opencv
-pip3 install opencv-contrib-python-headless==4.2.0.32 # 安装headless版本的open-cv
-cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
-python3 PPOCRLabel.py --lang ch
+cd ./PPOCRLabel  # 切换到PPOCRLabel目录
+python PPOCRLabel.py --lang ch
 ```
 
 
 
-## 使用
+## 2. 使用
 
-### 操作步骤
+### 2.1 操作步骤
 
 1. 安装与运行：使用上述命令安装与运行程序。
 2. 打开文件夹：在菜单栏点击 “文件” - "打开目录" 选择待标记图片的文件夹<sup>[1]</sup>.
@@ -112,7 +105,7 @@ python3 PPOCRLabel.py --lang ch
 9. 删除：点击 “删除图像”，图片将会被删除至回收站。
 10. 导出结果：用户可以通过菜单中“文件-导出标记结果”手动导出，同时也可以点击“文件 - 自动导出标记结果”开启自动导出。手动确认过的标记将会被存放在所打开图片文件夹下的*Label.txt*中。在菜单栏点击 “文件” - "导出识别结果"后，会将此类图片的识别训练数据保存在*crop_img*文件夹下，识别标签保存在*rec_gt.txt*中<sup>[4]</sup>。
 
-### 注意
+### 2.2 注意
 
 [1] PPOCRLabel以文件夹为基本标记单位，打开待标记的图片文件夹后，不会在窗口栏中显示图片，而是在点击 "选择文件夹" 之后直接将文件夹下的图片导入到程序中。
 
@@ -130,9 +123,11 @@ python3 PPOCRLabel.py --lang ch
 |  rec_gt.txt   | 识别标签。可直接用于PPOCR识别模型训练。需用户手动点击菜单栏“文件” - "导出识别结果"后产生。 |
 |   crop_img    |   识别数据。按照检测框切割后的图片。与rec_gt.txt同时产生。   |
 
-## 说明
 
-### 快捷键
+
+## 3. 说明
+
+### 3.1 快捷键
 
 | 快捷键           | 说明                         |
 | ---------------- | ---------------------------- |
@@ -152,29 +147,53 @@ python3 PPOCRLabel.py --lang ch
 | Ctrl--           | 放大                         |
 | ↑→↓←             | 移动标记框                   |
 
-### 内置模型
+### 3.2 内置模型
 
  - 默认模型：PPOCRLabel默认使用PaddleOCR中的中英文超轻量OCR模型，支持中英文与数字识别，多种语言检测。
 
  - 模型语言切换：用户可通过菜单栏中 "PaddleOCR" - "选择模型" 切换内置模型语言，目前支持的语言包括法文、德文、韩文、日文。具体模型下载链接可参考[PaddleOCR模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/models_list.md).
 
- - 自定义模型：用户可根据[自定义模型代码使用](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md#%E8%87%AA%E5%AE%9A%E4%B9%89%E6%A8%A1%E5%9E%8B)，通过修改PPOCRLabel.py中针对[PaddleOCR类的实例化](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/PPOCRLabel.py#L110)替换成自己训练的模型。
+ - **自定义模型**：如果用户想将内置模型更换为自己的推理模型，可根据[自定义模型代码使用](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/whl.md#%E8%87%AA%E5%AE%9A%E4%B9%89%E6%A8%A1%E5%9E%8B)，通过修改PPOCRLabel.py中针对[PaddleOCR类的实例化](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/PPOCRLabel.py#L116) 实现，例如指定检测模型：`self.ocr = PaddleOCR(det=True, cls=True, use_gpu=gpu, lang=lang) `，在 `det_model_dir` 中传入  自己的模型即可。 
 
-### 导出标记结果
+### 3.3 导出标记结果
 
 PPOCRLabel支持三种导出方式：
 
 - 自动导出：点击“文件 - 自动导出标记结果”后，用户每确认过一张图片，程序自动将标记结果写入Label.txt中。若未开启此选项，则检测到用户手动确认过5张图片后进行自动导出。
+
+  > 默认情况下自动导出功能为关闭状态
+
 - 手动导出：点击“文件 - 导出标记结果”手动导出标记。
+
 - 关闭应用程序导出
 
-### 导出部分识别结果
+### 3.4 导出部分识别结果
+
+针对部分难以识别的数据，通过在识别结果的复选框中**取消勾选**相应的标记，其识别结果不会被导出。被取消勾选的识别结果在标记文件 `label.txt` 中的 `difficult` 变量保存为 `True` 。
+
+> *注意：识别结果中的复选框状态仍需用户手动点击确认后才能保留*
+
+### 3.5 数据集划分
+
+在终端中输入以下命令执行数据集划分脚本：
+
+```
+cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+python gen_ocr_train_val_test.py --trainValTestRatio 6:2:2 --labelRootPath ../train_data/label --detRootPath ../train_data/det --recRootPath ../train_data/rec
+```
+
+参数说明：
+
+- `trainValTestRatio` 是训练集、验证集、测试集的图像数量划分比例，根据实际情况设定，默认是`6:2:2`
 
-针对部分难以识别的数据，通过在识别结果的复选框中**取消勾选**相应的标记，其识别结果不会被导出。
+- `labelRootPath` 是PPOCRLabel标注的数据集存放路径，默认是`../train_data/label`
 
-*注意：识别结果中的复选框状态仍需用户手动点击确认后才能保留*
+- `detRootPath` 是根据PPOCRLabel标注的数据集划分后的文本检测数据集存放的路径，默认是`../train_data/det `
+
+- `recRootPath` 是根据PPOCRLabel标注的数据集划分后的字符识别数据集存放的路径，默认是`../train_data/rec`
+
+### 3.6 错误提示
 
-### 错误提示
 - 如果同时使用whl包安装了paddleocr，其优先级大于通过paddleocr.py调用PaddleOCR类，whl包未更新时会导致程序异常。
 
 - PPOCRLabel**不支持对中文文件名**的图片进行自动标注。
@@ -194,6 +213,8 @@ PPOCRLabel支持三种导出方式：
     pip install opencv-contrib-python-headless==4.2.0.32
     ```
 
-### 参考资料
+
+
+### 4. 参考资料
 
 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)

PPOCRLabel/gen_ocr_train_val_test.py

+# coding:utf8
+import os
+import shutil
+import random
+import argparse
+
+
+# 删除划分的训练集、验证集、测试集文件夹，重新创建一个空的文件夹
+def isCreateOrDeleteFolder(path, flag):
+    flagPath = os.path.join(path, flag)
+
+    if os.path.exists(flagPath):
+        shutil.rmtree(flagPath)
+
+    os.makedirs(flagPath)
+    flagAbsPath = os.path.abspath(flagPath)
+    return flagAbsPath
+
+
+def splitTrainVal(root, dir, absTrainRootPath, absValRootPath, absTestRootPath, trainTxt, valTxt, testTxt, flag):
+    # 按照指定的比例划分训练集、验证集、测试集
+    labelPath = os.path.join(root, dir)
+    labelAbsPath = os.path.abspath(labelPath)
+
+    if flag == "det":
+        labelFilePath = os.path.join(labelAbsPath, args.detLabelFileName)
+    elif flag == "rec":
+        labelFilePath = os.path.join(labelAbsPath, args.recLabelFileName)
+
+    labelFileRead = open(labelFilePath, "r", encoding="UTF-8")
+    labelFileContent = labelFileRead.readlines()
+    random.shuffle(labelFileContent)
+    labelRecordLen = len(labelFileContent)
+
+    for index, labelRecordInfo in enumerate(labelFileContent):
+        imageRelativePath = labelRecordInfo.split('\t')[0]
+        imageLabel = labelRecordInfo.split('\t')[1]
+        imageName = os.path.basename(imageRelativePath)
+
+        if flag == "det":
+            imagePath = os.path.join(labelAbsPath, imageName)
+        elif flag == "rec":
+            imagePath = os.path.join(labelAbsPath, "{}\\{}".format(args.recImageDirName, imageName))
+
+        # 按预设的比例划分训练集、验证集、测试集
+        trainValTestRatio = args.trainValTestRatio.split(":")
+        trainRatio = eval(trainValTestRatio[0]) / 10
+        valRatio = trainRatio + eval(trainValTestRatio[1]) / 10
+        curRatio = index / labelRecordLen
+
+        if curRatio < trainRatio:
+            imageCopyPath = os.path.join(absTrainRootPath, imageName)
+            shutil.copy(imagePath, imageCopyPath)
+            trainTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
+        elif curRatio >= trainRatio and curRatio < valRatio:
+            imageCopyPath = os.path.join(absValRootPath, imageName)
+            shutil.copy(imagePath, imageCopyPath)
+            valTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
+        else:
+            imageCopyPath = os.path.join(absTestRootPath, imageName)
+            shutil.copy(imagePath, imageCopyPath)
+            testTxt.write("{}\t{}".format(imageCopyPath, imageLabel))
+
+
+# 删掉存在的文件
+def removeFile(path):
+    if os.path.exists(path):
+        os.remove(path)
+
+
+def genDetRecTrainVal(args):
+    detAbsTrainRootPath = isCreateOrDeleteFolder(args.detRootPath, "train")
+    detAbsValRootPath = isCreateOrDeleteFolder(args.detRootPath, "val")
+    detAbsTestRootPath = isCreateOrDeleteFolder(args.detRootPath, "test")
+    recAbsTrainRootPath = isCreateOrDeleteFolder(args.recRootPath, "train")
+    recAbsValRootPath = isCreateOrDeleteFolder(args.recRootPath, "val")
+    recAbsTestRootPath = isCreateOrDeleteFolder(args.recRootPath, "test")
+
+    removeFile(os.path.join(args.detRootPath, "train.txt"))
+    removeFile(os.path.join(args.detRootPath, "val.txt"))
+    removeFile(os.path.join(args.detRootPath, "test.txt"))
+    removeFile(os.path.join(args.recRootPath, "train.txt"))
+    removeFile(os.path.join(args.recRootPath, "val.txt"))
+    removeFile(os.path.join(args.recRootPath, "test.txt"))
+
+    detTrainTxt = open(os.path.join(args.detRootPath, "train.txt"), "a", encoding="UTF-8")
+    detValTxt = open(os.path.join(args.detRootPath, "val.txt"), "a", encoding="UTF-8")
+    detTestTxt = open(os.path.join(args.detRootPath, "test.txt"), "a", encoding="UTF-8")
+    recTrainTxt = open(os.path.join(args.recRootPath, "train.txt"), "a", encoding="UTF-8")
+    recValTxt = open(os.path.join(args.recRootPath, "val.txt"), "a", encoding="UTF-8")
+    recTestTxt = open(os.path.join(args.recRootPath, "test.txt"), "a", encoding="UTF-8")
+
+    for root, dirs, files in os.walk(args.labelRootPath):
+        for dir in dirs:
+            splitTrainVal(root, dir, detAbsTrainRootPath, detAbsValRootPath, detAbsTestRootPath, detTrainTxt, detValTxt,
+                          detTestTxt, "det")
+            splitTrainVal(root, dir, recAbsTrainRootPath, recAbsValRootPath, recAbsTestRootPath, recTrainTxt, recValTxt,
+                          recTestTxt, "rec")
+        break
+
+
+if __name__ == "__main__":
+    # 功能描述：分别划分检测和识别的训练集、验证集、测试集
+    # 说明：可以根据自己的路径和需求调整参数，图像数据往往多人合作分批标注，每一批图像数据放在一个文件夹内用PPOCRLabel进行标注，
+    # 如此会有多个标注好的图像文件夹汇总并划分训练集、验证集、测试集的需求
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--trainValTestRatio",
+        type=str,
+        default="6:2:2",
+        help="ratio of trainset:valset:testset")
+    parser.add_argument(
+        "--labelRootPath",
+        type=str,
+        default="../train_data/label",
+        help="path to the dataset marked by ppocrlabel, E.g, dataset folder named 1,2,3..."
+    )
+    parser.add_argument(
+        "--detRootPath",
+        type=str,
+        default="../train_data/det",
+        help="the path where the divided detection dataset is placed")
+    parser.add_argument(
+        "--recRootPath",
+        type=str,
+        default="../train_data/rec",
+        help="the path where the divided recognition dataset is placed"
+    )
+    parser.add_argument(
+        "--detLabelFileName",
+        type=str,
+        default="Label.txt",
+        help="the name of the detection annotation file")
+    parser.add_argument(
+        "--recLabelFileName",
+        type=str,
+        default="rec_gt.txt",
+        help="the name of the recognition annotation file"
+    )
+    parser.add_argument(
+        "--recImageDirName",
+        type=str,
+        default="crop_img",
+        help="the name of the folder where the cropped recognition dataset is located"
+    )
+    args = parser.parse_args()
+    genDetRecTrainVal(args)

PPOCRLabel/libs/stringBundle.py

@@ -19,6 +19,9 @@ import sys
 import locale
 from libs.ustr import ustr
 
+__dir__ = os.path.dirname(os.path.abspath(__file__)) # 获取本程序文件路径
+__dirpath__ = os.path.abspath(os.path.join(__dir__, '../resources/strings'))
+
 try:
     from PyQt5.QtCore import *
 except ImportError:
@@ -57,7 +60,7 @@ class StringBundle:
 
     def __createLookupFallbackList(self, localeStr):
         resultPaths = []
-        basePath = ":/strings"
+        basePath = "\strings" if os.name == 'nt' else ":/strings"
         resultPaths.append(basePath)
         if localeStr is not None:
             # Don't follow standard BCP47. Simple fallback
@@ -65,6 +68,7 @@ class StringBundle:
             for tag in tags:
                 lastPath = resultPaths[-1]
                 resultPaths.append(lastPath + '-' + tag)
+            resultPaths[-1] = __dirpath__ + resultPaths[-1] + ".properties"
 
         return resultPaths
 

PPOCRLabel/libs/utils.py

@@ -17,6 +17,10 @@ import re
 import sys
 import cv2
 import numpy as np
+import os
+
+__dir__ = os.path.dirname(os.path.abspath(__file__)) # 获取本程序文件路径
+__iconpath__ = os.path.abspath(os.path.join(__dir__, '../resources/icons'))
 
 try:
     from PyQt5.QtGui import *
@@ -29,9 +33,9 @@ except ImportError:
 
 def newIcon(icon, iconSize=None):
     if iconSize is not None:
-        return QIcon(QIcon(':/' + icon).pixmap(iconSize,iconSize))
+        return QIcon(QIcon(__iconpath__ + "/" + icon + ".png").pixmap(iconSize,iconSize))
     else:
-        return QIcon(':/' + icon)
+        return QIcon(__iconpath__ + "/" + icon + ".png")
 
 
 def newButton(text, icon=None, slot=None):

PPOCRLabel/requirements.txt

+pyqt5
+paddleocr
\ No newline at end of file

PPOCRLabel/resources.qrc

@@ -35,7 +35,7 @@
 <file alias="prev">resources/icons/prev.png</file>
 <file alias="resetall">resources/icons/resetall.png</file>
 <file alias="verify">resources/icons/verify.png</file>
-<file alias="strings">resources/strings/strings.properties</file>
+<file alias="strings">resources/strings/strings-en.properties</file>
 <file alias="strings-zh-CN">resources/strings/strings-zh-CN.properties</file>
 </qresource>
 </RCC>

PPOCRLabel/setup.py

-# Copyright (c) <2015-Present> Tzutalin
-# Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba,
-# William T. Freeman. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
-# associated documentation files (the "Software"), to deal in the Software without restriction, including without
-# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the
-# Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-# The above copyright notice and this permission notice shall be included in all copies or substantial portions of
-# the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
-# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
-# SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
-# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-# THE SOFTWARE.
-
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-from setuptools import setup, find_packages, Command
-from sys import platform as _platform
-from shutil import rmtree
-import sys
-import os
-
-here = os.path.abspath(os.path.dirname(__file__))
-NAME = 'labelImg'
-REQUIRES_PYTHON = '>=3.0.0'
-REQUIRED_DEP = ['pyqt5', 'lxml']
-about = {}
-
-with open(os.path.join(here, 'libs', '__init__.py')) as f:
-    exec(f.read(), about)
-
-with open('README.rst') as readme_file:
-    readme = readme_file.read()
-
-with open('HISTORY.rst') as history_file:
-    history = history_file.read()
-
-
-# OS specific settings
-SET_REQUIRES = []
-if _platform == "linux" or _platform == "linux2":
-   # linux
-   print('linux')
-elif _platform == "darwin":
-   # MAC OS X
-   SET_REQUIRES.append('py2app')
-
-required_packages = find_packages()
-required_packages.append('labelImg')
-
-APP = [NAME + '.py']
-OPTIONS = {
-    'argv_emulation': True,
-    'iconfile': 'resources/icons/app.icns'
-}
-
-class UploadCommand(Command):
-    """Support setup.py upload."""
-
-    description=readme + '\n\n' + history,
-
-    user_options = []
-
-    @staticmethod
-    def status(s):
-        """Prints things in bold."""
-        print('\033[1m{0}\033[0m'.format(s))
-
-    def initialize_options(self):
-        pass
-
-    def finalize_options(self):
-        pass
-
-    def run(self):
-        try:
-            self.status('Removing previous builds…')
-            rmtree(os.path.join(here, 'dist'))
-        except OSError:
-            self.status('Fail to remove previous builds..')
-            pass
-
-        self.status('Building Source and Wheel (universal) distribution…')
-        os.system(
-            '{0} setup.py sdist bdist_wheel --universal'.format(sys.executable))
-
-        self.status('Uploading the package to PyPI via Twine…')
-        os.system('twine upload dist/*')
-
-        self.status('Pushing git tags…')
-        os.system('git tag -d v{0}'.format(about['__version__']))
-        os.system('git tag v{0}'.format(about['__version__']))
-        # os.system('git push --tags')
-
-        sys.exit()
+# Copyright (c) 2020 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 setuptools import setup
+from io import open
+
+with open('requirements.txt', encoding="utf-8-sig") as f:
+    requirements = f.readlines()
+    requirements.append('tqdm')
+
+
+def readme():
+    with open('README.md', encoding="utf-8-sig") as f:
+        README = f.read()
+    return README
 
 
 setup(
-    app=APP,
-    name=NAME,
-    version=about['__version__'],
-    description="LabelImg is a graphical image annotation tool and label object bounding boxes in images",
-    long_description=readme + '\n\n' + history,
-    author="TzuTa Lin",
-    author_email='tzu.ta.lin@gmail.com',
-    url='https://github.com/tzutalin/labelImg',
-    python_requires=REQUIRES_PYTHON,
-    package_dir={'labelImg': '.'},
-    packages=required_packages,
-    entry_points={
-        'console_scripts': [
-            'labelImg=labelImg.labelImg:main'
-        ]
-    },
+    name='PPOCRLabel',
+    packages=['PPOCRLabel'],
+    package_data = {'PPOCRLabel': ['libs/*','resources/strings/*','resources/icons/*']},
+    package_dir={'PPOCRLabel': ''},
     include_package_data=True,
-    install_requires=REQUIRED_DEP,
-    license="MIT license",
-    zip_safe=False,
-    keywords='labelImg labelTool development annotation deeplearning',
+    entry_points={"console_scripts": ["PPOCRLabel= PPOCRLabel.PPOCRLabel:main"]},
+    version='1.0.0',
+    install_requires=requirements,
+    license='Apache License 2.0',
+    description='PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field, with built-in PPOCR model to automatically detect and re-recognize data. It is written in python3 and pyqt5, supporting rectangular box annotation and four-point annotation modes. Annotations can be directly used for the training of PPOCR detection and recognition models',
+    long_description=readme(),
+    long_description_content_type='text/markdown',
+    url='https://github.com/PaddlePaddle/PaddleOCR',
+    download_url='https://github.com/PaddlePaddle/PaddleOCR.git',
+    keywords=[
+        'ocr textdetection textrecognition paddleocr crnn east star-net rosetta ocrlite db chineseocr chinesetextdetection chinesetextrecognition'
+    ],
     classifiers=[
-        'Development Status :: 5 - Production/Stable',
-        'Intended Audience :: Developers',
-        'License :: OSI Approved :: MIT License',
+        'Intended Audience :: Developers', 'Operating System :: OS Independent',
         'Natural Language :: English',
-        'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.3',
-        'Programming Language :: Python :: 3.4',
-        'Programming Language :: Python :: 3.5',
         'Programming Language :: Python :: 3.6',
-        'Programming Language :: Python :: 3.7',
-    ],
-    package_data={'data/predefined_classes.txt': ['data/predefined_classes.txt']},
+        'Programming Language :: Python :: 3.7', 'Topic :: Utilities'
+    ], )
\ No newline at end of file
-    options={'py2app': OPTIONS},
-    setup_requires=SET_REQUIRES,
-    # $ setup.py publish support.
-    cmdclass={
-        'upload': UploadCommand,
-    }
-)

README.md

@@ -25,7 +25,7 @@ PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools
 
 **Recent updates**
 
-- 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).
+- PaddleOCR R&D team would like to share the key points of PP-OCRv2, at 20:15 pm on September 8th, [Course Address](https://aistudio.baidu.com/aistudio/education/group/info/6758).
 - 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.
@@ -86,9 +86,9 @@ Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Andr
 
 | Model introduction                                           | Model name                   | Recommended scene | Detection model                                              | Direction classifier                                         | Recognition model                                            |
 | ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| 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)  |  
+| 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) / [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) / [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) / [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) / [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) / [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) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.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) / [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) / [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) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |
 
 
 For more model downloads (including multiple languages), please refer to [PP-OCR series model downloads](./doc/doc_en/models_list_en.md).
@@ -102,7 +102,6 @@ For a new language request, please refer to [Guideline for new language_requests
 - PP-OCR Industry Landing: from Training to Deployment
     - [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 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)
@@ -119,7 +118,7 @@ For a new language request, please refer to [Guideline for new language_requests
     - [Table Recognition](./ppstructure/table/README.md)
 - Academic Circles
     - [Two-stage Algorithm](./doc/doc_en/algorithm_overview_en.md)
-    - [PGNet Algorithm](./doc/doc_en/algorithm_overview_en.md)
+    - [PGNet Algorithm](./doc/doc_en/pgnet_en.md)
     - [Python Inference](./doc/doc_en/inference_en.md)
 - Data Annotation and Synthesis
     - [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md)

README_ch.md

@@ -24,7 +24,7 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 **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)。
+- 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，[课程回放](https://aistudio.baidu.com/aistudio/education/group/info/6758)。
 - 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。
@@ -81,9 +81,9 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 
 | 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/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)  |  
+| 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/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) / [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) / [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) / [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) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.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) / [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) / [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) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |
 
 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)
 
@@ -94,7 +94,6 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - PP-OCR产业落地：从训练到部署
     - [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)
         - [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)
@@ -109,15 +108,16 @@ PaddleOCR旨在打造一套丰富、领先、且实用的OCR工具库，助力
 - [PP-Structure信息提取](./ppstructure/README_ch.md)
     - [版面分析](./ppstructure/layout/README_ch.md)
     - [表格识别](./ppstructure/table/README_ch.md)
+- 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)
+    - [使用PaddleOCR架构添加新算法](./doc/doc_ch/add_new_algorithm.md)
 - 数据标注与合成
     - [半自动标注工具PPOCRLabel](./PPOCRLabel/README_ch.md)
     - [数据合成工具Style-Text](./StyleText/README_ch.md)
     - [其它数据标注工具](./doc/doc_ch/data_annotation.md)
     - [其它数据合成工具](./doc/doc_ch/data_synthesis.md)
-- 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)

benchmark/readme.md

 
-# PaddleOCR DB/EAST 算法训练benchmark测试
+# PaddleOCR DB/EAST/PSE 算法训练benchmark测试
 
 PaddleOCR/benchmark目录下的文件用于获取并分析训练日志。
 训练采用icdar2015数据集，包括1000张训练图像和500张测试图像。模型配置采用resnet18_vd作为backbone，分别训练batch_size=8和batch_size=16的情况。
@@ -18,7 +18,7 @@ run_det.sh 执行方式如下:
 
 ```
 # cd PaddleOCR/
-bash benchmark/run_det.sh 
+bash benchmark/run_det.sh
 ```
 
 以DB为例，将得到四个日志文件，如下：
@@ -28,7 +28,3 @@ det_res18_db_v2.0_sp_bs8_fp32_1
 det_res18_db_v2.0_mp_bs16_fp32_1
 det_res18_db_v2.0_mp_bs8_fp32_1
 ```
-
-
-
-

benchmark/run_benchmark_det.sh

@@ -5,28 +5,36 @@ set -xe
 function _set_params(){
     run_mode=${1:-"sp"}          # 单卡sp|多卡mp
     batch_size=${2:-"64"}
-    fp_item=${3:-"fp32"}        # fp32|fp16
-    max_iter=${4:-"500"}       # 可选，如果需要修改代码提前中断
-    model_name=${5:-"model_name"}
+    fp_item=${3:-"fp32"}         # fp32|fp16
+    max_epoch=${4:-"10"}         # 可选，如果需要修改代码提前中断
+    model_item=${5:-"model_item"}
     run_log_path=${TRAIN_LOG_DIR:-$(pwd)}  # TRAIN_LOG_DIR 后续QA设置该参数
-
+#   日志解析所需参数
+    base_batch_size=${batch_size}
+    mission_name="OCR"
+    direction_id="0"
+    ips_unit="images/sec"
+    skip_steps=2                 # 解析日志，有些模型前几个step耗时长，需要跳过                                    (必填)
+    keyword="ips:"               # 解析日志，筛选出数据所在行的关键字                                             (必填)
+    index="1"
+    model_name=${model_item}_${run_mode}_bs${batch_size}_${fp_item}        # model_item 用于yml文件名匹配，model_name 用于数据入库前端展示
 #   以下不用修改   
     device=${CUDA_VISIBLE_DEVICES//,/ }
     arr=(${device})
     num_gpu_devices=${#arr[*]}
-    log_file=${run_log_path}/${model_name}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
+    log_file=${run_log_path}/${model_item}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
 }
 function _train(){
     echo "Train on ${num_gpu_devices} GPUs"
     echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
 
-    train_cmd="-c configs/det/${model_name}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_iter} "   
+    train_cmd="-c configs/det/${model_item}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_epoch} Global.eval_batch_step=[0,20000] Global.print_batch_step=2"
     case ${run_mode} in
       sp) 
-        train_cmd="python3.7 tools/train.py "${train_cmd}""
+        train_cmd="python tools/train.py "${train_cmd}""
         ;;
       mp)
-        train_cmd="python3.7 -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}"
+        train_cmd="python -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}"
         ;;
       *) echo "choose run_mode(sp or mp)"; exit 1;
     esac
@@ -39,18 +47,14 @@ function _train(){
         echo -e "${model_name}, SUCCESS"
         export job_fail_flag=0
     fi
-    kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'`
 
     if [ $run_mode = "mp" -a -d mylog ]; then
         rm ${log_file}
         cp mylog/workerlog.0 ${log_file}
     fi
-
-    # run log analysis
-    analysis_cmd="python3.7 benchmark/analysis.py --filename ${log_file}  --mission_name ${model_name} --run_mode ${mode} --direction_id 0 --keyword 'ips:' --base_batch_size ${batch_szie} --skip_steps 1 --gpu_num ${num_gpu_devices}  --index 1  --model_mode=-1  --ips_unit=samples/sec"
-    eval $analysis_cmd
 }
 
+source ${BENCHMARK_ROOT}/scripts/run_model.sh      # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在连调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开
 _set_params $@
-_train
-
+#_train      # 如果只想产出训练log,不解析,可取消注释
+_run         # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开

benchmark/run_det.sh

+#!/bin/bash
 # 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7  paddle=2.1.2  py=37
 # 执行目录: ./PaddleOCR
 # 1 安装该模型需要的依赖 (如需开启优化策略请注明)
-python3.7 -m pip install -r requirements.txt
+python -m pip install -r requirements.txt
 # 2 拷贝该模型需要数据、预训练模型
-wget -c  -p ./tain_data/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data  && tar xf icdar2015.tar && cd ../
-wget -c -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
+wget -P ./train_data/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data  && tar xf icdar2015.tar && cd ../
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
+wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
 # 3 批量运行（如不方便批量，1，2需放到单个模型中）
 
-model_mode_list=(det_res18_db_v2.0 det_r50_vd_east)
+model_mode_list=(det_res18_db_v2.0 det_r50_vd_east det_r50_vd_pse)
 fp_item_list=(fp32)
 bs_list=(8 16)
 for model_mode in ${model_mode_list[@]}; do
@@ -15,11 +18,13 @@ for model_mode in ${model_mode_list[@]}; do
           for bs_item in ${bs_list[@]}; do
             echo "index is speed, 1gpus, begin, ${model_name}"
             run_mode=sp
-            CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}     #  (5min)
+            log_name=ocr_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}
+            CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode} | tee ${log_path}/${log_name}_speed_1gpus 2>&1    #  (5min)
             sleep 60
             echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
             run_mode=mp
-            CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} 
+            log_name=ocr_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}
+            CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
             sleep 60
             done
       done

configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml → configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml

@@ -141,6 +141,7 @@ Train:
           img_mode: BGR
           channel_first: False
       - DetLabelEncode: # Class handling label
+      - CopyPaste:
       - IaaAugment:
           augmenter_args:
             - { 'type': Fliplr, 'args': { 'p': 0.5 } }

configs/det/ch_PP-OCRv2/ch_PP-OCR_det_distill.yml → configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_distill.yml

@@ -68,8 +68,7 @@ Loss:
       ohem_ratio: 3
   - DistillationDBLoss:
       weight: 1.0
-      model_name_list: ["Student", "Teacher"]
-      # key: maps
+      model_name_list: ["Student"]
       name: DBLoss
       balance_loss: true
       main_loss_type: DiceLoss
@@ -91,7 +90,7 @@ Optimizer:
 
 PostProcess:
   name: DistillationDBPostProcess
-  model_name: ["Student", "Student2"]
+  model_name: ["Student"]
   key: head_out
   thresh: 0.3
   box_thresh: 0.6
@@ -116,6 +115,7 @@ Train:
           img_mode: BGR
           channel_first: False
       - DetLabelEncode: # Class handling label
+      - CopyPaste:
       - IaaAugment:
           augmenter_args:
             - { 'type': Fliplr, 'args': { 'p': 0.5 } }

configs/det/ch_PP-OCRv2/ch_PP-OCR_det_dml.yml → configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_dml.yml

@@ -118,6 +118,7 @@ Train:
           img_mode: BGR
           channel_first: False
       - DetLabelEncode: # Class handling label
+      - CopyPaste:
       - IaaAugment:
           augmenter_args:
             - { 'type': Fliplr, 'args': { 'p': 0.5 } }

configs/det/det_r50_vd_sast_icdar15.yml

@@ -8,7 +8,7 @@ Global:
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
   cal_metric_during_train: False
-  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
   checkpoints:
   save_inference_dir:
   use_visualdl: False
@@ -106,4 +106,4 @@ Eval:
     shuffle: False
     drop_last: False
     batch_size_per_card: 1 # must be 1
-    num_workers: 2
+    num_workers: 2
\ No newline at end of file

configs/det/det_r50_vd_sast_totaltext.yml

@@ -8,7 +8,7 @@ Global:
   # evaluation is run every 5000 iterations after the 4000th iteration
   eval_batch_step: [4000, 5000]
   cal_metric_during_train: False
-  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
   checkpoints: 
   save_inference_dir:
   use_visualdl: False

configs/e2e/e2e_r50_vd_pg.yml

@@ -94,7 +94,7 @@ Eval:
     label_file_list: [./train_data/total_text/test/test.txt]
     transforms:
       - DecodeImage: # load image
-          img_mode: RGB
+          img_mode: BGR
           channel_first: False
       - E2ELabelEncodeTest:
       - E2EResizeForTest:

configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml

@@ -14,7 +14,6 @@ Global:
   use_visualdl: false
   infer_img: doc/imgs_words/ch/word_1.jpg
   character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: ch
   max_text_length: 25
   infer_mode: false
   use_space_char: true

configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_distillation.yml

@@ -14,7 +14,6 @@ Global:
   use_visualdl: false
   infer_img: doc/imgs_words/ch/word_1.jpg
   character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: ch
   max_text_length: 25
   infer_mode: false
   use_space_char: true

configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec_enhanced_ctc_loss.yml

@@ -14,7 +14,6 @@ Global:
   use_visualdl: false
   infer_img: doc/imgs_words/ch/word_1.jpg
   character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: ch
   max_text_length: 25
   infer_mode: false
   use_space_char: true
@@ -63,8 +62,7 @@ Loss:
       weight: 0.05
       num_classes: 6625
       feat_dim: 96
-      init_center: false
-      center_file_path: "./train_center.pkl"
+      center_file_path:
   # you can also try to add ace loss on your own dataset
   # - ACELoss:
   #     weight: 0.1

configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml

@@ -15,7 +15,6 @@ Global:
   infer_img: doc/imgs_words/ch/word_1.jpg
   # for data or label process
   character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: ch
   max_text_length: 25
   infer_mode: False
   use_space_char: True

configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml

@@ -15,7 +15,6 @@ Global:
   infer_img: doc/imgs_words/ch/word_1.jpg
   # for data or label process
   character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  character_type: ch
   max_text_length: 25
   infer_mode: False
   use_space_char: True

configs/rec/multi_language/rec_arabic_lite_train.yml

@@ -15,7 +15,6 @@ Global:
   use_visualdl: false
   infer_img: null
   character_dict_path: ppocr/utils/dict/arabic_dict.txt
-  character_type: arabic
   max_text_length: 25
   infer_mode: false
   use_space_char: true

configs/rec/multi_language/rec_cyrillic_lite_train.yml

@@ -15,7 +15,6 @@ Global:
   use_visualdl: false
   infer_img: null
   character_dict_path: ppocr/utils/dict/cyrillic_dict.txt
-  character_type: cyrillic
   max_text_length: 25
   infer_mode: false
   use_space_char: true

configs/rec/multi_language/rec_devanagari_lite_train.yml

@@ -15,7 +15,6 @@ Global:
   use_visualdl: false
   infer_img: null
   character_dict_path: ppocr/utils/dict/devanagari_dict.txt
-  character_type: devanagari
   max_text_length: 25
   infer_mode: false
   use_space_char: true

configs/rec/multi_language/rec_en_number_lite_train.yml

@@ -16,7 +16,6 @@ Global:
   infer_img:
   # for data or label process
   character_dict_path: ppocr/utils/en_dict.txt
-  character_type: EN
   max_text_length: 25
   infer_mode: False
   use_space_char: True

configs/rec/multi_language/rec_french_lite_train.yml

@@ -16,7 +16,6 @@ Global:
   infer_img:
   # for data or label process
   character_dict_path: ppocr/utils/dict/french_dict.txt
-  character_type: french
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/multi_language/rec_german_lite_train.yml

@@ -16,7 +16,6 @@ Global:
   infer_img:
   # for data or label process
   character_dict_path: ppocr/utils/dict/german_dict.txt
-  character_type: german
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/multi_language/rec_japan_lite_train.yml

@@ -16,7 +16,6 @@ Global:
   infer_img:
   # for data or label process
   character_dict_path: ppocr/utils/dict/japan_dict.txt
-  character_type: japan
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/multi_language/rec_korean_lite_train.yml

@@ -16,7 +16,6 @@ Global:
   infer_img:
   # for data or label process
   character_dict_path: ppocr/utils/dict/korean_dict.txt
-  character_type: korean
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/multi_language/rec_latin_lite_train.yml

@@ -15,7 +15,6 @@ Global:
   use_visualdl: false
   infer_img: null
   character_dict_path: ppocr/utils/dict/latin_dict.txt
-  character_type: latin
   max_text_length: 25
   infer_mode: false
   use_space_char: true

configs/rec/rec_icdar15_train.yml

@@ -15,7 +15,6 @@ Global:
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
   character_dict_path: ppocr/utils/en_dict.txt
-  character_type: EN
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_mtb_nrtr.yml

@@ -14,11 +14,10 @@ Global:
   use_visualdl: False
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
-  character_dict_path: 
-  character_type: EN_symbol
+  character_dict_path: ppocr/utils/EN_symbol_dict.txt
   max_text_length: 25
   infer_mode: False
-  use_space_char: True
+  use_space_char: False
   save_res_path: ./output/rec/predicts_nrtr.txt
 
 Optimizer:

configs/rec/rec_mv3_none_bilstm_ctc.yml

@@ -14,8 +14,7 @@ Global:
   use_visualdl: False
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
-  character_dict_path: 
-  character_type: en
+  character_dict_path:
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_mv3_none_none_ctc.yml

@@ -15,7 +15,6 @@ Global:
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
   character_dict_path:
-  character_type: en
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_mv3_tps_bilstm_att.yml

@@ -14,8 +14,7 @@ Global:
   use_visualdl: False
   infer_img: doc/imgs_words/ch/word_1.jpg
   # for data or label process
-  character_dict_path: 
-  character_type: en
+  character_dict_path:
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_mv3_tps_bilstm_ctc.yml

@@ -15,7 +15,6 @@ Global:
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
   character_dict_path:
-  character_type: en
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_r31_sar.yml

@@ -15,7 +15,6 @@ Global:
   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

configs/rec/rec_r34_vd_none_bilstm_ctc.yml

@@ -14,8 +14,7 @@ Global:
   use_visualdl: False
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
-  character_dict_path: 
-  character_type: en
+  character_dict_path:
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_r34_vd_none_none_ctc.yml

@@ -15,7 +15,6 @@ Global:
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
   character_dict_path:
-  character_type: en
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_r34_vd_tps_bilstm_att.yml

@@ -14,8 +14,7 @@ Global:
   use_visualdl: False
   infer_img: doc/imgs_words/ch/word_1.jpg
   # for data or label process
-  character_dict_path: 
-  character_type: en
+  character_dict_path:
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_r34_vd_tps_bilstm_ctc.yml

@@ -14,8 +14,7 @@ Global:
   use_visualdl: False
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
-  character_dict_path: 
-  character_type: en
+  character_dict_path:
   max_text_length: 25
   infer_mode: False
   use_space_char: False

configs/rec/rec_r50_fpn_srn.yml

@@ -14,8 +14,7 @@ Global:
   use_visualdl: False
   infer_img: doc/imgs_words/ch/word_1.jpg
   # for data or label process
-  character_dict_path: 
-  character_type: en
+  character_dict_path:
   max_text_length: 25
   num_heads: 8
   infer_mode: False

configs/rec/rec_resnet_stn_bilstm_att.yml

@@ -14,8 +14,7 @@ Global:
   use_visualdl: False
   infer_img: doc/imgs_words_en/word_10.png
   # for data or label process
-  character_dict_path: 
-  character_type: EN_symbol
+  character_dict_path: ppocr/utils/EN_symbol_dict.txt
   max_text_length: 100
   infer_mode: False
   use_space_char: False

configs/table/table_mv3.yml

 Global:
   use_gpu: true
-  epoch_num: 50
+  epoch_num: 400
   log_smooth_window: 20
   print_batch_step: 5
   save_model_dir: ./output/table_mv3/
-  save_epoch_step: 5
+  save_epoch_step: 3
   # evaluation is run every 400 iterations after the 0th iteration
   eval_batch_step: [0, 400]
   cal_metric_during_train: True
-  pretrained_model: 
+  pretrained_model:
   checkpoints: 
   save_inference_dir:
   use_visualdl: False
-  infer_img: doc/imgs_words/ch/word_1.jpg
+  infer_img: doc/table/table.jpg
   # for data or label process
   character_dict_path: ppocr/utils/dict/table_structure_dict.txt
   character_type: en
   max_text_length: 100
-  max_elem_length: 500
+  max_elem_length: 800
   max_cell_num: 500
   infer_mode: False
   process_total_num: 0
   process_cut_num: 0
 
-
 Optimizer:
   name: Adam
   beta1: 0.9
@@ -41,13 +40,15 @@ Architecture:
   Backbone:
     name: MobileNetV3
     scale: 1.0
-    model_name: small
-    disable_se: True
+    model_name: large
   Head:
     name: TableAttentionHead
     hidden_size: 256
     l2_decay: 0.00001
     loc_type: 2
+    max_text_length: 100
+    max_elem_length: 800
+    max_cell_num: 500
 
 Loss:
   name: TableAttentionLoss

deploy/cpp_infer/include/ocr_rec.h

@@ -44,7 +44,8 @@ public:
                           const int &gpu_id, const int &gpu_mem,
                           const int &cpu_math_library_num_threads,
                           const bool &use_mkldnn, const string &label_path,
-                          const bool &use_tensorrt, const std::string &precision) {
+                          const bool &use_tensorrt, const std::string &precision,
+                          const int &rec_batch_num) {
     this->use_gpu_ = use_gpu;
     this->gpu_id_ = gpu_id;
     this->gpu_mem_ = gpu_mem;
@@ -52,6 +53,7 @@ public:
     this->use_mkldnn_ = use_mkldnn;
     this->use_tensorrt_ = use_tensorrt;
     this->precision_ = precision;
+    this->rec_batch_num_ = rec_batch_num;
 
     this->label_list_ = Utility::ReadDict(label_path);
     this->label_list_.insert(this->label_list_.begin(),
@@ -64,7 +66,7 @@ public:
   // Load Paddle inference model
   void LoadModel(const std::string &model_dir);
 
-  void Run(cv::Mat &img, std::vector<double> *times);
+  void Run(std::vector<cv::Mat> img_list, std::vector<double> *times);
 
 private:
   std::shared_ptr<Predictor> predictor_;
@@ -82,10 +84,12 @@ private:
   bool is_scale_ = true;
   bool use_tensorrt_ = false;
   std::string precision_ = "fp32";
+  int rec_batch_num_ = 6;
+    
   // pre-process
   CrnnResizeImg resize_op_;
   Normalize normalize_op_;
-  Permute permute_op_;
+  PermuteBatch permute_op_;
 
   // post-process
   PostProcessor post_processor_;

deploy/cpp_infer/include/preprocess_op.h

@@ -44,6 +44,11 @@ public:
   virtual void Run(const cv::Mat *im, float *data);
 };
 
+class PermuteBatch {
+public:
+  virtual void Run(const std::vector<cv::Mat> imgs, float *data);
+};
+    
 class ResizeImgType0 {
 public:
   virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,

deploy/cpp_infer/include/utility.h

@@ -50,6 +50,9 @@ public:
     
   static cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
                           std::vector<std::vector<int>> box);
+    
+  static std::vector<int> argsort(const std::vector<float>& array);
+
 };
 
 } // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/readme.md

@@ -34,10 +34,10 @@ PaddleOCR模型部署。
 
 * 首先需要从opencv官网上下载在Linux环境下源码编译的包，以opencv3.4.7为例，下载命令如下。
 
-```
+```bash
 cd deploy/cpp_infer
-wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
-tar -xf 3.4.7.tar.gz
+wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
+tar -xf opencv-3.4.7.tar.gz
 ```
 
 最终可以在当前目录下看到`opencv-3.4.7/`的文件夹。
@@ -45,12 +45,13 @@ tar -xf 3.4.7.tar.gz
 * 编译opencv，设置opencv源码路径(`root_path`)以及安装路径(`install_path`)。进入opencv源码路径下，按照下面的方式进行编译。
 
 ```shell
-root_path=your_opencv_root_path
+root_path="your_opencv_root_path"
 install_path=${root_path}/opencv3
+build_dir=${root_path}/build
 
-rm -rf build
-mkdir build
-cd build
+rm -rf ${build_dir}
+mkdir ${build_dir}
+cd ${build_dir}
 
 cmake .. \
     -DCMAKE_INSTALL_PREFIX=${install_path} \
@@ -74,6 +75,11 @@ make -j
 make install
 ```
 
+也可以直接修改`tools/build_opencv.sh`的内容，然后直接运行下面的命令进行编译。
+
+```shell
+sh tools/build_opencv.sh
+```
 
 其中`root_path`为下载的opencv源码路径，`install_path`为opencv的安装路径，`make install`完成之后，会在该文件夹下生成opencv头文件和库文件，用于后面的OCR代码编译。
 
@@ -233,12 +239,12 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --image_dir=../../doc/imgs/12.jpg
 ```
 
-更多参数如下：
+更多支持的可调节参数解释如下：
 
 - 通用参数
 
 |参数名称|类型|默认参数|意义|
-| --- | --- | --- | --- |
+| :---: | :---: | :---: | :---: |
 |use_gpu|bool|false|是否使用GPU|
 |gpu_id|int|0|GPU id，使用GPU时有效|
 |gpu_mem|int|4000|申请的GPU内存|
@@ -248,7 +254,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 - 检测模型相关
 
 |参数名称|类型|默认参数|意义|
-| --- | --- | --- | --- |
+| :---: | :---: | :---: | :---: |
 |det_model_dir|string|-|检测模型inference model地址|
 |max_side_len|int|960|输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960|
 |det_db_thresh|float|0.3|用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显|
@@ -260,7 +266,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 - 方向分类器相关
 
 |参数名称|类型|默认参数|意义|
-| --- | --- | --- | --- |
+| :---: | :---: | :---: | :---: |
 |use_angle_cls|bool|false|是否使用方向分类器|
 |cls_model_dir|string|-|方向分类器inference model地址|
 |cls_thresh|float|0.9|方向分类器的得分阈值|
@@ -268,7 +274,7 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 - 识别模型相关
 
 |参数名称|类型|默认参数|意义|
-| --- | --- | --- | --- |
+| :---: | :---: | :---: | :---: |
 |rec_model_dir|string|-|识别模型inference model地址|
 |char_list_file|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件|
 

deploy/cpp_infer/readme_en.md

@@ -17,10 +17,10 @@ PaddleOCR model deployment.
 
 * First of all, you need to download the source code compiled package in the Linux environment from the opencv official website. Taking opencv3.4.7 as an example, the download command is as follows.
 
-```
+```bash
 cd deploy/cpp_infer
-wget https://github.com/opencv/opencv/archive/3.4.7.tar.gz
-tar -xf 3.4.7.tar.gz
+wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
+tar -xf opencv-3.4.7.tar.gz
 ```
 
 Finally, you can see the folder of `opencv-3.4.7/` in the current directory.

deploy/cpp_infer/src/main.cpp

@@ -61,7 +61,7 @@ DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
 DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
 // recognition related
 DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
-DEFINE_int32(rec_batch_num, 1, "rec_batch_num.");
+DEFINE_int32(rec_batch_num, 6, "rec_batch_num.");
 DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");
 
 
@@ -146,8 +146,9 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
     CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
                        FLAGS_gpu_mem, FLAGS_cpu_threads,
                        FLAGS_enable_mkldnn, char_list_file,
-                       FLAGS_use_tensorrt, FLAGS_precision);
+                       FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num);
 
+    std::vector<cv::Mat> img_list;
     for (int i = 0; i < cv_all_img_names.size(); ++i) {
       LOG(INFO) << "The predict img: " << cv_all_img_names[i];
 
@@ -156,22 +157,21 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
         std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
         exit(1);
       }
-
-      std::vector<double> rec_times;
-      rec.Run(srcimg, &rec_times);
-        
-      time_info[0] += rec_times[0];
-      time_info[1] += rec_times[1];
-      time_info[2] += rec_times[2];
+      img_list.push_back(srcimg);
     }
-        
+    std::vector<double> rec_times;
+    rec.Run(img_list, &rec_times);
+    time_info[0] += rec_times[0];
+    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, 
+                           FLAGS_rec_batch_num, 
                            "dynamic", 
                            FLAGS_precision, 
                            time_info, 
@@ -209,7 +209,7 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
     CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
                        FLAGS_gpu_mem, FLAGS_cpu_threads,
                        FLAGS_enable_mkldnn, char_list_file,
-                       FLAGS_use_tensorrt, FLAGS_precision);
+                       FLAGS_use_tensorrt, FLAGS_precision, FLAGS_rec_batch_num);
 
     for (int i = 0; i < cv_all_img_names.size(); ++i) {
       LOG(INFO) << "The predict img: " << cv_all_img_names[i];
@@ -228,19 +228,22 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
       time_info_det[1] += det_times[1];
       time_info_det[2] += det_times[2];
         
-      cv::Mat crop_img;
+      std::vector<cv::Mat> img_list;
       for (int j = 0; j < boxes.size(); j++) {
-        crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
-
-        if (cls != nullptr) {
-          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];
+          cv::Mat crop_img;
+          crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
+          if (cls != nullptr) {
+              crop_img = cls->Run(crop_img);
+          }
+          img_list.push_back(crop_img);
       }
+
+      rec.Run(img_list, &rec_times);
+      time_info_rec[0] += rec_times[0];
+      time_info_rec[1] += rec_times[1];
+      time_info_rec[2] += rec_times[2];
     }
+    
     if (FLAGS_benchmark) {
         AutoLogger autolog_det("ocr_det", 
                             FLAGS_use_gpu,
@@ -257,7 +260,7 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
                             FLAGS_use_tensorrt,
                             FLAGS_enable_mkldnn,
                             FLAGS_cpu_threads,
-                            1, 
+                            FLAGS_rec_batch_num, 
                             "dynamic", 
                             FLAGS_precision, 
                             time_info_rec, 

deploy/cpp_infer/src/ocr_rec.cpp

@@ -15,83 +15,108 @@
 #include <include/ocr_rec.h>
 
 namespace PaddleOCR {
-
-void CRNNRecognizer::Run(cv::Mat &img, std::vector<double> *times) {
-  cv::Mat srcimg;
-  img.copyTo(srcimg);
-  cv::Mat resize_img;
-
-  float wh_ratio = float(srcimg.cols) / float(srcimg.rows);
-  auto preprocess_start = std::chrono::steady_clock::now();
-  this->resize_op_.Run(srcimg, resize_img, wh_ratio, this->use_tensorrt_);
-
-  this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
-                          this->is_scale_);
-
-  std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
-
-  this->permute_op_.Run(&resize_img, input.data());
-  auto preprocess_end = std::chrono::steady_clock::now();
-
-  // Inference.
-  auto input_names = this->predictor_->GetInputNames();
-  auto input_t = this->predictor_->GetInputHandle(input_names[0]);
-  input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
-  auto inference_start = std::chrono::steady_clock::now();
-  input_t->CopyFromCpu(input.data());
-  this->predictor_->Run();
-
-  std::vector<float> predict_batch;
-  auto output_names = this->predictor_->GetOutputNames();
-  auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
-  auto predict_shape = output_t->shape();
-
-  int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
+    
+void CRNNRecognizer::Run(std::vector<cv::Mat> img_list, std::vector<double> *times) {
+    std::chrono::duration<float> preprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+    std::chrono::duration<float> inference_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+    std::chrono::duration<float> postprocess_diff = std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+
+    int img_num = img_list.size();
+    std::vector<float> width_list;
+    for (int i = 0; i < img_num; i++) {
+        width_list.push_back(float(img_list[i].cols) / img_list[i].rows);
+    }
+    std::vector<int> indices = Utility::argsort(width_list);
+
+    for (int beg_img_no = 0; beg_img_no < img_num; beg_img_no += this->rec_batch_num_) {
+        auto preprocess_start = std::chrono::steady_clock::now();
+        int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
+        float max_wh_ratio = 0;
+        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
+            int h = img_list[indices[ino]].rows;
+            int w = img_list[indices[ino]].cols;
+            float wh_ratio = w * 1.0 / h;
+            max_wh_ratio = max(max_wh_ratio, wh_ratio);
+        }
+        std::vector<cv::Mat> norm_img_batch;
+        for (int ino = beg_img_no; ino < end_img_no; ino ++) {
+            cv::Mat srcimg;
+            img_list[indices[ino]].copyTo(srcimg);
+            cv::Mat resize_img;
+            this->resize_op_.Run(srcimg, resize_img, max_wh_ratio, this->use_tensorrt_);
+            this->normalize_op_.Run(&resize_img, this->mean_, this->scale_, this->is_scale_);
+            norm_img_batch.push_back(resize_img);
+        }
+        
+        int batch_width = int(ceilf(32 * max_wh_ratio)) - 1;
+        std::vector<float> input(this->rec_batch_num_ * 3 * 32 * batch_width, 0.0f);
+        this->permute_op_.Run(norm_img_batch, input.data());
+        auto preprocess_end = std::chrono::steady_clock::now();
+        preprocess_diff += preprocess_end - preprocess_start;
+
+        // Inference.
+        auto input_names = this->predictor_->GetInputNames();
+        auto input_t = this->predictor_->GetInputHandle(input_names[0]);
+        input_t->Reshape({this->rec_batch_num_, 3, 32, batch_width});
+        auto inference_start = std::chrono::steady_clock::now();
+        input_t->CopyFromCpu(input.data());
+        this->predictor_->Run();
+
+        std::vector<float> predict_batch;
+        auto output_names = this->predictor_->GetOutputNames();
+        auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
+        auto predict_shape = output_t->shape();
+
+        int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
                                 std::multiplies<int>());
-  predict_batch.resize(out_num);
-
-  output_t->CopyToCpu(predict_batch.data());
-  auto inference_end = std::chrono::steady_clock::now();
-
-  // ctc decode
-  auto postprocess_start = std::chrono::steady_clock::now();
-  std::vector<std::string> str_res;
-  int argmax_idx;
-  int last_index = 0;
-  float score = 0.f;
-  int count = 0;
-  float max_value = 0.0f;
-
-  for (int n = 0; n < predict_shape[1]; n++) {
-    argmax_idx =
-        int(Utility::argmax(&predict_batch[n * predict_shape[2]],
-                            &predict_batch[(n + 1) * predict_shape[2]]));
-    max_value =
-        float(*std::max_element(&predict_batch[n * predict_shape[2]],
-                                &predict_batch[(n + 1) * predict_shape[2]]));
-
-    if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
-      score += max_value;
-      count += 1;
-      str_res.push_back(label_list_[argmax_idx]);
+        predict_batch.resize(out_num);
+
+        output_t->CopyToCpu(predict_batch.data());
+        auto inference_end = std::chrono::steady_clock::now();
+        inference_diff += inference_end - inference_start;
+        
+        // ctc decode
+        auto postprocess_start = std::chrono::steady_clock::now();
+        for (int m = 0; m < predict_shape[0]; m++) {
+            std::vector<std::string> str_res;
+            int argmax_idx;
+            int last_index = 0;
+            float score = 0.f;
+            int count = 0;
+            float max_value = 0.0f;
+
+            for (int n = 0; n < predict_shape[1]; n++) {
+                argmax_idx =
+                    int(Utility::argmax(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
+                                        &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
+                max_value =
+                    float(*std::max_element(&predict_batch[(m * predict_shape[1] + n) * predict_shape[2]],
+                                            &predict_batch[(m * predict_shape[1] + n + 1) * predict_shape[2]]));
+
+                if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
+                    score += max_value;
+                    count += 1;
+                    str_res.push_back(label_list_[argmax_idx]);
+                }
+                last_index = argmax_idx;
+            }
+            score /= count;
+            if (isnan(score))
+                continue;
+            for (int i = 0; i < str_res.size(); i++) {
+                std::cout << str_res[i];
+            }
+            std::cout << "\tscore: " << score << std::endl;
+        }
+        auto postprocess_end = std::chrono::steady_clock::now();
+        postprocess_diff += postprocess_end - postprocess_start;
     }
-    last_index = argmax_idx;
-  }
-  auto postprocess_end = std::chrono::steady_clock::now();
-  score /= count;
-  for (int i = 0; i < str_res.size(); i++) {
-    std::cout << str_res[i];
-  }
-  std::cout << "\tscore: " << score << std::endl;
-
-  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
-  times->push_back(double(preprocess_diff.count() * 1000));
-  std::chrono::duration<float> inference_diff = inference_end - inference_start;
-  times->push_back(double(inference_diff.count() * 1000));
-  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
-  times->push_back(double(postprocess_diff.count() * 1000));
+    times->push_back(double(preprocess_diff.count() * 1000));
+    times->push_back(double(inference_diff.count() * 1000));
+    times->push_back(double(postprocess_diff.count() * 1000));
 }
 
+    
 void CRNNRecognizer::LoadModel(const std::string &model_dir) {
   //   AnalysisConfig config;
   paddle_infer::Config config;

deploy/cpp_infer/src/preprocess_op.cpp

@@ -40,6 +40,17 @@ void Permute::Run(const cv::Mat *im, float *data) {
   }
 }
 
+void PermuteBatch::Run(const std::vector<cv::Mat> imgs, float *data) {
+    for (int j = 0; j < imgs.size(); j ++){
+        int rh = imgs[j].rows;
+        int rw = imgs[j].cols;
+        int rc = imgs[j].channels();
+        for (int i = 0; i < rc; ++i) {
+            cv::extractChannel(imgs[j], cv::Mat(rh, rw, CV_32FC1, data + (j * rc + i) * rh * rw), i);
+        }
+    }
+}
+    
 void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
                     const std::vector<float> &scale, const bool is_scale) {
   double e = 1.0;
@@ -90,16 +101,17 @@ void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio,
   imgC = rec_image_shape[0];
   imgH = rec_image_shape[1];
   imgW = rec_image_shape[2];
-
+    
   imgW = int(32 * wh_ratio);
 
   float ratio = float(img.cols) / float(img.rows);
   int resize_w, resize_h;
+
   if (ceilf(imgH * ratio) > imgW)
     resize_w = imgW;
   else
     resize_w = int(ceilf(imgH * ratio));
-
+    
   cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
              cv::INTER_LINEAR);
   cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0,

deploy/cpp_infer/src/utility.cpp

@@ -147,4 +147,17 @@ cv::Mat Utility::GetRotateCropImage(const cv::Mat &srcimage,
   }
 }
 
+std::vector<int> Utility::argsort(const std::vector<float>& array)
+{
+    const int array_len(array.size());
+    std::vector<int> array_index(array_len, 0);
+    for (int i = 0; i < array_len; ++i)
+        array_index[i] = i;
+
+    std::sort(array_index.begin(), array_index.end(),
+        [&array](int pos1, int pos2) {return (array[pos1] < array[pos2]); });
+
+    return array_index;
+}
+
 } // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/tools/build_opencv.sh

+root_path="/paddle/PaddleOCR/deploy/cpp_infer/opencv-3.4.7"
+install_path=${root_path}/opencv3
+build_dir=${root_path}/build
+
+rm -rf ${build_dir}
+mkdir ${build_dir}
+cd ${build_dir}
+
+cmake .. \
+    -DCMAKE_INSTALL_PREFIX=${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

deploy/lite/ocr_db_crnn.cc

@@ -12,12 +12,14 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle_api.h" // NOLINT
 #include <chrono>
+#include "paddle_api.h" // NOLINT
+#include "paddle_place.h"
 
 #include "cls_process.h"
 #include "crnn_process.h"
 #include "db_post_process.h"
+#include "AutoLog/auto_log/lite_autolog.h"
 
 using namespace paddle::lite_api; // NOLINT
 using namespace std;
@@ -27,7 +29,7 @@ void NeonMeanScale(const float *din, float *dout, int size,
                    const std::vector<float> mean,
                    const std::vector<float> scale) {
   if (mean.size() != 3 || scale.size() != 3) {
-    std::cerr << "[ERROR] mean or scale size must equal to 3\n";
+    std::cerr << "[ERROR] mean or scale size must equal to 3" << std::endl;
     exit(1);
   }
   float32x4_t vmean0 = vdupq_n_f32(mean[0]);
@@ -159,7 +161,8 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
                  std::vector<float> &rec_text_score,
                  std::vector<std::string> charactor_dict,
                  std::shared_ptr<PaddlePredictor> predictor_cls,
-                 int use_direction_classify) {
+                 int use_direction_classify,
+                 std::vector<double> *times) {
   std::vector<float> mean = {0.5f, 0.5f, 0.5f};
   std::vector<float> scale = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
 
@@ -169,7 +172,10 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
   cv::Mat resize_img;
 
   int index = 0;
+
+  std::vector<double> time_info = {0, 0, 0};
   for (int i = boxes.size() - 1; i >= 0; i--) {
+    auto preprocess_start = std::chrono::steady_clock::now();
     crop_img = GetRotateCropImage(srcimg, boxes[i]);
     if (use_direction_classify >= 1) {
       crop_img = RunClsModel(crop_img, predictor_cls);
@@ -188,7 +194,9 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
     auto *data0 = input_tensor0->mutable_data<float>();
 
     NeonMeanScale(dimg, data0, resize_img.rows * resize_img.cols, mean, scale);
+    auto preprocess_end = std::chrono::steady_clock::now();
     //// Run CRNN predictor
+    auto inference_start = std::chrono::steady_clock::now();
     predictor_crnn->Run();
 
     // Get output and run postprocess
@@ -196,8 +204,10 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
         std::move(predictor_crnn->GetOutput(0)));
     auto *predict_batch = output_tensor0->data<float>();
     auto predict_shape = output_tensor0->shape();
+    auto inference_end = std::chrono::steady_clock::now();
 
     // ctc decode
+    auto postprocess_start = std::chrono::steady_clock::now();
     std::string str_res;
     int argmax_idx;
     int last_index = 0;
@@ -221,19 +231,33 @@ void RunRecModel(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat img,
     score /= count;
     rec_text.push_back(str_res);
     rec_text_score.push_back(score);
+    auto postprocess_end = std::chrono::steady_clock::now();
+
+    std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
+    time_info[0] += double(preprocess_diff.count() * 1000);
+    std::chrono::duration<float> inference_diff = inference_end - inference_start;
+    time_info[1] += double(inference_diff.count() * 1000);
+    std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
+    time_info[2] += double(postprocess_diff.count() * 1000);
+
   }
+
+times->push_back(time_info[0]);
+times->push_back(time_info[1]);
+times->push_back(time_info[2]);
 }
 
 std::vector<std::vector<std::vector<int>>>
 RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
-            std::map<std::string, double> Config) {
+            std::map<std::string, double> Config, std::vector<double> *times) {
   // Read img
   int max_side_len = int(Config["max_side_len"]);
   int det_db_use_dilate = int(Config["det_db_use_dilate"]);
 
   cv::Mat srcimg;
   img.copyTo(srcimg);
-
+  
+  auto preprocess_start = std::chrono::steady_clock::now();
   std::vector<float> ratio_hw;
   img = DetResizeImg(img, max_side_len, ratio_hw);
   cv::Mat img_fp;
@@ -248,8 +272,10 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
   std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
   const float *dimg = reinterpret_cast<const float *>(img_fp.data);
   NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
+  auto preprocess_end = std::chrono::steady_clock::now();
 
   // Run predictor
+  auto inference_start = std::chrono::steady_clock::now();
   predictor->Run();
 
   // Get output and post process
@@ -257,8 +283,10 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
       std::move(predictor->GetOutput(0)));
   auto *outptr = output_tensor->data<float>();
   auto shape_out = output_tensor->shape();
+  auto inference_end = std::chrono::steady_clock::now();
 
   // Save output
+  auto postprocess_start = std::chrono::steady_clock::now();
   float pred[shape_out[2] * shape_out[3]];
   unsigned char cbuf[shape_out[2] * shape_out[3]];
 
@@ -287,14 +315,23 @@ RunDetModel(std::shared_ptr<PaddlePredictor> predictor, cv::Mat img,
 
   std::vector<std::vector<std::vector<int>>> filter_boxes =
       FilterTagDetRes(boxes, ratio_hw[0], ratio_hw[1], srcimg);
+  auto postprocess_end = std::chrono::steady_clock::now();
+
+  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
+  times->push_back(double(preprocess_diff.count() * 1000));
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  times->push_back(double(inference_diff.count() * 1000));
+  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
+  times->push_back(double(postprocess_diff.count() * 1000));
 
   return filter_boxes;
 }
 
-std::shared_ptr<PaddlePredictor> loadModel(std::string model_file) {
+std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, int num_threads) {
   MobileConfig config;
   config.set_model_from_file(model_file);
 
+  config.set_threads(num_threads);
   std::shared_ptr<PaddlePredictor> predictor =
       CreatePaddlePredictor<MobileConfig>(config);
   return predictor;
@@ -354,60 +391,285 @@ std::map<std::string, double> LoadConfigTxt(std::string config_path) {
   return dict;
 }
 
-int main(int argc, char **argv) {
-  if (argc < 5) {
-    std::cerr << "[ERROR] usage: " << argv[0]
-              << " det_model_file cls_model_file rec_model_file image_path "
-                 "charactor_dict\n";
+void check_params(int argc, char **argv) {
+  if (argc<=1 || (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0)) {
+    std::cerr << "Please choose one mode of [det, rec, system] !" << std::endl;
     exit(1);
   }
-  std::string det_model_file = argv[1];
-  std::string rec_model_file = argv[2];
-  std::string cls_model_file = argv[3];
-  std::string img_path = argv[4];
-  std::string dict_path = argv[5];
+  if (strcmp(argv[1], "det") == 0) {
+      if (argc < 9){
+        std::cerr << "[ERROR] usage:" << argv[0]
+                  << " det det_model runtime_device num_threads batchsize img_dir det_config lite_benchmark_value" << std::endl;
+        exit(1);
+      }
+  }
+
+  if (strcmp(argv[1], "rec") == 0) {
+      if (argc < 9){
+        std::cerr << "[ERROR] usage:" << argv[0]
+                  << " rec rec_model runtime_device num_threads batchsize img_dir key_txt lite_benchmark_value" << std::endl;
+        exit(1);
+      }
+  }
+
+  if (strcmp(argv[1], "system") == 0) {
+      if (argc < 12){
+        std::cerr << "[ERROR] usage:" << argv[0]
+                  << " system det_model rec_model clas_model runtime_device num_threads batchsize img_dir det_config key_txt lite_benchmark_value" << std::endl;
+        exit(1);
+      }
+  }
+}
+
+void system(char **argv){
+  std::string det_model_file = argv[2];
+  std::string rec_model_file = argv[3];
+  std::string cls_model_file = argv[4];
+  std::string runtime_device = argv[5];
+  std::string precision = argv[6];
+  std::string num_threads = argv[7];
+  std::string batchsize = argv[8];
+  std::string img_dir = argv[9];
+  std::string det_config_path = argv[10];
+  std::string dict_path = argv[11];
+
+  if (strcmp(argv[6], "FP32") != 0 && strcmp(argv[6], "INT8") != 0) {
+      std::cerr << "Only support FP32 or INT8." << std::endl;
+      exit(1);
+  }
+
+  std::vector<cv::String> cv_all_img_names;
+  cv::glob(img_dir, cv_all_img_names);
 
   //// load config from txt file
-  auto Config = LoadConfigTxt("./config.txt");
+  auto Config = LoadConfigTxt(det_config_path);
   int use_direction_classify = int(Config["use_direction_classify"]);
 
-  auto start = std::chrono::system_clock::now();
+  auto charactor_dict = ReadDict(dict_path);
+  charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
+  charactor_dict.push_back(" ");
+
+  auto det_predictor = loadModel(det_model_file, std::stoi(num_threads));
+  auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));
+  auto cls_predictor = loadModel(cls_model_file, std::stoi(num_threads));
+
+  std::vector<double> det_time_info = {0, 0, 0};
+  std::vector<double> rec_time_info = {0, 0, 0};
 
-  auto det_predictor = loadModel(det_model_file);
-  auto rec_predictor = loadModel(rec_model_file);
-  auto cls_predictor = loadModel(cls_model_file);
+  for (int i = 0; i < cv_all_img_names.size(); ++i) {
+    std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
+    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] << std::endl;
+      exit(1);
+    }
+
+    std::vector<double> det_times;
+    auto boxes = RunDetModel(det_predictor, srcimg, Config, &det_times);
+  
+    std::vector<std::string> rec_text;
+    std::vector<float> rec_text_score;
+  
+    std::vector<double> rec_times;
+    RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
+                charactor_dict, cls_predictor, use_direction_classify, &rec_times);
+  
+    //// visualization
+    auto img_vis = Visualization(srcimg, boxes);
+  
+    //// print recognized text
+    for (int i = 0; i < rec_text.size(); i++) {
+      std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
+                <<  std::endl;
+
+    }
+
+    det_time_info[0] += det_times[0];
+    det_time_info[1] += det_times[1];
+    det_time_info[2] += det_times[2];
+    rec_time_info[0] += rec_times[0];
+    rec_time_info[1] += rec_times[1];
+    rec_time_info[2] += rec_times[2];
+  }
+  if (strcmp(argv[12], "True") == 0) {
+    AutoLogger autolog_det(det_model_file, 
+                       runtime_device,
+                       std::stoi(num_threads),
+                       std::stoi(batchsize), 
+                       "dynamic", 
+                       precision, 
+                       det_time_info, 
+                       cv_all_img_names.size());
+    AutoLogger autolog_rec(rec_model_file, 
+                       runtime_device,
+                       std::stoi(num_threads),
+                       std::stoi(batchsize), 
+                       "dynamic", 
+                       precision, 
+                       rec_time_info, 
+                       cv_all_img_names.size());
+
+    autolog_det.report();
+    std::cout << std::endl;
+    autolog_rec.report();
+  }
+}
+
+void det(int argc, char **argv) {
+  std::string det_model_file = argv[2];
+  std::string runtime_device = argv[3];
+  std::string precision = argv[4];
+  std::string num_threads = argv[5];
+  std::string batchsize = argv[6];
+  std::string img_dir = argv[7];
+  std::string det_config_path = argv[8];
+
+  if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0) {
+      std::cerr << "Only support FP32 or INT8." << std::endl;
+      exit(1);
+  }
+
+  std::vector<cv::String> cv_all_img_names;
+  cv::glob(img_dir, cv_all_img_names);
+
+  //// load config from txt file
+  auto Config = LoadConfigTxt(det_config_path);
+
+  auto det_predictor = loadModel(det_model_file, std::stoi(num_threads));
+
+  std::vector<double> time_info = {0, 0, 0};
+  for (int i = 0; i < cv_all_img_names.size(); ++i) {
+    std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
+    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] << std::endl;
+      exit(1);
+    }
+
+    std::vector<double> times;
+    auto boxes = RunDetModel(det_predictor, srcimg, Config, &times);
+
+    //// visualization
+    auto img_vis = Visualization(srcimg, boxes);
+    std::cout << boxes.size() << " bboxes have detected:" << std::endl;
+
+    for (int i=0; i<boxes.size(); i++){
+      std::cout << "The " << i << " box:" << std::endl;
+      for (int j=0; j<4; j++){
+        for (int k=0; k<2; k++){
+          std::cout << boxes[i][j][k] << "\t";
+        }
+      }
+      std::cout << std::endl;
+    }
+    time_info[0] += times[0];
+    time_info[1] += times[1];
+    time_info[2] += times[2];
+  }
+
+  if (strcmp(argv[9], "True") == 0) {
+    AutoLogger autolog(det_model_file, 
+                       runtime_device,
+                       std::stoi(num_threads),
+                       std::stoi(batchsize), 
+                       "dynamic", 
+                       precision, 
+                       time_info, 
+                       cv_all_img_names.size());
+    autolog.report();
+  }
+}
+
+void rec(int argc, char **argv) {
+  std::string rec_model_file = argv[2];
+  std::string runtime_device = argv[3];
+  std::string precision = argv[4];
+  std::string num_threads = argv[5];
+  std::string batchsize = argv[6];
+  std::string img_dir = argv[7];
+  std::string dict_path = argv[8];
+
+  if (strcmp(argv[4], "FP32") != 0 && strcmp(argv[4], "INT8") != 0) {
+      std::cerr << "Only support FP32 or INT8." << std::endl;
+      exit(1);
+  }
+
+  std::vector<cv::String> cv_all_img_names;
+  cv::glob(img_dir, cv_all_img_names);
 
   auto charactor_dict = ReadDict(dict_path);
   charactor_dict.insert(charactor_dict.begin(), "#"); // blank char for ctc
   charactor_dict.push_back(" ");
 
-  cv::Mat srcimg = cv::imread(img_path, cv::IMREAD_COLOR);
-  auto boxes = RunDetModel(det_predictor, srcimg, Config);
+  auto rec_predictor = loadModel(rec_model_file, std::stoi(num_threads));
 
-  std::vector<std::string> rec_text;
-  std::vector<float> rec_text_score;
+  std::shared_ptr<PaddlePredictor> cls_predictor;
 
-  RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
-              charactor_dict, cls_predictor, use_direction_classify);
+  std::vector<double> time_info = {0, 0, 0};
+  for (int i = 0; i < cv_all_img_names.size(); ++i) {
+    std::cout << "The predict img: " << cv_all_img_names[i] << std::endl;
+    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
 
-  auto end = std::chrono::system_clock::now();
-  auto duration =
-      std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+    if (!srcimg.data) {
+      std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << std::endl;
+      exit(1);
+    }
 
-  //// visualization
-  auto img_vis = Visualization(srcimg, boxes);
+    int width = srcimg.cols;
+    int height = srcimg.rows;
+    std::vector<int> upper_left = {0, 0};
+    std::vector<int> upper_right = {width, 0};
+    std::vector<int> lower_right = {width, height};
+    std::vector<int> lower_left  = {0, height};
+    std::vector<std::vector<int>> box = {upper_left, upper_right, lower_right, lower_left};
+    std::vector<std::vector<std::vector<int>>> boxes = {box};
+
+    std::vector<std::string> rec_text;
+    std::vector<float> rec_text_score;
+    std::vector<double> times;
+    RunRecModel(boxes, srcimg, rec_predictor, rec_text, rec_text_score,
+                charactor_dict, cls_predictor, 0, &times);
+  
+    //// print recognized text
+    for (int i = 0; i < rec_text.size(); i++) {
+      std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
+                << std::endl;
+    }
+    time_info[0] += times[0];
+    time_info[1] += times[1];
+    time_info[2] += times[2];
+  }
+  // TODO: support autolog
+  if (strcmp(argv[9], "True") == 0) {
+    AutoLogger autolog(rec_model_file, 
+                       runtime_device,
+                       std::stoi(num_threads),
+                       std::stoi(batchsize), 
+                       "dynamic", 
+                       precision, 
+                       time_info, 
+                       cv_all_img_names.size());
+    autolog.report();
+  }
+}
+
+int main(int argc, char **argv) {
+  check_params(argc, argv);
+  std::cout << "mode: " << argv[1] << endl;
 
-  //// print recognized text
-  for (int i = 0; i < rec_text.size(); i++) {
-    std::cout << i << "\t" << rec_text[i] << "\t" << rec_text_score[i]
-              << std::endl;
+  if (strcmp(argv[1], "system") == 0) {
+    system(argv);
   }
 
-  std::cout << "花费了"
-            << double(duration.count()) *
-                   std::chrono::microseconds::period::num /
-                   std::chrono::microseconds::period::den
-            << "秒" << std::endl;
+  if (strcmp(argv[1], "det") == 0) {
+    det(argc, argv);
+  }
+
+  if (strcmp(argv[1], "rec") == 0) {
+    rec(argc, argv);
+  }
 
   return 0;
-}
+}
\ No newline at end of file

deploy/paddle2onnx/readme.md

+# paddle2onnx 模型转化与预测
+
+本章节介绍 PaddleOCR 模型如何转化为 ONNX 模型，并基于 ONNX 引擎预测。
+
+## 1. 环境准备
+
+需要准备 Paddle2ONNX 模型转化环境，和 ONNX 模型预测环境
+
+###  Paddle2ONNX
+
+Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式，算子目前稳定支持导出 ONNX Opset 9~11，部分Paddle算子支持更低的ONNX Opset转换。
+更多细节可参考 [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX/blob/develop/README_zh.md)
+
+- 安装 Paddle2ONNX
+```
+python3.7 -m pip install paddle2onnx
+```
+
+- 安装 ONNX
+```
+# 建议安装 1.4.0 版本，可根据环境更换版本号
+python3.7 -m pip install onnxruntime==1.4.0
+```
+
+## 2. 模型转换
+
+
+- Paddle 模型下载
+
+有两种方式获取Paddle静态图模型：在 [model_list](../../doc/doc_ch/models_list.md) 中下载PaddleOCR提供的预测模型；
+参考[模型导出说明](../../doc/doc_ch/inference.md#训练模型转inference模型)把训练好的权重转为 inference_model。
+
+以 ppocr 检测模型为例：
+
+```
+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 && cd ..
+```
+
+- 模型转换
+
+使用 Paddle2ONNX 将Paddle静态图模型转换为ONNX模型格式：
+
+```
+paddle2onnx --model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ \
+--model_filename=inference.pdmodel \
+--params_filename=inference.pdiparams \
+--save_file=./inference/det_mobile_onnx/model.onnx \
+--opset_version=10 \
+--enable_onnx_checker=True
+```
+
+执行完毕后，ONNX 模型会被保存在 `./inference/det_mobile_onnx/` 路径下
+
+* 注意：以下几个模型暂不支持转换为 ONNX 模型：
+NRTR、SAR、RARE、SRN
+
+## 3. onnx 预测
+
+以检测模型为例，使用 ONNX 预测可执行如下命令：
+
+```
+python3.7 ../../tools/infer/predict_det.py --use_gpu=False --use_onnx=True \
+--det_model_dir=./inference/det_mobile_onnx/model.onnx \
+--image_dir=../../doc/imgs/1.jpg
+```
+
+执行命令后在终端会打印出预测的检测框坐标，并在 `./inference_results/` 下保存可视化结果。
+
+```
+root INFO: 1.jpg  [[[291, 295], [334, 292], [348, 844], [305, 847]], [[344, 296], [379, 294], [387, 669], [353, 671]]]
+The predict time of ../../doc/imgs/1.jpg: 0.06162881851196289
+The visualized image saved in ./inference_results/det_res_1.jpg
+```
+
+* 注意：ONNX暂时不支持变长预测，需要将输入resize到固定输入，预测结果可能与直接使用Paddle预测有细微不同。

deploy/pdserving/README.md

@@ -114,7 +114,7 @@ The recognition model is the same.
     git clone https://github.com/PaddlePaddle/PaddleOCR
 
     # Enter the working directory  
-    cd PaddleOCR/deploy/pdserver/
+    cd PaddleOCR/deploy/pdserving/
     ```
 
     The pdserver directory contains the code to start the pipeline service and send prediction requests, including:

deploy/pdserving/README_CN.md

@@ -112,7 +112,7 @@ python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_mobile_v2.0_rec_in
     git clone https://github.com/PaddlePaddle/PaddleOCR
 
     # 进入到工作目录
-    cd PaddleOCR/deploy/pdserver/
+    cd PaddleOCR/deploy/pdserving/
     ```
     pdserver目录包含启动pipeline服务和发送预测请求的代码，包括：
     ```
@@ -206,7 +206,7 @@ pip3 install paddle-serving-app==0.3.1
 1. 启动服务端程序
 
 ```
-cd win 
+cd win
 python3 ocr_web_server.py gpu(使用gpu方式)
 或者
 python3 ocr_web_server.py cpu(使用cpu方式)

deploy/pdserving/ocr_cpp_client.py

+# Copyright (c) 2020 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.
+# pylint: disable=doc-string-missing
+
+from paddle_serving_client import Client
+import sys
+import numpy as np
+import base64
+import os
+import cv2
+from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
+from paddle_serving_app.reader import Div, Normalize, Transpose
+from ocr_reader import OCRReader
+
+client = Client()
+# TODO:load_client need to load more than one client model.
+# this need to figure out some details.
+client.load_client_config(sys.argv[1:])
+client.connect(["127.0.0.1:9293"])
+
+import paddle
+test_img_dir = "test_img/"
+
+ocr_reader = OCRReader(char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt")
+
+
+def cv2_to_base64(image):
+    return base64.b64encode(image).decode(
+        'utf8')  #data.tostring()).decode('utf8')
+
+
+for img_file in os.listdir(test_img_dir):
+    with open(os.path.join(test_img_dir, img_file), 'rb') as file:
+        image_data = file.read()
+    image = cv2_to_base64(image_data)
+    res_list = []
+    #print(image)
+    fetch_map = client.predict(
+        feed={"x": image}, fetch=["save_infer_model/scale_0.tmp_1"], batch=True)
+    print("fetrch map:", fetch_map)
+    one_batch_res = ocr_reader.postprocess(fetch_map, with_score=True)
+    for res in one_batch_res:
+        res_list.append(res[0])
+    res = {"res": str(res_list)}
+    print(res)

deploy/pdserving/pipeline_http_client.py

@@ -18,13 +18,19 @@ import json
 import base64
 import os
 
+import argparse
+parser = argparse.ArgumentParser(description="args for paddleserving")
+parser.add_argument("--image_dir", type=str, default="../../doc/imgs/")
+args = parser.parse_args()
+
 
 def cv2_to_base64(image):
     return base64.b64encode(image).decode('utf8')
 
 
 url = "http://127.0.0.1:9998/ocr/prediction"
-test_img_dir = "../../doc/imgs/"
+test_img_dir = args.image_dir
+
 for idx, img_file in enumerate(os.listdir(test_img_dir)):
     with open(os.path.join(test_img_dir, img_file), 'rb') as file:
         image_data1 = file.read()
@@ -36,5 +42,4 @@ for idx, img_file in enumerate(os.listdir(test_img_dir)):
         r = requests.post(url=url, data=json.dumps(data))
         print(r.json())
 
-test_img_dir = "../../doc/imgs/"
 print("==> total number of test imgs: ", len(os.listdir(test_img_dir)))

deploy/pdserving/pipeline_rpc_client.py

@@ -30,12 +30,17 @@ def cv2_to_base64(image):
     return base64.b64encode(image).decode('utf8')
 
 
-test_img_dir = "imgs/"
+import argparse
+parser = argparse.ArgumentParser(description="args for paddleserving")
+parser.add_argument("--image_dir", type=str, default="../../doc/imgs/")
+args = parser.parse_args()
+test_img_dir = args.image_dir
+
 for img_file in os.listdir(test_img_dir):
     with open(os.path.join(test_img_dir, img_file), 'rb') as file:
         image_data = file.read()
     image = cv2_to_base64(image_data)
 
-for i in range(1):
-    ret = client.predict(feed_dict={"image": image}, fetch=["res"])
-    print(ret)
+    for i in range(1):
+        ret = client.predict(feed_dict={"image": image}, fetch=["res"])
+        print(ret)

deploy/slim/prune/export_prune_model.py

@@ -30,7 +30,7 @@ from ppocr.modeling.architectures import build_model
 
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 import tools.program as program
 
 
@@ -89,7 +89,7 @@ def main(config, device, logger, vdl_writer):
     logger.info(f"FLOPs after pruning: {flops}")
 
     # load pretrain model
-    pre_best_model_dict = init_model(config, model, logger, None)
+    load_model(config, model)
     metric = program.eval(model, valid_dataloader, post_process_class,
                           eval_class)
     logger.info(f"metric['hmean']: {metric['hmean']}")

deploy/slim/prune/sensitivity_anal.py

@@ -32,7 +32,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 import tools.program as program
 
 dist.get_world_size()
@@ -94,7 +94,7 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # load pretrain model
-    pre_best_model_dict = init_model(config, model, logger, optimizer)
+    pre_best_model_dict = load_model(config, model, optimizer)
 
     logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
                 format(len(train_dataloader), len(valid_dataloader)))

deploy/slim/quantization/export_model.py

@@ -28,7 +28,7 @@ from paddle.jit import to_static
 
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.logging import get_logger
 from tools.program import load_config, merge_config, ArgsParser
 from ppocr.metrics import build_metric
@@ -101,7 +101,7 @@ def main():
     quanter = QAT(config=quant_config)
     quanter.quantize(model)
 
-    init_model(config, model)
+    load_model(config, model)
     model.eval()
 
     # build metric

deploy/slim/quantization/quant.py

@@ -37,7 +37,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 import tools.program as program
 from paddleslim.dygraph.quant import QAT
 
@@ -137,7 +137,7 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # load pretrain model
-    pre_best_model_dict = init_model(config, model, logger, optimizer)
+    pre_best_model_dict = load_model(config, model, optimizer)
 
     logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
                 format(len(train_dataloader), len(valid_dataloader)))

deploy/slim/quantization/quant_kl.py

@@ -37,7 +37,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 import tools.program as program
 import paddleslim
 from paddleslim.dygraph.quant import QAT

doc/doc_ch/algorithm_overview.md

-<a name="算法介绍"></a>
-## 算法介绍
+# 两阶段算法
+
+- [两阶段算法](#-----)
+  * [1. 算法介绍](#1)
+    + [1.1 文本检测算法](#11)
+    + [1.2 文本识别算法](#12)
+  * [2. 模型训练](#2)
+  * [3. 模型推理](#3)
+
+<a name="1"></a>
+
+## 1. 算法介绍
 本文给出了PaddleOCR已支持的文本检测算法和文本识别算法列表，以及每个算法在**英文公开数据集**上的模型和指标，主要用于算法简介和算法性能对比，更多包括中文在内的其他数据集上的模型请参考[PP-OCR v2.0 系列模型下载](./models_list.md)。
 
-- [1.文本检测算法](#文本检测算法)
-- [2.文本识别算法](#文本识别算法)
+<a name="11"></a>
 
-<a name="文本检测算法"></a>
-### 1.文本检测算法
+### 1.1 文本检测算法
 
 PaddleOCR开源的文本检测算法列表：
-- [x]  DB([paper]( https://arxiv.org/abs/1911.08947))（ppocr推荐）
-- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
-- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))
+- [x]  DB([paper]( https://arxiv.org/abs/1911.08947)) [2]（ppocr推荐）
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[1]
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
 - [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
 
 在ICDAR2015文本检测公开数据集上，算法效果如下：
-
 |模型|骨干网络|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- |
-|EAST|ResNet50_vd|85.80%|86.71%|86.25%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
-|EAST|MobileNetV3|79.42%|80.64%|80.03%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
-|DB|ResNet50_vd|86.41%|78.72%|82.38%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
-|DB|MobileNetV3|77.29%|73.08%|75.12%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
-|SAST|ResNet50_vd|91.39%|83.77%|87.42%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
-|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
-|PSE|MobileNetV3|82.20%|70.48%|75.89%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|EAST|ResNet50_vd|85.80%|86.71%|86.25%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
+|EAST|MobileNetV3|79.42%|80.64%|80.03%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
+|DB|ResNet50_vd|86.41%|78.72%|82.38%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
+|DB|MobileNetV3|77.29%|73.08%|75.12%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+|SAST|ResNet50_vd|91.39%|83.77%|87.42%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
+|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 
 在Total-text文本检测公开数据集上，算法效果如下：
 
 |模型|骨干网络|precision|recall|Hmean|下载链接|
 | --- | --- | --- | --- | --- | --- |
-|SAST|ResNet50_vd|89.63%|78.44%|83.66%|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
+|SAST|ResNet50_vd|89.63%|78.44%|83.66%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
 
 **说明：** SAST模型训练额外加入了icdar2013、icdar2017、COCO-Text、ArT等公开数据集进行调优。PaddleOCR用到的经过整理格式的英文公开数据集下载：
 * [百度云地址](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (提取码: 2bpi)
 * [Google Drive下载地址](https://drive.google.com/drive/folders/1ll2-XEVyCQLpJjawLDiRlvo_i4BqHCJe?usp=sharing)
 
-PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./detection.md)。
-
+<a name="12"></a>
 
-<a name="文本识别算法"></a>
-### 2.文本识别算法
+### 1.2 文本识别算法
 
 PaddleOCR基于动态图开源的文本识别算法列表：
-- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))（ppocr推荐）
-- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
-- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
-- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
-- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))
-- [x]  NRTR([paper](https://arxiv.org/abs/1806.00926v2))
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐）
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
+- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
+- [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))[13]
 - [x]  SAR([paper](https://arxiv.org/abs/1811.00751v2))
-- [x]  SEED([paper](https://arxiv.org/pdf/2005.10977.pdf))
+- [x] SEED([paper](https://arxiv.org/pdf/2005.10977.pdf))
 
-参考[DTRB](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：
+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：
 
 |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
 |---|---|---|---|---|
-|Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
-|Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
-|CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
-|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
-|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
-|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
-|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
-|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) |
-|SEED| Aster_Resnet | 85.2% | rec_resnet_stn_bilstm_att | [下载链接](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.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)。
+|Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
+|Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
+|CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
+|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
+|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
+|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
+|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
+|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[训练模型](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 | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar) |
+|NRTR|NRTR_MTB| 84.3% | rec_mtb_nrtr | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar) |
+|SAR|Resnet31| 87.2% | rec_r31_sar | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
+|SEED|Aster_Resnet| 85.2% | rec_resnet_stn_bilstm_att | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
+
+<a name="2"></a>
+
+## 2. 模型训练
+
+PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训练/评估中的文本检测部分](./detection.md)。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)。
+
+<a name="3"></a>
+
+## 3. 模型推理
+
+上述模型中除PP-OCR系列模型以外，其余模型仅支持基于Python引擎的推理，具体内容可参考[基于Python预测引擎推理](./inference.md)

doc/doc_ch/angle_class.md

@@ -11,7 +11,7 @@
 ## 1. 方法介绍
 文本方向分类器主要用于图片非0度的场景下，在这种场景下需要对图片里检测到的文本行进行一个转正的操作。在PaddleOCR系统内，
 文字检测之后得到的文本行图片经过仿射变换之后送入识别模型，此时只需要对文字进行一个0和180度的角度分类，因此PaddleOCR内置的
-文字角度分类器**只支持了0和180度的分类**。如果想支持更多角度，可以自己修改算法进行支持。
+文本方向分类器**只支持了0和180度的分类**。如果想支持更多角度，可以自己修改算法进行支持。
 
 0和180度数据样本例子：
 
@@ -72,8 +72,6 @@ train/cls/train/word_002.jpg   180
 <a name="启动训练"></a>
 ## 3. 启动训练
 
-### 启动训练
-
 将准备好的txt文件和图片文件夹路径分别写入配置文件的 `Train/Eval.dataset.label_file_list` 和 `Train/Eval.dataset.data_dir` 字段下，`Train/Eval.dataset.data_dir`字段下的路径和文件里记载的图片名构成了图片的绝对路径。
 
 PaddleOCR提供了训练脚本、评估脚本和预测脚本。

doc/doc_ch/config.md

@@ -36,11 +36,10 @@
 |      pretrained_model    |    设置加载预训练模型路径      |  ./pretrain_models/CRNN/best_accuracy  |  \          |
 |      checkpoints         |    加载模型参数路径            |       None        |    用于中断后加载参数继续训练 |
 |      use_visualdl  |    设置是否启用visualdl进行可视化log展示 |          False        |    [教程地址](https://www.paddlepaddle.org.cn/paddle/visualdl) |
-|      infer_img            |    设置预测图像路径或文件夹路径     |       ./infer_img | \|
-|      character_dict_path |    设置字典路径            |  ./ppocr/utils/ppocr_keys_v1.txt  |    \                 |
+|      infer_img            |    设置预测图像路径或文件夹路径     |       ./infer_img | \||
+|      character_dict_path |    设置字典路径            |  ./ppocr/utils/ppocr_keys_v1.txt  | 如果为空，则默认使用小写字母+数字作为字典 |
 |      max_text_length     |    设置文本最大长度        |       25          |                \                 |
-|      character_type      |    设置字符类型            |       ch          |    en/ch, en时将使用默认dict，ch时使用自定义dict|
-|      use_space_char     |    设置是否识别空格             |        True      |          仅在 character_type=ch 时支持空格                 |
+|      use_space_char     |    设置是否识别空格             |        True      |          \|               |
 |      label_list          |    设置方向分类器支持的角度       |    ['0','180']    |     仅在方向分类器中生效 |
 |      save_res_path          |    设置检测模型的结果保存地址       |    ./output/det_db/predicts_db.txt    |     仅在检测模型中生效 |
 
@@ -191,7 +190,6 @@ PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi
       use_gpu: True
       epoch_num: 500
       ...
-      character_type: it  # 需要识别的语种
       character_dict_path:  {path/of/dict} # 字典文件所在路径
    
    Train:
@@ -212,17 +210,17 @@ PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi
 
 目前PaddleOCR支持的多语言算法有：
 
-| 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |  language | character_type |
-| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
-| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 中文繁体  | chinese_cht|
-| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 英语（区分大小写）   | EN |
-| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 法语 |  french |
-| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   | german |
-| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语  | japan |
-| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语  | korean |
-| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 拉丁字母  | latin |
-| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 阿拉伯字母 |  ar |
-| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 斯拉夫字母  | cyrillic |
-| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 梵文字母  | devanagari |
+| 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |  language |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  |
+| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 中文繁体  |
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 英语（区分大小写）   |
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 法语 |
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   |
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语  |
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语  |
+| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 拉丁字母  |
+| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 阿拉伯字母 |
+| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 斯拉夫字母  |
+| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 梵文字母  |
 
 更多支持语种请参考: [多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/multi_languages.md#%E8%AF%AD%E7%A7%8D%E7%BC%A9%E5%86%99)

doc/doc_ch/detection.md

 
-# 目录
-- [1. 文字检测](#1-----)
-  * [1.1 数据准备](#11-----)
-  * [1.2 下载预训练模型](#12--------)
-  * [1.3 启动训练](#13-----)
-  * [1.4 断点训练](#14-----)
-  * [1.5 更换Backbone 训练](#15---backbone---)
-  * [1.6 指标评估](#16-----)
-  * [1.7 测试检测效果](#17-------)
-  * [1.8 转inference模型测试](#18--inference----)
-- [2. FAQ](#2-faq)
-
-
-<a name="1-----"></a>
-# 1. 文字检测
+# 文字检测
 
 本节以icdar2015数据集为例，介绍PaddleOCR中检测模型训练、评估、测试的使用方式。
 
+- [1. 准备数据和模型](#1--------)
+  * [1.1 数据准备](#11-----)
+  * [1.2 下载预训练模型](#12--------)
+- [2. 开始训练](#2-----)
+  * [2.1 启动训练](#21-----)
+  * [2.2 断点训练](#22-----)
+  * [2.3 更换Backbone 训练](#23---backbone---)
+- [3. 模型评估与预测](#3--------)
+  * [3.1 指标评估](#31-----)
+  * [3.2 测试检测效果](#32-------)
+- [4. 模型导出与预测](#4--------)
+- [5. FAQ](#5-faq)
+
+<a name="1--------"></a>
+# 1. 准备数据和模型
+
 <a name="11-----"></a>
 ## 1.1 数据准备
 
@@ -83,8 +85,11 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg
 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
 ```
 
-<a name="13-----"></a>
-## 1.3 启动训练
+<a name="2-----"></a>
+# 2. 开始训练
+
+<a name="21-----"></a>
+## 2.1 启动训练
 
 *如果您安装的是cpu版本，请将配置文件中的 `use_gpu` 字段修改为false*
 
@@ -96,6 +101,10 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml \
 # 单机多卡训练，通过 --gpus 参数设置使用的GPU ID
 python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
      -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+
+# 多机多卡训练，通过 --ips 参数设置使用的机器IP地址，通过 --gpus 参数设置使用的GPU ID
+python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
+     -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
 ```
 
 上述指令中，通过-c 选择训练使用configs/det/det_db_mv3.yml配置文件。
@@ -106,8 +115,17 @@ python3 -m paddle.distributed.launch --gpus '0,1,2,3' tools/train.py -c configs/
 python3 tools/train.py -c configs/det/det_mv3_db.yml -o Optimizer.base_lr=0.0001
 ```
 
-<a name="14-----"></a>
-## 1.4 断点训练
+**注意:** 采用多机多卡训练时，需要替换上面命令中的ips值为您机器的地址，机器之间需要能够相互ping通。另外，训练时需要在多个机器上分别启动命令。查看机器ip地址的命令为`ifconfig`。
+
+如果您想进一步加快训练速度，可以使用[自动混合精度训练](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_cn.html)， 以单机单卡为例，命令如下：
+```shell
+python3 tools/train.py -c configs/det/det_mv3_db.yml \
+     -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained \
+     Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
+ ```
+
+<a name="22-----"></a>
+## 2.2 断点训练
 
 如果训练程序中断，如果希望加载训练中断的模型从而恢复训练，可以通过指定Global.checkpoints指定要加载的模型路径：
 ```shell
@@ -116,8 +134,8 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o Global.checkpoints=./you
 
 **注意**：`Global.checkpoints`的优先级高于`Global.pretrained_model`的优先级，即同时指定两个参数时，优先加载`Global.checkpoints`指定的模型，如果`Global.checkpoints`指定的模型路径有误，会加载`Global.pretrained_model`指定的模型。
 
-<a name="15---backbone---"></a>
-## 1.5 更换Backbone 训练
+<a name="23---backbone---"></a>
+## 2.3 更换Backbone 训练
 
 PaddleOCR将网络划分为四部分，分别在[ppocr/modeling](../../ppocr/modeling)下。 进入网络的数据将按照顺序(transforms->backbones->
 necks->heads)依次通过这四个部分。
@@ -164,8 +182,11 @@ args1: args1
 
 **注意**：如果要更换网络的其他模块，可以参考[文档](./add_new_algorithm.md)。
 
-<a name="16-----"></a>
-## 1.6 指标评估
+<a name="3--------"></a>
+# 3. 模型评估与预测
+
+<a name="31-----"></a>
+## 3.1 指标评估
 
 PaddleOCR计算三个OCR检测相关的指标，分别是：Precision、Recall、Hmean（F-Score）。
 
@@ -177,8 +198,8 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{pat
 
 * 注：`box_thresh`、`unclip_ratio`是DB后处理所需要的参数，在评估EAST模型时不需要设置
 
-<a name="17-------"></a>
-## 1.7 测试检测效果
+<a name="32-------"></a>
+## 3.2 测试检测效果
 
 测试单张图像的检测效果
 ```shell
@@ -195,8 +216,8 @@ python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./
 python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy"
 ```
 
-<a name="18--inference----"></a>
-## 1.8 转inference模型测试
+<a name="4--------"></a>
+# 4. 模型导出与预测
 
 inference 模型（`paddle.jit.save`保存的模型）
 一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。
@@ -218,10 +239,11 @@ python3 tools/infer/predict_det.py --det_algorithm="DB" --det_model_dir="./outpu
 python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./output/det_db_inference/" --image_dir="./doc/imgs/" --use_gpu=True
 ```
 
-<a name="2"></a>
-# 2. FAQ
+<a name="5-faq"></a>
+# 5. FAQ
 
 Q1: 训练模型转inference 模型之后预测效果不一致？
+
 **A**：此类问题出现较多，问题多是trained model预测时候的预处理、后处理参数和inference model预测的时候的预处理、后处理参数不一致导致的。以det_mv3_db.yml配置文件训练的模型为例，训练模型、inference模型预测结果不一致问题解决方式如下：
 - 检查[trained model预处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L116)，和[inference model的预测预处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/predict_det.py#L42)函数是否一致。算法在评估的时候，输入图像大小会影响精度，为了和论文保持一致，训练icdar15配置文件中将图像resize到[736, 1280]，但是在inference model预测的时候只有一套默认参数，会考虑到预测速度问题，默认限制图像最长边为960做resize的。训练模型预处理和inference模型的预处理函数位于[ppocr/data/imaug/operators.py](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/ppocr/data/imaug/operators.py#L147)
 - 检查[trained model后处理](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L51)，和[inference 后处理参数](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/utility.py#L50)是否一致。

doc/doc_ch/enhanced_ctc_loss.md

+# Enhanced CTC Loss
+
+在OCR识别中， CRNN是一种在工业界广泛使用的文字识别算法。 在训练阶段，其采用CTCLoss来计算网络损失； 在推理阶段，其采用CTCDecode来获得解码结果。虽然CRNN算法在实际业务中被证明能够获得很好的识别效果， 然而用户对识别准确率的要求却是无止境的，如何进一步提升文字识别的准确率呢？ 本文以CTCLoss为切人点，分别从难例挖掘、 多任务学习、 Metric Learning 3个不同的角度探索了CTCLoss的改进融合方案，提出了EnhancedCTCLoss，其包括如下3个组成部分： Focal-CTC Loss，A-CTC Loss， C-CTC Loss。
+
+## 1. Focal-CTC Loss
+Focal Loss 出自论文《Focal Loss for Dense Object Detection》, 该loss最先提出的时候主要是为了解决one-stage目标检测中正负样本比例严重失衡的问题。该损失函数降低了大量简单负样本在训练中所占的权重，也可理解为一种困难样本挖掘。
+其损失函数形式如下：
+<div align="center"> 
+<img src="./focal_loss_formula.png" width = "600" /> 
+</div>
+ 
+其中,  y' 是经过激活函数的输出，取值在0-1之间。其在原始的交叉熵损失的基础上加了一个调制系数（1 – y’)^ &gamma;和平衡因子&alpha;。 当&alpha; = 1，y=1时，其损失函数与交叉熵损失的对比如下图所示:   
+<div align="center"> 
+<img src="./focal_loss_image.png" width = "600" /> 
+</div>
+
+从上图可以看到, 当&gamma;> 0时，调整系数（1-y’）^&gamma; 赋予易分类样本损失一个更小的权重，使得网络更关注于困难的、错分的样本。 调整因子&gamma;用于调节简单样本权重降低的速率，当&gamma;为0时即为交叉熵损失函数，当&gamma;增加时，调整因子的影响也会随之增大。实验发现&gamma;为2是最优。平衡因子&alpha;用来平衡正负样本本身的比例不均，文中&alpha;取0.25。
+
+对于经典的CTC算法，假设某个特征序列（f<sub>1</sub>, f<sub>2</sub>, ......f<sub>t</sub>), 经过CTC解码之后结果等于label的概率为y’, 则CTC解码结果不为label的概率即为（1-y’)；不难发现, CTCLoss值和y’有如下关系：
+<div align="center"> 
+<img src="./equation_ctcloss.png" width = "250" /> 
+</div>
+
+结合Focal Loss的思想，赋予困难样本较大的权重，简单样本较小的权重，可以使网络更加聚焦于对困难样本的挖掘，进一步提升识别的准确率，由此我们提出了Focal-CTC Loss； 其定义如下所示：
+<div align="center"> 
+<img src="./equation_focal_ctc.png" width = "500" /> 
+</div>
+
+实验中，&gamma;取值为2, &alpha;= 1, 具体实现见:  [rec_ctc_loss.py](../../ppocr/losses/rec_ctc_loss.py)
+
+## 2. A-CTC Loss
+A-CTC Loss是CTC Loss + ACE Loss的简称。 其中ACE Loss出自论文< Aggregation Cross-Entropy for Sequence Recognition>.  ACE Loss相比于CTCLoss，主要有如下两点优势: 
++ ACE Loss能够解决2-D文本的识别问题;  CTCLoss只能够处理1-D文本
++ ACE Loss 在时间复杂度和空间复杂度上优于CTC loss
+
+前人总结的OCR识别算法的优劣如下图所示：
+<div align="center">
+<img src="./rec_algo_compare.png" width = "1000" /> 
+</div>
+ 
+虽然ACELoss确实如上图所说，可以处理2D预测，在内存占用及推理速度方面具备优势，但在实践过程中，我们发现单独使用ACE Loss,  识别效果并不如CTCLoss.  因此，我们尝试将CTCLoss和ACELoss进行结合，同时以CTCLoss为主，将ACELoss 定位为一个辅助监督loss。 这一尝试收到了效果，在我们内部的实验数据集上，相比单独使用CTCLoss，识别准确率可以提升1%左右。
+A_CTC Loss定义如下:  
+<div align="center">
+<img src="./equation_a_ctc.png" width = "300" /> 
+</div>
+
+实验中，λ = 0.1.  ACE loss实现代码见:  [ace_loss.py](../../ppocr/losses/ace_loss.py)
+
+## 3. C-CTC Loss
+C-CTC Loss是CTC Loss + Center Loss的简称。 其中Center Loss出自论文 < A Discriminative Feature Learning Approach for Deep Face Recognition>.  最早用于人脸识别任务，用于增大类间距离，减小类内距离,  是Metric Learning领域一种较早的、也比较常用的一种算法。 
+在中文OCR识别任务中，通过对badcase分析， 我们发现中文识别的一大难点是相似字符多，容易误识。 由此我们想到是否可以借鉴Metric Learing的想法， 增大相似字符的类间距，从而提高识别准确率。然而，MetricLearning主要用于图像识别领域，训练数据的标签为一个固定的值；而对于OCR识别来说，其本质上是一个序列识别任务，特征和label之间并不具有显式的对齐关系，因此两者如何结合依然是一个值得探索的方向。
+通过尝试Arcmargin, Cosmargin等方法， 我们最终发现Centerloss 有助于进一步提升识别的准确率。C_CTC Loss定义如下：
+<div align="center">
+<img src="./equation_c_ctc.png" width = "300" /> 
+</div>
+
+实验中，我们设置λ=0.25. center_loss实现代码见:  [center_loss.py](../../ppocr/losses/center_loss.py)
+
+值得一提的是， 在C-CTC Loss中，选择随机初始化Center并不能够带来明显的提升. 我们的Center初始化方法如下：
++ 基于原始的CTCLoss， 训练得到一个网络N
++ 挑选出训练集中，识别完全正确的部分, 组成集合G
++ 将G中的每个样本送入网络，进行前向计算， 提取最后一个FC层的输入（即feature）及其经过argmax计算的结果（即index）之间的对应关系
++ 将相同index的feature进行聚合，计算平均值，得到各自字符的初始center. 
+
+以配置文件`configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml`为例， center提取命令如下所示:
+```
+python tools/export_center.py -c configs/rec/ch_PP-OCRv2/ch_PP-OCRv2_rec.yml -o Global.pretrained_model="./output/rec_mobile_pp-OCRv2/best_accuracy"
+```
+运行完后，会在PaddleOCR主目录下生成`train_center.pkl`.
+
+## 4. 实验
+对于上述的三种方案，我们基于百度内部数据集进行了训练、评测，实验情况如下表所示：
+|algorithm| Focal_CTC | A_CTC | C-CTC |
+|:------| :------| ------: | :------: |
+|gain| +0.3% | +0.7% | +1.7% | 
+
+基于上述实验结论，我们在PP-OCRv2中，采用了C-CTC的策略。 值得一提的是，由于PP-OCRv2 处理的是6625个中文字符的识别任务，字符集比较大，形似字较多，所以在该任务上C-CTC 方案带来的提升较大。 但如果换做其他OCR识别任务，结论可能会有所不同。大家可以尝试Focal-CTC，A-CTC, C-CTC以及组合方案EnhancedCTC，相信会带来不同程度的提升效果。
+统一的融合方案见如下文件：  [rec_enhanced_ctc_loss.py](../../ppocr/losses/rec_enhanced_ctc_loss.py)

doc/doc_ch/environment.md

 # 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。
 
+推荐环境：
+- PaddlePaddle >= 2.0.0 (2.1.2)
+- python3.7
+- CUDA10.1 / CUDA10.2
+- CUDNN 7.6
+
 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)
@@ -123,13 +130,13 @@ Windows和Mac用户推荐使用Anaconda搭建Python环境，Linux用户建议使
     # !! Contents within this block are managed by 'conda init' !!
     __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
     if [ $? -eq 0 ]; then
-    		eval "$__conda_setup"
+            eval "$__conda_setup"
     else
-    		if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
-    				. "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
-    		else
-    				export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
-    		fi
+            if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+                    . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+            else
+                    export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+            fi
     fi
     unset __conda_setup
     # <<< conda initialize <<<
@@ -294,11 +301,12 @@ cd /home/Projects
 # 首次运行需创建一个docker容器，再次运行时不需要运行当前命令
 # 创建一个名字为ppocr的docker容器，并将当前目录映射到容器的/paddle目录下
 
-如果您希望在CPU环境下使用docker，使用docker而不是nvidia-docker创建docker
-sudo docker run --name ppocr -v $PWD:/paddle --network=host -it paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
+#如果您希望在CPU环境下使用docker，使用docker而不是nvidia-docker创建docker
+sudo docker run --name ppocr -v $PWD:/paddle --network=host -it registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda10.2-cudnn7 /bin/bash
 
-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
+#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
+# 如果是CUDA11+CUDNN8，推荐使用镜像registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda11.2-cudnn8
+sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda10.2-cudnn7 /bin/bash
 
 # 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
@@ -321,8 +329,3 @@ python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
 ```
 
 更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
-
-
-
-
-

doc/doc_ch/inference.md

@@ -273,7 +273,7 @@ python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o G
 CRNN 文本识别模型推理，可以执行如下命令：
 
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/rec_crnn/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
 ```
 
 ![](../imgs_words_en/word_336.png)
@@ -288,7 +288,7 @@ Predicts of ./doc/imgs_words_en/word_336.png:('super', 0.9999073)
 
 - 训练时采用的图像分辨率不同，训练上述模型采用的图像分辨率是[3，32，100]，而中文模型训练时，为了保证长文本的识别效果，训练时采用的图像分辨率是[3, 32, 320]。预测推理程序默认的的形状参数是训练中文采用的图像分辨率，即[3, 32, 320]。因此，这里推理上述英文模型时，需要通过参数rec_image_shape设置识别图像的形状。
 
-- 字符列表，DTRB论文中实验只是针对26个小写英文本母和10个数字进行实验，总共36个字符。所有大小字符都转成了小写字符，不在上面列表的字符都忽略，认为是空格。因此这里没有输入字符字典，而是通过如下命令生成字典.因此在推理时需要设置参数rec_char_type，指定为英文"en"。
+- 字符列表，DTRB论文中实验只是针对26个小写英文本母和10个数字进行实验，总共36个字符。所有大小字符都转成了小写字符，不在上面列表的字符都忽略，认为是空格。因此这里没有输入字符字典，而是通过如下命令生成字典.因此在推理时需要设置参数rec_char_dict_path，指定为英文字典"./ppocr/utils/ic15_dict.txt"。
 
 ```
 self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
@@ -303,15 +303,15 @@ dict_character = list(self.character_str)
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
                                    --rec_model_dir="./inference/srn/" \
                                    --rec_image_shape="1, 64, 256" \
-                                   --rec_char_type="en" \
+                                   --rec_char_dict_path="./ppocr/utils/ic15_dict.txt" \
                                    --rec_algorithm="SRN"
 ```
 
 ### 4. 自定义文本识别字典的推理
-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`
+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
 
 ```
-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"
+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_dict_path="your text dict path"
 ```
 
 <a name="多语言模型的推理"></a>
@@ -320,7 +320,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
 需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
 
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model"  --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
 
@@ -388,7 +388,7 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --de
 下面给出基于EAST文本检测和STAR-Net文本识别执行命令：
 
 ```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
 ```
 
 执行命令后，识别结果图像如下：

doc/doc_ch/inference_ppocr.md

-# PP-OCR模型库快速推理
+# 基于Python引擎的PP-OCR模型库推理
 
 本文介绍针对PP-OCR模型库的Python推理引擎使用方法，内容依次为文本检测、文本识别、方向分类器以及三者串联在CPU、GPU上的预测方法。
 
 
 - [1. 文本检测模型推理](#文本检测模型推理)
-
 - [2. 文本识别模型推理](#文本识别模型推理)
   - [2.1 超轻量中文识别模型推理](#超轻量中文识别模型推理)
   - [2.2 多语言模型的推理](#多语言模型的推理)
-
 - [3. 方向分类模型推理](#方向分类模型推理)
-
 - [4. 文本检测、方向分类和文字识别串联推理](#文本检测、方向分类和文字识别串联推理)
 
 <a name="文本检测模型推理"></a>
@@ -21,12 +18,15 @@
 
 ```
 # 下载超轻量中文检测模型：
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tartar xf ch_ppocr_mobile_v2.0_det_infer.tarpython3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_ppocr_mobile_v2.0_det_infer/"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar xf ch_PP-OCRv2_det_infer.tar
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer/"
+
 ```
 
 可视化文本检测结果默认保存到`./inference_results`文件夹里面，结果文件的名称前缀为'det_res'。结果示例如下：
 
-![](/Users/zhulingfeng01/OCR/PaddleOCR/doc/imgs_results/det_res_00018069.jpg)
+![](../imgs_results/det_res_00018069.jpg)
 
 通过参数`limit_type`和`det_limit_side_len`来对图片的尺寸进行限制，
 `limit_type`可选参数为[`max`, `min`]，
@@ -39,13 +39,13 @@ wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_i
 如果输入图片的分辨率比较大，而且想使用更大的分辨率预测，可以设置det_limit_side_len 为想要的值，比如1216：
 
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216
 ```
 
 如果想使用CPU进行预测，执行命令如下
 
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/"  --use_gpu=False
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/"  --use_gpu=False
 ```
 
 
@@ -62,12 +62,12 @@ python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_di
 
 ```
 # 下载超轻量中文识别模型：
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar xf ch_PP-OCRv2_rec_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/"
 ```
 
-![](/Users/zhulingfeng01/OCR/PaddleOCR/doc/imgs_words/ch/word_4.jpg)
+![](../imgs_words/ch/word_4.jpg)
 
 执行命令后，上面图像的预测结果（识别的文本和得分）会打印到屏幕上，示例如下：
 
@@ -79,14 +79,13 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
 
 ### 2.2 多语言模型的推理
 
-如果您需要预测的是其他语言模型，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, 同时为了得到正确的可视化结果，
-需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
-
+如果您需要预测的是其他语言模型，可以在[此链接](./models_list.md#%E5%A4%9A%E8%AF%AD%E8%A8%80%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)中找到对应语言的inference模型，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, 同时为了得到正确的可视化结果，需要通过 `--vis_font_path` 指定可视化的字体路径，`doc/fonts/` 路径下有默认提供的小语种字体，例如韩文识别：
 ```
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 
-![](/Users/zhulingfeng01/OCR/PaddleOCR/doc/imgs_words/korean/1.jpg)
+![](../imgs_words/korean/1.jpg)
 
 执行命令后，上图的预测结果为：
 
@@ -107,7 +106,7 @@ tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
 python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
 ```
 
-![](/Users/zhulingfeng01/OCR/PaddleOCR/doc/imgs_words/ch/word_1.jpg)
+![](../imgs_words/ch/word_1.jpg)
 
 执行命令后，上面图像的预测结果（分类的方向和得分）会打印到屏幕上，示例如下：
 
@@ -123,14 +122,13 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
 
 ```shell
 # 使用方向分类器
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true
 # 不使用方向分类器
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false
 # 使用多进程
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
 ```
 
 执行命令后，识别结果图像如下：
 
-![](/Users/zhulingfeng01/OCR/PaddleOCR/doc/imgs_results/system_res_00018069.jpg)
-
+![](../imgs_results/system_res_00018069.jpg)

doc/doc_ch/models.md

+
+# PP-OCR模型库
+PP-OCR模型一节主要补充一些OCR模型的基本概念以及如何快速运用PP-OCR模型库中的模型。
+
+本节包含两个部分，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[基于Python引擎的PP-OCR模型库推理](./inference_ppocr.md)中介绍PP-OCR模型库的使用方法，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。
+
+------
+
+下面我们首先了解一些OCR相关的基本概念：
+
+- [1. OCR 简要介绍](#1-ocr-----)
+  * [1.1 OCR 检测模型基本概念](#11-ocr---------)
+  * [1.2 OCR 识别模型基本概念](#12-ocr---------)
+  * [1.3 PP-OCR模型](#13-pp-ocr--)
+
+<a name="1-ocr-----"></a>
+## 1. OCR 简要介绍
+本节简要介绍OCR检测模型、识别模型的基本概念，并介绍PaddleOCR的PP-OCR模型。
+
+OCR（Optical Character Recognition，光学字符识别）目前是文字识别的统称，已不限于文档或书本文字识别，更包括识别自然场景下的文字，又可以称为STR（Scene Text Recognition）。
+
+OCR文字识别一般包括两个部分，文本检测和文本识别；文本检测首先利用检测算法检测到图像中的文本行；然后检测到的文本行用识别算法去识别到具体文字。
+
+<a name="11-ocr---------"></a>
+### 1.1 OCR 检测模型基本概念
+
+文本检测就是要定位图像中的文字区域，然后通常以边界框的形式将单词或文本行标记出来。传统的文字检测算法多是通过手工提取特征的方式，特点是速度快，简单场景效果好，但是面对自然场景，效果会大打折扣。当前多是采用深度学习方法来做。
+
+基于深度学习的文本检测算法可以大致分为以下几类：
+1. 基于目标检测的方法；一般是预测得到文本框后，通过NMS筛选得到最终文本框，多是四点文本框，对弯曲文本场景效果不理想。典型算法为EAST、Text Box等方法。
+2. 基于分割的方法；将文本行当成分割目标，然后通过分割结果构建外接文本框，可以处理弯曲文本，对于文本交叉场景问题效果不理想。典型算法为DB、PSENet等方法。
+3. 混合目标检测和分割的方法；
+
+<a name="12-ocr---------"></a>
+### 1.2 OCR 识别模型基本概念
+
+OCR识别算法的输入数据一般是文本行，背景信息不多，文字占据主要部分，识别算法目前可以分为两类算法：
+1. 基于CTC的方法；即识别算法的文字预测模块是基于CTC的，常用的算法组合为CNN+RNN+CTC。目前也有一些算法尝试在网络中加入transformer模块等等。
+2. 基于Attention的方法；即识别算法的文字预测模块是基于Attention的，常用算法组合是CNN+RNN+Attention。
+
+<a name="13-pp-ocr--"></a>
+### 1.3 PP-OCR模型
+
+PaddleOCR 中集成了很多OCR算法，文本检测算法有DB、EAST、SAST等等，文本识别算法有CRNN、RARE、StarNet、Rosetta、SRN等算法。
+
+其中PaddleOCR针对中英文自然场景通用OCR，推出了PP-OCR系列模型，PP-OCR模型由DB+CRNN算法组成，利用海量中文数据训练加上模型调优方法，在中文场景上具备较高的文本检测识别能力。并且PaddleOCR推出了高精度超轻量PP-OCRv2模型，检测模型仅3M，识别模型仅8.5M，利用[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)的模型量化方法，可以在保持精度不降低的情况下，将检测模型压缩到0.8M，识别压缩到3M，更加适用于移动端部署场景。
+

doc/doc_ch/models_list.md

-## OCR模型列表（V2.1，2021年9月6日更新）
+# OCR模型列表（V2.1，2021年9月6日更新）
 
 > **说明**
 > 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
@@ -6,13 +6,13 @@
 > 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)
-- [二、文本识别模型](#文本识别模型)
-    - [1. 中文识别模型](#中文识别模型)
-    - [2. 英文识别模型](#英文识别模型)
-    - [3. 多语言识别模型](#多语言识别模型)
-- [三、文本方向分类模型](#文本方向分类模型)
-- [四、Paddle-Lite 模型](#Paddle-Lite模型)
+- [1. 文本检测模型](#文本检测模型)
+- [2. 文本识别模型](#文本识别模型)
+    - [2.1 中文识别模型](#中文识别模型)
+    - [2.2 英文识别模型](#英文识别模型)
+    - [2.3 多语言识别模型](#多语言识别模型)
+- [3. 文本方向分类模型](#文本方向分类模型)
+- [4. Paddle-Lite 模型](#Paddle-Lite模型)
 
 PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训练模型`、`slim模型`，模型区别说明如下：
 
@@ -29,27 +29,28 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 
 
 <a name="文本检测模型"></a>
-### 一、文本检测模型
+## 1. 文本检测模型
 
 |模型名称|模型简介|配置文件|推理模型大小|下载地址|
 | --- | --- | --- | --- | --- |
-|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_PP-OCRv2_det_slim|【最新】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-OCRv2_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|【最新】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-OCRv2_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)|
 
 
 <a name="文本识别模型"></a>
-### 二、文本识别模型
+## 2. 文本识别模型
 
 <a name="中文识别模型"></a>
-#### 1. 中文识别模型
+
+### 2.1 中文识别模型
 
 |模型名称|模型简介|配置文件|推理模型大小|下载地址|
 | --- | --- | --- | --- | --- |
-|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_PP-OCRv2_rec_slim|【最新】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|【最新】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) |
@@ -57,7 +58,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 **说明：** `训练模型`是基于预训练模型在真实数据与竖排合成文本数据上finetune得到的模型，在真实应用场景中有着更好的表现，`预训练模型`则是直接基于全量真实数据与合成数据训练得到，更适合用于在自己的数据集上finetune。
 
 <a name="英文识别模型"></a>
-#### 2. 英文识别模型
+### 2.2 英文识别模型
 
 |模型名称|模型简介|配置文件|推理模型大小|下载地址|
 | --- | --- | --- | --- | --- |
@@ -65,7 +66,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 |en_number_mobile_v2.0_rec|原始超轻量模型，支持英文、数字识别|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.6M|[推理模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) |
 
 <a name="多语言识别模型"></a>
-#### 3. 多语言识别模型（更多语言持续更新中...）
+### 2.3 多语言识别模型（更多语言持续更新中...）
 
 |模型名称|字典文件|模型简介|配置文件|推理模型大小|下载地址|
 | --- | --- | --- | --- |--- | --- |
@@ -86,7 +87,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 
 
 <a name="文本方向分类模型"></a>
-### 三、文本方向分类模型
+## 3. 文本方向分类模型
 
 |模型名称|模型简介|配置文件|推理模型大小|下载地址|
 | --- | --- | --- | --- | --- |
@@ -95,7 +96,7 @@ PaddleOCR提供的可下载模型包括`推理模型`、`训练模型`、`预训
 
 
 <a name="Paddle-Lite模型"></a>
-### 四、Paddle-Lite 模型
+## 4. 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|
 |---|---|---|---|---|---|---|

doc/doc_ch/pgnet.md

@@ -28,9 +28,9 @@ PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.Wang
 
 ### 性能指标
 
-测试集: Total Text
+#### 测试集: Total Text
 
-测试环境: NVIDIA Tesla V100-SXM2-16GB
+#### 测试环境: NVIDIA Tesla V100-SXM2-16GB
 
 |PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|下载|
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
@@ -43,7 +43,7 @@ PGNet算法细节详见[论文](https://www.aaai.org/AAAI21Papers/AAAI-2885.Wang
 
 <a name="环境配置"></a>
 ## 二、环境配置
-请先参考[快速安装](./installation.md)配置PaddleOCR运行环境。
+请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《PaddleOCR全景图与项目克隆》](./paddleOCR_overview.md)克隆项目
 
 <a name="快速使用"></a>
 ## 三、快速使用
@@ -92,7 +92,7 @@ python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/im
   |- train.txt       # total_text数据集的训练标注
 ```
 
-total_text.txt标注文件格式如下，文件名和标注信息中间用"\t"分隔：
+train.txt标注文件格式如下，文件名和标注信息中间用"\t"分隔：
 ```
 " 图像文件名                    json.dumps编码的图像标注信息"
 rgb/img11.jpg    [{"transcription": "ASRAMA", "points": [[214.0, 325.0], [235.0, 308.0], [259.0, 296.0], [286.0, 291.0], [313.0, 295.0], [338.0, 305.0], [362.0, 320.0], [349.0, 347.0], [330.0, 337.0], [310.0, 329.0], [290.0, 324.0], [269.0, 328.0], [249.0, 336.0], [231.0, 346.0]]}, {...}]

doc/doc_ch/quickstart.md

@@ -47,10 +47,10 @@ cd /path/to/ppocr_img
 <a name="211"></a>
 #### 2.1.1 中英文模型
 
-* 检测+方向分类器+识别全流程：设置方向分类器参数`--use_angle_cls true`后可对竖排文本进行识别。
+* 检测+方向分类器+识别全流程：`--use_angle_cls true`设置使用方向分类器识别180度旋转文字，`--use_gpu false`设置不使用GPU
 
   ```bash
-  paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true
+  paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true --use_gpu false
   ```
 
   结果是一个list，每个item包含了文本框，文字和识别置信度

doc/doc_ch/recognition.md

@@ -159,7 +159,6 @@ PaddleOCR内置了一部分字典，可以按需使用。
 - 自定义字典
 
 如需自定义dic文件，请在 `configs/rec/rec_icdar15_train.yml` 中添加 `character_dict_path` 字段, 指向您的字典路径。
-并将 `character_type` 设置为 `ch`。
 
 <a name="支持空格"></a>
 ### 1.4 添加空格类别
@@ -246,8 +245,6 @@ Global:
   ...
   # 添加自定义字典，如修改字典请将路径指向新字典
   character_dict_path: ppocr/utils/ppocr_keys_v1.txt
-  # 修改字符类型
-  character_type: ch
   ...
   # 识别空格
   use_space_char: True
@@ -311,18 +308,18 @@ PaddleOCR目前已支持80种（除中文外）语种识别，`configs/rec/multi
 
 按语系划分，目前PaddleOCR支持的语种有：
 
-| 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |  language | character_type |
-| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
-| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 中文繁体  | chinese_cht|
-| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 英语（区分大小写）   | EN |
-| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 法语 |  french |
-| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   | german |
-| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语  | japan |
-| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语  | korean |
-| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 拉丁字母  | latin |
-| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 阿拉伯字母 |  ar |
-| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 斯拉夫字母  | cyrillic |
-| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 梵文字母  | devanagari |
+| 配置文件 |  算法名称 |   backbone |   trans   |   seq      |     pred     |  language |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  |
+| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 中文繁体  |
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 英语（区分大小写）   |
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 法语 |  
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 德语   |
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 日语  |
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 韩语  |
+| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 拉丁字母  |
+| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 阿拉伯字母 |
+| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 斯拉夫字母  |
+| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | 梵文字母  |
 
 更多支持语种请参考: [多语言模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_ch/multi_languages.md#%E8%AF%AD%E7%A7%8D%E7%BC%A9%E5%86%99)
 

doc/doc_ch/reference.md

@@ -112,4 +112,14 @@
   year={2016}
 }
 
+13.NRTR
+@misc{sheng2019nrtr,
+      title={NRTR: A No-Recurrence Sequence-to-Sequence Model For Scene Text Recognition},
+      author={Fenfen Sheng and Zhineng Chen and Bo Xu},
+      year={2019},
+      eprint={1806.00926},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+
 ```

doc/doc_ch/thirdparty.md

+# 社区贡献说明
+
+感谢大家长久以来对PaddleOCR的支持和关注，与广大开发者共同构建一个专业、和谐、相互帮助的开源社区是PaddleOCR的目标。本文档展示了已有的社区贡献、对于各类贡献说明、新的机会与流程，希望贡献流程更加高效、路径更加清晰。
+
+PaddleOCR希望可以通过AI的力量助力任何一位有梦想的开发者实现自己的想法，享受创造价值带来的愉悦。
+
+<a href="https://github.com/PaddlePaddle/PaddleOCR/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=PaddlePaddle/PaddleOCR" />
+</a>
+
+> 上图为PaddleOCR目前的Contributor，定期更新
+
+## 1. 社区贡献
+
+### 1.1 为PaddleOCR新增功能
+
+- 非常感谢 [authorfu](https://github.com/authorfu) 贡献Android([#340](https://github.com/PaddlePaddle/PaddleOCR/pull/340))和[xiadeye](https://github.com/xiadeye) 贡献IOS的demo代码([#325](https://github.com/PaddlePaddle/PaddleOCR/pull/325))
+- 非常感谢 [tangmq](https://gitee.com/tangmq) 给PaddleOCR增加Docker化部署服务，支持快速发布可调用的Restful API服务([#507](https://github.com/PaddlePaddle/PaddleOCR/pull/507))。
+- 非常感谢 [lijinhan](https://github.com/lijinhan) 给PaddleOCR增加java SpringBoot 调用OCR Hubserving接口完成对OCR服务化部署的使用([#1027](https://github.com/PaddlePaddle/PaddleOCR/pull/1027))。
+- 非常感谢 [Evezerest](https://github.com/Evezerest)， [ninetailskim](https://github.com/ninetailskim)， [edencfc](https://github.com/edencfc)， [BeyondYourself](https://github.com/BeyondYourself)， [1084667371](https://github.com/1084667371) 贡献了[PPOCRLabel](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/PPOCRLabel/README_ch.md) 的完整代码。
+
+### 1.2 基于PaddleOCR的社区贡献
+
+- 【最新】完整的C#版本标注工具 [FastOCRLabel](https://gitee.com/BaoJianQiang/FastOCRLabel) (@ [包建强](https://gitee.com/BaoJianQiang) )
+- 通用型桌面级即时翻译工具 [DangoOCR离线版](https://github.com/PantsuDango/DangoOCR) (@ [PantsuDango](https://github.com/PantsuDango))
+- 获取OCR识别结果的key-value [paddleOCRCorrectOutputs](https://github.com/yuranusduke/paddleOCRCorrectOutputs) (@ [yuranusduke](https://github.com/yuranusduke))
+- 截屏转文字工具  [scr2txt](https://github.com/lstwzd/scr2txt) (@ [lstwzd](https://github.com/lstwzd))
+- 身份证复印件识别 [id_card_ocr](https://github.com/baseli/id_card_ocr)(@ [baseli](https://github.com/baseli))
+- 能看懂表格图片的数据助手：[Paddle_Table_Image_Reader](https://github.com/thunder95/Paddle_Table_Image_Reader) (@ [thunder95][https://github.com/thunder95])
+- 英文视频自动生成字幕 [AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/1054614?channelType=0&channel=0)( @ [叶月水狐](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/322052))
+
+### 1.3 代码与文档优化
+
+
+- 非常感谢 [zhangxin](https://github.com/ZhangXinNan)([Blog](https://blog.csdn.net/sdlypyzq)) 贡献新的可视化方式、添加.gitgnore、处理手动设置PYTHONPATH环境变量的问题([#210](https://github.com/PaddlePaddle/PaddleOCR/pull/210))。
+- 非常感谢 [lyl120117](https://github.com/lyl120117) 贡献打印网络结构的代码([#304](https://github.com/PaddlePaddle/PaddleOCR/pull/304))。
+- 非常感谢 [BeyondYourself](https://github.com/BeyondYourself) 给PaddleOCR提了很多非常棒的建议，并简化了PaddleOCR的部分代码风格([so many commits)](https://github.com/PaddlePaddle/PaddleOCR/commits?author=BeyondYourself)。
+
+- 非常感谢 [Khanh Tran](https://github.com/xxxpsyduck) 和 [Karl Horky](https://github.com/karlhorky) 贡献修改英文文档。
+
+### 1.4 多语言语料
+
+- 非常感谢 [xiangyubo](https://github.com/xiangyubo) 贡献手写中文OCR数据集([#321](https://github.com/PaddlePaddle/PaddleOCR/pull/321))。
+- 非常感谢 [Mejans](https://github.com/Mejans) 给PaddleOCR增加新语言奥克西坦语Occitan的字典和语料([#954](https://github.com/PaddlePaddle/PaddleOCR/pull/954))。
+
+## 2. 贡献说明
+### 2.1 新增功能类
+
+PaddleOCR非常欢迎社区贡献以PaddleOCR为核心的各种服务、部署实例与软件应用，经过认证的社区贡献会被添加在上述社区贡献表中，为广大开发者增加曝光，也是PaddleOCR的荣耀，其中：
+
+- 项目形式：官方社区认证的项目代码应有良好的规范和结构，同时，还应配备一个详细的README.md，说明项目的使用方法。通过在requirements.txt文件中增加一行 `paddleocr` 可以自动收录到PaddleOCR的usedby中。
+
+- 合入方式：如果是对PaddleOCR现有工具的更新升级，则会合入主repo。如果为PaddleOCR拓展了新功能，请先与官方人员联系，确认项目是否合入主repo，*即使新功能未合入主repo，我们同样也会以社区贡献的方式为您的个人项目增加曝光。*
+
+
+### 2.2 代码优化
+
+如果您在使用PaddleOCR时遇到了代码bug、功能不符合预期等问题，可以为PaddleOCR贡献您的修改，其中：
+
+- Python代码规范可参考[附录1：Python代码规范](./code_and_doc.md/#附录1)。
+
+-  提交代码前请再三确认不会引入新的bug，并在PR中描述优化点。如果该PR解决了某个issue，请在PR中连接到该issue。所有的PR都应该遵守附录3中的[3.2.10 提交代码的一些约定。](./code_and_doc.md/#提交代码的一些约定)
+
+- 请在提交之前参考下方的[附录3：Pull Request说明](./code_and_doc.md/#附录3)。如果您对git的提交流程不熟悉，同样可以参考附录3的3.2节。
+
+**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理**。
+
+### 2.3 文档优化
+
+如果您在使用PaddleOCR时遇到了文档表述不清楚、描述缺失、链接失效等问题，可以为PaddleOCR贡献您的修改。文档书写规范请参考[附录2：文档规范](./code_and_doc.md/#附录2)。**最后请在PR的题目中加上标签`【third-party】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理。**
+
+## 3. 更多贡献机会
+
+我们非常鼓励开发者使用PaddleOCR实现自己的想法，同时我们也列出一些经过分析后认为有价值的拓展方向，供大家参考
+
+- 功能类：IOS端侧demo、前后处理工具、针对各种垂类场景的检测识别模型（如手写体、公式）。
+- 文档类：PaddleOCR在各种垂类行业的应用案例（可在公众号中推广）。
+
+## 4. 联系我们
+
+PaddleOCR非常欢迎广大开发者在有意向贡献前与我们联系，这样可以大大降低PR过程中的沟通成本。同时，如果您觉得某些想法个人难以实现，我们也可以通过SIG的形式定向为项目招募志同道合的开发者一起共建。通过SIG渠道贡献的项目将会获得深层次的研发支持与运营资源。
+
+我们推荐的贡献流程是：
+
+- 通过在github issue的题目中增加  `【third-party】` 标记，说明遇到的问题（以及解决的思路）或想拓展的功能，等待值班人员回复。例如 `【third-party】为PaddleOCR贡献IOS示例`
+- 与我们沟通确认技术方案或bug、优化点准确无误后进行功能新增或相应的修改，代码与文档遵循相关规范。
+- PR链接到上述issue，等待review。
+
+## 5. 致谢与后续
+
+  - 合入代码之后，首页README末尾新增感谢贡献，默认链接为github名字及主页，如果有需要更换主页，也可以联系我们。
+  - 新增重要功能类，会在用户群广而告之，享受开源社区荣誉时刻。
+  - **如果您有基于PaddleOCR的贡献，但未出现在上述列表中，请按照 `4. 联系我们` 的步骤与我们联系。**

doc/doc_ch/training.md

-# 模型训练
+# PP-OCR模型训练
 
 本文将介绍模型训练时需掌握的基本概念，和训练时的调优方法。
 
 同时会简单介绍PaddleOCR模型训练数据的组成部分，以及如何在垂类场景中准备数据finetune模型。
 
-- [1. 基本概念](#基本概念)
-  * [1.1 学习率](#学习率)
-  * [1.2 正则化](#正则化)
-  * [1.3 评估指标](#评估指标)
-- [2. 数据与垂类场景](#数据与垂类场景)
-  * [2.1 训练数据](#训练数据)
-  * [2.2 垂类场景](#垂类场景)
-  * [2.3 自己构建数据集](#自己构建数据集)
-* [3. 常见问题](#常见问题)
+- [1.配置文件说明](#配置文件)
+- [2. 基本概念](#基本概念)
+  * [2.1 学习率](#学习率)
+  * [2.2 正则化](#正则化)
+  * [2.3 评估指标](#评估指标)
+- [3. 数据与垂类场景](#数据与垂类场景)
+  * [3.1 训练数据](#训练数据)
+  * [3.2 垂类场景](#垂类场景)
+  * [3.3 自己构建数据集](#自己构建数据集)
+* [4. 常见问题](#常见问题)
+
+<a name="配置文件"></a>
+
+## 1. 配置文件说明
+
+PaddleOCR模型使用配置文件管理网络训练、评估的参数。在配置文件中，可以设置组建模型、优化器、损失函数、模型前后处理的参数，PaddleOCR从配置文件中读取到这些参数，进而组建出完整的训练流程，完成模型训练，在需要对模型进行优化的时，可以通过修改配置文件中的参数完成配置，使用简单且方便修改。
+
+完整的配置文件说明可以参考[配置文件](./config.md)
 
 <a name="基本概念"></a>
-## 1. 基本概念
 
-OCR(Optical Character Recognition,光学字符识别)是指对图像进行分析识别处理，获取文字和版面信息的过程，是典型的计算机视觉任务，
-通常由文本检测和文本识别两个子任务构成。
+## 2. 基本概念
 
-模型调优时需要关注以下参数：
+模型训练过程中需要手动调整一些超参数，帮助模型以最小的代价获得最优指标。不同的数据量可能需要不同的超参，当您希望在自己的数据上finetune或对模型效果调优时，有以下几个参数调整策略可供参考：
 
 <a name="学习率"></a>
-### 1.1 学习率
+### 2.1 学习率
 
 学习率是训练神经网络的重要超参数之一，它代表在每一次迭代中梯度向损失函数最优解移动的步长。
 在PaddleOCR中提供了多种学习率更新策略,可以通过配置文件修改，例如：
@@ -42,7 +49,7 @@ Piecewise 代表分段常数衰减，在不同的学习阶段指定不同的学
 warmup_epoch 代表在前5个epoch中，学习率将逐渐从0增加到base_lr。全部策略可以参考代码[learning_rate.py](../../ppocr/optimizer/learning_rate.py) 。
 
 <a name="正则化"></a>
-### 1.2 正则化
+### 2.2 正则化
 
 正则化可以有效的避免算法过拟合，PaddleOCR中提供了L1、L2正则方法，L1 和 L2 正则化是最常用的正则化方法。L1 正则化向目标函数添加正则化项，以减少参数的绝对值总和；而 L2 正则化中，添加正则化项的目的在于减少参数平方的总和。配置方法如下：
 
@@ -55,7 +62,7 @@ Optimizer:
 ```
 
 <a name="评估指标"></a>
-### 1.3 评估指标
+### 2.3 评估指标
 
 （1）检测阶段：先按照检测框和标注框的IOU评估，IOU大于某个阈值判断为检测准确。这里检测框和标注框不同于一般的通用目标检测框，是采用多边形进行表示。检测准确率：正确的检测框个数在全部检测框的占比，主要是判断检测指标。检测召回率：正确的检测框个数在全部标注框的占比，主要是判断漏检的指标。
 
@@ -65,10 +72,10 @@ Optimizer:
 
 <a name="数据与垂类场景"></a>
 
-## 2. 数据与垂类场景
+## 3. 数据与垂类场景
 
 <a name="训练数据"></a>
-### 2.1 训练数据
+### 3.1 训练数据
 目前开源的模型，数据集和量级如下：
 
     - 检测：  
@@ -83,13 +90,14 @@ Optimizer:
 其中，公开数据集都是开源的，用户可自行搜索下载，也可参考[中文数据集](./datasets.md)，合成数据暂不开源，用户可使用开源合成工具自行合成，可参考的合成工具包括[text_renderer](https://github.com/Sanster/text_renderer) 、[SynthText](https://github.com/ankush-me/SynthText) 、[TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator) 等。
 
 <a name="垂类场景"></a>
-### 2.2 垂类场景
+### 3.2 垂类场景
 
 PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+垂类数据自己训练；
 如果缺少带标注的数据，或者不想投入研发成本，建议直接调用开放的API，开放的API覆盖了目前比较常见的一些垂类。
 
 <a name="自己构建数据集"></a>
-### 2.3 自己构建数据集
+
+### 3.3 自己构建数据集
 
 在构建数据集时有几个经验可供参考：
 
@@ -107,7 +115,7 @@ PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+
 
 <a name="常见问题"></a>
 
-## 3. 常见问题
+## 4. 常见问题
 
 **Q**：训练CRNN识别时，如何选择合适的网络输入shape？
 
@@ -129,3 +137,14 @@ PaddleOCR主要聚焦通用OCR，如果有垂类需求，您可以用PaddleOCR+
 
     A：识别模型训练初期acc为0是正常的，多训一段时间指标就上来了。
 
+
+
+***
+
+具体的训练教程可点击下方链接跳转： 
+
+\- [文本检测模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md) 
+
+\- [文本识别模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)  
+
+\- [文本方向分类器训练](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)  

doc/doc_ch/whl.md

@@ -420,3 +420,5 @@ im_show.save('result.jpg')
 | cls                     | 前向时是否启动分类 (命令行模式下使用use_angle_cls控制前向是否启动分类)                                                                                                                                                                                                | FALSE                    |
 | show_log                     | 是否打印det和rec等信息                                                                                                                                                                                                | FALSE                    |
 | type                     | 执行ocr或者表格结构化, 值可选['ocr','structure']                                                                                                                                                                                             | ocr                    |
+| ocr_version                     | OCR模型版本，可选PP-OCRv2, PP-OCR。PP-OCRv2 目前仅支持中文的检测和识别模型，PP-OCR支持中文的检测，识别，多语种识别，方向分类器等模型                                                                                                                                        | PP-OCRv2                   |
+| structure_version                     | 表格结构化模型版本，可选 STRUCTURE。STRUCTURE支持表格结构化模型                                                                                                                                                                                        | STRUCTURE                    |

doc/doc_en/add_new_algorithm_en.md

-# Add new algorithm
+# Add New Algorithm
 
 PaddleOCR decomposes an algorithm into the following parts, and modularizes each part to make it more convenient to develop new algorithms.
 
@@ -263,7 +263,7 @@ Metric:
   main_indicator: acc
 ```
 
-## 优化器
+## Optimizer
 
 The optimizer is used to train the network. The optimizer also contains network regularization and learning rate decay modules. This part is under [ppocr/optimizer](../../ppocr/optimizer). PaddleOCR has built-in
 Commonly used optimizer modules such as `Momentum`, `Adam` and `RMSProp`, common regularization modules such as `Linear`, `Cosine`, `Step` and `Piecewise`, and common learning rate decay modules such as `L1Decay` and `L2Decay`.

doc/doc_en/algorithm_overview_en.md

+# Two-stage Algorithm
+
+- [1. Algorithm Introduction](#1-algorithm-introduction)
+  * [1.1 Text Detection Algorithm](#11-text-detection-algorithm)
+  * [1.2 Text Recognition Algorithm](#12-text-recognition-algorithm)
+- [2. Training](#2-training)
+- [3. Inference](#3-inference)
+
 <a name="Algorithm_introduction"></a>
-## Algorithm introduction
+
+## 1. Algorithm Introduction
 
 This tutorial lists the text detection algorithms and text recognition algorithms supported by PaddleOCR, as well as the models and metrics of each algorithm on **English public datasets**. It is mainly used for algorithm introduction and algorithm performance comparison. For more models on other datasets including Chinese, please refer to [PP-OCR v2.0 models list](./models_list_en.md).
 
@@ -8,31 +17,32 @@ This tutorial lists the text detection algorithms and text recognition algorithm
 - [2. Text Recognition Algorithm](#TEXTRECOGNITIONALGORITHM)
 
 <a name="TEXTDETECTIONALGORITHM"></a>
-### 1. Text Detection Algorithm
+
+### 1.1 Text Detection Algorithm
 
 PaddleOCR open source text detection algorithms list:
-- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
-- [x]  DB([paper](https://arxiv.org/abs/1911.08947))
-- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))
-- [x]  PSE([paper](https://arxiv.org/abs/1903.12473v2))
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[2]
+- [x]  DB([paper](https://arxiv.org/abs/1911.08947))[1]
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
+- [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
 
 On the ICDAR2015 dataset, the text detection result is as follows:
 
-|Model|Backbone|precision|recall|Hmean|Download link|
+|Model|Backbone|Precision|Recall|Hmean|Download link|
 | --- | --- | --- | --- | --- | --- |
-|EAST|ResNet50_vd|85.80%|86.71%|86.25%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
-|EAST|MobileNetV3|79.42%|80.64%|80.03%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
-|DB|ResNet50_vd|86.41%|78.72%|82.38%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
-|DB|MobileNetV3|77.29%|73.08%|75.12%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
-|SAST|ResNet50_vd|91.39%|83.77%|87.42%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
-|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
-|PSE|MobileNetV3|82.20%|70.48%|75.89%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|EAST|ResNet50_vd|85.80%|86.71%|86.25%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_east_v2.0_train.tar)|
+|EAST|MobileNetV3|79.42%|80.64%|80.03%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_east_v2.0_train.tar)|
+|DB|ResNet50_vd|86.41%|78.72%|82.38%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar)|
+|DB|MobileNetV3|77.29%|73.08%|75.12%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar)|
+|SAST|ResNet50_vd|91.39%|83.77%|87.42%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
+|PSE|ResNet50_vd|85.81%|79.53%|82.55%|[trianed model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
+|PSE|MobileNetV3|82.20%|70.48%|75.89%|[trianed model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
 
 On Total-Text dataset, the text detection result is as follows:
 
-|Model|Backbone|precision|recall|Hmean|Download link|
+|Model|Backbone|Precision|Recall|Hmean|Download link|
 | --- | --- | --- | --- | --- | --- |
-|SAST|ResNet50_vd|89.63%|78.44%|83.66%|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
+|SAST|ResNet50_vd|89.63%|78.44%|83.66%|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_totaltext_v2.0_train.tar)|
 
 **Note：** Additional data, like icdar2013, icdar2017, COCO-Text, ArT, was added to the model training of SAST. Download English public dataset in organized format used by PaddleOCR from:
 * [Baidu Drive](https://pan.baidu.com/s/12cPnZcVuV1zn5DOd4mqjVw) (download code: 2bpi).
@@ -41,31 +51,41 @@ On Total-Text dataset, the text detection result is as follows:
 For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./detection_en.md)
 
 <a name="TEXTRECOGNITIONALGORITHM"></a>
-### 2. Text Recognition Algorithm
+### 1.2 Text Recognition Algorithm
 
 PaddleOCR open-source text recognition algorithms list:
-- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
-- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
-- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
-- [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
-- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))
-- [x]  NRTR([paper](https://arxiv.org/abs/1806.00926v2))
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
+- [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11]
+- [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))[13]
 - [x]  SAR([paper](https://arxiv.org/abs/1811.00751v2))
+- [x] SEED([paper](https://arxiv.org/pdf/2005.10977.pdf))
 
 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:
 
 |Model|Backbone|Avg Accuracy|Module combination|Download link|
 |---|---|---|---|---|
-|Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
-|Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
-|CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
-|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
-|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
-|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
-|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[Download link](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
-|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)
+|Rosetta|Resnet34_vd|80.9%|rec_r34_vd_none_none_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_none_ctc_v2.0_train.tar)|
+|Rosetta|MobileNetV3|78.05%|rec_mv3_none_none_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_none_ctc_v2.0_train.tar)|
+|CRNN|Resnet34_vd|82.76%|rec_r34_vd_none_bilstm_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_none_bilstm_ctc_v2.0_train.tar)|
+|CRNN|MobileNetV3|79.97%|rec_mv3_none_bilstm_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar)|
+|StarNet|Resnet34_vd|84.44%|rec_r34_vd_tps_bilstm_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r34_vd_tps_bilstm_ctc_v2.0_train.tar)|
+|StarNet|MobileNetV3|81.42%|rec_mv3_tps_bilstm_ctc|[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_ctc_v2.0_train.tar)|
+|RARE|MobileNetV3|82.5%|rec_mv3_tps_bilstm_att |[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_tps_bilstm_att_v2.0_train.tar)|
+|RARE|Resnet34_vd|83.6%|rec_r34_vd_tps_bilstm_att |[trained model](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 |[trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_r50_vd_srn_train.tar)|
+|NRTR|NRTR_MTB| 84.3% | rec_mtb_nrtr | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mtb_nrtr_train.tar) |
+|SAR|Resnet31| 87.2% | rec_r31_sar | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_r31_sar_train.tar) |
+|SEED|Aster_Resnet| 85.2% | rec_resnet_stn_bilstm_att | [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
+
+Please refer to the document for training guide and use of PaddleOCR 
+
+## 2. Training
+
+For the training guide and use of PaddleOCR text detection algorithms, please refer to the document [Text detection model training/evaluation/prediction](./detection_en.md). For text recognition algorithms, please refer to [Text recognition model training/evaluation/prediction](./recognition_en.md)
+
+## 3. Inference
+
+Except for the PP-OCR series models of the above models, the other models only support inference based on the Python engine. For details, please refer to [Inference based on Python prediction engine](./inference_en.md)

doc/doc_en/angle_class_en.md

@@ -15,7 +15,6 @@ The text line image obtained after text detection is sent to the recognition mod
 Example of 0 and 180 degree data samples：
 
 ![](../imgs_results/angle_class_example.jpg)
-### DATA PREPARATION
 
 <a name="data-preparation"></a>
 ## 2. Data Preparation
@@ -131,8 +130,6 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/
 <a name="prediction"></a>
 ## 5. Prediction
 
-### PREDICTION
-
 * Training engine prediction
 
 Using the model trained by paddleocr, you can quickly get prediction through the following script.

doc/doc_en/config_en.md

@@ -36,11 +36,10 @@ Take rec_chinese_lite_train_v2.0.yml as an example
 |      pretrained_model    |    Set the path of the pre-trained model      |  ./pretrain_models/CRNN/best_accuracy  |  \          |
 |      checkpoints         |    set model parameter path            |       None        |   Used to load parameters after interruption to continue training|
 |      use_visualdl  |    Set whether to enable visualdl for visual log display |          False        |    [Tutorial](https://www.paddlepaddle.org.cn/paddle/visualdl) |
-|      infer_img            |    Set inference image path or folder path     |       ./infer_img | \|
-|      character_dict_path |    Set dictionary path            |  ./ppocr/utils/ppocr_keys_v1.txt  |    \                 |
+|      infer_img            |    Set inference image path or folder path     |       ./infer_img | \||
+|      character_dict_path |    Set dictionary path            |  ./ppocr/utils/ppocr_keys_v1.txt  | If the character_dict_path is None, model can only recognize number and lower letters |
 |      max_text_length     |    Set the maximum length of text        |       25          |                \                 |
-|      character_type      |    Set character type            |       ch          |    en/ch, the default dict will be used for en, and the custom dict will be used for ch |
-|      use_space_char     |    Set whether to recognize spaces             |        True      |          Only support in character_type=ch mode                 |
+|      use_space_char     |    Set whether to recognize spaces             |        True      |          \|               |
 |      label_list          |    Set the angle supported by the direction classifier       |    ['0','180']    |     Only valid in angle classifier model |
 |      save_res_path          |    Set the save address of the test model results       |    ./output/det_db/predicts_db.txt    |     Only valid in the text detection model |
 
@@ -196,7 +195,6 @@ Italian is made up of Latin letters, so after executing the command, you will ge
       use_gpu: True
       epoch_num: 500
       ...
-      character_type: it  # language
       character_dict_path:  {path/of/dict} # path of dict
    
    Train:
@@ -218,18 +216,18 @@ Italian is made up of Latin letters, so after executing the command, you will ge
 
 Currently, the multi-language algorithms supported by PaddleOCR are:
 
-| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language | character_type |
-| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
-| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  | chinese_cht|
-| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   | EN |
-| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  french |
-| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   | german |
-| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese | japan |
-| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  | korean |
-| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  | latin |
-| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |  ar |
-| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   | cyrillic |
-| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  | devanagari |
+| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  |
+| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  |
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   |
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   |
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese |
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  |
+| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  |
+| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |
+| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   |
+| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  |
 
 For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
 

doc/doc_en/detection_en.md

@@ -99,6 +99,18 @@ python3 tools/train.py -c configs/det/det_mv3_db.yml -o   \
 # Set the GPU ID used by the '--gpus' parameter.
 python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
 
+# multi-Node, multi-GPU training
+# Set the IPs of your nodes used by the '--ips' parameter. Set the GPU ID used by the '--gpus' parameter.
+python3 -m paddle.distributed.launch --ips="xx.xx.xx.xx,xx.xx.xx.xx" --gpus '0,1,2,3' tools/train.py -c configs/det/det_mv3_db.yml \
+     -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+```
+**Note:** For multi-Node multi-GPU training, you need to replace the `ips` value in the preceding command with the address of your machine, and the machines must be able to ping each other. In addition, it requires activating commands separately on multiple machines when we start the training. The command for viewing the IP address of the machine is `ifconfig`.
+
+If you want to further speed up the training, you can use [automatic mixed precision training](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/01_paddle2.0_introduction/basic_concept/amp_en.html). for single card training, the command is as follows:
+```
+python3 tools/train.py -c configs/det/det_mv3_db.yml \
+     -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained \
+     Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True
 ```
 
 ### 2.2 Load Trained Model and Continue Training
@@ -223,6 +235,7 @@ python3 tools/infer/predict_det.py --det_algorithm="EAST" --det_model_dir="./out
 ## 5. FAQ
 
 Q1: The prediction results of trained model and inference model are inconsistent?
+
 **A**: Most of the problems are caused by the inconsistency of the pre-processing and post-processing parameters during the prediction of the trained model and the pre-processing and post-processing parameters during the prediction of the inference model. Taking the model trained by the det_mv3_db.yml configuration file as an example, the solution to the problem of inconsistent prediction results between the training model and the inference model is as follows:
 - Check whether the [trained model preprocessing](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L116) is consistent with the prediction [preprocessing function of the inference model](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/predict_det.py#L42). When the algorithm is evaluated, the input image size will affect the accuracy. In order to be consistent with the paper, the image is resized to [736, 1280] in the training icdar15 configuration file, but there is only a set of default parameters when the inference model predicts, which will be considered To predict the speed problem, the longest side of the image is limited to 960 for resize by default. The preprocessing function of the training model preprocessing and the inference model is located in [ppocr/data/imaug/operators.py](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/ppocr/data/imaug/operators.py#L147)
 - Check whether the [post-processing of the trained model](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/configs/det/det_mv3_db.yml#L51) is consistent with the [post-processing parameters of the inference](https://github.com/PaddlePaddle/PaddleOCR/blob/c1ed243fb68d5d466258243092e56cbae32e2c14/tools/infer/utility.py#L50).

doc/doc_en/environment_en.md

 # Environment Preparation
 
+Windows and Mac users are recommended to use Anaconda to build a Python environment, and Linux users are recommended to use docker to build a Python environment. If you are familiar with the Python environment, you can skip to step 2 to install PaddlePaddle.
+
+Recommended working environment:
+- PaddlePaddle >= 2.0.0 (2.1.2)
+- python3.7
+- CUDA10.1 / CUDA10.2
+- CUDNN 7.6
+
 * [1. Python Environment Setup](#1)
   + [1.1 Windows](#1.1)
   + [1.2 Mac](#1.2)
   + [1.3 Linux](#1.3)
 * [2. Install PaddlePaddle 2.0](#2)
 
+
 <a name="1"></a>
 
 ## 1. Python Environment Setup
@@ -38,7 +47,7 @@
   - Check conda to add environment variables and ignore the warning that
 
     <img src="../install/windows/anaconda_install_env.png" alt="add conda to path" width="500" align="center"/>
-    
+
 
 #### 1.1.2 Opening the terminal and creating the conda environment
 
@@ -69,7 +78,7 @@
   # View the current location of python
   where python
   ```
-  
+
   <img src="../install/windows/conda_list_env.png" alt="create environment" width="600" align="center"/>
 
 The above anaconda environment and python environment are installed
@@ -133,13 +142,13 @@ The above anaconda environment and python environment are installed
     # !!! Contents within this block are managed by 'conda init' !!!
     __conda_setup="$('/Users/xxx/opt/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
     if [ $? -eq 0 ]; then
-    		eval "$__conda_setup"
+            eval "$__conda_setup"
     else
-    		if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
-    				. "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
-    		else
-    				export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
-    		fi
+            if [ -f "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh" ]; then
+                    . "/Users/xxx/opt/anaconda3/etc/profile.d/conda.sh"
+            else
+                    export PATH="/Users/xxx/opt/anaconda3/bin:$PATH"
+            fi
     fi
     unset __conda_setup
     # <<< conda initialize <<<
@@ -197,11 +206,10 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
 - **Download Anaconda**.
 
   - Download at: https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/?C=M&O=D
-    
+
 
   <img src="../install/linux/anaconda_download.png" akt="anaconda download" width="800" align="center"/>
 
-  
 
   - Select the appropriate version for your operating system
       - Type `uname -m` in the terminal to check the command set used by your system
@@ -216,12 +224,12 @@ Linux users can choose to run either Anaconda or Docker. If you are familiar wit
     sudo yum install wget # CentOS
     ```
     ```bash
-    # Then use wget to download from Tsinghua source 
+    # Then use wget to download from Tsinghua source
     # If you want to download Anaconda3-2021.05-Linux-x86_64.sh, the download command is as follows
     wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2021.05-Linux-x86_64.sh
     # If you want to download another version, you need to change the file name after the last 1 / to the version you want to download
     ```
-  
+
 - To install Anaconda.
 
   - Type `sh Anaconda3-2021.05-Linux-x86_64.sh` at the command line
@@ -309,7 +317,18 @@ cd /home/Projects
 # Create a docker container named ppocr and map the current directory to the /paddle directory of the container
 
 # If using CPU, use docker instead of nvidia-docker to create docker
-sudo docker run --name ppocr -v $PWD:/paddle --network=host -it  paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82  /bin/bash
+sudo docker run --name ppocr -v $PWD:/paddle --network=host -it  registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda10.2-cudnn7  /bin/bash
+
+# If using GPU, use nvidia-docker to create docker
+# docker image registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda11.2-cudnn8 is recommended for CUDA11.2 + CUDNN8.
+sudo nvidia-docker run --name ppocr -v $PWD:/paddle --shm-size=64G --network=host -it registry.baidubce.com/paddlepaddle/paddle:2.1.3-gpu-cuda10.2-cudnn7 /bin/bash
+
+```
+You can also visit [DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/) to get the image that fits your machine.
+
+```
+# ctrl+P+Q to exit docker, to re-enter docker using the following command:
+sudo docker container exec -it ppocr /bin/bash
 ```
 
 <a name="2"></a>
@@ -329,4 +348,3 @@ python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
 ```
 
 For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/install/quick) for operation.
-

doc/doc_en/inference_en.md

@@ -21,7 +21,7 @@ Next, we first introduce how to convert a trained model into an inference model,
     - [2.2 DB Text Detection Model Inference](#DB_DETECTION)
     - [2.3 East Text Detection Model Inference](#EAST_DETECTION)
     - [2.4 Sast Text Detection Model Inference](#SAST_DETECTION)
-    
+
 - [3. Text Recognition Model Inference](#RECOGNITION_MODEL_INFERENCE)
     - [3.1 Lightweight Chinese Text Recognition Model Reference](#LIGHTWEIGHT_RECOGNITION)
     - [3.2 CTC-Based Text Recognition Model Inference](#CTC-BASED_RECOGNITION)
@@ -281,7 +281,7 @@ python3 tools/export_model.py -c configs/det/rec_r34_vd_none_bilstm_ctc.yml -o G
 For CRNN text recognition model inference, execute the following commands:
 
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
 ```
 
 ![](../imgs_words_en/word_336.png)
@@ -314,7 +314,7 @@ with the training, such as: --rec_image_shape="1, 64, 256"
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png" \
                                     --rec_model_dir="./inference/srn/" \
                                     --rec_image_shape="1, 64, 256" \
-                                    --rec_char_type="en" \
+                                    --rec_char_dict_path="./ppocr/utils/ic15_dict.txt" \
                                     --rec_algorithm="SRN"
 ```
 
@@ -323,7 +323,7 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_336.png
 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"
+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_dict_path="your text dict path"
 ```
 
 <a name="MULTILINGUAL_MODEL_INFERENCE"></a>
@@ -333,7 +333,7 @@ If you need to predict other language models, when using inference model predict
 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:
 
 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model"  --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
 
@@ -399,7 +399,7 @@ If you want to try other detection algorithms or recognition algorithms, please
 The following command uses the combination of the EAST text detection and STAR-Net text recognition:
 
 ```
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_type="en"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --det_model_dir="./inference/det_east/" --det_algorithm="EAST" --rec_model_dir="./inference/starnet/" --rec_image_shape="3, 32, 100" --rec_char_dict_path="./ppocr/utils/ic15_dict.txt"
 ```
 
 After executing the command, the recognition result image is as follows:

doc/doc_en/inference_ppocr_en.md

 
-# Python Inference for PP-OCR Model Library
+# Python Inference for PP-OCR Model Zoo
 
 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 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)
-
 - [Text Detection Angle Classification and Recognition Inference Concatenation](#CONCATENATION)
 
 <a name="DETECTION_MODEL_INFERENCE"></a>
@@ -22,10 +19,10 @@ The default configuration is based on the inference setting of the DB text detec
 
 ```
 # download DB text detection inference model
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
-tar xf ch_ppocr_mobile_v2.0_det_infer.tar
-# predict
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar
+tar xf ch_PP-OCRv2_det_infer.tar
+# run inference
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./ch_PP-OCRv2_det_infer.tar/"
 ```
 
 The visual text detection results are saved to the ./inference_results folder by default, and the name of the result file is prefixed with'det_res'. Examples of results are as follows:
@@ -42,12 +39,12 @@ Set as `limit_type='min', det_limit_side_len=960`, it means that the shortest si
 
 If the resolution of the input picture is relatively large and you want to use a larger resolution prediction, you can set det_limit_side_len to the desired value, such as 1216:
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --det_limit_type=max --det_limit_side_len=1216
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --det_limit_type=max --det_limit_side_len=1216
 ```
 
 If you want to use the CPU for prediction, execute the command as follows
 ```
-python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/det_db/" --use_gpu=False
+python3 tools/infer/predict_det.py --image_dir="./doc/imgs/1.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/"  --use_gpu=False
 ```
 
 <a name="RECOGNITION_MODEL_INFERENCE"></a>
@@ -62,9 +59,10 @@ For lightweight Chinese recognition model inference, you can execute the followi
 
 ```
 # download CRNN text recognition inference model
-wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
-tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
+wget  https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar
+tar xf ch_PP-OCRv2_rec_infer.tar
+# run inference
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./ch_PP-OCRv2_rec_infer/"
 ```
 
 ![](../imgs_words_en/word_10.png)
@@ -78,10 +76,12 @@ Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
 <a name="MULTILINGUAL_MODEL_INFERENCE"></a>
 
 ### 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,
+If you need to predict [other language models](./models_list_en.md#Multilingual), 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:
 
 ```
+wget wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/korean_mobile_v2.0_rec_infer.tar
+
 python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" --rec_model_dir="./your inference model" --rec_char_type="korean" --rec_char_dict_path="ppocr/utils/dict/korean_dict.txt" --vis_font_path="doc/fonts/korean.ttf"
 ```
 ![](../imgs_words/korean/1.jpg)
@@ -120,13 +120,13 @@ When performing prediction, you need to specify the path of a single image or a
 
 ```shell
 # use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=true
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --cls_model_dir="./inference/cls/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=true
 
 # not use use direction classifier
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/"
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false
 
 # use multi-process
-python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/det_db/" --rec_model_dir="./inference/rec_crnn/" --use_angle_cls=false --use_mp=True --total_process_num=6
+python3 tools/infer/predict_system.py --image_dir="./doc/imgs/00018069.jpg" --det_model_dir="./inference/ch_PP-OCRv2_det_infer/" --rec_model_dir="./inference/ch_PP-OCRv2_rec_infer/" --use_angle_cls=false --use_mp=True --total_process_num=6
 ```
 
 

doc/doc_en/models_list_en.md

-## OCR model list（V2.1, updated on 2021.9.6）
+# OCR Model List（V2.1, updated on 2021.9.6）
 > **Note**
 > 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.
@@ -6,9 +6,9 @@
 
 - [1. Text Detection Model](#Detection)
 - [2. Text Recognition Model](#Recognition)
-    - [Chinese Recognition Model](#Chinese)
-    - [English Recognition Model](#English)
-    - [Multilingual Recognition Model](#Multilingual)
+    - [2.1 Chinese Recognition Model](#Chinese)
+    - [2.2 English Recognition Model](#English)
+    - [2.3 Multilingual Recognition Model](#Multilingual)
 - [3. Text Angle Classification Model](#Angle)
 - [4. Paddle-Lite Model](#Paddle-Lite)
 
@@ -25,26 +25,26 @@ Relationship of the above models is as follows.
 ![](../imgs_en/model_prod_flow_en.png)
 
 <a name="Detection"></a>
-### 1. Text Detection Model
+## 1. Text Detection Model
 
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
-|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_PP-OCRv2_det_slim|[New] 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-OCRv2_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|[New] Original lightweight model, supporting Chinese, English, multilingual text detection|[ch_PP-OCRv2_det_cml.yml](../../configs/det/ch_PP-OCRv2/ch_PP-OCRv2_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)|
 
 <a name="Recognition"></a>
-### 2. Text Recognition Model
+## 2. Text Recognition Model
 
 <a name="Chinese"></a>
-#### Chinese Recognition Model
+### 2.1 Chinese Recognition Model
 
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
-|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_PP-OCRv2_rec_slim|[New] 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|[New] 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) |
@@ -53,7 +53,7 @@ Relationship of the above models is as follows.
 **Note:** The `trained model` is finetuned on the `pre-trained model` with real data and synthsized vertical text data, which achieved better performance in real scene. The `pre-trained model` is directly trained on the full amount of real data and synthsized data, which is more suitable for finetune on your own dataset.
 
 <a name="English"></a>
-#### English Recognition Model
+### 2.2 English Recognition Model
 
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
@@ -61,7 +61,7 @@ Relationship of the above models is as follows.
 |en_number_mobile_v2.0_rec|Original lightweight model, supporting English and number recognition|[rec_en_number_lite_train.yml](../../configs/rec/multi_language/rec_en_number_lite_train.yml)|2.6M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar) |
 
 <a name="Multilingual"></a>
-#### Multilingual Recognition Model（Updating...）
+### 2.3 Multilingual Recognition Model（Updating...）
 
 |model name| dict file | description|config|model size|download|
 | --- | --- | --- |--- | --- | --- |
@@ -82,7 +82,7 @@ For more supported languages, please refer to : [Multi-language model](./multi_l
 
 
 <a name="Angle"></a>
-### 3. Text Angle Classification Model
+## 3. Text Angle Classification Model
 
 |model name|description|config|model size|download|
 | --- | --- | --- | --- | --- |
@@ -90,7 +90,7 @@ For more supported languages, please refer to : [Multi-language model](./multi_l
 |ch_ppocr_mobile_v2.0_cls|Original model for text angle classification|[cls_mv3.yml](../../configs/cls/cls_mv3.yml)|1.38M|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |
 
 <a name="Paddle-Lite"></a>
-### 4. Paddle-Lite Model
+## 4. Paddle-Lite Model
 |Version|Introduction|Model size|Detection model|Text Direction model|Recognition model|Paddle-Lite branch|
 |---|---|---|---|---|---|---|
 |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|

doc/doc_en/pgnet_en.md

@@ -24,9 +24,9 @@ The results of detection and recognition are as follows:
 ![](../imgs_results/e2e_res_img293_pgnet.png)
 ![](../imgs_results/e2e_res_img295_pgnet.png)
 ### Performance
-####Test set: Total Text
+#### Test set: Total Text
 
-####Test environment: NVIDIA Tesla V100-SXM2-16GB
+#### Test environment: NVIDIA Tesla V100-SXM2-16GB
 |PGNetA|det_precision|det_recall|det_f_score|e2e_precision|e2e_recall|e2e_f_score|FPS|download|
 | --- | --- | --- | --- | --- | --- | --- | --- | --- |
 |Paper|85.30|86.80|86.1|-|-|61.7|38.20 (size=640)|-|
@@ -36,7 +36,7 @@ The results of detection and recognition are as follows:
 
 <a name="Environment_Configuration"></a>
 ## 2. Environment Configuration
-Please refer to [Quick Installation](./installation_en.md) Configure the PaddleOCR running environment.
+Please refer to [Operation Environment Preparation](./environment_en.md) to configure PaddleOCR operating environment first, refer to [PaddleOCR Overview and Project Clone](./paddleOCR_overview_en.md) to clone the project
 
 <a name="Quick_Use"></a>
 ## 3. Quick Use

doc/doc_en/quickstart_en.md

@@ -53,10 +53,10 @@ If you do not use the provided test image, you can replace the following `--imag
 
 #### 2.1.1 Chinese and English Model
 
-* Detection, direction classification and recognition: set the direction classifier parameter`--use_angle_cls true` to recognize vertical text.
+* Detection, direction classification and recognition: set the parameter`--use_gpu false` to disable the gpu device
 
   ```bash
-  paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en
+  paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en --use_gpu false
   ```
 
   Output will be a list, each item contains bounding box, text and recognition confidence

doc/doc_en/recognition_en.md

@@ -161,7 +161,7 @@ The current multi-language model is still in the demo stage and will continue to
 If you like, you can submit the dictionary file to [dict](../../ppocr/utils/dict) and we will thank you in the Repo.
 
 
-To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` and set `character_type` to `ch`.
+To customize the dict file, please modify the `character_dict_path` field in `configs/rec/rec_icdar15_train.yml` .
 
 - Custom dictionary
 
@@ -172,8 +172,6 @@ If you need to customize dic file, please add character_dict_path field in confi
 
 If you want to support the recognition of the `space` category, please set the `use_space_char` field in the yml file to `True`.
 
-**Note: use_space_char only takes effect when character_type=ch**
-
 <a name="TRAINING"></a>
 ## 2.Training
 
@@ -250,7 +248,6 @@ Global:
   # Add a custom dictionary, such as modify the dictionary, please point the path to the new dictionary
   character_dict_path: ppocr/utils/ppocr_keys_v1.txt
   # Modify character type
-  character_type: ch
   ...
   # Whether to recognize spaces
   use_space_char: True
@@ -312,18 +309,18 @@ Eval:
 
 Currently, the multi-language algorithms supported by PaddleOCR are:
 
-| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language | character_type |
-| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  | :-----:  |
-| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  | chinese_cht|
-| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   | EN |
-| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  french |
-| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   | german |
-| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese | japan |
-| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  | korean |
-| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  | latin |
-| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |  ar |
-| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   | cyrillic |
-| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  | devanagari |
+| Configuration file |  Algorithm name |   backbone |   trans   |   seq      |     pred     |  language |
+| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   | :-----:  |
+| rec_chinese_cht_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | chinese traditional  |
+| rec_en_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | English(Case sensitive)   |
+| rec_french_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | French |  
+| rec_ger_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | German   |
+| rec_japan_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Japanese |
+| rec_korean_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Korean  |
+| rec_latin_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | Latin  |
+| rec_arabic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | arabic |
+| rec_cyrillic_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | cyrillic   |
+| rec_devanagari_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  | devanagari  |
 
 For more supported languages, please refer to : [Multi-language model](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.1/doc/doc_en/multi_languages_en.md#4-support-languages-and-abbreviations)
 
@@ -471,6 +468,3 @@ inference/det_db/
   ```
   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/training_en.md

@@ -25,11 +25,10 @@ The PaddleOCR model uses configuration files to manage network training and eval
 For the complete configuration file description, please refer to [Configuration File](./config_en.md)
 
 <a name="1-basic-concepts"></a>
-# 1. Basic concepts
 
 ## 2. Basic Concepts
 
-The following parameters need to be paid attention to when tuning the model:
+In the process of model training, some hyperparameters need to be manually adjusted to help the model obtain the optimal index at the least loss. Different data volumes may require different hyper-parameters. When you want to finetune your own data or tune the model effect, there are several parameter adjustment strategies for reference:
 
 <a name="11-learning-rate"></a>
 ### 2.1 Learning Rate
@@ -53,7 +52,7 @@ and the learning rate is the same in each stage.
 warmup_epoch means that in the first 5 epochs, the learning rate will gradually increase from 0 to base_lr. For all strategies, please refer to the code [learning_rate.py](../../ppocr/optimizer/learning_rate.py).
 
 <a name="12-regularization"></a>
-## 1.2 Regularization
+### 2.2 Regularization
 
 Regularization can effectively avoid algorithm overfitting. PaddleOCR provides L1 and L2 regularization methods.
 L1 and L2 regularization are the most commonly used regularization methods.
@@ -125,7 +124,7 @@ There are several experiences for reference when constructing the data set:
 
 <a name="3-faq"></a>
 
-# 3. FAQ
+## 4. FAQ
 
 **Q**: How to choose a suitable network input shape when training CRNN recognition?
 
@@ -147,3 +146,10 @@ There are several experiences for reference when constructing the data set:
 
     A: It is normal for the acc to be 0 at the beginning of the recognition model training, and the indicator will come up after a longer training period.
 
+***
+
+Click the following links for detailed training tutorial:  
+
+- [text detection model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/detection.md)  
+- [text recognition model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/recognition.md)  
+- [text direction classification model training](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.3/doc/doc_ch/angle_class.md)  

doc/doc_en/whl_en.md

@@ -366,4 +366,6 @@ im_show.save('result.jpg')
 | rec                     | Enable recognition when `ppocr.ocr` func exec                                                                                                                                                                                                   | TRUE                    |
 | cls                     | Enable classification when `ppocr.ocr` func exec((Use use_angle_cls in command line mode to control whether to start classification in the forward direction)                                                                                                                                                                                                   | FALSE                    |
 | show_log                     | Whether to print log in det and rec | FALSE                    |
-| type                     | Perform ocr or table structuring, the value is selected in ['ocr','structure']                                                                                                                                                                                             | ocr                    |
+| type                     | Perform ocr or table structuring, the value is selected in ['ocr','structure']                                                                                                                                                                                             | ocr                    |
\ No newline at end of file
+| ocr_version                     | OCR Model version number, the current model support list is as follows: PP-OCRv2 support Chinese detection and recognition model, PP-OCR support Chinese detection, recognition and direction classifier, multilingual recognition model | PP-OCRv2                 |
+| structure_version                     | table structure Model version number, the current model support list is as follows: STRUCTURE support english table structure model | STRUCTURE                 |

paddleocr.py

@@ -16,6 +16,9 @@ import os
 import sys
 
 __dir__ = os.path.dirname(__file__)
+
+import paddle
+
 sys.path.append(os.path.join(__dir__, ''))
 
 import cv2
@@ -29,7 +32,7 @@ from ppocr.utils.logging import get_logger
 logger = get_logger()
 from ppocr.utils.utility import check_and_read_gif, get_image_file_list
 from ppocr.utils.network import maybe_download, download_with_progressbar, is_link, confirm_model_dir_url
-from tools.infer.utility import draw_ocr, str2bool
+from tools.infer.utility import draw_ocr, str2bool, check_gpu
 from ppstructure.utility import init_args, draw_structure_result
 from ppstructure.predict_system import OCRSystem, save_structure_res
 
@@ -39,130 +42,137 @@ __all__ = [
 ]
 
 SUPPORT_DET_MODEL = ['DB']
-VERSION = '2.2.1'
+VERSION = '2.3.0.2'
 SUPPORT_REC_MODEL = ['CRNN']
 BASE_DIR = os.path.expanduser("~/.paddleocr/")
 
-DEFAULT_MODEL_VERSION = '2.0'
+DEFAULT_OCR_MODEL_VERSION = 'PP-OCR'
+DEFAULT_STRUCTURE_MODEL_VERSION = 'STRUCTURE'
 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'
-            }
-        }
-    },
-    '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',
+    'OCR': {
+        'PP-OCRv2': {
+            'det': {
+                'ch': {
+                    'url':
+                    'https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar',
+                },
             },
-            'structure': {
-                'url':
-                'https://paddleocr.bj.bcebos.com/dygraph_v2.0/table/en_ppocr_mobile_v2.0_table_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'
-            },
-            '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':
-                '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':
-                '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':
-                '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':
-                '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':
-                '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':
-                'https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/ta_mobile_v2.0_rec_infer.tar',
-                'dict_path': './ppocr/utils/dict/ta_dict.txt'
+        DEFAULT_OCR_MODEL_VERSION: {
+            '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'
+                }
             },
-            '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'
+            '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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':
+                    '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'
+                },
+                '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'
+                }
             },
-            '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'
+            'cls': {
+                'ch': {
+                    'url':
+                    'https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar',
+                }
             },
-            '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':
-                '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':
-                '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':
-                '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'
-            },
-            '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',
-            }
-        },
-        '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'
+        }
+    },
+    'STRUCTURE': {
+        DEFAULT_STRUCTURE_MODEL_VERSION: {
+            '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'
+                }
             }
         }
     }
@@ -177,7 +187,20 @@ 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')
+    parser.add_argument(
+        "--ocr_version",
+        type=str,
+        default='PP-OCRv2',
+        help='OCR Model version, the current model support list is as follows: '
+        '1. PP-OCRv2 Support Chinese detection and recognition model. '
+        '2. PP-OCR support Chinese detection, recognition and direction classifier and multilingual recognition model.'
+    )
+    parser.add_argument(
+        "--structure_version",
+        type=str,
+        default='STRUCTURE',
+        help='Model version, the current model support list is as follows:'
+        ' 1. STRUCTURE Support en table structure model.')
 
     for action in parser._actions:
         if action.dest in ['rec_char_dict_path', 'table_char_dict_path']:
@@ -215,9 +238,9 @@ def parse_lang(lang):
         lang = "cyrillic"
     elif lang in devanagari_lang:
         lang = "devanagari"
-    assert lang in MODEL_URLS[DEFAULT_MODEL_VERSION][
+    assert lang in MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION][
         'rec'], 'param lang must in {}, but got {}'.format(
-            MODEL_URLS[DEFAULT_MODEL_VERSION]['rec'].keys(), lang)
+            MODEL_URLS['OCR'][DEFAULT_OCR_MODEL_VERSION]['rec'].keys(), lang)
     if lang == "ch":
         det_lang = "ch"
     elif lang == 'structure':
@@ -227,33 +250,41 @@ 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))
+def get_model_config(type, version, model_type, lang):
+    if type == 'OCR':
+        DEFAULT_MODEL_VERSION = DEFAULT_OCR_MODEL_VERSION
+    elif type == 'STRUCTURE':
+        DEFAULT_MODEL_VERSION = DEFAULT_STRUCTURE_MODEL_VERSION
+    else:
+        raise NotImplementedError
+    model_urls = MODEL_URLS[type]
+    if version not in model_urls:
+        logger.warning('version {} not in {}, auto switch to version {}'.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]:
+    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'.
+                'version {} not support {} models, auto switch to version {}'.
                 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()))
+                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))
+    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 {}, auto switch to version {}'.
+                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(
+                format(lang, model_urls[DEFAULT_MODEL_VERSION][model_type].keys(
                 ), model_type))
             sys.exit(-1)
-    return MODEL_URLS[version][model_type][lang]
+    return model_urls[version][model_type][lang]
 
 
 class PaddleOCR(predict_system.TextSystem):
@@ -265,23 +296,28 @@ class PaddleOCR(predict_system.TextSystem):
         """
         params = parse_args(mMain=False)
         params.__dict__.update(**kwargs)
+        params.use_gpu = check_gpu(params.use_gpu)
+
         if not params.show_log:
             logger.setLevel(logging.INFO)
         self.use_angle_cls = params.use_angle_cls
         lang, det_lang = parse_lang(params.lang)
 
         # init model dir
-        det_model_config = get_model_config(params.version, 'det', det_lang)
+        det_model_config = get_model_config('OCR', params.ocr_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)
+        rec_model_config = get_model_config('OCR', params.ocr_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')
+        cls_model_config = get_model_config('OCR', params.ocr_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'),
@@ -362,22 +398,27 @@ class PPStructure(OCRSystem):
     def __init__(self, **kwargs):
         params = parse_args(mMain=False)
         params.__dict__.update(**kwargs)
+        params.use_gpu = check_gpu(params.use_gpu)
+
         if not params.show_log:
             logger.setLevel(logging.INFO)
         lang, det_lang = parse_lang(params.lang)
 
         # init model dir
-        det_model_config = get_model_config(params.version, 'det', det_lang)
+        det_model_config = get_model_config('OCR', params.ocr_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)
+        rec_model_config = get_model_config('OCR', params.ocr_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')
+        table_model_config = get_model_config(
+            'STRUCTURE', params.structure_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'),

ppocr/data/imaug/east_process.py

@@ -11,7 +11,10 @@
 #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.
-
+"""
+This code is refered from: 
+https://github.com/songdejia/EAST/blob/master/data_utils.py
+"""
 import math
 import cv2
 import numpy as np
@@ -24,10 +27,10 @@ __all__ = ['EASTProcessTrain']
 
 class EASTProcessTrain(object):
     def __init__(self,
-                 image_shape = [512, 512],
-                 background_ratio = 0.125,
-                 min_crop_side_ratio = 0.1,
-                 min_text_size = 10,
+                 image_shape=[512, 512],
+                 background_ratio=0.125,
+                 min_crop_side_ratio=0.1,
+                 min_text_size=10,
                  **kwargs):
         self.input_size = image_shape[1]
         self.random_scale = np.array([0.5, 1, 2.0, 3.0])
@@ -282,12 +285,7 @@ class EASTProcessTrain(object):
                 1.0 / max(min(poly_h, poly_w), 1.0)
         return score_map, geo_map, training_mask
 
-    def crop_area(self,
-                  im,
-                  polys,
-                  tags,
-                  crop_background=False,
-                  max_tries=50):
+    def crop_area(self, im, polys, tags, crop_background=False, max_tries=50):
         """
         make random crop from the input image
         :param im:
@@ -435,5 +433,4 @@ class EASTProcessTrain(object):
         data['score_map'] = score_map
         data['geo_map'] = geo_map
         data['training_mask'] = training_mask
-        # print(im.shape, score_map.shape, geo_map.shape, training_mask.shape)
-        return data
+        return data
\ No newline at end of file

ppocr/data/imaug/iaa_augment.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/iaa_augment.py
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

ppocr/data/imaug/label_ops.py

@@ -22,6 +22,8 @@ import string
 from shapely.geometry import LineString, Point, Polygon
 import json
 
+from ppocr.utils.logging import get_logger
+
 
 class ClsLabelEncode(object):
     def __init__(self, label_list, **kwargs):
@@ -93,31 +95,23 @@ class BaseRecLabelEncode(object):
     def __init__(self,
                  max_text_length,
                  character_dict_path=None,
-                 character_type='ch',
                  use_space_char=False):
-        support_character_type = [
-            'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
-            'EN', 'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs',
-            'oc', 'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi',
-            'mr', 'ne', 'latin', 'arabic', 'cyrillic', 'devanagari'
-        ]
-        assert character_type in support_character_type, "Only {} are supported now but get {}".format(
-            support_character_type, character_type)
 
         self.max_text_len = max_text_length
         self.beg_str = "sos"
         self.end_str = "eos"
-        if character_type == "en":
+        self.lower = False
+
+        if character_dict_path is None:
+            logger = get_logger()
+            logger.warning(
+                "The character_dict_path is None, model can only recognize number and lower letters"
+            )
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
-        elif character_type == "EN_symbol":
-            # same with ASTER setting (use 94 char).
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
-        elif character_type in support_character_type:
+            self.lower = True
+        else:
             self.character_str = ""
-            assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
-                character_type)
             with open(character_dict_path, "rb") as fin:
                 lines = fin.readlines()
                 for line in lines:
@@ -126,7 +120,6 @@ class BaseRecLabelEncode(object):
             if use_space_char:
                 self.character_str += " "
             dict_character = list(self.character_str)
-        self.character_type = character_type
         dict_character = self.add_special_char(dict_character)
         self.dict = {}
         for i, char in enumerate(dict_character):
@@ -148,7 +141,7 @@ class BaseRecLabelEncode(object):
         """
         if len(text) == 0 or len(text) > self.max_text_len:
             return None
-        if self.character_type == "en":
+        if self.lower:
             text = text.lower()
         text_list = []
         for char in text:
@@ -168,13 +161,11 @@ class NRTRLabelEncode(BaseRecLabelEncode):
     def __init__(self,
                  max_text_length,
                  character_dict_path=None,
-                 character_type='EN_symbol',
                  use_space_char=False,
                  **kwargs):
 
-        super(NRTRLabelEncode,
-              self).__init__(max_text_length, character_dict_path,
-                             character_type, use_space_char)
+        super(NRTRLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
 
     def __call__(self, data):
         text = data['label']
@@ -201,12 +192,10 @@ class CTCLabelEncode(BaseRecLabelEncode):
     def __init__(self,
                  max_text_length,
                  character_dict_path=None,
-                 character_type='ch',
                  use_space_char=False,
                  **kwargs):
-        super(CTCLabelEncode,
-              self).__init__(max_text_length, character_dict_path,
-                             character_type, use_space_char)
+        super(CTCLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
 
     def __call__(self, data):
         text = data['label']
@@ -232,12 +221,10 @@ class E2ELabelEncodeTest(BaseRecLabelEncode):
     def __init__(self,
                  max_text_length,
                  character_dict_path=None,
-                 character_type='EN',
                  use_space_char=False,
                  **kwargs):
-        super(E2ELabelEncodeTest,
-              self).__init__(max_text_length, character_dict_path,
-                             character_type, use_space_char)
+        super(E2ELabelEncodeTest, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
 
     def __call__(self, data):
         import json
@@ -468,12 +455,10 @@ class AttnLabelEncode(BaseRecLabelEncode):
     def __init__(self,
                  max_text_length,
                  character_dict_path=None,
-                 character_type='ch',
                  use_space_char=False,
                  **kwargs):
-        super(AttnLabelEncode,
-              self).__init__(max_text_length, character_dict_path,
-                             character_type, use_space_char)
+        super(AttnLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
 
     def add_special_char(self, dict_character):
         self.beg_str = "sos"
@@ -516,12 +501,10 @@ class SEEDLabelEncode(BaseRecLabelEncode):
     def __init__(self,
                  max_text_length,
                  character_dict_path=None,
-                 character_type='ch',
                  use_space_char=False,
                  **kwargs):
-        super(SEEDLabelEncode,
-              self).__init__(max_text_length, character_dict_path,
-                             character_type, use_space_char)
+        super(SEEDLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
 
     def add_special_char(self, dict_character):
         self.end_str = "eos"
@@ -548,12 +531,10 @@ class SRNLabelEncode(BaseRecLabelEncode):
     def __init__(self,
                  max_text_length=25,
                  character_dict_path=None,
-                 character_type='en',
                  use_space_char=False,
                  **kwargs):
-        super(SRNLabelEncode,
-              self).__init__(max_text_length, character_dict_path,
-                             character_type, use_space_char)
+        super(SRNLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
 
     def add_special_char(self, dict_character):
         dict_character = dict_character + [self.beg_str, self.end_str]
@@ -761,12 +742,10 @@ class SARLabelEncode(BaseRecLabelEncode):
     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)
+        super(SARLabelEncode, self).__init__(
+            max_text_length, character_dict_path, use_space_char)
 
     def add_special_char(self, dict_character):
         beg_end_str = "<BOS/EOS>"

ppocr/data/imaug/make_border_map.py

-# -*- coding:utf-8 -*- 
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/make_border_map.py
+"""
 
 from __future__ import absolute_import
 from __future__ import division

ppocr/data/imaug/make_pse_gt.py

-# -*- coding:utf-8 -*- 
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from __future__ import absolute_import
 from __future__ import division
@@ -12,12 +24,8 @@ from shapely.geometry import Polygon
 
 __all__ = ['MakePseGt']
 
-class MakePseGt(object):
-    r'''
-    Making binary mask from detection data with ICDAR format.
-    Typically following the process of class `MakeICDARData`.
-    '''
 
+class MakePseGt(object):
     def __init__(self, kernel_num=7, size=640, min_shrink_ratio=0.4, **kwargs):
         self.kernel_num = kernel_num
         self.min_shrink_ratio = min_shrink_ratio
@@ -38,16 +46,20 @@ class MakePseGt(object):
             text_polys *= scale
 
         gt_kernels = []
-        for i in range(1,self.kernel_num+1):
+        for i in range(1, self.kernel_num + 1):
             # s1->sn, from big to small
-            rate = 1.0 - (1.0 - self.min_shrink_ratio) / (self.kernel_num - 1) * i
-            text_kernel, ignore_tags = self.generate_kernel(image.shape[0:2], rate, text_polys, ignore_tags)
+            rate = 1.0 - (1.0 - self.min_shrink_ratio) / (self.kernel_num - 1
+                                                          ) * i
+            text_kernel, ignore_tags = self.generate_kernel(
+                image.shape[0:2], rate, text_polys, ignore_tags)
             gt_kernels.append(text_kernel)
 
         training_mask = np.ones(image.shape[0:2], dtype='uint8')
         for i in range(text_polys.shape[0]):
             if ignore_tags[i]:
-                cv2.fillPoly(training_mask, text_polys[i].astype(np.int32)[np.newaxis, :, :], 0)
+                cv2.fillPoly(training_mask,
+                             text_polys[i].astype(np.int32)[np.newaxis, :, :],
+                             0)
 
         gt_kernels = np.array(gt_kernels)
         gt_kernels[gt_kernels > 0] = 1
@@ -59,16 +71,25 @@ class MakePseGt(object):
         data['mask'] = training_mask.astype('float32')
         return data
 
-    def generate_kernel(self, img_size, shrink_ratio, text_polys, ignore_tags=None):
+    def generate_kernel(self,
+                        img_size,
+                        shrink_ratio,
+                        text_polys,
+                        ignore_tags=None):
+        """
+        Refer to part of the code:
+        https://github.com/open-mmlab/mmocr/blob/main/mmocr/datasets/pipelines/textdet_targets/base_textdet_targets.py
+        """
+
         h, w = img_size
         text_kernel = np.zeros((h, w), dtype=np.float32)
         for i, poly in enumerate(text_polys):
             polygon = Polygon(poly)
-            distance = polygon.area * (1 - shrink_ratio * shrink_ratio) / (polygon.length + 1e-6)
+            distance = polygon.area * (1 - shrink_ratio * shrink_ratio) / (
+                polygon.length + 1e-6)
             subject = [tuple(l) for l in poly]
             pco = pyclipper.PyclipperOffset()
-            pco.AddPath(subject, pyclipper.JT_ROUND,
-                        pyclipper.ET_CLOSEDPOLYGON)
+            pco.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
             shrinked = np.array(pco.Execute(-distance))
 
             if len(shrinked) == 0 or shrinked.size == 0:

ppocr/data/imaug/make_shrink_map.py

-# -*- coding:utf-8 -*- 
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/make_shrink_map.py
+"""
 
 from __future__ import absolute_import
 from __future__ import division

ppocr/data/imaug/random_crop_data.py

-# -*- coding:utf-8 -*- 
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/random_crop_data.py
+"""
 
 from __future__ import absolute_import
 from __future__ import division

ppocr/data/imaug/rec_img_aug.py

@@ -87,17 +87,17 @@ class RecResizeImg(object):
     def __init__(self,
                  image_shape,
                  infer_mode=False,
-                 character_type='ch',
+                 character_dict_path='./ppocr/utils/ppocr_keys_v1.txt',
                  padding=True,
                  **kwargs):
         self.image_shape = image_shape
         self.infer_mode = infer_mode
-        self.character_type = character_type
+        self.character_dict_path = character_dict_path
         self.padding = padding
 
     def __call__(self, data):
         img = data['image']
-        if self.infer_mode and self.character_type == "ch":
+        if self.infer_mode and self.character_dict_path is not None:
             norm_img = resize_norm_img_chinese(img, self.image_shape)
         else:
             norm_img = resize_norm_img(img, self.image_shape, self.padding)

ppocr/data/imaug/sast_process.py

@@ -11,7 +11,10 @@
 #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.
-
+"""
+This part code is refered from: 
+https://github.com/songdejia/EAST/blob/master/data_utils.py
+"""
 import math
 import cv2
 import numpy as np

ppocr/data/imaug/text_image_aug/augment.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/RubanSeven/Text-Image-Augmentation-python/blob/master/augment.py
+"""
 
 import numpy as np
 from .warp_mls import WarpMLS

ppocr/data/imaug/text_image_aug/warp_mls.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/RubanSeven/Text-Image-Augmentation-python/blob/master/warp_mls.py
+"""
 
 import numpy as np
 
@@ -161,4 +165,4 @@ class WarpMLS:
         dst = np.clip(dst, 0, 255)
         dst = np.array(dst, dtype=np.uint8)
 
-        return dst
+        return dst
\ No newline at end of file

ppocr/losses/ace_loss.py

@@ -11,6 +11,9 @@
 # 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.
+
+# This code is refer from: https://github.com/viig99/LS-ACELoss
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -32,6 +35,7 @@ class ACELoss(nn.Layer):
     def __call__(self, predicts, batch):
         if isinstance(predicts, (list, tuple)):
             predicts = predicts[-1]
+
         B, N = predicts.shape[:2]
         div = paddle.to_tensor([N]).astype('float32')
 
@@ -42,9 +46,7 @@ class ACELoss(nn.Layer):
         length = batch[2].astype("float32")
         batch = batch[3].astype("float32")
         batch[:, 0] = paddle.subtract(div, length)
-
         batch = paddle.divide(batch, div)
 
         loss = self.loss_func(aggregation_preds, batch)
-
         return {"loss_ace": loss}

ppocr/losses/center_loss.py

@@ -12,6 +12,8 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
+# This code is refer from: https://github.com/KaiyangZhou/pytorch-center-loss
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -28,22 +30,17 @@ class CenterLoss(nn.Layer):
     Reference: Wen et al. A Discriminative Feature Learning Approach for Deep Face Recognition. ECCV 2016.
     """
 
-    def __init__(self,
-                 num_classes=6625,
-                 feat_dim=96,
-                 init_center=False,
-                 center_file_path=None):
+    def __init__(self, num_classes=6625, feat_dim=96, center_file_path=None):
         super().__init__()
         self.num_classes = num_classes
         self.feat_dim = feat_dim
         self.centers = paddle.randn(
-            shape=[self.num_classes, self.feat_dim]).astype(
-                "float64")  #random center
+            shape=[self.num_classes, self.feat_dim]).astype("float64")
 
-        if init_center:
+        if center_file_path is not None:
             assert os.path.exists(
                 center_file_path
-            ), f"center path({center_file_path}) must exist when init_center is set as True."
+            ), f"center path({center_file_path}) must exist when it is not None."
             with open(center_file_path, 'rb') as f:
                 char_dict = pickle.load(f)
                 for key in char_dict.keys():
@@ -60,22 +57,23 @@ class CenterLoss(nn.Layer):
 
         batch_size = feats_reshape.shape[0]
 
-        #calc feat * feat   
-        dist1 = paddle.sum(paddle.square(feats_reshape), axis=1, keepdim=True)
-        dist1 = paddle.expand(dist1, [batch_size, self.num_classes])
+        #calc l2 distance between feats and centers  
+        square_feat = paddle.sum(paddle.square(feats_reshape),
+                                 axis=1,
+                                 keepdim=True)
+        square_feat = paddle.expand(square_feat, [batch_size, self.num_classes])
 
-        #dist2 of centers
-        dist2 = paddle.sum(paddle.square(self.centers), axis=1,
-                           keepdim=True)  #num_classes
-        dist2 = paddle.expand(dist2,
-                              [self.num_classes, batch_size]).astype("float64")
-        dist2 = paddle.transpose(dist2, [1, 0])
+        square_center = paddle.sum(paddle.square(self.centers),
+                                   axis=1,
+                                   keepdim=True)
+        square_center = paddle.expand(
+            square_center, [self.num_classes, batch_size]).astype("float64")
+        square_center = paddle.transpose(square_center, [1, 0])
 
-        #first x * x + y * y
-        distmat = paddle.add(dist1, dist2)
-        tmp = paddle.matmul(feats_reshape,
-                            paddle.transpose(self.centers, [1, 0]))
-        distmat = distmat - 2.0 * tmp
+        distmat = paddle.add(square_feat, square_center)
+        feat_dot_center = paddle.matmul(feats_reshape,
+                                        paddle.transpose(self.centers, [1, 0]))
+        distmat = distmat - 2.0 * feat_dot_center
 
         #generate the mask
         classes = paddle.arange(self.num_classes).astype("int64")
@@ -83,7 +81,8 @@ class CenterLoss(nn.Layer):
             paddle.unsqueeze(label, 1), (batch_size, self.num_classes))
         mask = paddle.equal(
             paddle.expand(classes, [batch_size, self.num_classes]),
-            label).astype("float64")  #get mask
+            label).astype("float64")
         dist = paddle.multiply(distmat, mask)
+
         loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
         return {'loss_center': loss}

ppocr/losses/det_basic_loss.py

@@ -11,7 +11,10 @@
 # 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.
-
+"""
+This code is refer from:
+https://github.com/WenmuZhou/DBNet.pytorch/blob/master/models/losses/basic_loss.py
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -147,4 +150,4 @@ class BCELoss(nn.Layer):
 
     def forward(self, input, label, mask=None, weight=None, name=None):
         loss = F.binary_cross_entropy(input, label, reduction=self.reduction)
-        return loss
+        return loss
\ No newline at end of file

ppocr/losses/det_db_loss.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/WenmuZhou/DBNet.pytorch/blob/master/models/losses/DB_loss.py
+"""
 
 from __future__ import absolute_import
 from __future__ import division

ppocr/losses/det_pse_loss.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/whai362/PSENet/blob/python3/models/head/psenet_head.py
+"""
 
 import paddle
 from paddle import nn

ppocr/losses/rec_ctc_loss.py

@@ -38,7 +38,7 @@ class CTCLoss(nn.Layer):
         if self.use_focal_loss:
             weight = paddle.exp(-loss)
             weight = paddle.subtract(paddle.to_tensor([1.0]), weight)
-            weight = paddle.square(weight) * self.focal_loss_alpha
+            weight = paddle.square(weight)
             loss = paddle.multiply(loss, weight)
-        loss = loss.mean()  # sum
+        loss = loss.mean()
         return {'loss': loss}

ppocr/losses/rec_enhanced_ctc_loss.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+from paddle import nn
+from .ace_loss import ACELoss
+from .center_loss import CenterLoss
+from .rec_ctc_loss import CTCLoss
+
+
+class EnhancedCTCLoss(nn.Layer):
+    def __init__(self,
+                 use_focal_loss=False,
+                 use_ace_loss=False,
+                 ace_loss_weight=0.1,
+                 use_center_loss=False,
+                 center_loss_weight=0.05,
+                 num_classes=6625,
+                 feat_dim=96,
+                 init_center=False,
+                 center_file_path=None,
+                 **kwargs):
+        super(EnhancedCTCLoss, self).__init__()
+        self.ctc_loss_func = CTCLoss(use_focal_loss=use_focal_loss)
+
+        self.use_ace_loss = False
+        if use_ace_loss:
+            self.use_ace_loss = use_ace_loss
+            self.ace_loss_func = ACELoss()
+            self.ace_loss_weight = ace_loss_weight
+
+        self.use_center_loss = False
+        if use_center_loss:
+            self.use_center_loss = use_center_loss
+            self.center_loss_func = CenterLoss(
+                num_classes=num_classes,
+                feat_dim=feat_dim,
+                init_center=init_center,
+                center_file_path=center_file_path)
+            self.center_loss_weight = center_loss_weight
+
+    def __call__(self, predicts, batch):
+        loss = self.ctc_loss_func(predicts, batch)["loss"]
+
+        if self.use_center_loss:
+            center_loss = self.center_loss_func(
+                predicts, batch)["loss_center"] * self.center_loss_weight
+            loss = loss + center_loss
+
+        if self.use_ace_loss:
+            ace_loss = self.ace_loss_func(
+                predicts, batch)["loss_ace"] * self.ace_loss_weight
+            loss = loss + ace_loss
+
+        return {'enhanced_ctc_loss': loss}

ppocr/losses/rec_nrtr_loss.py

@@ -22,7 +22,7 @@ class NRTRLoss(nn.Layer):
             log_prb = F.log_softmax(pred, axis=1)
             non_pad_mask = paddle.not_equal(
                 tgt, paddle.zeros(
-                    tgt.shape, dtype='int64'))
+                    tgt.shape, dtype=tgt.dtype))
             loss = -(one_hot * log_prb).sum(axis=1)
             loss = loss.masked_select(non_pad_mask).mean()
         else:

ppocr/losses/rec_sar_loss.py

@@ -9,11 +9,14 @@ 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)
+        self.loss_func = paddle.nn.loss.CrossEntropyLoss(
+            reduction="mean", ignore_index=92)
 
     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
+        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, \

ppocr/modeling/architectures/distillation_model.py

@@ -21,7 +21,7 @@ from ppocr.modeling.backbones import build_backbone
 from ppocr.modeling.necks import build_neck
 from ppocr.modeling.heads import build_head
 from .base_model import BaseModel
-from ppocr.utils.save_load import init_model, load_pretrained_params
+from ppocr.utils.save_load import load_pretrained_params
 
 __all__ = ['DistillationModel']
 

ppocr/modeling/backbones/rec_mv1_enhance.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,30 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# This code is refer from: https://github.com/PaddlePaddle/PaddleClas/blob/develop/ppcls/arch/backbone/legendary_models/pp_lcnet.py
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import numpy as np
-import paddle
-from paddle import ParamAttr
-import paddle.nn as nn
-import paddle.nn.functional as F
-from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
-from paddle.nn.initializer import KaimingNormal
 import math
 import numpy as np
 import paddle
-from paddle import ParamAttr, reshape, transpose, concat, split
+from paddle import ParamAttr, reshape, transpose
 import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import KaimingNormal
-import math
-from paddle.nn.functional import hardswish, hardsigmoid
 from paddle.regularizer import L2Decay
+from paddle.nn.functional import hardswish, hardsigmoid
 
 
 class ConvBNLayer(nn.Layer):

ppocr/modeling/backbones/rec_resnet_31.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from: 
+https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/layers/conv_layer.py
+https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/backbones/resnet31_ocr.py
+"""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -18,12 +37,12 @@ def conv3x3(in_channel, out_channel, stride=1):
         kernel_size=3,
         stride=stride,
         padding=1,
-        bias_attr=False
-    )
+        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)
@@ -34,9 +53,13 @@ class BasicBlock(nn.Layer):
         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),
-            )
+                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
@@ -57,7 +80,7 @@ class BasicBlock(nn.Layer):
         out += residual
         out = self.relu(out)
 
-        return out        
+        return out
 
 
 class ResNet31(nn.Layer):
@@ -69,12 +92,13 @@ class ResNet31(nn.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):
+
+    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)
@@ -83,46 +107,56 @@ class ResNet31(nn.Layer):
         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.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.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.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.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.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.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.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.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.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.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):
@@ -130,19 +164,19 @@ class ResNet31(nn.Layer):
             if input_channels != output_channels:
                 downsample = nn.Sequential(
                     nn.Conv2D(
-                        input_channels, 
-                        output_channels, 
-                        kernel_size=1, 
-                        stride=1, 
+                        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))
+                    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)
@@ -166,11 +200,11 @@ class ResNet31(nn.Layer):
             x = block_layer(x)
             x = conv_layer(x)
             x = bn_layer(x)
-            x= relu_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/backbones/rec_resnet_aster.py

@@ -11,7 +11,10 @@
 # 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.
-
+"""
+This code is refer from:
+https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/resnet_aster.py
+"""
 import paddle
 import paddle.nn as nn
 

ppocr/modeling/heads/det_pse_head.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,22 +11,24 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/whai362/PSENet/blob/python3/models/head/psenet_head.py
+"""
+
 from paddle import nn
 
 
 class PSEHead(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 hidden_dim=256,
-                 out_channels=7,
-                 **kwargs):
+    def __init__(self, in_channels, hidden_dim=256, out_channels=7, **kwargs):
         super(PSEHead, self).__init__()
-        self.conv1 = nn.Conv2D(in_channels, hidden_dim, kernel_size=3, stride=1, padding=1)
+        self.conv1 = nn.Conv2D(
+            in_channels, hidden_dim, kernel_size=3, stride=1, padding=1)
         self.bn1 = nn.BatchNorm2D(hidden_dim)
         self.relu1 = nn.ReLU()
 
-        self.conv2 = nn.Conv2D(hidden_dim, out_channels, kernel_size=1, stride=1, padding=0)
-
+        self.conv2 = nn.Conv2D(
+            hidden_dim, out_channels, kernel_size=1, stride=1, padding=0)
 
     def forward(self, x, **kwargs):
         out = self.conv1(x)

ppocr/modeling/heads/rec_aster_head.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/attention_recognition_head.py
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

ppocr/modeling/heads/rec_att_head.py

@@ -53,7 +53,6 @@ class AttentionHead(nn.Layer):
                 output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
             output = paddle.concat(output_hiddens, axis=1)
             probs = self.generator(output)
-
         else:
             targets = paddle.zeros(shape=[batch_size], dtype="int32")
             probs = None
@@ -75,7 +74,8 @@ class AttentionHead(nn.Layer):
                             probs_step, axis=1)], axis=1)
                 next_input = probs_step.argmax(axis=1)
                 targets = next_input
-
+        if not self.training:
+            probs = paddle.nn.functional.softmax(probs, axis=2)
         return probs
 
 

ppocr/modeling/heads/rec_sar_head.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from: 
+https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/encoders/sar_encoder.py
+https://github.com/open-mmlab/mmocr/blob/main/mmocr/models/textrecog/decoders/sar_decoder.py
+"""
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -275,7 +294,6 @@ class ParallelSARDecoder(BaseDecoder):
         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)

ppocr/modeling/heads/table_att_head.py

@@ -23,32 +23,40 @@ import numpy as np
 
 
 class TableAttentionHead(nn.Layer):
-    def __init__(self, in_channels, hidden_size, loc_type, in_max_len=488, **kwargs):
+    def __init__(self,
+                 in_channels,
+                 hidden_size,
+                 loc_type,
+                 in_max_len=488,
+                 max_text_length=100,
+                 max_elem_length=800,
+                 max_cell_num=500,
+                 **kwargs):
         super(TableAttentionHead, self).__init__()
         self.input_size = in_channels[-1]
         self.hidden_size = hidden_size
         self.elem_num = 30
-        self.max_text_length = 100
-        self.max_elem_length = 500
-        self.max_cell_num = 500
+        self.max_text_length = max_text_length
+        self.max_elem_length = max_elem_length
+        self.max_cell_num = max_cell_num
 
         self.structure_attention_cell = AttentionGRUCell(
             self.input_size, hidden_size, self.elem_num, use_gru=False)
         self.structure_generator = nn.Linear(hidden_size, self.elem_num)
         self.loc_type = loc_type
         self.in_max_len = in_max_len
-        
+
         if self.loc_type == 1:
             self.loc_generator = nn.Linear(hidden_size, 4)
         else:
             if self.in_max_len == 640:
-                self.loc_fea_trans = nn.Linear(400, self.max_elem_length+1)
+                self.loc_fea_trans = nn.Linear(400, self.max_elem_length + 1)
             elif self.in_max_len == 800:
-                self.loc_fea_trans = nn.Linear(625, self.max_elem_length+1)
+                self.loc_fea_trans = nn.Linear(625, self.max_elem_length + 1)
             else:
-                self.loc_fea_trans = nn.Linear(256, self.max_elem_length+1)
+                self.loc_fea_trans = nn.Linear(256, self.max_elem_length + 1)
             self.loc_generator = nn.Linear(self.input_size + hidden_size, 4)
-            
+
     def _char_to_onehot(self, input_char, onehot_dim):
         input_ont_hot = F.one_hot(input_char, onehot_dim)
         return input_ont_hot
@@ -60,16 +68,16 @@ class TableAttentionHead(nn.Layer):
         if len(fea.shape) == 3:
             pass
         else:
-            last_shape = int(np.prod(fea.shape[2:])) # gry added
+            last_shape = int(np.prod(fea.shape[2:]))  # gry added
             fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], last_shape])
             fea = fea.transpose([0, 2, 1])  # (NTC)(batch, width, channels)
         batch_size = fea.shape[0]
-        
+
         hidden = paddle.zeros((batch_size, self.hidden_size))
         output_hiddens = []
         if self.training and targets is not None:
             structure = targets[0]
-            for i in range(self.max_elem_length+1):
+            for i in range(self.max_elem_length + 1):
                 elem_onehots = self._char_to_onehot(
                     structure[:, i], onehot_dim=self.elem_num)
                 (outputs, hidden), alpha = self.structure_attention_cell(
@@ -96,7 +104,7 @@ class TableAttentionHead(nn.Layer):
             alpha = None
             max_elem_length = paddle.to_tensor(self.max_elem_length)
             i = 0
-            while i < max_elem_length+1:
+            while i < max_elem_length + 1:
                 elem_onehots = self._char_to_onehot(
                     temp_elem, onehot_dim=self.elem_num)
                 (outputs, hidden), alpha = self.structure_attention_cell(
@@ -105,7 +113,7 @@ class TableAttentionHead(nn.Layer):
                 structure_probs_step = self.structure_generator(outputs)
                 temp_elem = structure_probs_step.argmax(axis=1, dtype="int32")
                 i += 1
-                
+
             output = paddle.concat(output_hiddens, axis=1)
             structure_probs = self.structure_generator(output)
             structure_probs = F.softmax(structure_probs)
@@ -119,9 +127,9 @@ class TableAttentionHead(nn.Layer):
                 loc_concat = paddle.concat([output, loc_fea], axis=2)
                 loc_preds = self.loc_generator(loc_concat)
                 loc_preds = F.sigmoid(loc_preds)
-        return {'structure_probs':structure_probs, 'loc_preds':loc_preds}
+        return {'structure_probs': structure_probs, 'loc_preds': loc_preds}
+
 
-    
 class AttentionGRUCell(nn.Layer):
     def __init__(self, input_size, hidden_size, num_embeddings, use_gru=False):
         super(AttentionGRUCell, self).__init__()

ppocr/modeling/necks/fpn.py

@@ -11,64 +11,102 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/whai362/PSENet/blob/python3/models/neck/fpn.py
+"""
 
 import paddle.nn as nn
 import paddle
 import math
 import paddle.nn.functional as F
 
+
 class Conv_BN_ReLU(nn.Layer):
-    def __init__(self, in_planes, out_planes, kernel_size=1, stride=1, padding=0):
+    def __init__(self,
+                 in_planes,
+                 out_planes,
+                 kernel_size=1,
+                 stride=1,
+                 padding=0):
         super(Conv_BN_ReLU, self).__init__()
-        self.conv = nn.Conv2D(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
-                              bias_attr=False)
+        self.conv = nn.Conv2D(
+            in_planes,
+            out_planes,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias_attr=False)
         self.bn = nn.BatchNorm2D(out_planes, momentum=0.1)
         self.relu = nn.ReLU()
 
         for m in self.sublayers():
             if isinstance(m, nn.Conv2D):
                 n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
-                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Normal(0, math.sqrt(2. / n)))
+                m.weight = paddle.create_parameter(
+                    shape=m.weight.shape,
+                    dtype='float32',
+                    default_initializer=paddle.nn.initializer.Normal(
+                        0, math.sqrt(2. / n)))
             elif isinstance(m, nn.BatchNorm2D):
-                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(1.0))
-                m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32', default_initializer=paddle.nn.initializer.Constant(0.0))
+                m.weight = paddle.create_parameter(
+                    shape=m.weight.shape,
+                    dtype='float32',
+                    default_initializer=paddle.nn.initializer.Constant(1.0))
+                m.bias = paddle.create_parameter(
+                    shape=m.bias.shape,
+                    dtype='float32',
+                    default_initializer=paddle.nn.initializer.Constant(0.0))
 
     def forward(self, x):
         return self.relu(self.bn(self.conv(x)))
 
+
 class FPN(nn.Layer):
     def __init__(self, in_channels, out_channels):
         super(FPN, self).__init__()
 
         # Top layer
-        self.toplayer_ = Conv_BN_ReLU(in_channels[3], out_channels, kernel_size=1, stride=1, padding=0)
+        self.toplayer_ = Conv_BN_ReLU(
+            in_channels[3], out_channels, kernel_size=1, stride=1, padding=0)
         # Lateral layers
-        self.latlayer1_ = Conv_BN_ReLU(in_channels[2], out_channels, kernel_size=1, stride=1, padding=0)
+        self.latlayer1_ = Conv_BN_ReLU(
+            in_channels[2], out_channels, kernel_size=1, stride=1, padding=0)
 
-        self.latlayer2_ = Conv_BN_ReLU(in_channels[1], out_channels, kernel_size=1, stride=1, padding=0)
+        self.latlayer2_ = Conv_BN_ReLU(
+            in_channels[1], out_channels, kernel_size=1, stride=1, padding=0)
 
-        self.latlayer3_ = Conv_BN_ReLU(in_channels[0], out_channels, kernel_size=1, stride=1, padding=0)
+        self.latlayer3_ = Conv_BN_ReLU(
+            in_channels[0], out_channels, kernel_size=1, stride=1, padding=0)
 
         # Smooth layers
-        self.smooth1_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-
-        self.smooth2_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.smooth1_ = Conv_BN_ReLU(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1)
 
-        self.smooth3_ = Conv_BN_ReLU(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.smooth2_ = Conv_BN_ReLU(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1)
 
+        self.smooth3_ = Conv_BN_ReLU(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1)
 
         self.out_channels = out_channels * 4
         for m in self.sublayers():
             if isinstance(m, nn.Conv2D):
                 n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
-                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32',
-                                                   default_initializer=paddle.nn.initializer.Normal(0,
-                                                                                                    math.sqrt(2. / n)))
+                m.weight = paddle.create_parameter(
+                    shape=m.weight.shape,
+                    dtype='float32',
+                    default_initializer=paddle.nn.initializer.Normal(
+                        0, math.sqrt(2. / n)))
             elif isinstance(m, nn.BatchNorm2D):
-                m.weight = paddle.create_parameter(shape=m.weight.shape, dtype='float32',
-                                                   default_initializer=paddle.nn.initializer.Constant(1.0))
-                m.bias = paddle.create_parameter(shape=m.bias.shape, dtype='float32',
-                                                 default_initializer=paddle.nn.initializer.Constant(0.0))
+                m.weight = paddle.create_parameter(
+                    shape=m.weight.shape,
+                    dtype='float32',
+                    default_initializer=paddle.nn.initializer.Constant(1.0))
+                m.bias = paddle.create_parameter(
+                    shape=m.bias.shape,
+                    dtype='float32',
+                    default_initializer=paddle.nn.initializer.Constant(0.0))
 
     def _upsample(self, x, scale=1):
         return F.upsample(x, scale_factor=scale, mode='bilinear')
@@ -81,15 +119,15 @@ class FPN(nn.Layer):
         p5 = self.toplayer_(f5)
 
         f4 = self.latlayer1_(f4)
-        p4 = self._upsample_add(p5, f4,2)
+        p4 = self._upsample_add(p5, f4, 2)
         p4 = self.smooth1_(p4)
 
         f3 = self.latlayer2_(f3)
-        p3 = self._upsample_add(p4, f3,2)
+        p3 = self._upsample_add(p4, f3, 2)
         p3 = self.smooth2_(p3)
 
         f2 = self.latlayer3_(f2)
-        p2 = self._upsample_add(p3, f2,2)
+        p2 = self._upsample_add(p3, f2, 2)
         p2 = self.smooth3_(p2)
 
         p3 = self._upsample(p3, 2)
@@ -97,4 +135,4 @@ class FPN(nn.Layer):
         p5 = self._upsample(p5, 8)
 
         fuse = paddle.concat([p2, p3, p4, p5], axis=1)
-        return fuse
+        return fuse
\ No newline at end of file

ppocr/modeling/necks/rnn.py

@@ -51,7 +51,7 @@ class EncoderWithFC(nn.Layer):
         super(EncoderWithFC, self).__init__()
         self.out_channels = hidden_size
         weight_attr, bias_attr = get_para_bias_attr(
-            l2_decay=0.00001, k=in_channels, name='reduce_encoder_fea')
+            l2_decay=0.00001, k=in_channels)
         self.fc = nn.Linear(
             in_channels,
             hidden_size,

ppocr/modeling/transforms/stn.py

@@ -11,7 +11,10 @@
 # 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.
-
+"""
+This code is refer from:
+https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/stn_head.py
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

ppocr/modeling/transforms/tps.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/clovaai/deep-text-recognition-benchmark/blob/master/modules/transformation.py
+"""
 
 from __future__ import absolute_import
 from __future__ import division

ppocr/modeling/transforms/tps_spatial_transformer.py

@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/tps_spatial_transformer.py
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

ppocr/postprocess/__init__.py

@@ -18,7 +18,6 @@ from __future__ import print_function
 from __future__ import unicode_literals
 
 import copy
-import platform
 
 __all__ = ['build_post_process']
 
@@ -26,24 +25,24 @@ from .db_postprocess import DBPostProcess, DistillationDBPostProcess
 from .east_postprocess import EASTPostProcess
 from .sast_postprocess import SASTPostProcess
 from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode, DistillationCTCLabelDecode, \
-    TableLabelDecode, NRTRLabelDecode, SARLabelDecode , SEEDLabelDecode
+    TableLabelDecode, NRTRLabelDecode, SARLabelDecode, SEEDLabelDecode
 from .cls_postprocess import ClsPostProcess
 from .pg_postprocess import PGPostProcess
 
-if platform.system() != "Windows":
-    # pse is not support in Windows
-    from .pse_postprocess import PSEPostProcess
-
 
 def build_post_process(config, global_config=None):
     support_dict = [
-        'DBPostProcess', 'PSEPostProcess', 'EASTPostProcess', 'SASTPostProcess',
-        'CTCLabelDecode', 'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode',
-        'PGPostProcess', 'DistillationCTCLabelDecode', 'TableLabelDecode',
+        'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
+        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess',
+        'DistillationCTCLabelDecode', 'TableLabelDecode',
         'DistillationDBPostProcess', 'NRTRLabelDecode', 'SARLabelDecode',
         'SEEDLabelDecode'
     ]
 
+    if config['name'] == 'PSEPostProcess':
+        from .pse_postprocess import PSEPostProcess
+        support_dict.append('PSEPostProcess')
+
     config = copy.deepcopy(config)
     module_name = config.pop('name')
     if module_name == "None":

ppocr/postprocess/db_postprocess.py

@@ -11,7 +11,10 @@
 # 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.
-
+"""
+This code is refered from:
+https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -190,7 +193,8 @@ class DBPostProcess(object):
 
 
 class DistillationDBPostProcess(object):
-    def __init__(self, model_name=["student"],
+    def __init__(self,
+                 model_name=["student"],
                  key=None,
                  thresh=0.3,
                  box_thresh=0.6,
@@ -201,12 +205,13 @@ class DistillationDBPostProcess(object):
                  **kwargs):
         self.model_name = model_name
         self.key = key
-        self.post_process = DBPostProcess(thresh=thresh,
-                 box_thresh=box_thresh,
-                 max_candidates=max_candidates,
-                 unclip_ratio=unclip_ratio,
-                 use_dilation=use_dilation,
-                 score_mode=score_mode)
+        self.post_process = DBPostProcess(
+            thresh=thresh,
+            box_thresh=box_thresh,
+            max_candidates=max_candidates,
+            unclip_ratio=unclip_ratio,
+            use_dilation=use_dilation,
+            score_mode=score_mode)
 
     def __call__(self, predicts, shape_list):
         results = {}

ppocr/postprocess/east_postprocess.py

@@ -20,6 +20,7 @@ import numpy as np
 from .locality_aware_nms import nms_locality
 import cv2
 import paddle
+import lanms
 
 import os
 import sys
@@ -29,6 +30,7 @@ class EASTPostProcess(object):
     """
     The post process for EAST.
     """
+
     def __init__(self,
                  score_thresh=0.8,
                  cover_thresh=0.1,
@@ -38,11 +40,6 @@ class EASTPostProcess(object):
         self.score_thresh = score_thresh
         self.cover_thresh = cover_thresh
         self.nms_thresh = nms_thresh
-        
-        # c++ la-nms is faster, but only support python 3.5
-        self.is_python35 = False
-        if sys.version_info.major == 3 and sys.version_info.minor == 5:
-            self.is_python35 = True
 
     def restore_rectangle_quad(self, origin, geometry):
         """
@@ -79,11 +76,8 @@ class EASTPostProcess(object):
         boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)
         boxes[:, :8] = text_box_restored.reshape((-1, 8))
         boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
-        if self.is_python35:
-            import lanms
-            boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh)
-        else:
-            boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
+        boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh)
+        # boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
         if boxes.shape[0] == 0:
             return []
         # Here we filter some low score boxes by the average score map, 
@@ -139,4 +133,4 @@ class EASTPostProcess(object):
                         continue
                     boxes_norm.append(box)
             dt_boxes_list.append({'points': np.array(boxes_norm)})
-        return dt_boxes_list
+        return dt_boxes_list
\ No newline at end of file

ppocr/postprocess/locality_aware_nms.py

 """
 Locality aware nms.
+This code is refered from: https://github.com/songdejia/EAST/blob/master/locality_aware_nms.py
 """
 
 import numpy as np

ppocr/postprocess/pse_postprocess/pse/README.md

 ## 编译
-code from https://github.com/whai362/pan_pp.pytorch
+This code is refer from:
+https://github.com/whai362/PSENet/blob/python3/models/post_processing/pse
 ```python
 python3 setup.py build_ext --inplace
 ```

ppocr/postprocess/pse_postprocess/pse/__init__.py

@@ -17,7 +17,13 @@ import subprocess
 
 python_path = sys.executable
 
-if subprocess.call('cd ppocr/postprocess/pse_postprocess/pse;{} setup.py build_ext --inplace;cd -'.format(python_path), shell=True) != 0:
-    raise RuntimeError('Cannot compile pse: {}'.format(os.path.dirname(os.path.realpath(__file__))))
+ori_path = os.getcwd()
+os.chdir('ppocr/postprocess/pse_postprocess/pse')
+if subprocess.call(
+        '{} setup.py build_ext --inplace'.format(python_path), shell=True) != 0:
+    raise RuntimeError(
+        'Cannot compile pse: {}, if your system is windows, you need to install all the default components of `desktop development using C++` in visual studio 2019+'.
+        format(os.path.dirname(os.path.realpath(__file__))))
+os.chdir(ori_path)
 
-from .pse import pse
+from .pse import pse
\ No newline at end of file

ppocr/postprocess/pse_postprocess/pse_postprocess.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#    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.
+"""
+This code is refer from:
+https://github.com/whai362/PSENet/blob/python3/models/head/psenet_head.py
+"""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -47,7 +51,8 @@ class PSEPostProcess(object):
         pred = outs_dict['maps']
         if not isinstance(pred, paddle.Tensor):
             pred = paddle.to_tensor(pred)
-        pred = F.interpolate(pred, scale_factor=4 // self.scale, mode='bilinear')
+        pred = F.interpolate(
+            pred, scale_factor=4 // self.scale, mode='bilinear')
 
         score = F.sigmoid(pred[:, 0, :, :])
 
@@ -60,7 +65,9 @@ class PSEPostProcess(object):
 
         boxes_batch = []
         for batch_index in range(pred.shape[0]):
-            boxes, scores = self.boxes_from_bitmap(score[batch_index], kernels[batch_index], shape_list[batch_index])
+            boxes, scores = self.boxes_from_bitmap(score[batch_index],
+                                                   kernels[batch_index],
+                                                   shape_list[batch_index])
 
             boxes_batch.append({'points': boxes, 'scores': scores})
         return boxes_batch
@@ -98,15 +105,14 @@ class PSEPostProcess(object):
                 mask = np.zeros((box_height, box_width), np.uint8)
                 mask[points[:, 1], points[:, 0]] = 255
 
-                contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+                contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL,
+                                               cv2.CHAIN_APPROX_SIMPLE)
                 bbox = np.squeeze(contours[0], 1)
             else:
                 raise NotImplementedError
 
-            bbox[:, 0] = np.clip(
-                np.round(bbox[:, 0] / ratio_w), 0, src_w)
-            bbox[:, 1] = np.clip(
-                np.round(bbox[:, 1] / ratio_h), 0, src_h)
+            bbox[:, 0] = np.clip(np.round(bbox[:, 0] / ratio_w), 0, src_w)
+            bbox[:, 1] = np.clip(np.round(bbox[:, 1] / ratio_h), 0, src_h)
             boxes.append(bbox)
             scores.append(score_i)
         return boxes, scores

ppocr/postprocess/rec_postprocess.py

@@ -21,33 +21,15 @@ import re
 class BaseRecLabelDecode(object):
     """ Convert between text-label and text-index """
 
-    def __init__(self,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False):
-        support_character_type = [
-            'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
-            'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
-            'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
-            'ne', 'EN', 'latin', 'arabic', 'cyrillic', 'devanagari'
-        ]
-        assert character_type in support_character_type, "Only {} are supported now but get {}".format(
-            support_character_type, character_type)
-
+    def __init__(self, character_dict_path=None, use_space_char=False):
         self.beg_str = "sos"
         self.end_str = "eos"
 
-        if character_type == "en":
+        self.character_str = []
+        if character_dict_path is None:
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
-        elif character_type == "EN_symbol":
-            # same with ASTER setting (use 94 char).
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
-        elif character_type in support_character_type:
-            self.character_str = []
-            assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
-                character_type)
+        else:
             with open(character_dict_path, "rb") as fin:
                 lines = fin.readlines()
                 for line in lines:
@@ -57,9 +39,6 @@ class BaseRecLabelDecode(object):
                 self.character_str.append(" ")
             dict_character = list(self.character_str)
 
-        else:
-            raise NotImplementedError
-        self.character_type = character_type
         dict_character = self.add_special_char(dict_character)
         self.dict = {}
         for i, char in enumerate(dict_character):
@@ -102,13 +81,10 @@ class BaseRecLabelDecode(object):
 class CTCLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 
-    def __init__(self,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False,
+    def __init__(self, character_dict_path=None, use_space_char=False,
                  **kwargs):
         super(CTCLabelDecode, self).__init__(character_dict_path,
-                                             character_type, use_space_char)
+                                             use_space_char)
 
     def __call__(self, preds, label=None, *args, **kwargs):
         if isinstance(preds, tuple):
@@ -136,13 +112,12 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
 
     def __init__(self,
                  character_dict_path=None,
-                 character_type='ch',
                  use_space_char=False,
                  model_name=["student"],
                  key=None,
                  **kwargs):
-        super(DistillationCTCLabelDecode, self).__init__(
-            character_dict_path, character_type, use_space_char)
+        super(DistillationCTCLabelDecode, self).__init__(character_dict_path,
+                                                         use_space_char)
         if not isinstance(model_name, list):
             model_name = [model_name]
         self.model_name = model_name
@@ -162,13 +137,9 @@ class DistillationCTCLabelDecode(CTCLabelDecode):
 class NRTRLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 
-    def __init__(self,
-                 character_dict_path=None,
-                 character_type='EN_symbol',
-                 use_space_char=True,
-                 **kwargs):
+    def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
         super(NRTRLabelDecode, self).__init__(character_dict_path,
-                                              character_type, use_space_char)
+                                              use_space_char)
 
     def __call__(self, preds, label=None, *args, **kwargs):
 
@@ -230,13 +201,10 @@ class NRTRLabelDecode(BaseRecLabelDecode):
 class AttnLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 
-    def __init__(self,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False,
+    def __init__(self, character_dict_path=None, use_space_char=False,
                  **kwargs):
         super(AttnLabelDecode, self).__init__(character_dict_path,
-                                              character_type, use_space_char)
+                                              use_space_char)
 
     def add_special_char(self, dict_character):
         self.beg_str = "sos"
@@ -313,13 +281,10 @@ class AttnLabelDecode(BaseRecLabelDecode):
 class SEEDLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 
-    def __init__(self,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False,
+    def __init__(self, character_dict_path=None, use_space_char=False,
                  **kwargs):
         super(SEEDLabelDecode, self).__init__(character_dict_path,
-                                              character_type, use_space_char)
+                                              use_space_char)
 
     def add_special_char(self, dict_character):
         self.beg_str = "sos"
@@ -394,13 +359,10 @@ class SEEDLabelDecode(BaseRecLabelDecode):
 class SRNLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 
-    def __init__(self,
-                 character_dict_path=None,
-                 character_type='en',
-                 use_space_char=False,
+    def __init__(self, character_dict_path=None, use_space_char=False,
                  **kwargs):
         super(SRNLabelDecode, self).__init__(character_dict_path,
-                                             character_type, use_space_char)
+                                             use_space_char)
         self.max_text_length = kwargs.get('max_text_length', 25)
 
     def __call__(self, preds, label=None, *args, **kwargs):
@@ -616,13 +578,10 @@ class TableLabelDecode(object):
 class SARLabelDecode(BaseRecLabelDecode):
     """ Convert between text-label and text-index """
 
-    def __init__(self,
-                 character_dict_path=None,
-                 character_type='ch',
-                 use_space_char=False,
+    def __init__(self, character_dict_path=None, use_space_char=False,
                  **kwargs):
         super(SARLabelDecode, self).__init__(character_dict_path,
-                                             character_type, use_space_char)
+                                             use_space_char)
 
         self.rm_symbol = kwargs.get('rm_symbol', False)
 

ppocr/utils/EN_symbol_dict.txt

+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+:
+;
+<
+=
+>
+?
+@
+[
+\
+]
+^
+_
+`
+{
+|
+}
+~
\ No newline at end of file

ppocr/utils/dict90.txt

+0
+1
+2
+3
+4
+5
+6
+7
+8
+9
+a
+b
+c
+d
+e
+f
+g
+h
+i
+j
+k
+l
+m
+n
+o
+p
+q
+r
+s
+t
+u
+v
+w
+x
+y
+z
+A
+B
+C
+D
+E
+F
+G
+H
+I
+J
+K
+L
+M
+N
+O
+P
+Q
+R
+S
+T
+U
+V
+W
+X
+Y
+Z
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+:
+;
+<
+=
+>
+?
+@
+[
+\
+]
+_
+`
+~
\ No newline at end of file

ppocr/utils/iou.py

-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,18 +11,23 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/whai362/PSENet/blob/python3/models/loss/iou.py
+"""
 
 import paddle
 
 EPS = 1e-6
 
+
 def iou_single(a, b, mask, n_class):
     valid = mask == 1
     a = a.masked_select(valid)
     b = b.masked_select(valid)
     miou = []
     for i in range(n_class):
-        if a.shape == [0] and a.shape==b.shape:
+        if a.shape == [0] and a.shape == b.shape:
             inter = paddle.to_tensor(0.0)
             union = paddle.to_tensor(0.0)
         else:
@@ -32,6 +37,7 @@ def iou_single(a, b, mask, n_class):
     miou = sum(miou) / len(miou)
     return miou
 
+
 def iou(a, b, mask, n_class=2, reduce=True):
     batch_size = a.shape[0]
 
@@ -39,10 +45,10 @@ def iou(a, b, mask, n_class=2, reduce=True):
     b = b.reshape([batch_size, -1])
     mask = mask.reshape([batch_size, -1])
 
-    iou = paddle.zeros((batch_size,), dtype='float32')
+    iou = paddle.zeros((batch_size, ), dtype='float32')
     for i in range(batch_size):
         iou[i] = iou_single(a[i], b[i], mask[i], n_class)
 
     if reduce:
         iou = paddle.mean(iou)
-    return iou
+    return iou
\ No newline at end of file

ppocr/utils/logging.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,6 +11,10 @@
 # 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.
+"""
+This code is refer from:
+https://github.com/WenmuZhou/PytorchOCR/blob/master/torchocr/utils/logging.py
+"""
 
 import os
 import sys

ppocr/utils/network.py

@@ -24,15 +24,17 @@ from ppocr.utils.logging import get_logger
 def download_with_progressbar(url, save_path):
     logger = get_logger()
     response = requests.get(url, stream=True)
-    total_size_in_bytes = int(response.headers.get('content-length', 0))
-    block_size = 1024  # 1 Kibibyte
-    progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
-    with open(save_path, 'wb') as file:
-        for data in response.iter_content(block_size):
-            progress_bar.update(len(data))
-            file.write(data)
-    progress_bar.close()
-    if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
+    if response.status_code == 200:
+        total_size_in_bytes = int(response.headers.get('content-length', 1))
+        block_size = 1024  # 1 Kibibyte
+        progress_bar = tqdm(
+            total=total_size_in_bytes, unit='iB', unit_scale=True)
+        with open(save_path, 'wb') as file:
+            for data in response.iter_content(block_size):
+                progress_bar.update(len(data))
+                file.write(data)
+        progress_bar.close()
+    else:
         logger.error("Something went wrong while downloading models")
         sys.exit(0)
 
@@ -45,7 +47,7 @@ def maybe_download(model_storage_directory, url):
     if not os.path.exists(
             os.path.join(model_storage_directory, 'inference.pdiparams')
     ) or not os.path.exists(
-        os.path.join(model_storage_directory, 'inference.pdmodel')):
+            os.path.join(model_storage_directory, 'inference.pdmodel')):
         assert url.endswith('.tar'), 'Only supports tar compressed package'
         tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
         print('download {} to {}'.format(url, tmp_path))

ppocr/utils/save_load.py

@@ -25,7 +25,7 @@ import paddle
 
 from ppocr.utils.logging import get_logger
 
-__all__ = ['init_model', 'save_model', 'load_dygraph_params']
+__all__ = ['load_model']
 
 
 def _mkdir_if_not_exist(path, logger):
@@ -44,7 +44,7 @@ def _mkdir_if_not_exist(path, logger):
                 raise OSError('Failed to mkdir {}'.format(path))
 
 
-def init_model(config, model, optimizer=None, lr_scheduler=None):
+def load_model(config, model, optimizer=None):
     """
     load model from checkpoint or pretrained_model
     """
@@ -54,15 +54,31 @@ def init_model(config, model, optimizer=None, lr_scheduler=None):
     pretrained_model = global_config.get('pretrained_model')
     best_model_dict = {}
     if checkpoints:
+        if checkpoints.endswith('.pdparams'):
+            checkpoints = checkpoints.replace('.pdparams', '')
         assert os.path.exists(checkpoints + ".pdparams"), \
-            "Given dir {}.pdparams not exist.".format(checkpoints)
-        assert os.path.exists(checkpoints + ".pdopt"), \
-            "Given dir {}.pdopt not exist.".format(checkpoints)
-        para_dict = paddle.load(checkpoints + '.pdparams')
-        opti_dict = paddle.load(checkpoints + '.pdopt')
-        model.set_state_dict(para_dict)
+            "The {}.pdparams does not exists!".format(checkpoints)
+
+        # load params from trained model
+        params = paddle.load(checkpoints + '.pdparams')
+        state_dict = model.state_dict()
+        new_state_dict = {}
+        for key, value in state_dict.items():
+            if key not in params:
+                logger.warning("{} not in loaded params {} !".format(
+                    key, params.keys()))
+            pre_value = params[key]
+            if list(value.shape) == list(pre_value.shape):
+                new_state_dict[key] = pre_value
+            else:
+                logger.warning(
+                    "The shape of model params {} {} not matched with loaded params shape {} !".
+                    format(key, value.shape, pre_value.shape))
+        model.set_state_dict(new_state_dict)
+
+        optim_dict = paddle.load(checkpoints + '.pdopt')
         if optimizer is not None:
-            optimizer.set_state_dict(opti_dict)
+            optimizer.set_state_dict(optim_dict)
 
         if os.path.exists(checkpoints + '.states'):
             with open(checkpoints + '.states', 'rb') as f:
@@ -73,70 +89,31 @@ def init_model(config, model, optimizer=None, lr_scheduler=None):
                 best_model_dict['start_epoch'] = states_dict['epoch'] + 1
         logger.info("resume from {}".format(checkpoints))
     elif pretrained_model:
-        if not isinstance(pretrained_model, list):
-            pretrained_model = [pretrained_model]
-        for pretrained in pretrained_model:
-            if not (os.path.isdir(pretrained) or
-                    os.path.exists(pretrained + '.pdparams')):
-                raise ValueError("Model pretrain path {} does not "
-                                 "exists.".format(pretrained))
-            param_state_dict = paddle.load(pretrained + '.pdparams')
-            model.set_state_dict(param_state_dict)
-            logger.info("load pretrained model from {}".format(
-                pretrained_model))
+        load_pretrained_params(model, pretrained_model)
     else:
         logger.info('train from scratch')
     return best_model_dict
 
 
-def load_dygraph_params(config, model, logger, optimizer):
-    ckp = config['Global']['checkpoints']
-    if ckp and os.path.exists(ckp + ".pdparams"):
-        pre_best_model_dict = init_model(config, model, optimizer)
-        return pre_best_model_dict
-    else:
-        pm = config['Global']['pretrained_model']
-        if pm is None:
-            return {}
-        if not os.path.exists(pm) and not os.path.exists(pm + ".pdparams"):
-            logger.info(f"The pretrained_model {pm} does not exists!")
-            return {}
-        pm = pm if pm.endswith('.pdparams') else pm + '.pdparams'
-        params = paddle.load(pm)
-        state_dict = model.state_dict()
-        new_state_dict = {}
-        for k1, k2 in zip(state_dict.keys(), params.keys()):
-            if list(state_dict[k1].shape) == list(params[k2].shape):
-                new_state_dict[k1] = params[k2]
-            else:
-                logger.info(
-                    f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
-                )
-        model.set_state_dict(new_state_dict)
-        logger.info(f"loaded pretrained_model successful from {pm}")
-        return {}
-
-
 def load_pretrained_params(model, path):
-    if path is None:
-        return False
-    if not os.path.exists(path) and not os.path.exists(path + ".pdparams"):
-        print(f"The pretrained_model {path} does not exists!")
-        return False
-
-    path = path if path.endswith('.pdparams') else path + '.pdparams'
-    params = paddle.load(path)
+    logger = get_logger()
+    if path.endswith('.pdparams'):
+        path = path.replace('.pdparams', '')
+    assert os.path.exists(path + ".pdparams"), \
+        "The {}.pdparams does not exists!".format(path)
+
+    params = paddle.load(path + '.pdparams')
     state_dict = model.state_dict()
     new_state_dict = {}
     for k1, k2 in zip(state_dict.keys(), params.keys()):
         if list(state_dict[k1].shape) == list(params[k2].shape):
             new_state_dict[k1] = params[k2]
         else:
-            print(
-                f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
-            )
+            logger.warning(
+                "The shape of model params {} {} not matched with loaded params {} {} !".
+                format(k1, state_dict[k1].shape, k2, params[k2].shape))
     model.set_state_dict(new_state_dict)
-    print(f"load pretrain successful from {path}")
+    logger.info("load pretrain successful from {}".format(path))
     return model
 
 

ppstructure/layout/train_layoutparser_model.md

@@ -4,9 +4,9 @@
 
 ​  [1.1 Requirements](#Requirements)
 
-​  [1.2 Install PaddleDetection](#Install PaddleDetection)
+​  [1.2 Install PaddleDetection](#Install_PaddleDetection)
 
-[2.  Data preparation](#Data preparation)
+[2.  Data preparation](#Data_reparation)
 
 [3. Configuration](#Configuration)
 
@@ -16,7 +16,7 @@
 
 [6. Deployment](#Deployment)
 
-​  [6.1 Export model](#Export model)
+​  [6.1 Export model](#Export_model)
 
 ​  [6.2 Inference](#Inference)  
 
@@ -35,7 +35,7 @@
 - CUDA >= 10.1
 - cuDNN >= 7.6
 
-<a name="Install PaddleDetection"></a>
+<a name="Install_PaddleDetection"></a>
 
 ### 1.2 Install PaddleDetection
 
@@ -51,7 +51,7 @@ pip install -r requirements.txt
 
 For more installation tutorials, please refer to： [Install doc](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/INSTALL_cn.md)
 
-<a name="Data preparation"></a>
+<a name="Data_preparation"></a>
 
 ## 2. Data preparation
 
@@ -165,7 +165,7 @@ python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer
 
 Use your trained model in Layout Parser
 
-<a name="Export model"></a>
+<a name="Export_model"></a>
 
 ### 6.1 Export model
 

requirements.txt

 shapely
-scikit-image==0.17.2
+scikit-image
 imgaug==0.4.0
 pyclipper
 lmdb
@@ -12,4 +12,4 @@ cython
 lxml
 premailer
 openpyxl
-fasttext
+fasttext==0.9.1

test_tipc/common_func.sh

+#!/bin/bash
+
+function func_parser_key(){
+    strs=$1
+    IFS=":"
+    array=(${strs})
+    tmp=${array[0]}
+    echo ${tmp}
+}
+
+function func_parser_value(){
+    strs=$1
+    IFS=":"
+    array=(${strs})
+    tmp=${array[1]}
+    echo ${tmp}
+}
+
+function func_set_params(){
+    key=$1
+    value=$2
+    if [ ${key}x = "null"x ];then
+        echo " "
+    elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
+        echo " "
+    else 
+        echo "${key}=${value}"
+    fi
+}
+
+function func_parser_params(){
+    strs=$1
+    MODE=$2
+    IFS=":"
+    array=(${strs})
+    key=${array[0]}
+    tmp=${array[1]}
+    IFS="|"
+    res=""
+    for _params in ${tmp[*]}; do
+        IFS="="
+        array=(${_params})
+        mode=${array[0]}
+        value=${array[1]}
+        if [[ ${mode} = ${MODE} ]]; then
+            IFS="|"
+            #echo $(func_set_params "${mode}" "${value}")
+            echo $value
+            break
+        fi
+        IFS="|"
+    done
+    echo ${res}
+}
+
+function status_check(){
+    last_status=$1   # the exit code
+    run_command=$2
+    run_log=$3
+    if [ $last_status -eq 0 ]; then
+        echo -e "\033[33m Run successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+    else
+        echo -e "\033[33m Run failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+    fi
+}
+

tests/compare_results.py → test_tipc/compare_results.py

@@ -32,6 +32,7 @@ def run_shell_command(cmd):
     else:
         return None
 
+
 def parser_results_from_log_by_name(log_path, names_list):
     if not os.path.exists(log_path):
         raise ValueError("The log file {} does not exists!".format(log_path))
@@ -52,6 +53,7 @@ def parser_results_from_log_by_name(log_path, names_list):
         parser_results[name] = result
     return parser_results
 
+
 def load_gt_from_file(gt_file):
     if not os.path.exists(gt_file):
         raise ValueError("The log file {} does not exists!".format(gt_file))

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

+===========================ch_ppocr_mobile_v2.0===========================
+model_name:ch_PP-OCRv2
+python:python3.7
+infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_export:null
+infer_quant:True
+inference:tools/infer/predict_system.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--rec_model_dir:./inference/ch_PP-OCRv2_rec_infer/
+--benchmark:True
+null:null
+null:null

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt

+===========================lite_params===========================
+inference:./ocr_db_crnn system
+runtime_device:ARM_CPU
+det_infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
+rec_infer_model:ch_PP-OCRv2_rec_infer|ch_PP-OCRv2_rec_slim_quant_infer
+cls_infer_model:ch_ppocr_mobile_v2.0_cls_infer|ch_ppocr_mobile_v2.0_cls_slim_infer
+--cpu_threads:1|4
+--det_batch_size:1
+--rec_batch_size:1
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+--rec_dict_dir:./ppocr_keys_v1.txt
+--benchmark:True

test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt

+===========================lite_params===========================
+inference:./ocr_db_crnn system
+runtime_device:ARM_GPU_OPENCL
+det_infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
+rec_infer_model:ch_PP-OCRv2_rec_infer|ch_PP-OCRv2_rec_slim_quant_infer
+cls_infer_model:ch_ppocr_mobile_v2.0_cls_infer|ch_ppocr_mobile_v2.0_cls_slim_infer
+--cpu_threads:1|4
+--det_batch_size:1
+--rec_batch_size:1
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+--rec_dict_dir:./ppocr_keys_v1.txt
+--benchmark:True

test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt

+===========================lite_params===========================
+inference:./ocr_db_crnn det
+runtime_device:ARM_CPU
+det_infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
+null:null
+null:null
+--cpu_threads:1|4
+--det_batch_size:1
+null:null
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+null:null
+--benchmark:True
\ No newline at end of file

test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt

+===========================lite_params===========================
+inference:./ocr_db_crnn det
+runtime_device:ARM_GPU_OPENCL
+det_infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
+null:null
+null:null
+--cpu_threads:1|4
+--det_batch_size:1
+null:null
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+null:null
+--benchmark:True

test_tipc/configs/ch_PP-OCRv2_det/train_infer_python.txt

+===========================train_params===========================
+model_name:ch_PPOCRv2_det
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+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/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o 
+pact_train:deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
+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:tools/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o 
+quant_export:deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o 
+fpgm_export: 
+distill_export:null
+export1:null
+export2:null
+inference_dir:Student
+infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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/
+null:null
+--benchmark:True
+null:null

test_tipc/configs/ch_PP-OCRv2_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

+===========================kl_quant_params===========================
+model_name:PPOCRv2_ocr_det_kl
+python:python3.7
+infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
+infer_quant:True
+inference:tools/infer/predict_det.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+null:null

test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt

+===========================train_params===========================
+model_name:PPOCRv2_ocr_det
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:pact_train
+norm_train:null
+pact_train:deploy/slim/quantization/quant.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o
+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:deploy/slim/quantization/export_model.py -c configs/det/ch_PP-OCRv2/ch_PP-OCRv2_det_cml.yml -o 
+fpgm_export: 
+distill_export:null
+export1:null
+export2:null
+inference_dir:Student
+infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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/
+null:null
+--benchmark:True
+null:null

test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml

+Global:
+  debug: false
+  use_gpu: true
+  epoch_num: 800
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_pp-OCRv2_distillation
+  save_epoch_step: 3
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: true
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: false
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: false
+  use_space_char: true
+  distributed: true
+  save_res_path: ./output/rec/predicts_pp-OCRv2_distillation.txt
+
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Piecewise
+    decay_epochs : [700, 800]
+    values : [0.001, 0.0001]
+    warmup_epoch: 5
+  regularizer:
+    name: L2
+    factor: 2.0e-05
+
+Architecture:
+  model_type: &model_type "rec"
+  name: DistillationModel
+  algorithm: Distillation
+  Models:
+    Teacher:
+      pretrained:
+      freeze_params: false
+      return_all_feats: true
+      model_type: *model_type
+      algorithm: CRNN
+      Transform:
+      Backbone:
+        name: MobileNetV1Enhance
+        scale: 0.5
+      Neck:
+        name: SequenceEncoder
+        encoder_type: rnn
+        hidden_size: 64
+      Head:
+        name: CTCHead
+        mid_channels: 96
+        fc_decay: 0.00002
+    Student:
+      pretrained:
+      freeze_params: false
+      return_all_feats: true
+      model_type: *model_type
+      algorithm: CRNN
+      Transform:
+      Backbone:
+        name: MobileNetV1Enhance
+        scale: 0.5
+      Neck:
+        name: SequenceEncoder
+        encoder_type: rnn
+        hidden_size: 64
+      Head:
+        name: CTCHead
+        mid_channels: 96
+        fc_decay: 0.00002
+  
+
+Loss:
+  name: CombinedLoss
+  loss_config_list:
+  - DistillationCTCLoss:
+      weight: 1.0
+      model_name_list: ["Student", "Teacher"]
+      key: head_out
+  - DistillationDMLLoss:
+      weight: 1.0
+      act: "softmax"
+      use_log: true
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: head_out
+  - DistillationDistanceLoss:
+      weight: 1.0
+      mode: "l2"
+      model_name_pairs:
+      - ["Student", "Teacher"]
+      key: backbone_out
+
+PostProcess:
+  name: DistillationCTCLabelDecode
+  model_name: ["Student", "Teacher"]
+  key: head_out
+
+Metric:
+  name: DistillationMetric
+  base_metric_name: RecMetric
+  main_indicator: acc
+  key: "Student"
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list:
+    - ./train_data/ic15_data/rec_gt_train.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - RecAug:
+    - CTCLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 32, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: true
+    batch_size_per_card: 128
+    drop_last: true
+    num_sections: 1
+    num_workers: 8
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list:
+    - ./train_data/ic15_data/rec_gt_test.txt
+    transforms:
+    - DecodeImage:
+        img_mode: BGR
+        channel_first: false
+    - CTCLabelEncode:
+    - RecResizeImg:
+        image_shape: [3, 32, 320]
+    - KeepKeys:
+        keep_keys:
+        - image
+        - label
+        - length
+  loader:
+    shuffle: false
+    drop_last: false
+    batch_size_per_card: 128
+    num_workers: 8

test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt

+===========================train_params===========================
+model_name:PPOCRv2_ocr_rec
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o 
+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:tools/export_model.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o 
+quant_export:
+fpgm_export: 
+distill_export:null
+export1:null
+export2:null
+inference_dir:Student
+infer_model:./inference/ch_PP-OCRv2_rec_infer/
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:False|True
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:/inference/rec_inference
+null:null
+--benchmark:True
+null:null
+
+

test_tipc/configs/ch_PP-OCRv2_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

+===========================kl_quant_params===========================
+model_name:PPOCRv2_ocr_rec_kl
+python:python3.7
+infer_model:./inference/ch_PP-OCRv2_rec_infer/
+infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o
+infer_quant:True
+inference:tools/infer/predict_rec.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:False
+--precision:int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+null:null
+--benchmark:True
+null:null
+null:null

test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt

+===========================train_params===========================
+model_name:PPOCRv2_ocr_rec_pact
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=3|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:pact_train
+norm_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o 
+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:deploy/slim/quantization/export_model.py -c test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml -o 
+quant_export:
+fpgm_export: 
+distill_export:null
+export1:null
+export2:null
+inference_dir:Student
+infer_model:./inference/ch_PP-OCRv2_rec_infer/
+infer_export:null
+infer_quant:True
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:False|True
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:/inference/rec_inference
+null:null
+--benchmark:True
+null:null
+
+

tests/ocr_ppocr_server_params.txt → test_tipc/configs/ch_ppocr_mobile_V2.0_det_FPGM/train_infer_python.txt

 ===========================train_params===========================
-model_name:ocr_system_server
+model_name:ocr_det
 python:python3.7
-gpu_list:null
-Global.use_gpu:null
+gpu_list:0|0,1
+Global.use_gpu:True|True
 Global.auto_cast:null
-Global.epoch_num:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
 Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:null
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
-train_model_name:null
-train_infer_img_dir:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
-trainer:
+trainer:fpgm_train
 norm_train:null
 pact_train:null
-fpgm_train:null
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ppocr_det_mobile/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_params=========================== 
 eval:null
 null:null
 ##
@@ -29,16 +29,16 @@ Global.save_inference_dir:./output/
 Global.pretrained_model:
 norm_export:null
 quant_export:null
-fpgm_export:null
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
 distill_export:null
 export1:null
 export2:null
-##
-infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
+inference_dir:null
+train_model:null
 infer_export:null
 infer_quant:False
-inference:tools/infer/predict_system.py
---use_gpu:True
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
 --enable_mkldnn:True|False
 --cpu_threads:1|6
 --rec_batch_num:1
@@ -46,21 +46,6 @@ inference:tools/infer/predict_system.py
 --precision:fp32|fp16|int8
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
---save_log_path:null
+null:null
 --benchmark:True
---rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
-===========================cpp_infer_params===========================
+null:null
\ No newline at end of file
-use_opencv:True
-infer_model:./inference/ch_ppocr_server_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_server_v2.0_rec_infer/
---benchmark:True
\ No newline at end of file

test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

+===========================ch_ppocr_mobile_v2.0===========================
+model_name:ch_ppocr_mobile_v2.0
+python:python3.7
+infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
+infer_export:null
+infer_quant:True
+inference:tools/infer/predict_system.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:int8
+--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
+null:null
+null:null

test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt

+===========================lite_params===========================
+inference:./ocr_db_crnn system
+runtime_device:ARM_CPU
+det_infer_model:ch_ppocr_mobile_v2.0_det_infer|ch_ppocr_db_mobile_v2.0_det_quant_infer
+rec_infer_model:ch_ppocr_mobile_v2.0_rec_infer|ch_ppocr_mobile_v2.0_rec_slim_infer
+cls_infer_model:ch_ppocr_mobile_v2.0_cls_infer|ch_ppocr_mobile_v2.0_cls_slim_infer
+--cpu_threads:1|4
+--det_batch_size:1
+--rec_batch_size:1
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+--rec_dict_dir:./ppocr_keys_v1.txt
+--benchmark:True

test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt

+===========================lite_params===========================
+inference:./ocr_db_crnn system
+runtime_device:ARM_GPU_OPENCL
+det_infer_model:ch_ppocr_mobile_v2.0_det_infer|ch_ppocr_db_mobile_v2.0_det_quant_infer
+rec_infer_model:ch_ppocr_mobile_v2.0_rec_infer|ch_ppocr_mobile_v2.0_rec_slim_infer
+cls_infer_model:ch_ppocr_mobile_v2.0_cls_infer|ch_ppocr_mobile_v2.0_cls_slim_infer
+--cpu_threads:1|4
+--det_batch_size:1
+--rec_batch_size:1
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+--rec_dict_dir:./ppocr_keys_v1.txt
+--benchmark:True

test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt

+===========================cpp_infer_params===========================
+model_name:ocr_det
+use_opencv:True
+infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
+infer_quant:False
+inference:./deploy/cpp_infer/build/ppocr det
+--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/
+null:null
+--benchmark:True
\ No newline at end of file

test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt

+===========================infer_params===========================
+model_name:ocr_det
+python:python
+infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp16|fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null

test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt

+===========================lite_params===========================
+inference:./ocr_db_crnn det
+runtime_device:ARM_CPU
+det_infer_model:ch_ppocr_mobile_v2.0_det_infer|ch_ppocr_db_mobile_v2.0_det_quant_infer
+null:null
+null:null
+--cpu_threads:1|4
+--det_batch_size:1
+null:null
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+null:null
+--benchmark:True

test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt

+===========================lite_params===========================
+inference:./ocr_db_crnn det
+runtime_device:ARM_GPU_OPENCL
+det_infer_model:ch_ppocr_mobile_v2.0_det_infer|ch_ppocr_db_mobile_v2.0_det_quant_infer
+null:null
+null:null
+--cpu_threads:1|4
+--det_batch_size:1
+null:null
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/
+--config_dir:./config.txt
+null:null
+--benchmark:True

test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ocr_det_mobile
+python:python3.7
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/det_mobile_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
\ No newline at end of file

test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_det_mobile
+python:python3.7|cpp
+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_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs
\ No newline at end of file

test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt

+===========================train_params===========================
+model_name:ch_ppocr_mobile_v2.0_det
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+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:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
+infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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/
+null:null
+--benchmark:True
+null:null
\ No newline at end of file

test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt

+===========================train_params===========================
+model_name:ocr_det
+python:python3.7
+gpu_list:xx.xx.xx.xx,yy.yy.yy.yy;0,1
+Global.use_gpu:True
+Global.auto_cast:fp32|amp
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train|pact_train|fpgm_train
+norm_train:tools/train.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ppocr_det_mobile/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:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
+infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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/
+null:null
+--benchmark:True
+null:null

test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt

+===========================train_params===========================
+model_name:ocr_det
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:amp
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train|pact_train|fpgm_train
+norm_train:tools/train.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ppocr_det_mobile/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:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
+infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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/
+null:null
+--benchmark:True
+null:null

test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt

+===========================train_params===========================
+model_name:ocr_det
+python:python
+gpu_list:-1
+Global.use_gpu:False
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train|pact_train|fpgm_train
+norm_train:tools/train.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/det_mv3_db.yml -o
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/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:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/det_mv3_db.yml -o 
+quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/det_mv3_db.yml -o 
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/det_mv3_db.yml -o 
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
+infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:fp32
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null

tests/ocr_det_params.txt → test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt

 ===========================train_params===========================
 model_name:ocr_det
-python:python3.7
-gpu_list:0|0,1
-Global.use_gpu:True|True
-Global.auto_cast:null
-Global.epoch_num:lite_train_infer=1|whole_train_infer=300
+python:python
+gpu_list:0
+Global.use_gpu:True
+Global.auto_cast:fp32|amp
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
 Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
 train_model_name:latest
 train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
 trainer:norm_train|pact_train|fpgm_train
-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
+norm_train:tools/train.py -c test_tipc/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/det_mv3_db.yml -o
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/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
@@ -27,26 +27,26 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.pretrained_model:
-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 
+norm_export:tools/export_model.py -c test_tipc/configs/det_mv3_db.yml -o 
+quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/det_mv3_db.yml -o 
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/det_mv3_db.yml -o 
 distill_export:null
 export1:null
 export2:null
-##
+inference_dir:null
 train_model:./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy
 infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
 infer_quant:False
 inference:tools/infer/predict_det.py
 --use_gpu:True|False
 --enable_mkldnn:True|False
---cpu_threads:6
+--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
+null:null
 --benchmark:True
 null:null
 ===========================cpp_infer_params===========================
@@ -65,6 +65,8 @@ inference:./deploy/cpp_infer/build/ppocr det
 null:null
 --benchmark:True
 ===========================serving_params===========================
+model_name:ocr_det
+python:python3.7
 trans_model:-m paddle_serving_client.convert
 --dirname:./inference/ch_ppocr_mobile_v2.0_det_infer/
 --model_filename:inference.pdmodel
@@ -78,5 +80,32 @@ 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
-
+pipline:pipeline_http_client.py|pipeline_rpc_client.py
+--image_dir=../../doc/imgs
+===========================kl_quant_params===========================
+infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
+infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
+infer_quant:True
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+null:null
+===========================lite_params===========================
+inference:./ocr_db_crnn det
+infer_model:./models/ch_ppocr_mobile_v2.0_det_opt.nb|./models/ch_ppocr_mobile_v2.0_det_slim_opt.nb
+--cpu_threads:1|4
+--batch_size:1
+--power_mode:LITE_POWER_HIGH|LITE_POWER_LOW
+--image_dir:./test_data/icdar2015_lite/text_localization/ch4_test_images/|./test_data/icdar2015_lite/text_localization/ch4_test_images/img_233.jpg
+--config_dir:./config.txt
+--rec_dict_dir:./ppocr_keys_v1.txt
+--benchmark:True

test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

+===========================kl_quant_params===========================
+model_name:PPOCRv2_ocr_det
+python:python3.7
+infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
+infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
+infer_quant:True
+inference:tools/infer/predict_det.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+null:null

test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_mac_cpu.txt

+===========================kl_quant_params===========================
+infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
+infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
+infer_quant:True
+inference:tools/infer/predict_det.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+null:null

test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_windows_gpu_cpu.txt

+===========================kl_quant_params===========================
+infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
+infer_export:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o
+infer_quant:True
+inference:tools/infer/predict_det.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
+null:null

tests/ocr_det_server_params.txt → test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/train_infer_python.txt

 ===========================train_params===========================
-model_name:ocr_server_det
+model_name:ocr_det
 python:python3.7
 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
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
 Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:lite_train_infer=2|whole_train_infer=4
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
 train_model_name:latest
 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 tests/configs/det_r50_vd_db.yml -o Global.pretrained_model=""
-pact_train:null
+trainer:pact_train
+norm_train:null
+pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
 fpgm_train:null
 distill_train:null
 null:null
 null:null
 ##
 ===========================eval_params=========================== 
-eval:tools/eval.py -c tests/configs/det_r50_vd_db.yml -o
+eval:null
 null:null
 ##
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.pretrained_model:
-norm_export:tools/export_model.py -c tests/configs/det_r50_vd_db.yml -o 
-quant_export:null 
+norm_export:null
+quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
 fpgm_export:null
 distill_export:null
 export1:null
 export2:null
-##
-infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
+inference_dir:null
+train_model:null
 infer_export:null
 infer_quant:False
 inference:tools/infer/predict_det.py
@@ -46,7 +46,6 @@ inference:tools/infer/predict_det.py
 --precision:fp32|fp16|int8
 --det_model_dir:
 --image_dir:./inference/ch_det_data_50/all-sum-510/
---save_log_path:null
---benchmark:True
 null:null
-
+--benchmark:True
+null:null
\ No newline at end of file

test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ocr_rec_mobile
+python:python3.7
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/rec_mobile_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--rec_model_dir:
+--image_dir:./inference/rec_inference
\ No newline at end of file

test_tipc/configs/ch_ppocr_mobile_v2.0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_rec_mobile
+python:python3.7|cpp
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
+op.rec.local_service_conf.devices:null|0
+op.rec.local_service_conf.use_mkldnn:True|False
+op.rec.local_service_conf.thread_num:1|6
+op.rec.local_service_conf.use_trt:False|True
+op.rec.local_service_conf.precision:fp32|fp16|int8
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs_words_en
\ No newline at end of file

tests/ocr_rec_params.txt → test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_infer_python.txt

 ===========================train_params===========================
-model_name:ocr_rec
+model_name:ch_ppocr_mobile_v2.0_rec
 python:python3.7
 gpu_list:0|0,1
 Global.use_gpu:True|True
@@ -12,9 +12,9 @@ train_model_name:latest
 train_infer_img_dir:./inference/rec_inference
 null:null
 ##
-trainer:norm_train|pact_train
+trainer:norm_train
 norm_train:tools/train.py -c configs/rec/rec_icdar15_train.yml -o
-pact_train:deploy/slim/quantization/quant.py -c configs/rec/rec_icdar15_train.yml -o
+pact_train:null
 fpgm_train:null
 distill_train:null
 null:null
@@ -28,16 +28,16 @@ null:null
 Global.save_inference_dir:./output/
 Global.pretrained_model:
 norm_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
-quant_export:deploy/slim/quantization/export_model.py -c configs/rec/rec_icdar15_train.yml -o
+quant_export:null
 fpgm_export:null
 distill_export:null
 export1:null
 export2:null
 ##
-infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
-infer_export:null
+infer_model:null
+infer_export:tools/export_model.py -c configs/rec/rec_icdar15_train.yml -o
 infer_quant:False
-inference:tools/infer/predict_rec.py
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100" --rec_algorithm="RARE"
 --use_gpu:True|False
 --enable_mkldnn:True|False
 --cpu_threads:1|6
@@ -49,33 +49,3 @@ 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
-===========================serving_params===========================
-trans_model:-m paddle_serving_client.convert
---dirname:./inference/ch_ppocr_mobile_v2.0_rec_infer/
---model_filename:inference.pdmodel
---params_filename:inference.pdiparams
---serving_server:./deploy/pdserving/ppocr_rec_mobile_2.0_serving/
---serving_client:./deploy/pdserving/ppocr_rec_mobile_2.0_client/
-serving_dir:./deploy/pdserving
-web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
-op.rec.local_service_conf.devices:null|0
-op.rec.local_service_conf.use_mkldnn:True|False
-op.rec.local_service_conf.thread_num:1|6
-op.rec.local_service_conf.use_trt:False|True
-op.rec.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py --image_dir=../../doc/imgs_words_en
\ No newline at end of file

test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt

+===========================ch_ppocr_mobile_v2.0===========================
+model_name:ch_ppocr_server_v2.0
+python:python3.7
+infer_model:./inference/ch_ppocr_server_v2.0_det_infer/
+infer_export:null
+infer_quant:True
+inference:tools/infer/predict_system.py
+--use_gpu:False
+--enable_mkldnn:False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
+--benchmark:True
+null:null
+null:null

test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ocr_det_server
+python:python3.7
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_server_v2.0_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/det_server_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--det_model_dir:
+--image_dir:./inference/det_inference
\ No newline at end of file

test_tipc/configs/ch_ppocr_server_v2.0_det/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_det_server
+python:python3.7|cpp
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_server_v2.0_det_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_det_server_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_det_server_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_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs_words_en
\ No newline at end of file

test_tipc/configs/ch_ppocr_server_v2.0_det/train_infer_python.txt

+===========================train_params===========================
+model_name:ch_ppocr_server_v2.0_det
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_lite_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train|pact_train|fpgm_export
+norm_train:tools/train.py -c test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o 
+quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o 
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o 
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c test_tipc/configs/ppocr_det_server/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 test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml -o 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/ch_ppocr_server_v2.0_det_train/best_accuracy
+infer_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_res18_db_v2.0.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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
+null:null
\ No newline at end of file

test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt

+===========================paddle2onnx_params===========================
+model_name:ocr_rec_server
+python:python3.7
+2onnx: paddle2onnx
+--model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--save_file:./inference/rec_server_onnx/model.onnx
+--opset_version:10
+--enable_onnx_checker:True
+inference:tools/infer/predict_rec.py
+--use_gpu:True|False
+--rec_model_dir:
+--image_dir:./inference/rec_inference
\ No newline at end of file

test_tipc/configs/ch_ppocr_server_v2.0_rec/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt

+===========================serving_params===========================
+model_name:ocr_rec_server
+python:python3.7
+trans_model:-m paddle_serving_client.convert
+--dirname:./inference/ch_ppocr_server_v2.0_rec_infer/
+--model_filename:inference.pdmodel
+--params_filename:inference.pdiparams
+--serving_server:./deploy/pdserving/ppocr_rec_server_2.0_serving/
+--serving_client:./deploy/pdserving/ppocr_rec_server_2.0_client/
+serving_dir:./deploy/pdserving
+web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
+op.rec.local_service_conf.devices:null|0
+op.rec.local_service_conf.use_mkldnn:True|False
+op.rec.local_service_conf.thread_num:1|6
+op.rec.local_service_conf.use_trt:False|True
+op.rec.local_service_conf.precision:fp32|fp16|int8
+pipline:pipeline_rpc_client.py|pipeline_http_client.py
+--image_dir:../../doc/imgs_words_en
\ No newline at end of file

test_tipc/configs/ch_ppocr_server_v2.0_rec/train_infer_python.txt

+===========================train_params===========================
+model_name:ch_ppocr_server_v2.0_rec
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=100
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/ch_ppocr_server_v2.0_rec/rec_icdar15_train.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null

test_tipc/configs/det_mv3_db_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:det_mv3_db_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_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: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:tools/export_model.py -c configs/det/det_mv3_db.yml -o 
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/det_mv3_db_v2.0_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
+--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/
+null:null
+--benchmark:True
+null:null
\ No newline at end of file

test_tipc/configs/det_mv3_east_v2.0/det_mv3_east.yml

+Global:
+  use_gpu: true
+  epoch_num: 10000
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/east_mv3/
+  save_epoch_step: 1000
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [4000, 5000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: 
+  save_res_path: ./output/det_east/predicts_east.txt
+
+Architecture:
+  model_type: det
+  algorithm: EAST
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: EASTFPN
+    model_name: small
+  Head:
+    name: EASTHead
+    model_name: small
+
+Loss:
+  name: EASTLoss
+  
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+  #  name: Cosine
+    learning_rate: 0.001
+  #  warmup_epoch: 0
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: EASTPostProcess
+  score_thresh: 0.8
+  cover_thresh: 0.1
+  nms_thresh: 0.2
+
+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
+      - EASTProcessTrain:
+          image_shape: [512, 512]
+          background_ratio: 0.125
+          min_crop_side_ratio: 0.1
+          min_text_size: 10
+      - KeepKeys:
+          keep_keys: ['image', 'score_map', 'geo_map', 'training_mask'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 16
+    num_workers: 8
+
+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:
+          limit_side_len: 2400
+          limit_type: max
+      - 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: 2
\ No newline at end of file

tests/ocr_kl_quant_params.txt → test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt

 ===========================train_params===========================
-model_name:ocr_system
+model_name:det_mv3_east_v2.0
 python:python3.7
-gpu_list:null
-Global.use_gpu:null
-Global.auto_cast:null
-Global.epoch_num:null
+gpu_list:0
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
 Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:null
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
-train_model_name:null
-train_infer_img_dir:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
-trainer:
-norm_train:null
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/det_mv3_east_v2.0/det_mv3_east.yml -o 
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -27,18 +27,18 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.pretrained_model:
-norm_export:null 
+norm_export:tools/export_model.py -c test_tipc/configs/det_mv3_east_v2.0/det_mv3_east.yml -o 
 quant_export:null 
-fpgm_export:null 
+fpgm_export:null
 distill_export:null
 export1:null
 export2:null
 ##
-infer_model:./inference/ch_ppocr_mobile_v2.0_det_infer/
-kl_quant:deploy/slim/quantization/quant_kl.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml  -o  
-infer_quant:True
+train_model:./inference/det_mv3_east/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/cconfigs/det_mv3_east_v2.0/det_mv3_east.yml  -o
+infer_quant:False
 inference:tools/infer/predict_det.py
---use_gpu:TrueFalse
+--use_gpu:True|False
 --enable_mkldnn:True|False
 --cpu_threads:1|6
 --rec_batch_num:1
@@ -48,4 +48,4 @@ inference:tools/infer/predict_det.py
 --image_dir:./inference/ch_det_data_50/all-sum-510/
 --save_log_path:null
 --benchmark:True
-null:null
+--det_algorithm:EAST

test_tipc/configs/det_r50_db_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:det_r50_db_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=2|whole_train_whole_infer=300
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_lite_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c configs/det/det_r50_vd_db.yml -o 
+quant_export:null
+fpgm_export:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c configs/det/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 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/ch_ppocr_server_v2.0_det_train/best_accuracy
+infer_export:tools/export_model.py -c configs/det/det_r50_vd_db.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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
+null:null
\ No newline at end of file

test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml

+Global:
+  use_gpu: true
+  epoch_num: 10000
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/east_r50_vd/
+  save_epoch_step: 1000
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [4000, 5000]
+  cal_metric_during_train: False
+  pretrained_model: 
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: 
+  save_res_path: ./output/det_east/predicts_east.txt
+
+Architecture:
+  model_type: det
+  algorithm: EAST
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: EASTFPN
+    model_name: large
+  Head:
+    name: EASTHead
+    model_name: large
+
+Loss:
+  name: EASTLoss
+  
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+  #  name: Cosine
+    learning_rate: 0.001
+  #  warmup_epoch: 0
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: EASTPostProcess
+  score_thresh: 0.8
+  cover_thresh: 0.1
+  nms_thresh: 0.2
+
+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
+      - EASTProcessTrain:
+          image_shape: [512, 512]
+          background_ratio: 0.125
+          min_crop_side_ratio: 0.1
+          min_text_size: 10
+      - KeepKeys:
+          keep_keys: ['image', 'score_map', 'geo_map', 'training_mask'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 8
+    num_workers: 8
+
+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
+          Fa: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 2400
+          limit_type: max
+      - 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: 2
\ No newline at end of file

tests/ocr_ppocr_mobile_params.txt → test_tipc/configs/det_r50_vd_east_v2.0/train_infer_python.txt

 ===========================train_params===========================
-model_name:ocr_system_mobile
+model_name:det_r50_vd_east_v2.0
 python:python3.7
-gpu_list:null
-Global.use_gpu:null
-Global.auto_cast:null
-Global.epoch_num:null
+gpu_list:0
+Global.use_gpu:True|True
+Global.auto_cast:fp32
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
 Global.save_model_dir:./output/
-Train.loader.batch_size_per_card:null
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
 Global.pretrained_model:null
-train_model_name:null
-train_infer_img_dir:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
 null:null
 ##
-trainer:
-norm_train:null
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o 
 pact_train:null
 fpgm_train:null
 distill_train:null
@@ -27,18 +27,18 @@ null:null
 ===========================infer_params===========================
 Global.save_inference_dir:./output/
 Global.pretrained_model:
-norm_export:null 
+norm_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o 
 quant_export:null 
-fpgm_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
+train_model:./inference/det_mv3_east/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/cconfigs/det_r50_vd_east_v2.0/det_r50_vd_east.yml  -o
 infer_quant:False
-inference:tools/infer/predict_system.py
---use_gpu:True
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
 --enable_mkldnn:True|False
 --cpu_threads:1|6
 --rec_batch_num:1
@@ -48,20 +48,4 @@ inference:tools/infer/predict_system.py
 --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
-
+--det_algorithm:EAST

test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml

+Global:
+  use_gpu: true
+  epoch_num: 5000
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/sast_r50_vd_ic15/
+  save_epoch_step: 1000
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [4000, 5000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img:
+  save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt
+
+
+Architecture:
+  model_type: det
+  algorithm: SAST
+  Transform:
+  Backbone:
+    name: ResNet_SAST
+    layers: 50
+  Neck:
+    name: SASTFPN
+    with_cab: True
+  Head:
+    name: SASTHead
+
+Loss:
+  name: SASTLoss
+  
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+  #  name: Cosine
+    learning_rate: 0.001
+  #  warmup_epoch: 0
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: SASTPostProcess
+  score_thresh: 0.5
+  sample_pts_num: 2
+  nms_thresh: 0.2
+  expand_scale: 1.0
+  shrink_ratio_of_width: 0.3
+
+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: [0.1, 0.45, 0.3, 0.15]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - SASTProcessTrain:
+          image_shape: [512, 512]
+          min_crop_side_ratio: 0.3
+          min_crop_size: 24
+          min_text_size: 4
+          max_text_size: 512
+      - KeepKeys:
+          keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 4
+    num_workers: 4
+
+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:
+          resize_long: 1536
+      - 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: 2
+

test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:det_r50_vd_sast_icdar15_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=5000
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o Global.pretrained_model=./pretrain_models/ResNet50_vd_ssld_pretrained
+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:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o 
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/det_r50_vd_sast_icdar15_v2.0_train/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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/
+null:null
+--benchmark:True
+null:null

test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml

+Global:
+  use_gpu: true
+  epoch_num: 5000
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/sast_r50_vd_tt/
+  save_epoch_step: 1000
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [4000, 5000]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: 
+  save_res_path: ./output/sast_r50_vd_tt/predicts_sast.txt
+
+Architecture:
+  model_type: det
+  algorithm: SAST
+  Transform:
+  Backbone:
+    name: ResNet_SAST
+    layers: 50
+  Neck:
+    name: SASTFPN
+    with_cab: True
+  Head:
+    name: SASTHead
+
+Loss:
+  name: SASTLoss
+  
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+  #  name: Cosine
+    learning_rate: 0.001
+  #  warmup_epoch: 0
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+PostProcess:
+  name: SASTPostProcess
+  score_thresh: 0.5
+  sample_pts_num: 6
+  nms_thresh: 0.2
+  expand_scale: 1.2
+  shrink_ratio_of_width: 0.2
+
+Metric:
+  name: DetMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/total_text/train
+    label_file_list: [./train_data/total_text/train/train.txt]
+    ratio_list: [1.0]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - SASTProcessTrain:
+          image_shape: [512, 512]
+          min_crop_side_ratio: 0.3
+          min_crop_size: 24
+          min_text_size: 4
+          max_text_size: 512
+      - KeepKeys:
+          keep_keys: ['image', 'score_map', 'border_map', 'training_mask', 'tvo_map', 'tco_map'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 4
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/
+    label_file_list: 
+      - ./train_data/total_text/test/test.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          resize_long: 768
+      - 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: 2
\ No newline at end of file

test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:det_r50_vd_sast_totaltext_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=5000
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=4
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml -o Global.pretrained_model=./pretrain_models/ResNet50_vd_ssld_pretrained
+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:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml -o 
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/det_r50_vd_sast_totaltext_v2.0/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml -o
+infer_quant:False
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--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/
+null:null
+--benchmark:True
+null:null

test_tipc/configs/en_server_pgnetA/train_infer_python.txt

+===========================train_params===========================
+model_name:en_server_pgnetA
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=1|whole_train_whole_infer=500
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=2|whole_train_whole_infer=14
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/total_text/test/rgb/
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./pretrain_models/en_server_pgnetA/best_accuracy
+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:tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o 
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:./inference/en_server_pgnetA/best_accuracy
+infer_export:tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o
+infer_quant:False
+inference:tools/infer/predict_e2e.py
+--use_gpu:True|False
+--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/
+null:null
+--benchmark:True
+null:null

test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
+  save_epoch_step: 3
+  # 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: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_none_bilstm_ctc.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 96
+  Head:
+    name: CTCHead
+    fc_decay: 0
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4

test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_mv3_none_bilstm_ctc_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=100
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null

test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/mv3_none_none_ctc/
+  save_epoch_step: 3
+  # 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: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_none_none_ctc.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: Rosetta
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: SequenceEncoder
+    encoder_type: reshape
+  Head:
+    name: CTCHead
+    fc_decay: 0.0004
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 8

test_tipc/configs/rec_mv3_none_none_ctc_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_mv3_none_none_ctc_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=100
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null

test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml

+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/mv3_tps_bilstm_ctc/
+  save_epoch_step: 3
+  # 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: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_mv3_tps_bilstm_ctc.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: STARNet
+  Transform:
+    name: TPS
+    num_fiducial: 20
+    loc_lr: 0.1
+    model_name: small
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 96
+  Head:
+    name: CTCHead
+    fc_decay: 0.0004
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4

test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_mv3_tps_bilstm_ctc_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=100
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null

test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/r34_vd_none_bilstm_ctc/
+  save_epoch_step: 3
+  # 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: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_none_bilstm_ctc.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 34
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 256
+  Head:
+    name: CTCHead
+    fc_decay: 0
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4

test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_r34_vd_none_bilstm_ctc_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=100
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null

test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/r34_vd_none_none_ctc/
+  save_epoch_step: 3
+  # 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: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_none_none_ctc.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: Rosetta
+  Backbone:
+    name: ResNet
+    layers: 34
+  Neck:
+    name: SequenceEncoder
+    encoder_type: reshape
+  Head:
+    name: CTCHead
+    fc_decay: 0.0004
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4

test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_r34_vd_none_none_ctc_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=100
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null

test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml

+Global:
+  use_gpu: true
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/r34_vd_tps_bilstm_ctc/
+  save_epoch_step: 3
+  # 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: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_r34_vd_tps_bilstm_ctc.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0
+
+Architecture:
+  model_type: rec
+  algorithm: STARNet
+  Transform:
+    name: TPS
+    num_fiducial: 20
+    loc_lr: 0.1
+    model_name: large
+  Backbone:
+    name: ResNet
+    layers: 34
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 256
+  Head:
+    name: CTCHead
+    fc_decay: 0
+
+Loss:
+  name: CTCLoss
+
+PostProcess:
+  name: CTCLabelDecode
+
+Metric:
+  name: RecMetric
+  main_indicator: acc
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data/
+    label_file_list: ["./train_data/ic15_data/rec_gt_train.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 256
+    drop_last: True
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/ic15_data
+    label_file_list: ["./train_data/ic15_data/rec_gt_test.txt"]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 100]
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 256
+    num_workers: 4

test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/train_infer_python.txt

+===========================train_params===========================
+model_name:rec_r34_vd_tps_bilstm_ctc_v2.0
+python:python3.7
+gpu_list:0|0,1
+Global.use_gpu:True|True
+Global.auto_cast:null
+Global.epoch_num:lite_train_lite_infer=5|whole_train_whole_infer=100
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:lite_train_lite_infer=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./inference/rec_inference
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params===========================
+eval:tools/eval.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+norm_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+quant_export:null
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+infer_model:null
+infer_export:tools/export_model.py -c test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="3,32,100"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null

test_tipc/docs/install.md

+## 1. 环境准备
+
+本教程适用于test_tipc目录下基础功能测试的运行环境搭建。
+
+推荐环境：
+- CUDA 10.1/10.2
+- CUDNN 7.6/cudnn8.1
+- TensorRT 6.1.0.5 / 7.1 / 7.2
+
+环境配置可以选择docker镜像安装，或者在本地环境Python搭建环境。推荐使用docker镜像安装，避免不必要的环境配置。
+
+## 2. Docker 镜像安装
+
+推荐docker镜像安装，按照如下命令创建镜像，当前目录映射到镜像中的`/paddle`目录下
+```
+nvidia-docker run --name paddle -it -v $PWD:/paddle paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82 /bin/bash
+cd /paddle
+
+# 安装带TRT的paddle
+pip3.7 install https://paddle-wheel.bj.bcebos.com/with-trt/2.1.3/linux-gpu-cuda10.1-cudnn7-mkl-gcc8.2-trt6-avx/paddlepaddle_gpu-2.1.3.post101-cp37-cp37m-linux_x86_64.whl
+```
+
+## 3 Python 环境构建
+
+非docker环境下，环境配置比较灵活，推荐环境组合配置：
+- CUDA10.1 + CUDNN7.6 + TensorRT 6
+- CUDA10.2 + CUDNN8.1 + TensorRT 7
+- CUDA11.1 + CUDNN8.1 + TensorRT 7
+
+下面以 CUDA10.2 + CUDNN8.1 + TensorRT 7 配置为例，介绍环境配置的流程。
+
+### 3.1 安装CUDNN
+
+如果当前环境满足CUDNN版本的要求，可以跳过此步骤。
+
+以CUDNN8.1 安装安装为例，安装步骤如下，首先下载CUDNN，从[Nvidia官网](https://developer.nvidia.com/rdp/cudnn-archive)下载CUDNN8.1版本，下载符合当前系统版本的三个deb文件，分别是：
+- cuDNN Runtime Library ，如：libcudnn8_8.1.0.77-1+cuda10.2_amd64.deb
+- cuDNN Developer Library ，如：libcudnn8-dev_8.1.0.77-1+cuda10.2_amd64.deb
+- cuDNN Code Samples，如：libcudnn8-samples_8.1.0.77-1+cuda10.2_amd64.deb
+
+deb安装可以参考[官方文档](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#installlinux-deb)，安装方式如下
+```
+# x.x.x表示下载的版本号
+# $HOME为工作目录
+sudo dpkg -i libcudnn8_x.x.x-1+cudax.x_arm64.deb
+sudo dpkg -i libcudnn8-dev_8.x.x.x-1+cudax.x_arm64.deb
+sudo dpkg -i libcudnn8-samples_8.x.x.x-1+cudax.x_arm64.deb
+
+# 验证是否正确安装
+cp -r /usr/src/cudnn_samples_v8/ $HOME
+cd  $HOME/cudnn_samples_v8/mnistCUDNN
+
+# 编译
+make clean && make
+./mnistCUDNN
+```
+如果运行mnistCUDNN完后提示运行成功，则表示安装成功。如果运行后出现freeimage相关的报错，需要按照提示安装freeimage库:
+```
+sudo apt-get install libfreeimage-dev
+sudo apt-get install libfreeimage
+```
+
+### 3.2 安装TensorRT
+
+首先，从[Nvidia官网TensorRT板块](https://developer.nvidia.com/tensorrt-getting-started)下载TensorRT，这里选择7.1.3.4版本的TensorRT，注意选择适合自己系统版本和CUDA版本的TensorRT，另外建议下载TAR package的安装包。
+
+以Ubuntu16.04+CUDA10.2为例，下载并解压后可以参考[官方文档](https://docs.nvidia.com/deeplearning/tensorrt/archives/tensorrt-713/install-guide/index.html#installing-tar)的安装步骤，按照如下步骤安装:
+```
+# 以下安装命令中 '${version}' 为下载的TensorRT版本，如7.1.3.4
+# 设置环境变量，<TensorRT-${version}/lib> 为解压后的TensorRT的lib目录
+export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib>
+
+# 安装TensorRT
+cd TensorRT-${version}/python
+pip3.7 install tensorrt-*-cp3x-none-linux_x86_64.whl
+
+# 安装graphsurgeon
+cd TensorRT-${version}/graphsurgeon
+```
+
+
+### 3.3 安装PaddlePaddle
+
+下载支持TensorRT版本的Paddle安装包，注意安装包的TensorRT版本需要与本地TensorRT一致，下载[链接](https://paddleinference.paddlepaddle.org.cn/user_guides/download_lib.html#python)
+选择下载 linux-cuda10.2-trt7-gcc8.2 Python3.7版本的Paddle：
+```
+# 从下载链接中可以看到是paddle2.1.1-cuda10.2-cudnn8.1版本
+wget  https://paddle-wheel.bj.bcebos.com/with-trt/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddlepaddle_gpu-2.1.1-cp37-cp37m-linux_x86_64.whl
+pip3.7 install -U paddlepaddle_gpu-2.1.1-cp37-cp37m-linux_x86_64.whl
+```
+
+## 4. 安装PaddleOCR依赖
+```
+# 安装AutoLog
+git clone https://github.com/LDOUBLEV/AutoLog
+cd AutoLog
+pip3.7 install -r requirements.txt
+python3.7 setup.py bdist_wheel
+pip3.7 install ./dist/auto_log-1.0.0-py3-none-any.whl
+
+# 下载OCR代码
+cd ../
+git clone https://github.com/PaddlePaddle/PaddleOCR
+
+```
+
+安装PaddleOCR依赖：
+```
+cd PaddleOCR
+pip3.7 install -r requirements.txt
+```
+
+## FAQ :
+Q. You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found. Ignore this if TensorRT is not needed.
+
+A. 问题一般是当前安装paddle版本带TRT，但是本地环境找不到TensorRT的预测库，需要下载TensorRT库，解压后设置环境变量LD_LIBRARY_PATH;
+如：
+```
+export LD_LIBRARY_PATH=/usr/local/python3.7.0/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/paddle/package/TensorRT-6.0.1.5/lib
+```
+或者问题是下载的TensorRT版本和当前paddle中编译的TRT版本不匹配，需要下载版本相符的TensorRT重新安装。

test_tipc/docs/jeston_test_train_inference_python.md

+# Jeston端基础训练预测功能测试
+
+Jeston端基础训练预测功能测试的主程序为`test_train_inference_python.sh`，由于Jeston端CPU较差，Jeston只需要测试TIPC关于GPU和TensorRT预测推理的部分即可。
+
+## 1. 测试结论汇总
+
+- 预测相关：基于训练是否使用量化，可以将训练产出的模型可以分为`正常模型`和`量化模型`，这两类模型对应的预测功能汇总如下：
+
+| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
+|  ----   |  ---- |   ----   |  :----:  |   :----:   |  :----:  |
+| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
+| 量化模型 | GPU | 1/6 | int8 | - | - |
+
+
+## 2. 测试流程
+
+环境准备只需要Python环境即可，安装PaddlePaddle等依赖参考下述文档。
+
+### 2.1 安装依赖
+- 安装PaddlePaddle >= 2.0
+- 安装PaddleOCR依赖
+    ```
+    pip install  -r ../requirements.txt
+    ```
+- 安装autolog（规范化日志输出工具）
+    ```
+    git clone https://github.com/LDOUBLEV/AutoLog
+    cd AutoLog
+    pip install -r requirements.txt
+    python setup.py bdist_wheel
+    pip install ./dist/auto_log-1.0.0-py3-none-any.whl
+    cd ../
+    ```
+- 安装PaddleSlim (可选)
+   ```
+   # 如果要测试量化、裁剪等功能，需要安装PaddleSlim
+   pip install paddleslim
+   ```
+
+
+### 2.2 功能测试
+
+先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
+
+`test_train_inference_python.sh`包含5种[运行模式](./test_train_inference_python.md)，在Jeston端，仅需要测试预测推理的模式即可：
+
+```
+- 模式3：whole_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer'
+# 用法1:
+bash test_tipc/test_inference_jeston.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer'
+# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
+bash test_tipc/test_inference_jeston.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer' '1'
+```
+
+运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如`lite_train_lite_infer`模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：
+```
+test_tipc/output/
+|- results_python.log    # 运行指令状态的日志
+|- python_infer_gpu_usetensorrt_True_precision_fp32_batchsize_1.log  # GPU上开启TensorRT，batch_size=1条件下的预测运行日志
+......
+```
+
+其中`results_python.log`中包含了每条指令的运行状态，如果运行成功会输出：
+```
+Run successfully with command - python tools/infer/predict_det.py --use_gpu=True --use_tensorrt=False --precision=fp32 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark=True   > ./test_tipc/output/python_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log 2>&1 !  
+Run successfully with command - python tools/infer/predict_det.py --use_gpu=True --use_tensorrt=True --precision=fp32 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark=True   > ./test_tipc/output/python_infer_gpu_usetrt_True_precision_fp32_batchsize_1.log 2>&1 !  
+Run successfully with command - python tools/infer/predict_det.py --use_gpu=True --use_tensorrt=True --precision=fp16 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark=True   > ./test_tipc/output/python_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log 2>&1 !
+```
+如果运行失败，会输出：
+```
+Run failed with command - python tools/infer/predict_det.py --use_gpu=True --use_tensorrt=False --precision=fp32 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark=True   > ./test_tipc/output/python_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log 2>&1 !
+Run failed with command - python tools/infer/predict_det.py --use_gpu=True --use_tensorrt=True --precision=fp32 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark=True   > ./test_tipc/output/python_infer_gpu_usetrt_True_precision_fp32_batchsize_1.log 2>&1 !
+Run failed with command - python tools/infer/predict_det.py --use_gpu=True --use_tensorrt=True --precision=fp16 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark=True   > ./test_tipc/output/python_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log 2>&1 !
+```
+可以很方便的根据`results_python.log`中的内容判定哪一个指令运行错误。
+
+### 2.3 精度测试
+
+使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
+- 提取日志中的预测坐标；
+- 从本地文件中提取保存好的坐标结果；
+- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
+
+#### 使用方式
+运行命令：
+```shell
+python test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt  --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3
+```
+
+参数介绍：  
+- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
+- log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入
+- atol: 设置的绝对误差
+- rtol: 设置的相对误差
+
+#### 运行结果
+
+正常运行效果如下：
+```
+Assert allclose passed! The results of python_infer_gpu_usetrt_True_precision_fp32_batchsize_1.log and ./test_tipc/results/python_ppocr_det_mobile_results_fp32.txt are consistent!
+```
+
+出现不一致结果时的运行输出：
+```
+......
+Traceback (most recent call last):
+  File "test_tipc/compare_results.py", line 140, in <module>
+    format(filename, gt_filename))
+ValueError: The results of python_infer_gpu_usetrt_True_precision_fp32_batchsize_1.log and the results of ./test_tipc/results/python_ppocr_det_mobile_results_fp32.txt are inconsistent!
+```
+
+
+## 3. 更多教程
+本文档为功能测试用，更丰富的训练预测使用教程请参考：  
+[模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/training.md)  
+[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference.md)

test_tipc/docs/mac_test_train_inference_python.md

+# Mac端基础训练预测功能测试
+
+Mac端基础训练预测功能测试的主程序为`test_train_inference_python.sh`，可以测试基于Python的模型CPU训练，包括裁剪、量化、蒸馏训练，以及评估、CPU推理等基本功能。
+
+注：Mac端测试用法同linux端测试方法类似，但是无需测试需要在GPU上运行的测试。
+
+## 1. 测试结论汇总
+
+- 训练相关：
+
+| 算法名称 | 模型名称 | 单机单卡（CPU） | 单机多卡 | 多机多卡 | 模型压缩（CPU） |
+|  :----  |   :----  |    :----  |  :----   |  :----   |  :----   |
+|  DB  | ch_ppocr_mobile_v2.0_det| 正常训练 | - | - | 正常训练：FPGM裁剪、PACT量化 <br> 离线量化（无需训练） |
+
+
+- 预测相关：基于训练是否使用量化，可以将训练产出的模型可以分为`正常模型`和`量化模型`，这两类模型对应的预测功能汇总如下，
+
+| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
+|  ----   |  ---- |   ----   |  :----:  |   :----:   |  :----:  |
+| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
+| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
+
+
+## 2. 测试流程
+
+Mac端无GPU，环境准备只需要Python环境即可，安装PaddlePaddle等依赖参考下述文档。
+
+### 2.1 安装依赖
+- 安装PaddlePaddle >= 2.0
+- 安装PaddleOCR依赖
+    ```
+    pip install  -r ../requirements.txt
+    ```
+- 安装autolog（规范化日志输出工具）
+    ```
+    git clone https://github.com/LDOUBLEV/AutoLog
+    cd AutoLog
+    pip install -r requirements.txt
+    python setup.py bdist_wheel
+    pip install ./dist/auto_log-1.0.0-py3-none-any.whl
+    cd ../
+    ```
+- 安装PaddleSlim (可选)
+   ```
+   # 如果要测试量化、裁剪等功能，需要安装PaddleSlim
+   pip install paddleslim
+   ```
+
+
+### 2.2 功能测试
+
+先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
+
+`test_train_inference_python.sh`包含5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_lite_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
+# 同linux端运行不同的是，Mac端测试使用新的配置文件mac_ppocr_det_mobile_params.txt，
+# 配置文件中默认去掉了GPU和mkldnn相关的测试链条
+bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'lite_train_lite_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'lite_train_lite_infer'
+```  
+
+- 模式2：lite_train_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'lite_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt  'lite_train_whole_infer'
+```  
+
+- 模式3：whole_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'whole_infer'
+# 用法1:
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'whole_infer'
+# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'whole_infer' '1'
+```  
+
+- 模式4：whole_train_whole_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；（Mac端不建议运行此模式）
+```shell
+bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'whole_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'whole_train_whole_infer'
+```  
+
+- 模式5：klquant_whole_infer，测试离线量化；
+```shell
+bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_mac_cpu.txt  'klquant_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_mac_cpu.txt  'klquant_whole_infer'
+```
+
+运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如`lite_train_lite_infer`模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：
+```
+test_tipc/output/
+|- results_python.log    # 运行指令状态的日志
+|- norm_train_gpus_-1_autocast_null/  # CPU上正常训练的训练日志和模型保存文件夹
+|- pact_train_gpus_-1_autocast_null/  # CPU上量化训练的训练日志和模型保存文件夹
+......
+|- python_infer_cpu_usemkldnn_False_threads_1_batchsize_1.log  # CPU上关闭Mkldnn线程数设置为1，测试batch_size=1条件下的预测运行日志
+......
+```
+
+其中`results_python.log`中包含了每条指令的运行状态，如果运行成功会输出：
+```
+Run successfully with command - python3.7 tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained Global.use_gpu=False  Global.save_model_dir=./tests/output/norm_train_gpus_-1_autocast_null Global.epoch_num=1     Train.loader.batch_size_per_card=2   !
+Run successfully with command - python3.7 tools/export_model.py -c tests/configs/det_mv3_db.yml -o  Global.pretrained_model=./tests/output/norm_train_gpus_-1_autocast_null/latest Global.save_inference_dir=./tests/output/norm_train_gpus_-1_autocast_null!
+......
+```
+如果运行失败，会输出：
+```
+Run failed with command - python3.7 tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained Global.use_gpu=Faslse  Global.save_model_dir=./tests/output/norm_train_gpus_-1_autocast_null Global.epoch_num=1     Train.loader.batch_size_per_card=2   !
+Run failed with command - python3.7 tools/export_model.py -c tests/configs/det_mv3_db.yml -o  Global.pretrained_model=./tests/output/norm_train_gpus_0_autocast_null/latest Global.save_inference_dir=./tests/output/norm_train_gpus_-1_autocast_null!
+......
+```
+可以很方便的根据`results_python.log`中的内容判定哪一个指令运行错误。
+
+### 2.3 精度测试
+
+使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
+- 提取日志中的预测坐标；
+- 从本地文件中提取保存好的坐标结果；
+- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
+
+#### 使用方式
+运行命令：
+```shell
+python test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt  --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3
+```
+
+参数介绍：  
+- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
+- log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入
+- atol: 设置的绝对误差
+- rtol: 设置的相对误差
+
+#### 运行结果
+
+正常运行效果如下：
+```
+Assert allclose passed! The results of python_infer_cpu_usemkldnn_False_threads_1_batchsize_1.log and ./test_tipc/results/python_ppocr_det_mobile_results_fp32.txt are consistent!
+```
+
+出现不一致结果时的运行输出：
+```
+......
+Traceback (most recent call last):
+  File "test_tipc/compare_results.py", line 140, in <module>
+    format(filename, gt_filename))
+ValueError: The results of python_infer_cpu_usemkldnn_False_threads_1_batchsize_1.log and the results of ./test_tipc/results/python_ppocr_det_mobile_results_fp32.txt are inconsistent!
+```
+
+
+## 3. 更多教程
+本文档为功能测试用，更丰富的训练预测使用教程请参考：  
+[模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/training.md)  
+[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference.md)

test_tipc/docs/termux_for_android.md

+# 安卓手机通过Termux连接电脑
+
+由于通过adb方式连接手机后，很多linux命令无法运行，自动化测试受阻，所以此处特此介绍另外一种通过Termux的连接方式，不仅可以运行大部分linux命令，方便开发者在手机上在线调试，甚至还可以多实现台机器同时连接手机。Termux不是真实的Linux环境，但是Termux可以安装真实的Linux，而且不会损失性能，与此同时，Termux不需要root。在配置Termux之前，请确保电脑已经安装adb工具，安装方式请参考[Lite端部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md) 。在运行以下命令后确保可以显示安卓设备信息。
+
+```
+adb devices
+```
+连接成功信息提示：
+```
+List of devices attached
+744be294    device
+```
+
+## 1.安卓手机安装termux app
+
+### 1.1 下载termux apk文件
+
+由于目前该app目前各大商城暂无，所以可以直接下载如下apk文件。
+
+打开电脑终端，执行以下命令：
+
+```
+wget http://10.12.121.133:8911/cuicheng01/fullchain/termux-v1.0.3.apk
+```
+
+### 1.2 安装termux到手机上
+
+在手机端的开发者模式下，允许USB调试，允许USB安装。在电脑终端，执行如下命令，将termux app安装到手机上：
+
+```
+adb install termux-v1.0.3.apk
+```
+
+此处需要手机端确认安装，点击确认。
+
+### 1.3 验证是否安装成功
+
+打开手机，检验termux是否安装成功，如果没有，重新执行1.2，如果有相应的app，点击进入，会有如下显示。
+
+<img src="termux.jpg" width="300" height = "300">
+
+接下来的配置环境需要在手机上此终端运行相关命令。
+
+## 2.手机端配置termux
+
+首先将手机联网，最好可以连接外网，部分的配置需要外网。打开Termux终端，执行以下命令安装基础件`proot`，并使用`termux-chroot`命令可以模拟 root 环境与标准的 Linux 目录结构。
+
+```
+pkg i -y proot
+termux-chroot
+```
+
+Termux 默认只能访问自身内部的数据，如果要访问手机中其它的数据，输入下面的命令后，手机弹出对请求权限的窗口，允许即可（方便对部分运行出的结果在手机端可视化）。
+
+```
+termux-setup-storage
+```
+
+### 2.1 配置SSH
+
+作为 Linux 终端或者服务器，必须有SSH。不管你是 SSH 连接到 Termux还是使用Termux去连其它主机，都需要先安装openssh。如果安装失败，请重复执行命令。
+
+```
+pkg i -y openssh
+```
+
+启动 SSH 服务端，默认端口号为8022
+
+```
+sshd
+```
+
+
+### 2.2 电脑通过SSH方式连接手机
+
+1.保证手机和电脑处于同一局域网下
+手机端分别输入以下命令获得ip地址和当前用户：
+
+```
+# 获取ip地址
+ifconfig
+
+# 获取当前用户
+whoami
+```
+
+如获取到的ip地址和当前用户分别是`172.24.162.117`和`u0_a374`。
+
+2.电脑端通过SSH连接手机
+
+```
+#默认端口号为8022
+ssh u0_a374@172.24.162.117 -p 8022
+```
+
+3.运行ls命令后，会有如下显示：
+
+```
+ls
+```
+
+<img src="ssh_termux_ls.png" width="800">
+
+
+### 2.3 通过scp传输数据
+
+1.在当前目录上新建test目录
+
+```
+mkdir test
+```
+
+2.测试scp功能
+
+将电脑中的某个文件拷贝到手机上：
+```
+scp -P 8022 test.txt u0_a374@172.24.162.117:/home/storage/test
+```
+
+3.手机端查看
+
+打开手机终端，在`/home/storage/test`下查看是否存在`test.txt`
+
+
+## 3. 更多教程
+
+本教程可以完成Termux基本配置，更多关于Termux的用法，请参考：[Termux高级终端安装使用配置教程](https://www.sqlsec.com/2018/05/termux.html)。
+

test_tipc/docs/test_inference_cpp.md

+# C++预测功能测试
+
+C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于C++预测库的模型推理功能。
+
+## 1. 测试结论汇总
+
+基于训练是否使用量化，进行本测试的模型可以分为`正常模型`和`量化模型`，这两类模型对应的C++预测功能汇总如下：
+
+| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
+|  ----   |  ---- |   ----   |  :----:  |   :----:   |  :----:  |
+| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
+| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
+| 量化模型 | GPU | 1/6 | int8 | - | - |
+| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
+
+## 2. 测试流程
+运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
+
+### 2.1 功能测试
+先运行`prepare.sh`准备数据和模型，然后运行`test_inference_cpp.sh`进行测试，最终在```test_tipc/output```目录下生成`cpp_infer_*.log`后缀的日志文件。
+
+```shell
+bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt "cpp_infer"
+
+# 用法1:
+bash test_tipc/test_inference_cpp.sh test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
+# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
+bash test_tipc/test_inference_cpp.sh test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt '1'
+```  
+
+运行预测指令后，在`test_tipc/output`文件夹下自动会保存运行日志，包括以下文件：
+
+```shell
+test_tipc/output/
+|- results_cpp.log    # 运行指令状态的日志
+|- cpp_infer_cpu_usemkldnn_False_threads_1_precision_fp32_batchsize_1.log  # CPU上不开启Mkldnn，线程数设置为1，测试batch_size=1条件下的预测运行日志
+|- cpp_infer_cpu_usemkldnn_False_threads_6_precision_fp32_batchsize_1.log  # CPU上不开启Mkldnn，线程数设置为6，测试batch_size=1条件下的预测运行日志
+|- cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log # GPU上不开启TensorRT，测试batch_size=1的fp32精度预测日志
+|- cpp_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log  # GPU上开启TensorRT，测试batch_size=1的fp16精度预测日志
+......
+```
+其中results_cpp.log中包含了每条指令的运行状态，如果运行成功会输出：
+
+```
+Run successfully with command - ./deploy/cpp_infer/build/ppocr det --use_gpu=False --enable_mkldnn=False --cpu_threads=6 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmar    k=True   > ./test_tipc/output/cpp_infer_cpu_usemkldnn_False_threads_6_precision_fp32_batchsize_1.log 2>&1 !
+Run successfully with command - ./deploy/cpp_infer/build/ppocr det --use_gpu=True --use_tensorrt=False --precision=fp32 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark    =True   > ./test_tipc/output/cpp_infer_gpu_usetrt_False_precision_fp32_batchsize_1.log 2>&1 !
+......
+```
+如果运行失败，会输出：
+```
+Run failed with command - ./deploy/cpp_infer/build/ppocr det --use_gpu=True --use_tensorrt=True --precision=fp32 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark=True       > ./test_tipc/output/cpp_infer_gpu_usetrt_True_precision_fp32_batchsize_1.log 2>&1 !
+Run failed with command - ./deploy/cpp_infer/build/ppocr det --use_gpu=True --use_tensorrt=True --precision=fp16 --det_model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --rec_batch_num=1 --image_dir=./inference/ch_det_data_50/all-sum-510/ --benchmark=True       > ./test_tipc/output/cpp_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log 2>&1 !
+......
+```
+可以很方便的根据results_cpp.log中的内容判定哪一个指令运行错误。
+
+
+### 2.2 精度测试
+
+使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
+- 提取日志中的预测坐标；
+- 从本地文件中提取保存好的坐标结果；
+- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
+
+#### 使用方式
+运行命令：
+```shell
+python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/cpp_*.txt  --log_file=./test_tipc/output/cpp_*.log --atol=1e-3 --rtol=1e-3
+```
+
+参数介绍：  
+- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
+- log_file: 指向运行test_tipc/test_inference_cpp.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持cpp_infer_*.log格式传入
+- atol: 设置的绝对误差
+- rtol: 设置的相对误差
+
+#### 运行结果
+
+正常运行效果如下图：
+<img src="compare_cpp_right.png" width="1000">
+
+出现不一致结果时的运行输出：
+<img src="compare_cpp_wrong.png" width="1000">
+
+
+## 3. 更多教程
+
+本文档为功能测试用，更详细的c++预测使用教程请参考：[服务器端C++预测](https://github.com/PaddlePaddle/PaddleOCR/tree/dygraph/deploy/cpp_infer)  

test_tipc/docs/test_lite_arm_cpp.md

+# Lite\_arm\_cpp预测功能测试
+
+Lite\_arm\_cpp预测功能测试的主程序为`test_lite_arm_cpp.sh`，可以在ARM上基于Lite预测库测试模型的C++推理功能。
+
+## 1. 测试结论汇总
+
+目前Lite端的样本间支持以方式的组合：
+
+**字段说明：**
+- 模型类型：包括正常模型（FP32）和量化模型（INT8）
+- batch-size：包括1和4
+- threads：包括1和4
+- predictor数量：包括单predictor预测和多predictor预测
+- 预测库来源：包括下载方式和编译方式
+- 测试硬件：ARM\_CPU/ARM\_GPU_OPENCL
+
+| 模型类型 | batch-size | threads | predictor数量 | 预测库来源 | 测试硬件 |
+|  :----:   |  :----:  | :----:  |  :----:  |  :----:  |  :----:  |
+| 正常模型/量化模型 | 1 | 1/4 |  单/多 | 下载方式/编译方式 | ARM\_CPU/ARM\_GPU_OPENCL |
+
+
+## 2. 测试流程
+运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
+
+### 2.1 功能测试
+
+先运行`prepare_lite_cpp.sh`，运行后会在当前路径下生成`test_lite.tar`，其中包含了测试数据、测试模型和用于预测的可执行文件。将`test_lite.tar`上传到被测试的手机上，在手机的终端解压该文件，进入`test_lite`目录中，然后运行`test_lite_arm_cpp.sh`进行测试，最终在`test_lite/output`目录下生成`lite_*.log`后缀的日志文件。
+
+#### 2.1.1 基于ARM\_CPU测试
+
+```shell
+
+# 数据、模型、Paddle-Lite预测库准备
+#预测库为下载方式
+bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt download
+#预测库为编译方式
+bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt compile
+
+# 手机端测试:
+bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+
+```  
+
+#### 2.1.2 基于ARM\_GPU\_OPENCL测试
+
+```shell
+
+# 数据、模型、Paddle-Lite预测库准备
+#预测库下载方式
+bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt download
+#预测库编译方式
+bash test_tipc/prepare_lite_cpp.sh ./test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt compile
+
+# 手机端测试:
+bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
+
+```  
+
+
+**注意**：
+
+由于运行该项目需要bash等命令，传统的adb方式不能很好的安装。所以此处推荐通在手机上开启虚拟终端的方式连接电脑，连接方式可以参考[安卓手机termux连接电脑](./termux_for_android.md)。
+
+### 2.2 运行结果
+
+各测试的运行情况会打印在 `./output/` 中：
+运行成功时会输出：
+
+```
+Run successfully with command - ./ocr_db_crnn det ch_PP-OCRv2_det_infer_opt.nb ARM_CPU FP32 1 1  ./test_data/icdar2015_lite/text_localization/ch4_test_images/ ./config.txt True > ./output/lite_ch_PP-OCRv2_det_infer_opt.nb_runtime_device_ARM_CPU_precision_FP32_batchsize_1_threads_1.log 2>&1!
+Run successfully with command xxx
+...
+```
+
+运行失败时会输出：
+
+```
+Run failed with command - ./ocr_db_crnn det ch_PP-OCRv2_det_infer_opt.nb ARM_CPU FP32 1 1  ./test_data/icdar2015_lite/text_localization/ch4_test_images/ ./config.txt True > ./output/lite_ch_PP-OCRv2_det_infer_opt.nb_runtime_device_ARM_CPU_precision_FP32_batchsize_1_threads_1.log 2>&1!
+Run failed with command xxx
+...
+```
+
+在./output/文件夹下，会存在如下日志，每一个日志都是不同配置下的log结果：
+
+<img src="lite_log.png" width="1000">
+
+在每一个log中，都会调用autolog打印如下信息：
+
+<img src="lite_auto_log.png" width="1000">
+
+
+
+## 3. 更多教程
+
+本文档为功能测试用，更详细的Lite端预测使用教程请参考：[Lite端部署](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/deploy/lite/readme.md)。

test_tipc/docs/test_paddle2onnx.md

+# Paddle2onnx预测功能测试
+
+PaddleServing预测功能测试的主程序为`test_paddle2onnx.sh`，可以测试Paddle2ONNX的模型转化功能，并验证正确性。
+
+## 1. 测试结论汇总
+
+基于训练是否使用量化，进行本测试的模型可以分为`正常模型`和`量化模型`，这两类模型对应的Paddle2ONNX预测功能汇总如下：
+
+| 模型类型 |device |
+|  ----   |  ---- |  
+| 正常模型 | GPU |
+| 正常模型 | CPU |
+| 量化模型 | GPU |
+| 量化模型 | CPU |
+
+## 2. 测试流程
+### 2.1 功能测试
+先运行`prepare.sh`准备数据和模型，然后运行`test_paddle2onnx.sh`进行测试，最终在```test_tipc/output```目录下生成`paddle2onnx_infer_*.log`后缀的日志文件。
+
+```shell
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt "paddle2onnx_infer"
+
+# 用法:
+bash test_tipc/test_paddle2onnx.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
+```  
+
+#### 运行结果
+
+各测试的运行情况会打印在 `test_tipc/output/results_paddle2onnx.log` 中：
+运行成功时会输出：
+
+```
+Run successfully with command -  paddle2onnx  --model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --model_filename=inference.pdmodel --params_filename=inference.pdiparams --save_file=./inference/det_mobile_onnx/model.onnx --opset_version=10  --enable_onnx_checker=True!
+Run successfully with command - python test_tipc/onnx_inference/predict_det.py --use_gpu=False --image_dir=./inference/ch_det_data_50/all-sum-510/ --det_model_dir=./inference/det_mobile_onnx/model.onnx  2>&1 !
+```
+
+运行失败时会输出：
+
+```
+Run failed with command - paddle2onnx  --model_dir=./inference/ch_ppocr_mobile_v2.0_det_infer/ --model_filename=inference.pdmodel --params_filename=inference.pdiparams --save_file=./inference/det_mobile_onnx/model.onnx --opset_version=10  --enable_onnx_checker=True!
+...
+```
+
+
+## 3. 更多教程
+
+本文档为功能测试用，更详细的Paddle2onnx预测使用教程请参考：[Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX)  

test_tipc/docs/test_serving.md

+# PaddleServing预测功能测试
+
+PaddleServing预测功能测试的主程序为`test_serving.sh`，可以测试基于PaddleServing的部署功能。
+
+## 1. 测试结论汇总
+
+基于训练是否使用量化，进行本测试的模型可以分为`正常模型`和`量化模型`，这两类模型对应的Serving预测功能汇总如下：
+
+| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
+|  ----   |  ---- |   ----   |  :----:  |   :----:   |  :----:  |
+| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
+| 正常模型 | CPU | 1/6 | - | fp32 | 支持 |
+| 量化模型 | GPU | 1/6 | int8 | - | - |
+| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
+
+## 2. 测试流程
+运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
+
+### 2.1 功能测试
+先运行`prepare.sh`准备数据和模型，然后运行`test_serving.sh`进行测试，最终在```test_tipc/output```目录下生成`serving_infer_*.log`后缀的日志文件。
+
+```shell
+bash test_tipc/prepare.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt "serving_infer"
+
+# 用法:
+bash test_tipc/test_serving.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
+```  
+
+#### 运行结果
+
+各测试的运行情况会打印在 `test_tipc/output/results_serving.log` 中：
+运行成功时会输出：
+
+```
+Run successfully  with command - python3.7 pipeline_http_client.py --image_dir=../../doc/imgs > ../../tests/output/server_infer_cpu_usemkldnn_True_threads_1_batchsize_1.log 2>&1 !
+Run successfully  with command - xxxxx
+...
+```
+
+运行失败时会输出：
+
+```
+Run failed with command - python3.7 pipeline_http_client.py --image_dir=../../doc/imgs > ../../tests/output/server_infer_cpu_usemkldnn_True_threads_1_batchsize_1.log 2>&1 !
+Run failed with command - python3.7 pipeline_http_client.py --image_dir=../../doc/imgs > ../../tests/output/server_infer_cpu_usemkldnn_True_threads_6_batchsize_1.log 2>&1 !
+Run failed with command - xxxxx
+...
+```
+
+详细的预测结果会存在 test_tipc/output/ 文件夹下，例如`server_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log`中会返回检测框的坐标:
+
+```
+{'err_no': 0, 'err_msg': '', 'key': ['dt_boxes'], 'value': ['[[[ 78. 642.]\n  [409. 640.]\n  [409. 657.]\n  
+[ 78. 659.]]\n\n [[ 75. 614.]\n  [211. 614.]\n      [211. 635.]\n  [ 75. 635.]]\n\n
+[[103. 554.]\n  [135. 554.]\n  [135. 575.]\n  [103. 575.]]\n\n [[ 75. 531.]\n  
+[347. 531.]\n  [347. 549.]\n  [ 75. 549.]    ]\n\n [[ 76. 503.]\n  [309. 498.]\n  
+[309. 521.]\n  [ 76. 526.]]\n\n [[163. 462.]\n  [317. 462.]\n  [317. 493.]\n  
+[163. 493.]]\n\n [[324. 431.]\n  [414.     431.]\n  [414. 452.]\n  [324. 452.]]\n\n
+[[ 76. 412.]\n  [208. 408.]\n  [209. 424.]\n  [ 76. 428.]]\n\n [[307. 409.]\n  
+[428. 409.]\n  [428. 426.]\n  [307    . 426.]]\n\n [[ 74. 385.]\n  [217. 382.]\n  
+[217. 400.]\n  [ 74. 403.]]\n\n [[308. 381.]\n  [427. 380.]\n  [427. 400.]\n  
+[308. 401.]]\n\n [[ 74. 363.]\n      [195. 362.]\n  [195. 378.]\n  [ 74. 379.]]\n\n
+[[303. 359.]\n  [423. 357.]\n  [423. 375.]\n  [303. 377.]]\n\n [[ 70. 336.]\n  
+[239. 334.]\n  [239. 354.]\    n  [ 70. 356.]]\n\n [[ 70. 312.]\n  [204. 310.]\n  
+[204. 327.]\n  [ 70. 330.]]\n\n [[303. 308.]\n  [419. 306.]\n  [419. 326.]\n  
+[303. 328.]]\n\n [[113. 2    72.]\n  [246. 270.]\n  [247. 299.]\n  [113. 301.]]\n\n
+ [[361. 269.]\n  [384. 269.]\n  [384. 296.]\n  [361. 296.]]\n\n [[ 70. 250.]\n
+ [243. 246.]\n  [243.     265.]\n  [ 70. 269.]]\n\n [[ 65. 221.]\n  [187. 220.]\n  
+[187. 240.]\n  [ 65. 241.]]\n\n [[337. 216.]\n  [382. 216.]\n  [382. 240.]\n  
+[337. 240.]]\n\n [    [ 65. 196.]\n  [247. 193.]\n  [247. 213.]\n  [ 65. 216.]]\n\n
+[[296. 197.]\n  [423. 191.]\n  [424. 209.]\n  [296. 215.]]\n\n [[ 65. 167.]\n  [244. 167.]\n  
+[244. 186.]\n  [ 65. 186.]]\n\n [[ 67. 139.]\n  [290. 139.]\n  [290. 159.]\n  [ 67. 159.]]\n\n
+[[ 68. 113.]\n  [410. 113.]\n  [410. 128.]\n  [ 68. 129.]    ]\n\n [[277.  87.]\n  [416.  87.]\n  
+[416. 108.]\n  [277. 108.]]\n\n [[ 79.  28.]\n  [132.  28.]\n  [132.  62.]\n  [ 79.  62.]]\n\n
+[[163.  17.]\n  [410.      14.]\n  [410.  50.]\n  [163.  53.]]]']}
+```
+
+
+## 3. 更多教程
+
+本文档为功能测试用，更详细的Serving预测使用教程请参考：[PPOCR 服务化部署](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/deploy/pdserving/README_CN.md)  

test_tipc/docs/test_train_inference_python.md

+# Linux端基础训练预测功能测试
+
+Linux端基础训练预测功能测试的主程序为`test_train_inference_python.sh`，可以测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
+
+- Mac端基础训练预测功能测试参考[链接](./mac_test_train_inference_python.md)
+- Windows端基础训练预测功能测试参考[链接](./win_test_train_inference_python.md)
+
+## 1. 测试结论汇总
+
+- 训练相关：
+
+| 算法名称 | 模型名称 | 单机单卡 | 单机多卡 | 多机多卡 | 模型压缩（单机多卡） |
+|  :----  |   :----  |    :----  |  :----   |  :----   |  :----   |
+|  DB  | ch_ppocr_mobile_v2.0_det| 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练：FPGM裁剪、PACT量化 <br> 离线量化（无需训练） |
+|  DB  | ch_ppocr_server_v2.0_det| 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练：FPGM裁剪、PACT量化 <br> 离线量化（无需训练） |
+| CRNN | ch_ppocr_mobile_v2.0_rec| 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练：PACT量化 <br> 离线量化（无需训练） |
+| CRNN | ch_ppocr_server_v2.0_rec| 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练：PACT量化 <br> 离线量化（无需训练） |
+|PP-OCR| ch_ppocr_mobile_v2.0| 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | - |
+|PP-OCR| ch_ppocr_server_v2.0| 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | - |
+|PP-OCRv2| ch_PP-OCRv2 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | 正常训练 <br> 混合精度 | - |
+
+
+- 预测相关：基于训练是否使用量化，可以将训练产出的模型可以分为`正常模型`和`量化模型`，这两类模型对应的预测功能汇总如下，
+
+| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
+|  ----   |  ---- |   ----   |  :----:  |   :----:   |  :----:  |
+| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
+| 正常模型 | CPU | 1/6 | - | fp32/fp16 | 支持 |
+| 量化模型 | GPU | 1/6 | int8 | - | - |
+| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
+
+
+## 2. 测试流程
+
+运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
+
+### 2.1 安装依赖
+- 安装PaddlePaddle >= 2.0
+- 安装PaddleOCR依赖
+    ```
+    pip3 install  -r ../requirements.txt
+    ```
+- 安装autolog（规范化日志输出工具）
+    ```
+    git clone https://github.com/LDOUBLEV/AutoLog
+    cd AutoLog
+    pip3 install -r requirements.txt
+    python3 setup.py bdist_wheel
+    pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
+    cd ../
+    ```
+- 安装PaddleSlim (可选)
+   ```
+   # 如果要测试量化、裁剪等功能，需要安装PaddleSlim
+   pip3 install paddleslim
+   ```
+
+
+### 2.2 功能测试
+先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
+
+
+`test_train_inference_python.sh`包含5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_lite_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_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
+```  
+
+- 模式2：lite_train_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt  'lite_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ../test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_whole_infer'
+```  
+
+- 模式3：whole_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'whole_infer'
+# 用法1:
+bash test_tipc/test_train_inference_python.sh ../test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'whole_infer'
+# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'whole_infer' '1'
+```  
+
+- 模式4：whole_train_whole_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'whole_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'whole_train_whole_infer'
+```  
+
+- 模式5：klquant_whole_infer，测试离线量化；
+```shell
+bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt  'klquant_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt  'klquant_whole_infer'
+```
+
+运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：
+```
+test_tipc/output/
+|- results_python.log    # 运行指令状态的日志
+|- norm_train_gpus_0_autocast_null/  # GPU 0号卡上正常训练的训练日志和模型保存文件夹
+|- pact_train_gpus_0_autocast_null/  # GPU 0号卡上量化训练的训练日志和模型保存文件夹
+......
+|- python_infer_cpu_usemkldnn_True_threads_1_batchsize_1.log  # CPU上开启Mkldnn线程数设置为1，测试batch_size=1条件下的预测运行日志
+|- python_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log # GPU上开启TensorRT，测试batch_size=1的半精度预测日志
+......
+```
+
+其中`results_python.log`中包含了每条指令的运行状态，如果运行成功会输出：
+```
+Run successfully with command - python3.7 tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained Global.use_gpu=True  Global.save_model_dir=./tests/output/norm_train_gpus_0_autocast_null Global.epoch_num=1     Train.loader.batch_size_per_card=2   !
+Run successfully with command - python3.7 tools/export_model.py -c tests/configs/det_mv3_db.yml -o  Global.pretrained_model=./tests/output/norm_train_gpus_0_autocast_null/latest Global.save_inference_dir=./tests/output/norm_train_gpus_0_autocast_null!
+......
+```
+如果运行失败，会输出：
+```
+Run failed with command - python3.7 tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained Global.use_gpu=True  Global.save_model_dir=./tests/output/norm_train_gpus_0_autocast_null Global.epoch_num=1     Train.loader.batch_size_per_card=2   !
+Run failed with command - python3.7 tools/export_model.py -c tests/configs/det_mv3_db.yml -o  Global.pretrained_model=./tests/output/norm_train_gpus_0_autocast_null/latest Global.save_inference_dir=./tests/output/norm_train_gpus_0_autocast_null!
+......
+```
+可以很方便的根据`results_python.log`中的内容判定哪一个指令运行错误。
+
+
+### 2.3 精度测试
+
+使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
+- 提取日志中的预测坐标；
+- 从本地文件中提取保存好的坐标结果；
+- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
+
+#### 使用方式
+运行命令：
+```shell
+python3.7 test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt  --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3
+```
+
+参数介绍：  
+- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
+- log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入
+- atol: 设置的绝对误差
+- rtol: 设置的相对误差
+
+#### 运行结果
+
+正常运行效果如下图：
+<img src="compare_right.png" width="1000">
+
+出现不一致结果时的运行输出：
+<img src="compare_wrong.png" width="1000">
+
+
+## 3. 更多教程
+本文档为功能测试用，更丰富的训练预测使用教程请参考：  
+[模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/training.md)  
+[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference.md)

test_tipc/docs/win_test_train_inference_python.md

+# Windows端基础训练预测功能测试
+
+Windows端基础训练预测功能测试的主程序为`test_train_inference_python.sh`，可以测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
+
+## 1. 测试结论汇总
+
+- 训练相关：
+
+| 算法名称 | 模型名称 | 单机单卡 | 单机多卡 | 多机多卡 | 模型压缩（单机多卡） |
+|  :----  |   :----  |    :----  |  :----   |  :----   |  :----   |
+|  DB  | ch_ppocr_mobile_v2.0_det| 正常训练 <br> 混合精度 | - | - | 正常训练：FPGM裁剪、PACT量化 <br> 离线量化（无需训练） |
+
+
+- 预测相关：基于训练是否使用量化，可以将训练产出的模型可以分为`正常模型`和`量化模型`，这两类模型对应的预测功能汇总如下：
+
+| 模型类型 |device | batchsize | tensorrt | mkldnn | cpu多线程 |
+|  ----   |  ---- |   ----   |  :----:  |   :----:   |  :----:  |
+| 正常模型 | GPU | 1/6 | fp32/fp16 | - | - |
+| 正常模型 | CPU | 1/6 | - | fp32/fp16 | 支持 |
+| 量化模型 | GPU | 1/6 | int8 | - | - |
+| 量化模型 | CPU | 1/6 | - | int8 | 支持 |
+
+
+## 2. 测试流程
+
+运行环境配置请参考[文档](./install.md)的内容配置TIPC的运行环境。
+
+另外，由于Windows上和linux的路径管理方式不同，可以在win上安装gitbash终端，在gitbash中执行指令的方式和在linux端执行指令方式相同，更方便tipc测试。gitbash[下载链接](https://git-scm.com/download/win)。
+
+
+### 2.1 安装依赖
+- 安装PaddlePaddle >= 2.0
+- 安装PaddleOCR依赖
+    ```
+    pip install  -r ../requirements.txt
+    ```
+- 安装autolog（规范化日志输出工具）
+    ```
+    git clone https://github.com/LDOUBLEV/AutoLog
+    cd AutoLog
+    pip  install -r requirements.txt
+    python setup.py bdist_wheel
+    pip  install ./dist/auto_log-1.0.0-py3-none-any.whl
+    cd ../
+    ```
+- 安装PaddleSlim (可选)
+   ```
+   # 如果要测试量化、裁剪等功能，需要安装PaddleSlim
+   pip install paddleslim
+   ```
+
+
+### 2.2 功能测试
+先运行`prepare.sh`准备数据和模型，然后运行`test_train_inference_python.sh`进行测试，最终在```test_tipc/output```目录下生成`python_infer_*.log`格式的日志文件。
+
+
+`test_train_inference_python.sh`包含5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_lite_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_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'lite_train_lite_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'lite_train_lite_infer'
+```  
+
+- 模式2：lite_train_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'lite_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'lite_train_whole_infer'
+```  
+
+- 模式3：whole_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'whole_infer'
+# 用法1:
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'whole_infer'
+# 用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'whole_infer' '1'
+```  
+
+- 模式4：whole_train_whole_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 test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'whole_train_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'whole_train_whole_infer'
+```  
+
+- 模式5：klquant_whole_infer，测试离线量化；
+```shell
+bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_windows_gpu_cpu.txt  'klquant_whole_infer'
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_windows_gpu_cpu.txt  'klquant_whole_infer'
+```
+
+
+运行相应指令后，在`test_tipc/output`文件夹下自动会保存运行日志。如'lite_train_lite_infer'模式下，会运行训练+inference的链条，因此，在`test_tipc/output`文件夹有以下文件：
+```
+test_tipc/output/
+|- results_python.log    # 运行指令状态的日志
+|- norm_train_gpus_0_autocast_null/  # GPU 0号卡上正常训练的训练日志和模型保存文件夹
+|- pact_train_gpus_0_autocast_null/  # GPU 0号卡上量化训练的训练日志和模型保存文件夹
+......
+|- python_infer_cpu_usemkldnn_True_threads_1_batchsize_1.log  # CPU上开启Mkldnn线程数设置为1，测试batch_size=1条件下的预测运行日志
+|- python_infer_gpu_usetrt_True_precision_fp16_batchsize_1.log # GPU上开启TensorRT，测试batch_size=1的半精度预测日志
+......
+```
+
+其中`results_python.log`中包含了每条指令的运行状态，如果运行成功会输出：
+```
+Run successfully with command - python3.7 tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained Global.use_gpu=True  Global.save_model_dir=./tests/output/norm_train_gpus_0_autocast_null Global.epoch_num=1     Train.loader.batch_size_per_card=2   !
+Run successfully with command - python3.7 tools/export_model.py -c tests/configs/det_mv3_db.yml -o  Global.pretrained_model=./tests/output/norm_train_gpus_0_autocast_null/latest Global.save_inference_dir=./tests/output/norm_train_gpus_0_autocast_null!
+......
+```
+如果运行失败，会输出：
+```
+Run failed with command - python3.7 tools/train.py -c tests/configs/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained Global.use_gpu=True  Global.save_model_dir=./tests/output/norm_train_gpus_0_autocast_null Global.epoch_num=1     Train.loader.batch_size_per_card=2   !
+Run failed with command - python3.7 tools/export_model.py -c tests/configs/det_mv3_db.yml -o  Global.pretrained_model=./tests/output/norm_train_gpus_0_autocast_null/latest Global.save_inference_dir=./tests/output/norm_train_gpus_0_autocast_null!
+......
+```
+可以很方便的根据`results_python.log`中的内容判定哪一个指令运行错误。
+
+
+### 2.3 精度测试
+
+使用compare_results.py脚本比较模型预测的结果是否符合预期，主要步骤包括：
+- 提取日志中的预测坐标；
+- 从本地文件中提取保存好的坐标结果；
+- 比较上述两个结果是否符合精度预期，误差大于设置阈值时会报错。
+
+#### 使用方式
+运行命令：
+```shell
+python test_tipc/compare_results.py --gt_file=./test_tipc/results/python_*.txt  --log_file=./test_tipc/output/python_*.log --atol=1e-3 --rtol=1e-3
+```
+
+参数介绍：  
+- gt_file： 指向事先保存好的预测结果路径，支持*.txt 结尾，会自动索引*.txt格式的文件，文件默认保存在test_tipc/result/ 文件夹下
+- log_file: 指向运行test_tipc/test_train_inference_python.sh 脚本的infer模式保存的预测日志，预测日志中打印的有预测结果，比如：文本框，预测文本，类别等等，同样支持python_infer_*.log格式传入
+- atol: 设置的绝对误差
+- rtol: 设置的相对误差
+
+#### 运行结果
+
+正常运行效果如下：
+```
+Assert allclose passed! The results of python_infer_cpu_usemkldnn_False_threads_1_batchsize_1.log and ./test_tipc/results/python_ppocr_det_mobile_results_fp32.txt are consistent!
+```
+
+出现不一致结果时的运行输出：
+```
+......
+Traceback (most recent call last):
+  File "test_tipc/compare_results.py", line 140, in <module>
+    format(filename, gt_filename))
+ValueError: The results of python_infer_cpu_usemkldnn_False_threads_1_batchsize_1.log and the results of ./test_tipc/results/python_ppocr_det_mobile_results_fp32.txt are inconsistent!
+```
+
+
+## 3. 更多教程
+本文档为功能测试用，更丰富的训练预测使用教程请参考：  
+[模型训练](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/training.md)  
+[基于Python预测引擎推理](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_ch/inference.md)

tests/prepare.sh → test_tipc/prepare.sh

 #!/bin/bash
+source test_tipc/common_func.sh
+
 FILENAME=$1
 
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer', 'serving_infer']
+# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer',  
+#                 'whole_infer', 'klquant_whole_infer',
+#                 'cpp_infer', 'serving_infer']
+
 MODE=$2
 
 dataline=$(cat ${FILENAME})
@@ -9,108 +14,178 @@ dataline=$(cat ${FILENAME})
 # parser params
 IFS=$'\n'
 lines=(${dataline})
-function func_parser_key(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[0]}
-    echo ${tmp}
-}
-function func_parser_value(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[1]}
-    echo ${tmp}
-}
-IFS=$'\n'
+
 # The training params
 model_name=$(func_parser_value "${lines[1]}")
 
 trainer_list=$(func_parser_value "${lines[14]}")
 
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
-MODE=$2
 
-if [ ${MODE} = "lite_train_infer" ];then
+if [ ${MODE} = "lite_train_lite_infer" ];then
     # pretrain lite train data
-    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-    wget -nc -P ./pretrain_models/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
+    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams  --no-check-certificate
+    wget -nc -P ./pretrain_models/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar  --no-check-certificate
+    if [ ${model_name} == "ch_PPOCRv2_det" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
+    fi
     cd ./pretrain_models/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
     rm -rf ./train_data/icdar2015
     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
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar --no-check-certificate
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar --no-check-certificate
+    wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar --no-check-certificate
+    wget -nc -P ./deploy/slim/prune https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle --no-check-certificate
     
     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
+    if [ ${model_name} == "en_server_pgnetA" ]; then
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf en_server_pgnetA.tar && cd ../
+        cd ./train_data && tar xf total_text_lite.tar && ln -s total_text && cd ../
+    fi
+    if [ ${model_name} == "det_r50_vd_sast_icdar15_v2.0" ] || [ ${model_name} == "det_r50_vd_sast_totaltext_v2.0" ]; then
+        wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
+        wget -nc -P ./train_data/ wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/total_text_lite.tar --no-check-certificate
+        cd ./train_data && tar xf total_text_lite.tar && ln -s total_text && cd ../
+    fi
+    if [ ${model_name} == "det_mv3_db_v2.0" ]; then
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar  --no-check-certificate
+        cd ./inference/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
+    fi
+    if [ ${model_name} == "det_r50_db_v2.0" ]; then
+        wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar  --no-check-certificate
+        cd ./inference/ && tar xf det_r50_vd_db_v2.0_train.tar && cd ../
+    fi
+
+elif [ ${MODE} = "whole_train_whole_infer" ];then
+    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
     rm -rf ./train_data/icdar2015
     rm -rf ./train_data/ic15_data
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar --no-check-certificate
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar --no-check-certificate
     cd ./train_data/ && tar xf icdar2015.tar && tar xf ic15_data.tar && cd ../
-elif [ ${MODE} = "whole_infer" ];then
-    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
+    if [ ${model_name} == "ch_PPOCRv2_det" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
+    fi
+    if [ ${model_name} == "en_server_pgnetA" ]; then
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/total_text.tar --no-check-certificate
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf en_server_pgnetA.tar && cd ../
+        cd ./train_data && tar xf total_text.tar && ln -s total_text && cd ../
+    fi
+    if [ ${model_name} == "det_r50_vd_sast_totaltext_v2.0" ]; then
+        wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams --no-check-certificate
+        wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dataset/total_text.tar --no-check-certificate
+        cd ./train_data && tar xf total_text.tar && ln -s total_text && cd ../
+    fi
+elif [ ${MODE} = "lite_train_whole_infer" ];then
+    wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams --no-check-certificate
     rm -rf ./train_data/icdar2015
     rm -rf ./train_data/ic15_data
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar --no-check-certificate
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar --no-check-certificate
     cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
     ln -s ./icdar2015_infer ./icdar2015
     cd ../
-elif [ ${MODE} = "infer" ];then
-    if [ ${model_name} = "ocr_det" ]; then
+    if [ ${model_name} == "ch_PPOCRv2_det" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf ch_PP-OCRv2_det_distill_train.tar && cd ../
+    fi
+elif [ ${MODE} = "whole_infer" ];then
+    wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
+    if [ ${model_name} = "ch_ppocr_mobile_v2.0_det" ]; then
         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_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_mobile" ]; 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
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
+        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && cd ../
+    elif [ ${model_name} = "ch_ppocr_server_v2.0_det" ]; then
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar --no-check-certificate
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar  --no-check-certificate
+        cd ./inference && tar xf ch_ppocr_server_v2.0_det_train.tar && tar xf ch_det_data_50.tar && cd ../
+    elif  [ ${model_name} = "ch_ppocr_mobile_v2.0" ]; then
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar --no-check-certificate
         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 ../
-    elif  [ ${model_name} = "ocr_system_server" ]; 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
-        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar
+    elif  [ ${model_name} = "ch_ppocr_server_v2.0" ]; then
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar --no-check-certificate
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar --no-check-certificate
         cd ./inference && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_det_data_50.tar && cd ../
-    elif [ ${model_name} = "ocr_rec" ]; then
-        rm -rf ./train_data/ic15_data
+    elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec" ]; then
         eval_model_name="ch_ppocr_mobile_v2.0_rec_infer"
-        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
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar --no-check-certificate
         cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../
-    elif [ ${model_name} = "ocr_server_rec" ]; then
-        rm -rf ./train_data/ic15_data
+    elif [ ${model_name} = "ch_ppocr_server_v2.0_rec" ]; then
         eval_model_name="ch_ppocr_server_v2.0_rec_infer"
-        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_server_v2.0_rec_infer.tar
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar --no-check-certificate
         cd ./inference && tar xf ${eval_model_name}.tar && tar xf rec_inference.tar && cd ../
     fi 
-elif [ ${MODE} = "cpp_infer" ];then
+    if [ ${model_name} = "ch_PPOCRv2_det" ]; then
+        eval_model_name="ch_PP-OCRv2_det_infer"
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar --no-check-certificate
+        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
+    fi
+    if [ ${model_name} = "ch_PPOCRv2_det" ]; then
+        wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar --no-check-certificate
+        wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/e2e_server_pgnetA_infer.tar --no-check-certificate
+         cd ./inference && tar xf e2e_server_pgnetA_infer.tar && tar xf ch_det_data_50.tar && cd ../ 
+    fi
+    if [ ${model_name} == "en_server_pgnetA" ]; then
+        wget -nc -P ./inference/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar  --no-check-certificate
+        cd ./inference && tar xf en_server_pgnetA.tar && cd ../
+    fi
+    if [ ${model_name} == "det_r50_vd_sast_icdar15_v2.0" ]; then
+        wget -nc -P  ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar --no-check-certificate
+        cd ./inference/ && tar xf det_r50_vd_sast_icdar15_v2.0_train.tar && cd ../
+    fi
+    if [ ${model_name} == "det_mv3_db_v2.0" ]; then
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar  --no-check-certificate
+        cd ./inference/ && tar xf det_mv3_db_v2.0_train.tar && cd ../
+    fi
+    if [ ${model_name} == "det_r50_db_v2.0" ]; then
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_db_v2.0_train.tar  --no-check-certificate
+        cd ./inference/ && tar xf det_r50_vd_db_v2.0_train.tar && cd ../
+    fi
+fi
+if [ ${MODE} = "klquant_whole_infer" ]; then
+    if [ ${model_name} = "ch_ppocr_mobile_v2.0_det" ]; then
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar --no-check-certificate
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar  --no-check-certificate
+        cd ./inference && tar xf ch_ppocr_mobile_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && cd ../ 
+    fi
+    if [ ${model_name} = "ch_PPOCRv2_det" ]; then
+        eval_model_name="ch_PP-OCRv2_det_infer"
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar  --no-check-certificate
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar  --no-check-certificate
+        cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
+    fi 
+fi
+
+if [ ${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
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar  --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar  --no-check-certificate
         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
+    elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec" ]; then
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/rec_inference.tar  --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar  --no-check-certificate
         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
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar  --no-check-certificate
+        wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar  --no-check-certificate
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar  --no-check-certificate
         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
@@ -126,79 +201,22 @@ if [ ${MODE} = "serving_infer" ];then
     wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
     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/ch/ch_ppocr_server_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_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.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_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_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
-        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)/opencv3
-
-            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
-
-            make -j
-            make install
-            cd ../
-            echo "################### build opencv finished ###################"
-        fi
-    fi
-
-
-    echo "################### build PaddleOCR demo ####################"
-    if [ ${use_opencv} = "True" ]; then
-        OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
-    else
-        OPENCV_DIR=''
-    fi
-    LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
-    CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
-    CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
+if [ ${MODE} = "paddle2onnx_infer" ];then
+    # prepare serving env
+    python_name=$(func_parser_value "${lines[2]}")
+    ${python_name} -m pip install install paddle2onnx
+    ${python_name} -m pip install onnxruntime==1.4.0
+    # wget model
+    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
+    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/ch/ch_ppocr_server_v2.0_rec_infer.tar
+    # wget data
+    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/test/rec_inference.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_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_det_data_50.tar && tar xf rec_inference.tar && cd ../
     
-    BUILD_DIR=build
-    rm -rf ${BUILD_DIR}
-    mkdir ${BUILD_DIR}
-    cd ${BUILD_DIR}
-    cmake .. \
-        -DPADDLE_LIB=${LIB_DIR} \
-        -DWITH_MKL=ON \
-        -DWITH_GPU=OFF \
-        -DWITH_STATIC_LIB=OFF \
-        -DWITH_TENSORRT=OFF \
-        -DOPENCV_DIR=${OPENCV_DIR} \
-        -DCUDNN_LIB=${CUDNN_LIB_DIR} \
-        -DCUDA_LIB=${CUDA_LIB_DIR} \
-        -DTENSORRT_DIR=${TENSORRT_DIR} \
-
-    make -j
-    echo "################### build PaddleOCR demo finished ###################"
 fi

test_tipc/prepare_lite_cpp.sh

+#!/bin/bash
+source ./test_tipc/common_func.sh
+FILENAME=$1
+dataline=$(cat ${FILENAME})
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+IFS=$'\n'
+paddlelite_library_source=$2
+
+inference_cmd=$(func_parser_value "${lines[1]}")
+DEVICE=$(func_parser_value "${lines[2]}")
+det_lite_model_list=$(func_parser_value "${lines[3]}")
+rec_lite_model_list=$(func_parser_value "${lines[4]}")
+cls_lite_model_list=$(func_parser_value "${lines[5]}")
+
+if [[ $inference_cmd =~ "det" ]]; then
+    lite_model_list=${det_lite_model_list}
+elif [[ $inference_cmd =~ "rec" ]]; then
+    lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
+elif [[ $inference_cmd =~ "system" ]]; then
+    lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
+else
+    echo "inference_cmd is wrong, please check."
+    exit 1
+fi
+
+if [ ${DEVICE} = "ARM_CPU" ]; then
+    valid_targets="arm"
+    paddlelite_library_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz"
+    end_index="66"
+    compile_with_opencl="OFF"
+elif [ ${DEVICE} = "ARM_GPU_OPENCL" ]; then
+    valid_targets="opencl"
+    paddlelite_library_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.armv8.clang.with_exception.with_extra.with_cv.opencl.tar.gz"
+    end_index="71"
+    compile_with_opencl="ON"
+else
+    echo "DEVICE only support ARM_CPU, ARM_GPU_OPENCL."
+    exit 2    
+fi
+
+# prepare paddlelite model
+pip install paddlelite==2.10-rc
+current_dir=${PWD}
+IFS="|"
+model_path=./inference_models
+
+for model in ${lite_model_list[*]}; do
+    if [[ $model =~ "PP-OCRv2" ]]; then
+        inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
+    elif [[ $model =~ "v2.0" ]]; then
+        inference_model_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/${model}.tar
+    else 
+        echo "Model is wrong, please check."
+        exit 3
+    fi
+    inference_model=${inference_model_url##*/}
+    wget -nc  -P ${model_path} ${inference_model_url}
+    cd ${model_path} && tar -xf ${inference_model} && cd ../
+    model_dir=${model_path}/${inference_model%.*}
+    model_file=${model_dir}/inference.pdmodel
+    param_file=${model_dir}/inference.pdiparams
+    paddle_lite_opt --model_dir=${model_dir} --model_file=${model_file} --param_file=${param_file} --valid_targets=${valid_targets} --optimize_out=${model_dir}_opt
+done
+
+# prepare test data
+data_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
+data_file=${data_url##*/}
+wget -nc  -P ./test_data ${data_url}
+cd ./test_data && tar -xf ${data_file} && rm ${data_file} && cd ../
+
+# prepare paddlelite predict library
+if [[ ${paddlelite_library_source} = "download" ]]; then
+    paddlelite_library_zipfile=$(echo $paddlelite_library_url | awk -F "/" '{print $NF}')
+    paddlelite_library_file=${paddlelite_library_zipfile:0:${end_index}}
+    wget ${paddlelite_library_url} && tar -xf ${paddlelite_library_zipfile}
+    cd ${paddlelite_library_zipfile}
+elif [[ ${paddlelite_library_source} = "compile" ]]; then
+    git clone -b release/v2.10 https://github.com/PaddlePaddle/Paddle-Lite.git
+    cd Paddle-Lite
+    ./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON --toolchain=clang --with_opencl=${compile_with_opencl}
+    cd ../
+    cp -r Paddle-Lite/build.lite.android.armv8.clang/inference_lite_lib.android.armv8/ .
+    paddlelite_library_file=inference_lite_lib.android.armv8
+else
+    echo "paddlelite_library_source only support 'download' and 'compile'"
+    exit 3
+fi
+
+# organize the required files  
+mkdir -p  ${paddlelite_library_file}/demo/cxx/ocr/test_lite
+cp -r ${model_path}/*_opt.nb test_data ${paddlelite_library_file}/demo/cxx/ocr/test_lite
+cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_library_file}/demo/cxx/ocr/test_lite
+cp -r ./deploy/lite/* ${paddlelite_library_file}/demo/cxx/ocr/
+cp ${paddlelite_library_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_library_file}/demo/cxx/ocr/test_lite
+cp ${FILENAME} test_tipc/test_lite_arm_cpp.sh test_tipc/common_func.sh ${paddlelite_library_file}/demo/cxx/ocr/test_lite
+cd ${paddlelite_library_file}/demo/cxx/ocr/
+git clone https://github.com/cuicheng01/AutoLog.git
+
+# compile and do some postprocess
+make -j
+sleep 1
+make -j
+cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
+tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
+rm -rf ${paddlelite_library_file}* && rm -rf ${model_path}

test_tipc/readme.md

+
+# 飞桨训推一体认证（TIPC）
+
+## 1. 简介
+
+飞桨除了基本的模型训练和预测，还提供了支持多端多平台的高性能推理部署工具。本文档提供了PaddleOCR中所有模型的飞桨训推一体认证 (Training and Inference Pipeline Certification(TIPC)) 信息和测试工具，方便用户查阅每种模型的训练推理部署打通情况，并可以进行一键测试。
+
+<div align="center">
+    <img src="docs/guide.png" width="1000">
+</div>
+
+## 2. 汇总信息
+
+打通情况汇总如下，已填写的部分表示可以使用本工具进行一键测试，未填写的表示正在支持中。
+
+**字段说明：**
+- 基础训练预测：包括模型训练、Paddle Inference Python预测。
+- 更多训练方式：包括多机多卡、混合精度。
+- 模型压缩：包括裁剪、离线/在线量化、蒸馏。
+- 其他预测部署：包括Paddle Inference C++预测、Paddle Serving部署、Paddle-Lite部署等。
+
+更详细的mkldnn、Tensorrt等预测加速相关功能的支持情况可以查看各测试工具的[更多教程](#more)。
+
+| 算法论文 | 模型名称 | 模型类型 | 基础<br>训练预测 | 更多<br>训练方式 | 模型压缩 |  其他预测部署  |
+| :--- | :--- |  :----:  | :--------: |  :----  |   :----  |   :----  |
+| DB     |ch_ppocr_mobile_v2.0_det | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| DB     |ch_ppocr_mobile_v2.0_det_FPGM | 检测  | 支持 | 多机多卡 <br> 混合精度 | FPGM裁剪 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| DB     |ch_ppocr_mobile_v2.0_det_PACT | 检测  | 支持 | 多机多卡 <br> 混合精度 | PACT量化 | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| DB     |ch_ppocr_mobile_v2.0_det_KL | 检测  | 支持 | 多机多卡 <br> 混合精度 | 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| DB     |ch_ppocr_server_v2.0_det | 检测  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
+| DB     |ch_PP-OCRv2_det          | 检测  |
+| CRNN   |ch_ppocr_mobile_v2.0_rec | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| CRNN   |ch_ppocr_server_v2.0_rec | 识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
+| CRNN   |ch_PP-OCRv2_rec          | 识别  |
+| PP-OCR |ch_ppocr_mobile_v2.0 | 检测+识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| PP-OCR |ch_ppocr_server_v2.0 | 检测+识别  | 支持 | 多机多卡 <br> 混合精度 | - | Paddle Inference: C++ <br> Paddle Serving: Python, C++ |
+|PP-OCRv2|ch_PP-OCRv2 | 检测+识别  |
+| DB     |det_mv3_db_v2.0                | 检测  |
+| DB     |det_r50_vd_db_v2.0             | 检测  |
+| EAST   |det_mv3_east_v2.0              | 检测  |
+| EAST   |det_r50_vd_east_v2.0           | 检测  |
+| PSENet |det_mv3_pse_v2.0               | 检测  |
+| PSENet |det_r50_vd_pse_v2.0            | 检测  |
+| SAST   |det_r50_vd_sast_totaltext_v2.0 | 检测  |
+| Rosetta|rec_mv3_none_none_ctc_v2.0     | 识别  |
+| Rosetta|rec_r34_vd_none_none_ctc_v2.0  | 识别  |
+| CRNN   |rec_mv3_none_bilstm_ctc_v2.0   | 识别  |
+| CRNN   |rec_r34_vd_none_bilstm_ctc_v2.0| 识别  |
+| StarNet|rec_mv3_tps_bilstm_ctc_v2.0    | 识别  |
+| StarNet|rec_r34_vd_tps_bilstm_ctc_v2.0 | 识别  |
+| RARE   |rec_mv3_tps_bilstm_att_v2.0    | 识别  |
+| RARE   |rec_r34_vd_tps_bilstm_att_v2.0 | 识别  |
+| SRN    |rec_r50fpn_vd_none_srn         | 识别  |
+| NRTR   |rec_mtb_nrtr                   | 识别  |
+| SAR    |rec_r31_sar                    | 识别  |
+| PGNet  |rec_r34_vd_none_none_ctc_v2.0  | 端到端|
+
+
+
+## 3. 测试工具简介
+### 目录介绍
+
+```shell
+test_tipc/
+├── configs/  # 配置文件目录
+    ├── ch_ppocr_mobile_v2.0_det    # ch_ppocr_mobile_v2.0_det模型的测试配置文件目录
+        ├── train_infer_python.txt      # 测试Linux上python训练预测（基础训练预测）的配置文件
+        ├── model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt     # 测试Linux上c++预测的配置文件
+        ├── model_linux_gpu_normal_normal_infer_python_jetson.txt         # 测试Jetson上python预测的配置文件
+        ├── train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt      # 测试Linux上多机多卡、混合精度训练和python预测的配置文件
+        ├── ...  
+    ├── ch_ppocr_server_v2.0_det               # ch_ppocr_server_v2.0_det模型的测试配置文件目录
+        ├── ...  
+    ├── ch_ppocr_mobile_v2.0_rec               # ch_ppocr_mobile_v2.0_rec模型的测试配置文件目录
+        ├── ...  
+    ├── ch_ppocr_server_v2.0_det               # ch_ppocr_server_v2.0_det模型的测试配置文件目录
+        ├── ...  
+    ├── ...  
+├── results/   # 预先保存的预测结果，用于和实际预测结果进行精读比对
+    ├── python_ppocr_det_mobile_results_fp32.txt           # 预存的mobile版ppocr检测模型python预测fp32精度的结果
+    ├── python_ppocr_det_mobile_results_fp16.txt           # 预存的mobile版ppocr检测模型python预测fp16精度的结果
+    ├── cpp_ppocr_det_mobile_results_fp32.txt       # 预存的mobile版ppocr检测模型c++预测的fp32精度的结果
+    ├── cpp_ppocr_det_mobile_results_fp16.txt       # 预存的mobile版ppocr检测模型c++预测的fp16精度的结果
+    ├── ...
+├── prepare.sh                        # 完成test_*.sh运行所需要的数据和模型下载
+├── test_train_inference_python.sh    # 测试python训练预测的主程序
+├── test_inference_cpp.sh             # 测试c++预测的主程序
+├── test_serving.sh                   # 测试serving部署预测的主程序
+├── test_lite_arm_cpu_cpp.sh          # 测试lite在arm_cpu上部署的C++预测的主程序
+├── compare_results.py                # 用于对比log中的预测结果与results中的预存结果精度误差是否在限定范围内
+└── readme.md                         # 使用文档
+```
+
+### 测试流程概述
+
+使用本工具，可以测试不同功能的支持情况，以及预测结果是否对齐，测试流程概括如下：
+<div align="center">
+    <img src="docs/test.png" width="800">
+</div>
+
+1. 运行prepare.sh准备测试所需数据和模型；
+2. 运行要测试的功能对应的测试脚本`test_*.sh`，产出log，由log可以看到不同配置是否运行成功；
+3. 用`compare_results.py`对比log中的预测结果和预存在results目录下的结果，判断预测精度是否符合预期（在误差范围内）。
+
+测试单项功能仅需两行命令，**如需测试不同模型/功能，替换配置文件即可**，命令格式如下：
+```shell
+# 功能：准备数据
+# 格式：bash + 运行脚本 + 参数1: 配置文件选择 + 参数2: 模式选择
+bash test_tipc/prepare.sh  configs/[model_name]/[params_file_name]  [Mode]
+
+# 功能：运行测试
+# 格式：bash + 运行脚本 + 参数1: 配置文件选择 + 参数2: 模式选择
+bash test_tipc/test_train_inference_python.sh configs/[model_name]/[params_file_name]  [Mode]
+```
+
+例如，测试基本训练预测功能的`lite_train_lite_infer`模式，运行：
+```shell
+# 准备数据
+bash test_tipc/prepare.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
+# 运行测试
+bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_infer_python.txt 'lite_train_lite_infer'
+```  
+关于本示例命令的更多信息可查看[基础训练预测使用文档](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/test_tipc/docs/test_train_inference_python.md#22-%E5%8A%9F%E8%83%BD%E6%B5%8B%E8%AF%95)。
+
+### 配置文件命名规范
+在`configs`目录下，**按模型名称划分为子目录**，子目录中存放所有该模型测试需要用到的配置文件，配置文件的命名遵循如下规范：
+
+1. 基础训练预测配置简单命名为：`train_infer_python.txt`，表示**Linux环境下单机、不使用混合精度训练+python预测**，其完整命名对应`train_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt`，由于本配置文件使用频率较高，这里进行了名称简化。
+
+2. 其他带训练配置命名格式为：`train_训练硬件环境(linux_gpu/linux_dcu/…)_是否多机(fleet/normal)_是否混合精度(amp/normal)_预测模式(infer/lite/serving/js)_语言(cpp/python/java)_预测硬件环境(linux_gpu/mac/jetson/opencl_arm_gpu/...).txt`。如，linux gpu下多机多卡+混合精度链条测试对应配置 `train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt`，linux dcu下基础训练预测对应配置 `train_linux_dcu_normal_normal_infer_python_linux_dcu.txt`。
+
+3. 仅预测的配置（如serving、lite等）命名格式：`model_训练硬件环境(linux_gpu/linux_dcu/…)_是否多机(fleet/normal)_是否混合精度(amp/normal)_(infer/lite/serving/js)_语言(cpp/python/java)_预测硬件环境(linux_gpu/mac/jetson/opencl_arm_gpu/...).txt`，即，与2相比，仅第一个字段从train换为model，测试时模型直接下载获取，这里的“训练硬件环境”表示所测试的模型是在哪种环境下训练得到的。
+
+**根据上述命名规范，可以直接从子目录名称和配置文件名找到需要测试的场景和功能对应的配置文件。**
+
+
+<a name="more"></a>
+## 4. 开始测试
+各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：  
+- [test_train_inference_python 使用](docs/test_train_inference_python.md) ：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。 
+- [test_inference_cpp 使用](docs/test_inference_cpp.md)：测试基于C++的模型推理。
+- [test_serving 使用](docs/test_serving.md)：测试基于Paddle Serving的服务化部署功能。
+- [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md)：测试基于Paddle-Lite的ARM CPU端c++预测部署功能。
+- [test_paddle2onnx 使用](docs/test_paddle2onnx.md)：测试Paddle2ONNX的模型转化功能，并验证正确性。

test_tipc/test_inference_cpp.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+
+FILENAME=$1
+dataline=$(awk 'NR==1, NR==16{print}'  $FILENAME)
+
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+# parser cpp inference model 
+model_name=$(func_parser_value "${lines[1]}")
+use_opencv=$(func_parser_value "${lines[2]}")
+cpp_infer_model_dir_list=$(func_parser_value "${lines[3]}")
+cpp_infer_is_quant=$(func_parser_value "${lines[4]}")
+# parser cpp inference 
+inference_cmd=$(func_parser_value "${lines[5]}")
+cpp_use_gpu_key=$(func_parser_key "${lines[6]}")
+cpp_use_gpu_list=$(func_parser_value "${lines[6]}")
+cpp_use_mkldnn_key=$(func_parser_key "${lines[7]}")
+cpp_use_mkldnn_list=$(func_parser_value "${lines[7]}")
+cpp_cpu_threads_key=$(func_parser_key "${lines[8]}")
+cpp_cpu_threads_list=$(func_parser_value "${lines[8]}")
+cpp_batch_size_key=$(func_parser_key "${lines[9]}")
+cpp_batch_size_list=$(func_parser_value "${lines[9]}")
+cpp_use_trt_key=$(func_parser_key "${lines[10]}")
+cpp_use_trt_list=$(func_parser_value "${lines[10]}")
+cpp_precision_key=$(func_parser_key "${lines[11]}")
+cpp_precision_list=$(func_parser_value "${lines[11]}")
+cpp_infer_model_key=$(func_parser_key "${lines[12]}")
+cpp_image_dir_key=$(func_parser_key "${lines[13]}")
+cpp_infer_img_dir=$(func_parser_value "${lines[13]}")
+cpp_infer_key1=$(func_parser_key "${lines[14]}")
+cpp_infer_value1=$(func_parser_value "${lines[14]}")
+cpp_benchmark_key=$(func_parser_key "${lines[15]}")
+cpp_benchmark_value=$(func_parser_value "${lines[15]}")
+
+LOG_PATH="./test_tipc/output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results_cpp.log"
+
+
+function func_cpp_inference(){
+    IFS='|'
+    _script=$1
+    _model_dir=$2
+    _log_path=$3
+    _img_dir=$4
+    _flag_quant=$5
+    # inference 
+    for use_gpu in ${cpp_use_gpu_list[*]}; do
+        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
+            for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
+                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
+                    continue
+                fi
+                for threads in ${cpp_cpu_threads_list[*]}; do
+                    for batch_size in ${cpp_batch_size_list[*]}; do
+                        precision="fp32"
+                        if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
+                            precison="int8"
+                        fi
+                        _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
+                        set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
+                        set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
+                        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}" "${_model_dir}")
+                        set_infer_params1=$(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}"
+                        status_check $last_status "${command}" "${status_log}"
+                    done
+                done
+            done
+        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
+            for use_trt in ${cpp_use_trt_list[*]}; do
+                for precision in ${cpp_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} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
+                        continue
+                    fi
+                    for batch_size in ${cpp_batch_size_list[*]}; do
+                        _save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
+                        set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
+                        set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
+                        set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
+                        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}" "${_model_dir}")
+                        set_infer_params1=$(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}"
+                        status_check $last_status "${command}" "${status_log}"
+                        
+                    done
+                done
+            done
+        else
+            echo "Does not support hardware other than CPU and GPU Currently!"
+        fi
+    done
+}
+
+
+cd deploy/cpp_infer
+if [ ${use_opencv} = "True" ]; then
+    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)/opencv3
+
+        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
+
+        make -j
+        make install
+        cd ../
+        echo "################### build opencv finished ###################"
+    fi
+fi
+
+
+echo "################### build PaddleOCR demo ####################"
+if [ ${use_opencv} = "True" ]; then
+    OPENCV_DIR=$(pwd)/opencv-3.4.7/opencv3/
+else
+    OPENCV_DIR=''
+fi
+LIB_DIR=$(pwd)/Paddle/build/paddle_inference_install_dir/
+CUDA_LIB_DIR=$(dirname `find /usr -name libcudart.so`)
+CUDNN_LIB_DIR=$(dirname `find /usr -name libcudnn.so`)
+
+BUILD_DIR=build
+rm -rf ${BUILD_DIR}
+mkdir ${BUILD_DIR}
+cd ${BUILD_DIR}
+cmake .. \
+    -DPADDLE_LIB=${LIB_DIR} \
+    -DWITH_MKL=ON \
+    -DWITH_GPU=OFF \
+    -DWITH_STATIC_LIB=OFF \
+    -DWITH_TENSORRT=OFF \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DCUDNN_LIB=${CUDNN_LIB_DIR} \
+    -DCUDA_LIB=${CUDA_LIB_DIR} \
+    -DTENSORRT_DIR=${TENSORRT_DIR} \
+
+make -j
+cd ../../../
+echo "################### build PaddleOCR demo finished ###################"
+
+
+# set cuda device
+GPUID=$2
+if [ ${#GPUID} -le 0 ];then
+    env=" "
+else
+    env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+fi
+set CUDA_VISIBLE_DEVICES
+eval $env
+
+
+echo "################### run test ###################"
+export Count=0
+IFS="|"
+infer_quant_flag=(${cpp_infer_is_quant})
+for infer_model in ${cpp_infer_model_dir_list[*]}; do
+    #run inference
+    is_quant=${infer_quant_flag[Count]}
+    func_cpp_inference "${inference_cmd}" "${infer_model}" "${LOG_PATH}" "${cpp_infer_img_dir}" ${is_quant}
+    Count=$(($Count + 1))
+done

test_tipc/test_inference_jeston.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+source test_tipc/test_train_inference_python.sh
+
+FILENAME=$1
+# MODE be one of ['whole_infer']
+MODE=$2
+
+dataline=$(awk 'NR==1, NR==17{print}'  $FILENAME)
+
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+
+infer_model_dir_list=$(func_parser_value "${lines[3]}")
+infer_export_list=$(func_parser_value "${lines[4]}")
+infer_is_quant=$(func_parser_value "${lines[5]}")
+# parser inference 
+inference_py=$(func_parser_value "${lines[6]}")
+use_gpu_key=$(func_parser_key "${lines[7]}")
+use_gpu_list=$(func_parser_value "${lines[7]}")
+use_mkldnn_key=$(func_parser_key "${lines[8]}")
+use_mkldnn_list=$(func_parser_value "${lines[8]}")
+cpu_threads_key=$(func_parser_key "${lines[9]}")
+cpu_threads_list=$(func_parser_value "${lines[9]}")
+batch_size_key=$(func_parser_key "${lines[10]}")
+batch_size_list=$(func_parser_value "${lines[10]}")
+use_trt_key=$(func_parser_key "${lines[11]}")
+use_trt_list=$(func_parser_value "${lines[11]}")
+precision_key=$(func_parser_key "${lines[12]}")
+precision_list=$(func_parser_value "${lines[12]}")
+infer_model_key=$(func_parser_key "${lines[13]}")
+image_dir_key=$(func_parser_key "${lines[14]}")
+infer_img_dir=$(func_parser_value "${lines[14]}")
+save_log_key=$(func_parser_key "${lines[15]}")
+benchmark_key=$(func_parser_key "${lines[16]}")
+benchmark_value=$(func_parser_value "${lines[16]}")
+infer_key1=$(func_parser_key "${lines[17]}")
+infer_value1=$(func_parser_value "${lines[17]}")
+
+
+LOG_PATH="./test_tipc/output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results_python.log"
+
+
+if [ ${MODE} = "whole_infer" ]; then
+    GPUID=$3
+    if [ ${#GPUID} -le 0 ];then
+        env=" "
+    else
+        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+    fi
+    # set CUDA_VISIBLE_DEVICES
+    eval $env
+    export Count=0
+    IFS="|"
+    infer_run_exports=(${infer_export_list})
+    infer_quant_flag=(${infer_is_quant})
+    for infer_model in ${infer_model_dir_list[*]}; do
+        # run export
+        if [ ${infer_run_exports[Count]} != "null" ];then
+            save_infer_dir=$(dirname $infer_model)
+            set_export_weight=$(func_set_params "${export_weight}" "${infer_model}")
+            set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
+            export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}"
+            echo ${infer_run_exports[Count]} 
+            echo  $export_cmd
+            eval $export_cmd
+            status_export=$?
+            status_check $status_export "${export_cmd}" "${status_log}"
+        else
+            save_infer_dir=${infer_model}
+        fi
+        #run inference
+        is_quant=${infer_quant_flag[Count]}
+        if [ ${MODE} = "klquant_infer" ]; then
+            is_quant="True"
+        fi
+        func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
+        Count=$(($Count + 1))
+    done
+fi
+

test_tipc/test_lite_arm_cpp.sh

+#!/bin/bash
+source ./common_func.sh
+export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
+
+FILENAME=$1
+dataline=$(cat $FILENAME)
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+# parser lite inference
+inference_cmd=$(func_parser_value "${lines[1]}")
+runtime_device=$(func_parser_value "${lines[2]}")
+det_model_list=$(func_parser_value "${lines[3]}")
+rec_model_list=$(func_parser_value "${lines[4]}")
+cls_model_list=$(func_parser_value "${lines[5]}")
+cpu_threads_list=$(func_parser_value "${lines[6]}")
+det_batch_size_list=$(func_parser_value "${lines[7]}")
+rec_batch_size_list=$(func_parser_value "${lines[8]}")
+infer_img_dir_list=$(func_parser_value "${lines[9]}")
+config_dir=$(func_parser_value "${lines[10]}")
+rec_dict_dir=$(func_parser_value "${lines[11]}")
+benchmark_value=$(func_parser_value "${lines[12]}")
+
+if [[ $inference_cmd =~ "det" ]]; then
+    lite_model_list=${det_lite_model_list}
+elif [[ $inference_cmd =~ "rec" ]]; then
+    lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
+elif [[ $inference_cmd =~ "system" ]]; then
+    lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
+else
+    echo "inference_cmd is wrong, please check."
+    exit 1
+fi
+
+LOG_PATH="./output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results.log"
+
+
+function func_test_det(){
+    IFS='|'
+    _script=$1
+    _det_model=$2
+    _log_path=$3
+    _img_dir=$4
+    _config=$5
+    if [[ $_det_model =~ "slim" ]]; then
+        precision="INT8"
+    else
+        precision="FP32"
+    fi
+
+    # lite inference
+    for num_threads in ${cpu_threads_list[*]}; do
+	for det_batchsize in ${det_batch_size_list[*]}; do
+            _save_log_path="${_log_path}/lite_${_det_model}_runtime_device_${runtime_device}_precision_${precision}_det_batchsize_${det_batchsize}_threads_${num_threads}.log"
+            command="${_script} ${_det_model} ${runtime_device} ${precision} ${num_threads} ${det_batchsize}  ${_img_dir} ${_config} ${benchmark_value} > ${_save_log_path} 2>&1"
+            eval ${command}
+            status_check $? "${command}" "${status_log}"
+        done
+    done
+}
+
+function func_test_rec(){
+    IFS='|'
+    _script=$1
+    _rec_model=$2
+    _cls_model=$3
+    _log_path=$4
+    _img_dir=$5
+    _config=$6
+    _rec_dict_dir=$7
+
+    if [[ $_det_model =~ "slim" ]]; then
+        _precision="INT8"
+    else
+        _precision="FP32"
+    fi
+
+    # lite inference
+    for num_threads in ${cpu_threads_list[*]}; do
+	for rec_batchsize in ${rec_batch_size_list[*]}; do
+            _save_log_path="${_log_path}/lite_${_rec_model}_${cls_model}_runtime_device_${runtime_device}_precision_${_precision}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
+            command="${_script} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${rec_batchsize}  ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
+            eval ${command}
+            status_check $? "${command}" "${status_log}"
+        done
+    done
+}
+
+function func_test_system(){
+    IFS='|'
+    _script=$1
+    _det_model=$2
+    _rec_model=$3
+    _cls_model=$4
+    _log_path=$5
+    _img_dir=$6
+    _config=$7
+    _rec_dict_dir=$8
+    if [[ $_det_model =~ "slim" ]]; then
+        _precision="INT8"
+    else
+        _precision="FP32"
+    fi
+
+    # lite inference
+    for num_threads in ${cpu_threads_list[*]}; do
+	for det_batchsize in ${det_batch_size_list[*]}; do
+	   for rec_batchsize in ${rec_batch_size_list[*]}; do
+                _save_log_path="${_log_path}/lite_${_det_model}_${_rec_model}_${_cls_model}_runtime_device_${runtime_device}_precision_${_precision}_det_batchsize_${det_batchsize}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
+                command="${_script} ${_det_model} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${det_batchsize}  ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
+               eval ${command}
+               status_check $? "${command}" "${status_log}"
+	    done
+        done
+    done
+}
+
+
+echo "################### run test ###################"
+
+if [[ $inference_cmd =~ "det" ]]; then
+    IFS="|"
+    det_model_list=(${det_model_list[*]})
+
+    for i in {0..1}; do
+        #run lite inference
+        for img_dir in ${infer_img_dir_list[*]}; do
+            func_test_det "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}"
+        done
+    done
+
+elif [[ $inference_cmd =~ "rec" ]]; then
+    IFS="|"
+    rec_model_list=(${rec_model_list[*]})
+    cls_model_list=(${cls_model_list[*]})
+
+    for i in {0..1}; do
+        #run lite inference
+        for img_dir in ${infer_img_dir_list[*]}; do
+            func_test_rec "${inference_cmd}" "${rec_model}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${rec_dict_dir}" "${config_dir}"
+        done
+    done
+
+elif [[ $inference_cmd =~ "system" ]]; then
+    IFS="|"
+    det_model_list=(${det_model_list[*]})
+    rec_model_list=(${rec_model_list[*]})
+    cls_model_list=(${cls_model_list[*]})
+
+    for i in {0..1}; do
+	#run lite inference
+        for img_dir in ${infer_img_dir_list[*]}; do
+            func_test_system "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${rec_model_list[i]}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}" "${rec_dict_dir}"
+        done
+    done
+fi

test_tipc/test_paddle2onnx.sh

+#!/bin/bash
+source test_tipc/common_func.sh 
+
+FILENAME=$1
+
+dataline=$(cat ${FILENAME})
+lines=(${dataline})
+# common params
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+
+
+# parser params
+dataline=$(awk 'NR==1, NR==12{print}'  $FILENAME)
+IFS=$'\n'
+lines=(${dataline})
+
+# parser paddle2onnx
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+padlle2onnx_cmd=$(func_parser_value "${lines[3]}")
+infer_model_dir_key=$(func_parser_key "${lines[4]}")
+infer_model_dir_value=$(func_parser_value "${lines[4]}")
+model_filename_key=$(func_parser_key "${lines[5]}")
+model_filename_value=$(func_parser_value "${lines[5]}")
+params_filename_key=$(func_parser_key "${lines[6]}")
+params_filename_value=$(func_parser_value "${lines[6]}")
+save_file_key=$(func_parser_key "${lines[7]}")
+save_file_value=$(func_parser_value "${lines[7]}")
+opset_version_key=$(func_parser_key "${lines[8]}")
+opset_version_value=$(func_parser_value "${lines[8]}")
+enable_onnx_checker_key=$(func_parser_key "${lines[9]}")
+enable_onnx_checker_value=$(func_parser_value "${lines[9]}")
+# parser onnx inference 
+inference_py=$(func_parser_value "${lines[10]}")
+use_gpu_key=$(func_parser_key "${lines[11]}")
+use_gpu_value=$(func_parser_value "${lines[11]}")
+det_model_key=$(func_parser_key "${lines[12]}")
+image_dir_key=$(func_parser_key "${lines[13]}")
+image_dir_value=$(func_parser_value "${lines[13]}")
+
+
+LOG_PATH="./test_tipc/output"
+mkdir -p ./test_tipc/output
+status_log="${LOG_PATH}/results_paddle2onnx.log"
+
+
+function func_paddle2onnx(){
+    IFS='|'
+    _script=$1
+
+    # paddle2onnx
+    _save_log_path="${LOG_PATH}/paddle2onnx_infer_cpu.log"
+    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}" "${params_filename_value}")
+    set_save_model=$(func_set_params "${save_file_key}" "${save_file_value}")
+    set_opset_version=$(func_set_params "${opset_version_key}" "${opset_version_value}")
+    set_enable_onnx_checker=$(func_set_params "${enable_onnx_checker_key}" "${enable_onnx_checker_value}")
+    trans_model_cmd="${padlle2onnx_cmd} ${set_dirname} ${set_model_filename} ${set_params_filename} ${set_save_model} ${set_opset_version} ${set_enable_onnx_checker}"
+    eval $trans_model_cmd
+    last_status=${PIPESTATUS[0]}
+    status_check $last_status "${trans_model_cmd}" "${status_log}"
+    # python inference
+    set_gpu=$(func_set_params "${use_gpu_key}" "${use_gpu_value}")
+    set_model_dir=$(func_set_params "${det_model_key}" "${save_file_value}")
+    set_img_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
+    infer_model_cmd="${python} ${inference_py} ${set_gpu} ${set_img_dir} ${set_model_dir} --use_onnx=True > ${_save_log_path} 2>&1 "
+    eval $infer_model_cmd
+    status_check $last_status "${infer_model_cmd}" "${status_log}"
+}
+
+
+echo "################### run test ###################"
+
+export Count=0
+IFS="|"
+func_paddle2onnx 
\ No newline at end of file

test_tipc/test_serving.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+
+FILENAME=$1
+dataline=$(awk 'NR==1, NR==18{print}'  $FILENAME)
+
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+# parser serving
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+trans_model_py=$(func_parser_value "${lines[3]}")
+infer_model_dir_key=$(func_parser_key "${lines[4]}")
+infer_model_dir_value=$(func_parser_value "${lines[4]}")
+model_filename_key=$(func_parser_key "${lines[5]}")
+model_filename_value=$(func_parser_value "${lines[5]}")
+params_filename_key=$(func_parser_key "${lines[6]}")
+params_filename_value=$(func_parser_value "${lines[6]}")
+serving_server_key=$(func_parser_key "${lines[7]}")
+serving_server_value=$(func_parser_value "${lines[7]}")
+serving_client_key=$(func_parser_key "${lines[8]}")
+serving_client_value=$(func_parser_value "${lines[8]}")
+serving_dir_value=$(func_parser_value "${lines[9]}")
+web_service_py=$(func_parser_value "${lines[10]}")
+web_use_gpu_key=$(func_parser_key "${lines[11]}")
+web_use_gpu_list=$(func_parser_value "${lines[11]}")
+web_use_mkldnn_key=$(func_parser_key "${lines[12]}")
+web_use_mkldnn_list=$(func_parser_value "${lines[12]}")
+web_cpu_threads_key=$(func_parser_key "${lines[13]}")
+web_cpu_threads_list=$(func_parser_value "${lines[13]}")
+web_use_trt_key=$(func_parser_key "${lines[14]}")
+web_use_trt_list=$(func_parser_value "${lines[14]}")
+web_precision_key=$(func_parser_key "${lines[15]}")
+web_precision_list=$(func_parser_value "${lines[15]}")
+pipeline_py=$(func_parser_value "${lines[16]}")
+image_dir_key=$(func_parser_key "${lines[17]}")
+image_dir_value=$(func_parser_value "${lines[17]}")
+
+LOG_PATH="../../test_tipc/output"
+mkdir -p ./test_tipc/output
+status_log="${LOG_PATH}/results_serving.log"
+
+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}")
+    set_image_dir=$(func_set_params "${image_dir_key}" "${image_dir_value}")
+    trans_model_cmd="${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 python in ${python[*]}; do
+        if [ ${python} = "cpp"]; then
+            for use_gpu in ${web_use_gpu_list[*]}; do
+                if [ ${use_gpu} = "null" ]; then
+                    web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293"
+                    eval $web_service_cmd
+                    sleep 2s
+                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
+                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
+                    eval $pipeline_cmd
+                    status_check $last_status "${pipeline_cmd}" "${status_log}"
+                    sleep 2s
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9
+                else
+                    web_service_cpp_cmd="${python} -m paddle_serving_server.serve --model ppocr_det_mobile_2.0_serving/ ppocr_rec_mobile_2.0_serving/ --port 9293 --gpu_id=0"
+                    eval $web_service_cmd
+                    sleep 2s
+                    _save_log_path="${LOG_PATH}/server_infer_cpp_cpu_pipeline_usemkldnn_False_threads_4_batchsize_1.log"
+                    pipeline_cmd="${python} ocr_cpp_client.py ppocr_det_mobile_2.0_client/ ppocr_rec_mobile_2.0_client/"
+                    eval $pipeline_cmd
+                    status_check $last_status "${pipeline_cmd}" "${status_log}"
+                    sleep 2s
+                    ps ux | grep -E 'web_service|pipeline' | awk '{print $2}' | xargs kill -s 9                
+                fi
+            done
+        else
+            # python serving
+            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 ${web_cpu_threads_list[*]}; do
+                            set_cpu_threads=$(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} &"
+                            eval $web_service_cmd
+                            sleep 2s
+                            for pipeline in ${pipeline_py[*]}; do
+                                _save_log_path="${LOG_PATH}/server_infer_cpu_${pipeline%_client*}_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_1.log"
+                                pipeline_cmd="${python} ${pipeline} ${set_image_dir} > ${_save_log_path} 2>&1 "
+                                eval $pipeline_cmd
+                                last_status=${PIPESTATUS[0]}
+                                eval "cat ${_save_log_path}"
+                                status_check $last_status "${pipeline_cmd}" "${status_log}"
+                                sleep 2s
+                            done
+                            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} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
+                                continue
+                            fi
+                            set_tensorrt=$(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} & "
+                            eval $web_service_cmd
+                        
+                            sleep 2s
+                            for pipeline in ${pipeline_py[*]}; do
+                                _save_log_path="${LOG_PATH}/server_infer_gpu_${pipeline%_client*}_usetrt_${use_trt}_precision_${precision}_batchsize_1.log"
+                                pipeline_cmd="${python} ${pipeline} ${set_image_dir}> ${_save_log_path} 2>&1"
+                                eval $pipeline_cmd
+                                last_status=${PIPESTATUS[0]}
+                                eval "cat ${_save_log_path}"
+                                status_check $last_status "${pipeline_cmd}" "${status_log}"
+                                sleep 2s
+                            done
+                            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
+        fi
+    done
+}
+
+
+# set cuda device
+GPUID=$2
+if [ ${#GPUID} -le 0 ];then
+    env=" "
+else
+    env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+fi
+set CUDA_VISIBLE_DEVICES
+eval $env
+
+
+echo "################### run test ###################"
+
+export Count=0
+IFS="|"
+func_serving "${web_service_cmd}"

tests/test.sh → test_tipc/test_train_inference_python.sh

 #!/bin/bash
+source test_tipc/common_func.sh
+
 FILENAME=$1
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer', 'cpp_infer']
+# MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer']
 MODE=$2
 
-dataline=$(cat ${FILENAME})
+dataline=$(awk 'NR==1, NR==51{print}'  $FILENAME)
 
 # parser params
 IFS=$'\n'
 lines=(${dataline})
 
-function func_parser_key(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[0]}
-    echo ${tmp}
-}
-function func_parser_value(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    tmp=${array[1]}
-    echo ${tmp}
-}
-function func_set_params(){
-    key=$1
-    value=$2
-    if [ ${key} = "null" ];then
-        echo " "
-    elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
-        echo " "
-    else 
-        echo "${key}=${value}"
-    fi
-}
-function func_parser_params(){
-    strs=$1
-    IFS=":"
-    array=(${strs})
-    key=${array[0]}
-    tmp=${array[1]}
-    IFS="|"
-    res=""
-    for _params in ${tmp[*]}; do
-        IFS="="
-        array=(${_params})
-        mode=${array[0]}
-        value=${array[1]}
-        if [[ ${mode} = ${MODE} ]]; then
-            IFS="|"
-            #echo $(func_set_params "${mode}" "${value}")
-            echo $value
-            break
-        fi
-        IFS="|"
-    done
-    echo ${res}
-}
-function status_check(){
-    last_status=$1   # the exit code
-    run_command=$2
-    run_log=$3
-    if [ $last_status -eq 0 ]; then
-        echo -e "\033[33m Run successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
-    else
-        echo -e "\033[33m Run failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
-    fi
-}
-
-IFS=$'\n'
 # The training params
 model_name=$(func_parser_value "${lines[1]}")
 python=$(func_parser_value "${lines[2]}")
@@ -78,10 +20,10 @@ train_use_gpu_value=$(func_parser_value "${lines[4]}")
 autocast_list=$(func_parser_value "${lines[5]}")
 autocast_key=$(func_parser_key "${lines[5]}")
 epoch_key=$(func_parser_key "${lines[6]}")
-epoch_num=$(func_parser_params "${lines[6]}")
+epoch_num=$(func_parser_params "${lines[6]}" "${MODE}")
 save_model_key=$(func_parser_key "${lines[7]}")
 train_batch_key=$(func_parser_key "${lines[8]}")
-train_batch_value=$(func_parser_params "${lines[8]}")
+train_batch_value=$(func_parser_params "${lines[8]}" "${MODE}")
 pretrain_model_key=$(func_parser_key "${lines[9]}")
 pretrain_model_value=$(func_parser_value "${lines[9]}")
 train_model_name=$(func_parser_value "${lines[10]}")
@@ -117,6 +59,7 @@ export_key1=$(func_parser_key "${lines[33]}")
 export_value1=$(func_parser_value "${lines[33]}")
 export_key2=$(func_parser_key "${lines[34]}")
 export_value2=$(func_parser_value "${lines[34]}")
+inference_dir=$(func_parser_value "${lines[35]}")
 
 # parser inference model 
 infer_model_dir_list=$(func_parser_value "${lines[36]}")
@@ -144,64 +87,44 @@ 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 [ ${MODE} = "cpp_infer" ]; then
-    # parser cpp inference model 
-    cpp_infer_model_dir_list=$(func_parser_value "${lines[53]}")
-    cpp_infer_is_quant=$(func_parser_value "${lines[54]}")
-    # parser cpp inference 
-    inference_cmd=$(func_parser_value "${lines[55]}")
-    cpp_use_gpu_key=$(func_parser_key "${lines[56]}")
-    cpp_use_gpu_list=$(func_parser_value "${lines[56]}")
-    cpp_use_mkldnn_key=$(func_parser_key "${lines[57]}")
-    cpp_use_mkldnn_list=$(func_parser_value "${lines[57]}")
-    cpp_cpu_threads_key=$(func_parser_key "${lines[58]}")
-    cpp_cpu_threads_list=$(func_parser_value "${lines[58]}")
-    cpp_batch_size_key=$(func_parser_key "${lines[59]}")
-    cpp_batch_size_list=$(func_parser_value "${lines[59]}")
-    cpp_use_trt_key=$(func_parser_key "${lines[60]}")
-    cpp_use_trt_list=$(func_parser_value "${lines[60]}")
-    cpp_precision_key=$(func_parser_key "${lines[61]}")
-    cpp_precision_list=$(func_parser_value "${lines[61]}")
-    cpp_infer_model_key=$(func_parser_key "${lines[62]}")
-    cpp_image_dir_key=$(func_parser_key "${lines[63]}")
-    cpp_infer_img_dir=$(func_parser_value "${lines[63]}")
-    cpp_infer_key1=$(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]}")
+# parser klquant_infer
+if [ ${MODE} = "klquant_whole_infer" ]; then
+    dataline=$(awk 'NR==1 NR==17{print}'  $FILENAME)
+    lines=(${dataline})
+    model_name=$(func_parser_value "${lines[1]}")
+    python=$(func_parser_value "${lines[2]}")
+    # parser inference model 
+    infer_model_dir_list=$(func_parser_value "${lines[3]}")
+    infer_export_list=$(func_parser_value "${lines[4]}")
+    infer_is_quant=$(func_parser_value "${lines[5]}")
+    # parser inference 
+    inference_py=$(func_parser_value "${lines[6]}")
+    use_gpu_key=$(func_parser_key "${lines[7]}")
+    use_gpu_list=$(func_parser_value "${lines[7]}")
+    use_mkldnn_key=$(func_parser_key "${lines[8]}")
+    use_mkldnn_list=$(func_parser_value "${lines[8]}")
+    cpu_threads_key=$(func_parser_key "${lines[9]}")
+    cpu_threads_list=$(func_parser_value "${lines[9]}")
+    batch_size_key=$(func_parser_key "${lines[10]}")
+    batch_size_list=$(func_parser_value "${lines[10]}")
+    use_trt_key=$(func_parser_key "${lines[11]}")
+    use_trt_list=$(func_parser_value "${lines[11]}")
+    precision_key=$(func_parser_key "${lines[12]}")
+    precision_list=$(func_parser_value "${lines[12]}")
+    infer_model_key=$(func_parser_key "${lines[13]}")
+    image_dir_key=$(func_parser_key "${lines[14]}")
+    infer_img_dir=$(func_parser_value "${lines[14]}")
+    save_log_key=$(func_parser_key "${lines[15]}")
+    benchmark_key=$(func_parser_key "${lines[16]}")
+    benchmark_value=$(func_parser_value "${lines[16]}")
+    infer_key1=$(func_parser_key "${lines[17]}")
+    infer_value1=$(func_parser_value "${lines[17]}")
 fi
 
-
-LOG_PATH="./tests/output"
+LOG_PATH="./test_tipc/output"
 mkdir -p ${LOG_PATH}
-status_log="${LOG_PATH}/results.log"
+status_log="${LOG_PATH}/results_python.log"
 
 
 function func_inference(){
@@ -221,18 +144,28 @@ function func_inference(){
                 fi
                 for threads in ${cpu_threads_list[*]}; do
                     for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
-                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
-                        set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
-                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
-                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${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}"
-                        status_check $last_status "${command}" "${status_log}"
+                        for precision in ${precision_list[*]}; do
+                            if [ ${use_mkldnn} = "False" ] && [ ${precision} = "fp16" ]; then
+                                continue
+                            fi # skip when enable fp16 but disable mkldnn
+                            if [ ${_flag_quant} = "True" ] && [ ${precision} != "int8" ]; then
+                                continue
+                            fi # skip when quant model inference but precision is not int8
+                            set_precision=$(func_set_params "${precision_key}" "${precision}")
+                            
+                            _save_log_path="${_log_path}/python_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_precision_${precision}_batchsize_${batch_size}.log"
+                            set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
+                            set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
+                            set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
+                            set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
+                            set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                            set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
+                            command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} > ${_save_log_path} 2>&1 "
+                            eval $command
+                            last_status=${PIPESTATUS[0]}
+                            eval "cat ${_save_log_path}"
+                            status_check $last_status "${command}" "${status_log}"
+                        done
                     done
                 done
             done
@@ -249,7 +182,7 @@ function func_inference(){
                         continue
                     fi
                     for batch_size in ${batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
+                        _save_log_path="${_log_path}/python_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
                         set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
                         set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
                         set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
@@ -271,150 +204,8 @@ 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} = "False" || ${precision} =~ "int8" ]] && [[ ${_flag_quant} = "True" ]]; then
-                        continue
-                    fi
-                    _save_log_path="${_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='|'
-    _script=$1
-    _model_dir=$2
-    _log_path=$3
-    _img_dir=$4
-    _flag_quant=$5
-    # inference 
-    for use_gpu in ${cpp_use_gpu_list[*]}; do
-        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
-            for use_mkldnn in ${cpp_use_mkldnn_list[*]}; do
-                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
-                    continue
-                fi
-                for threads in ${cpp_cpu_threads_list[*]}; do
-                    for batch_size in ${cpp_batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/cpp_infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
-                        set_batchsize=$(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}")
-                        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}"
-                        status_check $last_status "${command}" "${status_log}"
-                    done
-                done
-            done
-        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
-            for use_trt in ${cpp_use_trt_list[*]}; do
-                for precision in ${cpp_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} = "False" || ${precision} =~ "int8" ]] && [ ${_flag_quant} = "True" ]; then
-                        continue
-                    fi
-                    for batch_size in ${cpp_batch_size_list[*]}; do
-                        _save_log_path="${_log_path}/cpp_infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
-                        set_infer_data=$(func_set_params "${cpp_image_dir_key}" "${_img_dir}")
-                        set_benchmark=$(func_set_params "${cpp_benchmark_key}" "${cpp_benchmark_value}")
-                        set_batchsize=$(func_set_params "${cpp_batch_size_key}" "${batch_size}")
-                        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}" "${_model_dir}")
-                        set_infer_params1=$(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}"
-                        status_check $last_status "${command}" "${status_log}"
-                        
-                    done
-                done
-            done
-        else
-            echo "Does not support hardware other than CPU and GPU Currently!"
-        fi
-    done
-}
 
-if [ ${MODE} = "infer" ]; then
+if [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then
     GPUID=$3
     if [ ${#GPUID} -le 0 ];then
         env=" "
@@ -435,7 +226,7 @@ if [ ${MODE} = "infer" ]; then
             set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_dir}")
             export_cmd="${python} ${infer_run_exports[Count]} ${set_export_weight} ${set_save_infer_key}"
             echo ${infer_run_exports[Count]} 
-            echo  $export_cmd
+            echo $export_cmd
             eval $export_cmd
             status_export=$?
             status_check $status_export "${export_cmd}" "${status_log}"
@@ -444,50 +235,20 @@ if [ ${MODE} = "infer" ]; then
         fi
         #run inference
         is_quant=${infer_quant_flag[Count]}
+        if [ ${MODE} = "klquant_infer" ]; then
+            is_quant="True"
+        fi
         func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
         Count=$(($Count + 1))
     done
-
-elif [ ${MODE} = "cpp_infer" ]; then
-    GPUID=$3
-    if [ ${#GPUID} -le 0 ];then
-        env=" "
-    else
-        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
-    fi
-    # set CUDA_VISIBLE_DEVICES
-    eval $env
-    export Count=0
-    IFS="|"
-    infer_quant_flag=(${cpp_infer_is_quant})
-    for infer_model in ${cpp_infer_model_dir_list[*]}; do
-        #run inference
-        is_quant=${infer_quant_flag[Count]}
-        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="|"
     export Count=0
     USE_GPU_KEY=(${train_use_gpu_value})
     for gpu in ${gpu_list[*]}; do
-        use_gpu=${USE_GPU_KEY[Count]}
+        train_use_gpu=${USE_GPU_KEY[Count]}
         Count=$(($Count + 1))
+        ips=""
         if [ ${gpu} = "-1" ];then
             env=""
         elif [ ${#gpu} -le 1 ];then
@@ -507,6 +268,11 @@ else
             env=" "
         fi
         for autocast in ${autocast_list[*]}; do 
+            if [ ${autocast} = "amp" ]; then
+                set_amp_config="Global.use_amp=True Global.scale_loss=1024.0 Global.use_dynamic_loss_scaling=True"
+            else
+                set_amp_config=" "
+            fi          
             for trainer in ${trainer_list[*]}; do 
                 flag_quant=False
                 if [ ${trainer} = ${pact_key} ]; then
@@ -533,27 +299,35 @@ else
                 if [ ${run_train} = "null" ]; then
                     continue
                 fi
-                
                 set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
                 set_epoch=$(func_set_params "${epoch_key}" "${epoch_num}")
                 set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
                 set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
                 set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
-                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${use_gpu}")
-                save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
-                
+                set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu}")
+                if [ ${#ips} -le 26 ];then
+                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
+                    nodes=1
+                else
+                    IFS=","
+                    ips_array=(${ips})
+                    IFS="|"
+                    nodes=${#ips_array[@]}
+                    save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}_nodes_${nodes}"
+                fi
+
                 # load pretrain from norm training if current trainer is pact or fpgm trainer
-                if [ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]; then
+                if ([ ${trainer} = ${pact_key} ] || [ ${trainer} = ${fpgm_key} ]) && [ ${nodes} -le 1 ]; then
                     set_pretrain="${load_norm_train_model}"
                 fi
 
                 set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
                 if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
-                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
-                elif [ ${#gpu} -le 15 ];then  # train with multi-gpu
-                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
+                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
+                elif [ ${#ips} -le 26 ];then  # train with multi-gpu
+                    cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
                 else     # train with multi-machine
-                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
+                    cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config}"
                 fi
                 # run train
                 eval "unset CUDA_VISIBLE_DEVICES"
@@ -562,7 +336,7 @@ else
 
                 set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${train_model_name}")
                 # save norm trained models to set pretrain for pact training and fpgm training 
-                if [ ${trainer} = ${trainer_norm} ]; then
+                if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1 ]; then
                     load_norm_train_model=${set_eval_pretrain}
                 fi
                 # run eval 
@@ -585,10 +359,17 @@ else
                     #run inference
                     eval $env
                     save_infer_path="${save_log}"
-                    func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
+                    if [[ ${inference_dir} != "null" ]] && [[ ${inference_dir} != '##' ]]; then
+                        infer_model_dir="${save_infer_path}/${inference_dir}"
+                    else
+                        infer_model_dir=${save_infer_path}
+                    fi
+                    func_inference "${python}" "${inference_py}" "${infer_model_dir}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
+                    
                     eval "unset CUDA_VISIBLE_DEVICES"
                 fi
             done  # done with:    for trainer in ${trainer_list[*]}; do 
         done      # done with:    for autocast in ${autocast_list[*]}; do 
     done          # done with:    for gpu in ${gpu_list[*]}; do
 fi  # end if [ ${MODE} = "infer" ]; then
+

tests/ocr_rec_server_params.txt

-===========================train_params===========================
-model_name:ocr_server_rec
-python:python3.7
-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
-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:./inference/rec_inference
-null:null
-##
-trainer:norm_train|pact_train
-norm_train:tools/train.py -c tests/configs/rec_icdar15_r34_train.yml -o
-pact_train:deploy/slim/quantization/quant.py -c tests/configs/rec_icdar15_r34_train.yml -o
-fpgm_train:null
-distill_train:null
-null:null
-null:null
-##
-===========================eval_params===========================
-eval:tools/eval.py -c tests/configs/rec_icdar15_r34_train.yml -o
-null:null
-##
-===========================infer_params===========================
-Global.save_inference_dir:./output/
-Global.pretrained_model:
-norm_export:tools/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o
-quant_export:deploy/slim/quantization/export_model.py -c tests/configs/rec_icdar15_r34_train.yml -o
-fpgm_export:null
-distill_export:null
-export1:null
-export2:null
-##
-infer_model:./inference/ch_ppocr_server_v2.0_rec_infer/
-infer_export:null
-infer_quant:False
-inference:tools/infer/predict_rec.py
---use_gpu:True|False
---enable_mkldnn:True|False
---cpu_threads:1|6
---rec_batch_num:1|6
---use_tensorrt:True|False
---precision:fp32|fp16|int8
---rec_model_dir:
---image_dir:./inference/rec_inference
---save_log_path:./test/output/
---benchmark:True
-null:null
-===========================cpp_infer_params===========================
-use_opencv:True
-infer_model:./inference/ch_ppocr_server_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
-===========================serving_params===========================
-trans_model:-m paddle_serving_client.convert
---dirname:./inference/ch_ppocr_server_v2.0_rec_infer/
---model_filename:inference.pdmodel
---params_filename:inference.pdiparams
---serving_server:./deploy/pdserving/ppocr_rec_server_2.0_serving/
---serving_client:./deploy/pdserving/ppocr_rec_server_2.0_client/
-serving_dir:./deploy/pdserving
-web_service:web_service_rec.py --config=config.yml --opt op.rec.concurrency=1
-op.rec.local_service_conf.devices:null|0
-op.rec.local_service_conf.use_mkldnn:True|False
-op.rec.local_service_conf.thread_num:1|6
-op.rec.local_service_conf.use_trt:False|True
-op.rec.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py --image_dir=../../doc/imgs_words_en
\ No newline at end of file

tests/readme.md

-
-# 从训练到推理部署工具链测试方法介绍
-
-test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。
-
-# 安装依赖
-- 安装PaddlePaddle >= 2.0
-- 安装PaddleOCR依赖
-    ```
-    pip3 install  -r ../requirements.txt
-    ```
-- 安装autolog
-    ```
-    git clone https://github.com/LDOUBLEV/AutoLog
-    cd AutoLog
-    pip3 install -r requirements.txt
-    python3 setup.py bdist_wheel
-    pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
-    cd ../
-    ```
-
-# 目录介绍
-
-```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
-├── 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；
-```shell
-bash tests/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；
-```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;
-```shell
-bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
-# 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
-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；
-```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'
-```  
-
-# 日志输出
-最终在```tests/output```目录下生成.log后缀的日志文件
-

tools/__init__.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+#
+# 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.

tools/eval.py

@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model, load_dygraph_params
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import print_dict
 import tools.program as program
 
@@ -61,7 +61,7 @@ def main():
     else:
         model_type = None
 
-    best_model_dict = load_dygraph_params(config, model, logger, None)
+    best_model_dict = load_model(config, model)
     if len(best_model_dict):
         logger.info('metric in ckpt ***************')
         for k, v in best_model_dict.items():

tools/export_center.py

+# Copyright (c) 2020 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import pickle
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+from ppocr.data import build_dataloader
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import load_model
+from ppocr.utils.utility import print_dict
+import tools.program as program
+
+
+def main():
+    global_config = config['Global']
+    # build dataloader
+    config['Eval']['dataset']['name'] = config['Train']['dataset']['name']
+    config['Eval']['dataset']['data_dir'] = config['Train']['dataset'][
+        'data_dir']
+    config['Eval']['dataset']['label_file_list'] = config['Train']['dataset'][
+        'label_file_list']
+    eval_dataloader = build_dataloader(config, 'Eval', device, logger)
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'],
+                                            global_config)
+
+    # build model
+    # for rec algorithm
+    if hasattr(post_process_class, 'character'):
+        char_num = len(getattr(post_process_class, 'character'))
+        config['Architecture']["Head"]['out_channels'] = char_num
+
+    #set return_features = True
+    config['Architecture']["Head"]["return_feats"] = True
+
+    model = build_model(config['Architecture'])
+
+    best_model_dict = load_model(config, model)
+    if len(best_model_dict):
+        logger.info('metric in ckpt ***************')
+        for k, v in best_model_dict.items():
+            logger.info('{}:{}'.format(k, v))
+
+    # get features from train data
+    char_center = program.get_center(model, eval_dataloader, post_process_class)
+
+    #serialize to disk
+    with open("train_center.pkl", 'wb') as f:
+        pickle.dump(char_center, f)
+    return
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main()

tools/export_model.py

@@ -26,7 +26,7 @@ from paddle.jit import to_static
 
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.logging import get_logger
 from tools.program import load_config, merge_config, ArgsParser
 
@@ -107,7 +107,7 @@ def main():
         else:  # base rec model
             config["Architecture"]["Head"]["out_channels"] = char_num
     model = build_model(config["Architecture"])
-    init_model(config, model)
+    load_model(config, model)
     model.eval()
 
     save_path = config["Global"]["save_inference_dir"]

tools/infer/predict_cls.py

@@ -47,6 +47,7 @@ class TextClassifier(object):
         self.postprocess_op = build_post_process(postprocess_params)
         self.predictor, self.input_tensor, self.output_tensors, _ = \
             utility.create_predictor(args, 'cls', logger)
+        self.use_onnx = args.use_onnx
 
     def resize_norm_img(self, img):
         imgC, imgH, imgW = self.cls_image_shape
@@ -100,10 +101,16 @@ class TextClassifier(object):
             norm_img_batch = np.concatenate(norm_img_batch)
             norm_img_batch = norm_img_batch.copy()
 
-            self.input_tensor.copy_from_cpu(norm_img_batch)
-            self.predictor.run()
-            prob_out = self.output_tensors[0].copy_to_cpu()
-            self.predictor.try_shrink_memory()
+            if self.use_onnx:
+                input_dict = {}
+                input_dict[self.input_tensor.name] = norm_img_batch
+                outputs = self.predictor.run(self.output_tensors, input_dict)
+                prob_out = outputs[0]
+            else:
+                self.input_tensor.copy_from_cpu(norm_img_batch)
+                self.predictor.run()
+                prob_out = self.output_tensors[0].copy_to_cpu()
+                self.predictor.try_shrink_memory()
             cls_result = self.postprocess_op(prob_out)
             elapse += time.time() - starttime
             for rno in range(len(cls_result)):
@@ -131,14 +138,9 @@ def main(args):
         img_list.append(img)
     try:
         img_list, cls_res, predict_time = text_classifier(img_list)
-    except:
+    except Exception as E:
         logger.info(traceback.format_exc())
-        logger.info(
-            "ERROR!!!! \n"
-            "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
-            "If your model has tps module:  "
-            "TPS does not support variable shape.\n"
-            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
+        logger.info(E)
         exit()
     for ino in range(len(img_list)):
         logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],

tools/infer/predict_det.py

@@ -38,6 +38,7 @@ class TextDetector(object):
     def __init__(self, args):
         self.args = args
         self.det_algorithm = args.det_algorithm
+        self.use_onnx = args.use_onnx
         pre_process_list = [{
             'DetResizeForTest': {
                 'limit_side_len': args.det_limit_side_len,
@@ -100,7 +101,12 @@ class TextDetector(object):
         else:
             logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
             sys.exit(0)
-
+        if self.use_onnx:
+            pre_process_list[0] = {
+                'DetResizeForTest': {
+                    'image_shape': [640, 640]
+                }
+            }
         self.preprocess_op = create_operators(pre_process_list)
         self.postprocess_op = build_post_process(postprocess_params)
         self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
@@ -198,15 +204,19 @@ class TextDetector(object):
 
         if self.args.benchmark:
             self.autolog.times.stamp()
-
-        self.input_tensor.copy_from_cpu(img)
-        self.predictor.run()
-        outputs = []
-        for output_tensor in self.output_tensors:
-            output = output_tensor.copy_to_cpu()
-            outputs.append(output)
-        if self.args.benchmark:
-            self.autolog.times.stamp()
+        if self.use_onnx:
+            input_dict = {}
+            input_dict[self.input_tensor.name] = img
+            outputs = self.predictor.run(self.output_tensors, input_dict)
+        else:
+            self.input_tensor.copy_from_cpu(img)
+            self.predictor.run()
+            outputs = []
+            for output_tensor in self.output_tensors:
+                output = output_tensor.copy_to_cpu()
+                outputs.append(output)
+            if self.args.benchmark:
+                self.autolog.times.stamp()
 
         preds = {}
         if self.det_algorithm == "EAST":

tools/infer/predict_e2e.py

@@ -38,6 +38,7 @@ class TextE2E(object):
     def __init__(self, args):
         self.args = args
         self.e2e_algorithm = args.e2e_algorithm
+        self.use_onnx = args.use_onnx
         pre_process_list = [{
             'E2EResizeForTest': {}
         }, {
@@ -67,7 +68,6 @@ class TextE2E(object):
             postprocess_params["character_dict_path"] = args.e2e_char_dict_path
             postprocess_params["valid_set"] = args.e2e_pgnet_valid_set
             postprocess_params["mode"] = args.e2e_pgnet_mode
-            self.e2e_pgnet_polygon = args.e2e_pgnet_polygon
         else:
             logger.info("unknown e2e_algorithm:{}".format(self.e2e_algorithm))
             sys.exit(0)
@@ -106,21 +106,31 @@ class TextE2E(object):
         img = img.copy()
         starttime = time.time()
 
-        self.input_tensor.copy_from_cpu(img)
-        self.predictor.run()
-        outputs = []
-        for output_tensor in self.output_tensors:
-            output = output_tensor.copy_to_cpu()
-            outputs.append(output)
-
-        preds = {}
-        if self.e2e_algorithm == 'PGNet':
+        if self.use_onnx:
+            input_dict = {}
+            input_dict[self.input_tensor.name] = img
+            outputs = self.predictor.run(self.output_tensors, input_dict)
+            preds = {}
             preds['f_border'] = outputs[0]
             preds['f_char'] = outputs[1]
             preds['f_direction'] = outputs[2]
             preds['f_score'] = outputs[3]
         else:
-            raise NotImplementedError
+            self.input_tensor.copy_from_cpu(img)
+            self.predictor.run()
+            outputs = []
+            for output_tensor in self.output_tensors:
+                output = output_tensor.copy_to_cpu()
+                outputs.append(output)
+
+            preds = {}
+            if self.e2e_algorithm == 'PGNet':
+                preds['f_border'] = outputs[0]
+                preds['f_char'] = outputs[1]
+                preds['f_direction'] = outputs[2]
+                preds['f_score'] = outputs[3]
+            else:
+                raise NotImplementedError
         post_result = self.postprocess_op(preds, shape_list)
         points, strs = post_result['points'], post_result['texts']
         dt_boxes = self.filter_tag_det_res_only_clip(points, ori_im.shape)
@@ -141,7 +151,6 @@ 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_rec.py

@@ -38,40 +38,34 @@ logger = get_logger()
 class TextRecognizer(object):
     def __init__(self, args):
         self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
-        self.character_type = args.rec_char_type
         self.rec_batch_num = args.rec_batch_num
         self.rec_algorithm = args.rec_algorithm
         postprocess_params = {
             'name': 'CTCLabelDecode',
-            "character_type": args.rec_char_type,
             "character_dict_path": args.rec_char_dict_path,
             "use_space_char": args.use_space_char
         }
         if self.rec_algorithm == "SRN":
             postprocess_params = {
                 'name': 'SRNLabelDecode',
-                "character_type": args.rec_char_type,
                 "character_dict_path": args.rec_char_dict_path,
                 "use_space_char": args.use_space_char
             }
         elif self.rec_algorithm == "RARE":
             postprocess_params = {
                 'name': 'AttnLabelDecode',
-                "character_type": args.rec_char_type,
                 "character_dict_path": args.rec_char_dict_path,
                 "use_space_char": args.use_space_char
             }
         elif self.rec_algorithm == 'NRTR':
             postprocess_params = {
                 'name': 'NRTRLabelDecode',
-                "character_type": args.rec_char_type,
                 "character_dict_path": args.rec_char_dict_path,
                 "use_space_char": args.use_space_char
             }
         elif self.rec_algorithm == "SAR":
             postprocess_params = {
                 'name': 'SARLabelDecode',
-                "character_type": args.rec_char_type,
                 "character_dict_path": args.rec_char_dict_path,
                 "use_space_char": args.use_space_char
             }
@@ -79,6 +73,7 @@ class TextRecognizer(object):
         self.predictor, self.input_tensor, self.output_tensors, self.config = \
             utility.create_predictor(args, 'rec', logger)
         self.benchmark = args.benchmark
+        self.use_onnx = args.use_onnx
         if args.benchmark:
             import auto_log
             pid = os.getpid()
@@ -96,7 +91,7 @@ class TextRecognizer(object):
                 time_keys=[
                     'preprocess_time', 'inference_time', 'postprocess_time'
                 ],
-                warmup=2,
+                warmup=0,
                 logger=logger)
 
     def resize_norm_img(self, img, max_wh_ratio):
@@ -112,8 +107,9 @@ class TextRecognizer(object):
             return norm_img.astype(np.float32) / 128. - 1.
 
         assert imgC == img.shape[2]
-        max_wh_ratio = max(max_wh_ratio, imgW / imgH)
         imgW = int((32 * max_wh_ratio))
+        if self.use_onnx:
+            imgW = 100
         h, w = img.shape[:2]
         ratio = w / float(h)
         if math.ceil(imgH * ratio) > imgW:
@@ -303,51 +299,72 @@ class TextRecognizer(object):
                     gsrm_slf_attn_bias1_list,
                     gsrm_slf_attn_bias2_list,
                 ]
-                input_names = self.predictor.get_input_names()
-                for i in range(len(input_names)):
-                    input_tensor = self.predictor.get_input_handle(input_names[
-                        i])
-                    input_tensor.copy_from_cpu(inputs[i])
-                self.predictor.run()
-                outputs = []
-                for output_tensor in self.output_tensors:
-                    output = output_tensor.copy_to_cpu()
-                    outputs.append(output)
-                if self.benchmark:
-                    self.autolog.times.stamp()
-                preds = {"predict": outputs[2]}
+                if self.use_onnx:
+                    input_dict = {}
+                    input_dict[self.input_tensor.name] = norm_img_batch
+                    outputs = self.predictor.run(self.output_tensors,
+                                                 input_dict)
+                    preds = {"predict": outputs[2]}
+                else:
+                    input_names = self.predictor.get_input_names()
+                    for i in range(len(input_names)):
+                        input_tensor = self.predictor.get_input_handle(
+                            input_names[i])
+                        input_tensor.copy_from_cpu(inputs[i])
+                    self.predictor.run()
+                    outputs = []
+                    for output_tensor in self.output_tensors:
+                        output = output_tensor.copy_to_cpu()
+                        outputs.append(output)
+                    if self.benchmark:
+                        self.autolog.times.stamp()
+                    preds = {"predict": outputs[2]}
             elif self.rec_algorithm == "SAR":
                 valid_ratios = np.concatenate(valid_ratios)
                 inputs = [
                     norm_img_batch,
                     valid_ratios,
                 ]
-                input_names = self.predictor.get_input_names()
-                for i in range(len(input_names)):
-                    input_tensor = self.predictor.get_input_handle(input_names[
-                        i])
-                    input_tensor.copy_from_cpu(inputs[i])
-                self.predictor.run()
-                outputs = []
-                for output_tensor in self.output_tensors:
-                    output = output_tensor.copy_to_cpu()
-                    outputs.append(output)
-                if self.benchmark:
-                    self.autolog.times.stamp()
-                preds = outputs[0]
-            else:
-                self.input_tensor.copy_from_cpu(norm_img_batch)
-                self.predictor.run()
-                outputs = []
-                for output_tensor in self.output_tensors:
-                    output = output_tensor.copy_to_cpu()
-                    outputs.append(output)
-                if self.benchmark:
-                    self.autolog.times.stamp()
-                if len(outputs) != 1:
-                    preds = outputs
+                if self.use_onnx:
+                    input_dict = {}
+                    input_dict[self.input_tensor.name] = norm_img_batch
+                    outputs = self.predictor.run(self.output_tensors,
+                                                 input_dict)
+                    preds = outputs[0]
                 else:
+                    input_names = self.predictor.get_input_names()
+                    for i in range(len(input_names)):
+                        input_tensor = self.predictor.get_input_handle(
+                            input_names[i])
+                        input_tensor.copy_from_cpu(inputs[i])
+                    self.predictor.run()
+                    outputs = []
+                    for output_tensor in self.output_tensors:
+                        output = output_tensor.copy_to_cpu()
+                        outputs.append(output)
+                    if self.benchmark:
+                        self.autolog.times.stamp()
                     preds = outputs[0]
+            else:
+                if self.use_onnx:
+                    input_dict = {}
+                    input_dict[self.input_tensor.name] = norm_img_batch
+                    outputs = self.predictor.run(self.output_tensors,
+                                                 input_dict)
+                    preds = outputs[0]
+                else:
+                    self.input_tensor.copy_from_cpu(norm_img_batch)
+                    self.predictor.run()
+                    outputs = []
+                    for output_tensor in self.output_tensors:
+                        output = output_tensor.copy_to_cpu()
+                        outputs.append(output)
+                    if self.benchmark:
+                        self.autolog.times.stamp()
+                    if len(outputs) != 1:
+                        preds = outputs
+                    else:
+                        preds = outputs[0]
             rec_result = self.postprocess_op(preds)
             for rno in range(len(rec_result)):
                 rec_res[indices[beg_img_no + rno]] = rec_result[rno]

tools/infer/predict_system.py

@@ -49,11 +49,19 @@ class TextSystem(object):
         if self.use_angle_cls:
             self.text_classifier = predict_cls.TextClassifier(args)
 
-    def print_draw_crop_rec_res(self, img_crop_list, rec_res):
+        self.args = args
+        self.crop_image_res_index = 0
+
+    def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res):
+        os.makedirs(output_dir, exist_ok=True)
         bbox_num = len(img_crop_list)
         for bno in range(bbox_num):
-            cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
-            logger.info(bno, rec_res[bno])
+            cv2.imwrite(
+                os.path.join(output_dir,
+                             f"mg_crop_{bno+self.crop_image_res_index}.jpg"),
+                img_crop_list[bno])
+            logger.debug(f"{bno}, {rec_res[bno]}")
+        self.crop_image_res_index += bbox_num
 
     def __call__(self, img, cls=True):
         ori_im = img.copy()
@@ -80,7 +88,9 @@ class TextSystem(object):
         rec_res, elapse = self.text_recognizer(img_crop_list)
         logger.debug("rec_res num  : {}, elapse : {}".format(
             len(rec_res), elapse))
-        # self.print_draw_crop_rec_res(img_crop_list, rec_res)
+        if self.args.save_crop_res:
+            self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,
+                                   rec_res)
         filter_boxes, filter_rec_res = [], []
         for box, rec_reuslt in zip(dt_boxes, rec_res):
             text, score = rec_reuslt
@@ -135,17 +145,17 @@ def main(args):
         if not flag:
             img = cv2.imread(image_file)
         if img is None:
-            logger.info("error in loading image:{}".format(image_file))
+            logger.debug("error in loading image:{}".format(image_file))
             continue
         starttime = time.time()
         dt_boxes, rec_res = text_sys(img)
         elapse = time.time() - starttime
         total_time += elapse
 
-        logger.info(
+        logger.debug(
             str(idx) + "  Predict time of %s: %.3fs" % (image_file, elapse))
         for text, score in rec_res:
-            logger.info("{}, {:.3f}".format(text, score))
+            logger.debug("{}, {:.3f}".format(text, score))
 
         if is_visualize:
             image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
@@ -160,19 +170,17 @@ def main(args):
                 scores,
                 drop_score=drop_score,
                 font_path=font_path)
-            draw_img_save = "./inference_results/"
-            if not os.path.exists(draw_img_save):
-                os.makedirs(draw_img_save)
+            draw_img_save_dir = args.draw_img_save_dir
+            os.makedirs(draw_img_save_dir, exist_ok=True)
             if flag:
                 image_file = image_file[:-3] + "png"
             cv2.imwrite(
-                os.path.join(draw_img_save, os.path.basename(image_file)),
+                os.path.join(draw_img_save_dir, os.path.basename(image_file)),
                 draw_img[:, :, ::-1])
-            logger.info("The visualized image saved in {}".format(
-                os.path.join(draw_img_save, os.path.basename(image_file))))
+            logger.debug("The visualized image saved in {}".format(
+                os.path.join(draw_img_save_dir, os.path.basename(image_file))))
 
     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()

tools/infer/utility.py

@@ -17,7 +17,7 @@ import os
 import sys
 import cv2
 import numpy as np
-import json
+import paddle
 from PIL import Image, ImageDraw, ImageFont
 import math
 from paddle import inference
@@ -74,7 +74,6 @@ def init_args():
     parser.add_argument("--rec_algorithm", type=str, default='CRNN')
     parser.add_argument("--rec_model_dir", type=str)
     parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
-    parser.add_argument("--rec_char_type", type=str, default='ch')
     parser.add_argument("--rec_batch_num", type=int, default=6)
     parser.add_argument("--max_text_length", type=int, default=25)
     parser.add_argument(
@@ -97,7 +96,6 @@ def init_args():
     parser.add_argument(
         "--e2e_char_dict_path", type=str, default="./ppocr/utils/ic15_dict.txt")
     parser.add_argument("--e2e_pgnet_valid_set", type=str, default='totaltext')
-    parser.add_argument("--e2e_pgnet_polygon", type=str2bool, default=True)
     parser.add_argument("--e2e_pgnet_mode", type=str, default='fast')
 
     # params for text classifier
@@ -111,7 +109,13 @@ def init_args():
     parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
     parser.add_argument("--cpu_threads", type=int, default=10)
     parser.add_argument("--use_pdserving", type=str2bool, default=False)
-    parser.add_argument("--warmup", type=str2bool, default=True)
+    parser.add_argument("--warmup", type=str2bool, default=False)
+
+    #
+    parser.add_argument(
+        "--draw_img_save_dir", type=str, default="./inference_results")
+    parser.add_argument("--save_crop_res", type=str2bool, default=False)
+    parser.add_argument("--crop_res_save_dir", type=str, default="./output")
 
     # multi-process
     parser.add_argument("--use_mp", type=str2bool, default=False)
@@ -122,6 +126,7 @@ def init_args():
     parser.add_argument("--save_log_path", type=str, default="./log_output/")
 
     parser.add_argument("--show_log", type=str2bool, default=True)
+    parser.add_argument("--use_onnx", type=str2bool, default=False)
     return parser
 
 
@@ -145,158 +150,167 @@ def create_predictor(args, mode, logger):
     if model_dir is None:
         logger.info("not find {} model file path {}".format(mode, model_dir))
         sys.exit(0)
-    model_file_path = model_dir + "/inference.pdmodel"
-    params_file_path = model_dir + "/inference.pdiparams"
-    if not os.path.exists(model_file_path):
-        raise ValueError("not find model file path {}".format(model_file_path))
-    if not os.path.exists(params_file_path):
-        raise ValueError("not find params file path {}".format(
-            params_file_path))
-
-    config = inference.Config(model_file_path, params_file_path)
-
-    if hasattr(args, 'precision'):
-        if args.precision == "fp16" and args.use_tensorrt:
-            precision = inference.PrecisionType.Half
-        elif args.precision == "int8":
-            precision = inference.PrecisionType.Int8
-        else:
-            precision = inference.PrecisionType.Float32
+    if args.use_onnx:
+        import onnxruntime as ort
+        model_file_path = model_dir
+        if not os.path.exists(model_file_path):
+            raise ValueError("not find model file path {}".format(
+                model_file_path))
+        sess = ort.InferenceSession(model_file_path)
+        return sess, sess.get_inputs()[0], None, None
+
     else:
-        precision = inference.PrecisionType.Float32
-
-    if args.use_gpu:
-        gpu_id = get_infer_gpuid()
-        if gpu_id is None:
-            raise ValueError(
-                "Not found GPU in current device. Please check your device or set args.use_gpu as False"
-            )
-        config.enable_use_gpu(args.gpu_mem, 0)
-        if args.use_tensorrt:
-            config.enable_tensorrt_engine(
-                precision_mode=precision,
-                max_batch_size=args.max_batch_size,
-                min_subgraph_size=args.min_subgraph_size)
-            # skip the minmum trt subgraph
-        if mode == "det":
-            min_input_shape = {
-                "x": [1, 3, 50, 50],
-                "conv2d_92.tmp_0": [1, 120, 20, 20],
-                "conv2d_91.tmp_0": [1, 24, 10, 10],
-                "conv2d_59.tmp_0": [1, 96, 20, 20],
-                "nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
-                "nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
-                "conv2d_124.tmp_0": [1, 256, 20, 20],
-                "nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
-                "nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
-                "nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
-                "elementwise_add_7": [1, 56, 2, 2],
-                "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
-            }
-            max_input_shape = {
-                "x": [1, 3, 2000, 2000],
-                "conv2d_92.tmp_0": [1, 120, 400, 400],
-                "conv2d_91.tmp_0": [1, 24, 200, 200],
-                "conv2d_59.tmp_0": [1, 96, 400, 400],
-                "nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
-                "conv2d_124.tmp_0": [1, 256, 400, 400],
-                "nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
-                "nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
-                "nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
-                "nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
-                "elementwise_add_7": [1, 56, 400, 400],
-                "nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
-            }
-            opt_input_shape = {
-                "x": [1, 3, 640, 640],
-                "conv2d_92.tmp_0": [1, 120, 160, 160],
-                "conv2d_91.tmp_0": [1, 24, 80, 80],
-                "conv2d_59.tmp_0": [1, 96, 160, 160],
-                "nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
-                "nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
-                "conv2d_124.tmp_0": [1, 256, 160, 160],
-                "nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
-                "nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
-                "nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
-                "elementwise_add_7": [1, 56, 40, 40],
-                "nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
-            }
-            min_pact_shape = {
-                "nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
-                "nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
-                "nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
-                "nearest_interp_v2_29.tmp_0": [1, 64, 20, 20]
-            }
-            max_pact_shape = {
-                "nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
-                "nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
-                "nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
-                "nearest_interp_v2_29.tmp_0": [1, 64, 400, 400]
-            }
-            opt_pact_shape = {
-                "nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
-                "nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
-                "nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
-                "nearest_interp_v2_29.tmp_0": [1, 64, 160, 160]
-            }
-            min_input_shape.update(min_pact_shape)
-            max_input_shape.update(max_pact_shape)
-            opt_input_shape.update(opt_pact_shape)
-        elif mode == "rec":
-            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": [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]}
+        model_file_path = model_dir + "/inference.pdmodel"
+        params_file_path = model_dir + "/inference.pdiparams"
+        if not os.path.exists(model_file_path):
+            raise ValueError("not find model file path {}".format(
+                model_file_path))
+        if not os.path.exists(params_file_path):
+            raise ValueError("not find params file path {}".format(
+                params_file_path))
+
+        config = inference.Config(model_file_path, params_file_path)
+
+        if hasattr(args, 'precision'):
+            if args.precision == "fp16" and args.use_tensorrt:
+                precision = inference.PrecisionType.Half
+            elif args.precision == "int8":
+                precision = inference.PrecisionType.Int8
+            else:
+                precision = inference.PrecisionType.Float32
         else:
-            min_input_shape = {"x": [1, 3, 10, 10]}
-            max_input_shape = {"x": [1, 3, 1000, 1000]}
-            opt_input_shape = {"x": [1, 3, 500, 500]}
-        config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
-                                          opt_input_shape)
+            precision = inference.PrecisionType.Float32
+
+        if args.use_gpu:
+            gpu_id = get_infer_gpuid()
+            if gpu_id is None:
+                logger.warning(
+                    "GPU is not found in current device by nvidia-smi. Please check your device or ignore it if run on jeston."
+                )
+            config.enable_use_gpu(args.gpu_mem, 0)
+            if args.use_tensorrt:
+                config.enable_tensorrt_engine(
+                    workspace_size=1 << 30,
+                    precision_mode=precision,
+                    max_batch_size=args.max_batch_size,
+                    min_subgraph_size=args.min_subgraph_size)
+                # skip the minmum trt subgraph
+            if mode == "det":
+                min_input_shape = {
+                    "x": [1, 3, 50, 50],
+                    "conv2d_92.tmp_0": [1, 120, 20, 20],
+                    "conv2d_91.tmp_0": [1, 24, 10, 10],
+                    "conv2d_59.tmp_0": [1, 96, 20, 20],
+                    "nearest_interp_v2_1.tmp_0": [1, 256, 10, 10],
+                    "nearest_interp_v2_2.tmp_0": [1, 256, 20, 20],
+                    "conv2d_124.tmp_0": [1, 256, 20, 20],
+                    "nearest_interp_v2_3.tmp_0": [1, 64, 20, 20],
+                    "nearest_interp_v2_4.tmp_0": [1, 64, 20, 20],
+                    "nearest_interp_v2_5.tmp_0": [1, 64, 20, 20],
+                    "elementwise_add_7": [1, 56, 2, 2],
+                    "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
+                }
+                max_input_shape = {
+                    "x": [1, 3, 1280, 1280],
+                    "conv2d_92.tmp_0": [1, 120, 400, 400],
+                    "conv2d_91.tmp_0": [1, 24, 200, 200],
+                    "conv2d_59.tmp_0": [1, 96, 400, 400],
+                    "nearest_interp_v2_1.tmp_0": [1, 256, 200, 200],
+                    "conv2d_124.tmp_0": [1, 256, 400, 400],
+                    "nearest_interp_v2_2.tmp_0": [1, 256, 400, 400],
+                    "nearest_interp_v2_3.tmp_0": [1, 64, 400, 400],
+                    "nearest_interp_v2_4.tmp_0": [1, 64, 400, 400],
+                    "nearest_interp_v2_5.tmp_0": [1, 64, 400, 400],
+                    "elementwise_add_7": [1, 56, 400, 400],
+                    "nearest_interp_v2_0.tmp_0": [1, 256, 400, 400]
+                }
+                opt_input_shape = {
+                    "x": [1, 3, 640, 640],
+                    "conv2d_92.tmp_0": [1, 120, 160, 160],
+                    "conv2d_91.tmp_0": [1, 24, 80, 80],
+                    "conv2d_59.tmp_0": [1, 96, 160, 160],
+                    "nearest_interp_v2_1.tmp_0": [1, 256, 80, 80],
+                    "nearest_interp_v2_2.tmp_0": [1, 256, 160, 160],
+                    "conv2d_124.tmp_0": [1, 256, 160, 160],
+                    "nearest_interp_v2_3.tmp_0": [1, 64, 160, 160],
+                    "nearest_interp_v2_4.tmp_0": [1, 64, 160, 160],
+                    "nearest_interp_v2_5.tmp_0": [1, 64, 160, 160],
+                    "elementwise_add_7": [1, 56, 40, 40],
+                    "nearest_interp_v2_0.tmp_0": [1, 256, 40, 40]
+                }
+                min_pact_shape = {
+                    "nearest_interp_v2_26.tmp_0": [1, 256, 20, 20],
+                    "nearest_interp_v2_27.tmp_0": [1, 64, 20, 20],
+                    "nearest_interp_v2_28.tmp_0": [1, 64, 20, 20],
+                    "nearest_interp_v2_29.tmp_0": [1, 64, 20, 20]
+                }
+                max_pact_shape = {
+                    "nearest_interp_v2_26.tmp_0": [1, 256, 400, 400],
+                    "nearest_interp_v2_27.tmp_0": [1, 64, 400, 400],
+                    "nearest_interp_v2_28.tmp_0": [1, 64, 400, 400],
+                    "nearest_interp_v2_29.tmp_0": [1, 64, 400, 400]
+                }
+                opt_pact_shape = {
+                    "nearest_interp_v2_26.tmp_0": [1, 256, 160, 160],
+                    "nearest_interp_v2_27.tmp_0": [1, 64, 160, 160],
+                    "nearest_interp_v2_28.tmp_0": [1, 64, 160, 160],
+                    "nearest_interp_v2_29.tmp_0": [1, 64, 160, 160]
+                }
+                min_input_shape.update(min_pact_shape)
+                max_input_shape.update(max_pact_shape)
+                opt_input_shape.update(opt_pact_shape)
+            elif mode == "rec":
+                min_input_shape = {"x": [1, 3, 32, 10]}
+                max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1024]}
+                opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
+            elif mode == "cls":
+                min_input_shape = {"x": [1, 3, 48, 10]}
+                max_input_shape = {"x": [args.rec_batch_num, 3, 48, 1024]}
+                opt_input_shape = {"x": [args.rec_batch_num, 3, 48, 320]}
+            else:
+                min_input_shape = {"x": [1, 3, 10, 10]}
+                max_input_shape = {"x": [1, 3, 512, 512]}
+                opt_input_shape = {"x": [1, 3, 256, 256]}
+            config.set_trt_dynamic_shape_info(min_input_shape, max_input_shape,
+                                              opt_input_shape)
 
-    else:
-        config.disable_gpu()
-        if hasattr(args, "cpu_threads"):
-            config.set_cpu_math_library_num_threads(args.cpu_threads)
         else:
-            # default cpu threads as 10
-            config.set_cpu_math_library_num_threads(10)
-        if args.enable_mkldnn:
-            # cache 10 different shapes for mkldnn to avoid memory leak
-            config.set_mkldnn_cache_capacity(10)
-            config.enable_mkldnn()
-
-    # enable memory optim
-    config.enable_memory_optim()
-    #config.disable_glog_info()
-
-    config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
-    if mode == 'table':
-        config.delete_pass("fc_fuse_pass")  # not supported for table
-    config.switch_use_feed_fetch_ops(False)
-    config.switch_ir_optim(True)
-
-    # create predictor
-    predictor = inference.create_predictor(config)
-    input_names = predictor.get_input_names()
-    for name in input_names:
-        input_tensor = predictor.get_input_handle(name)
-    output_names = predictor.get_output_names()
-    output_tensors = []
-    for output_name in output_names:
-        output_tensor = predictor.get_output_handle(output_name)
-        output_tensors.append(output_tensor)
-    return predictor, input_tensor, output_tensors, config
+            config.disable_gpu()
+            if hasattr(args, "cpu_threads"):
+                config.set_cpu_math_library_num_threads(args.cpu_threads)
+            else:
+                # default cpu threads as 10
+                config.set_cpu_math_library_num_threads(10)
+            if args.enable_mkldnn:
+                # cache 10 different shapes for mkldnn to avoid memory leak
+                config.set_mkldnn_cache_capacity(10)
+                config.enable_mkldnn()
+                if args.precision == "fp16":
+                    config.enable_mkldnn_bfloat16()
+        # enable memory optim
+        config.enable_memory_optim()
+        config.disable_glog_info()
+
+        config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
+        if mode == 'table':
+            config.delete_pass("fc_fuse_pass")  # not supported for table
+        config.switch_use_feed_fetch_ops(False)
+        config.switch_ir_optim(True)
+
+        # create predictor
+        predictor = inference.create_predictor(config)
+        input_names = predictor.get_input_names()
+        for name in input_names:
+            input_tensor = predictor.get_input_handle(name)
+        output_names = predictor.get_output_names()
+        output_tensors = []
+        for output_name in output_names:
+            output_tensor = predictor.get_output_handle(output_name)
+            output_tensors.append(output_tensor)
+        return predictor, input_tensor, output_tensors, config
 
 
 def get_infer_gpuid():
-    cmd = "nvidia-smi"
-    res = os.popen(cmd).readlines()
-    if len(res) == 0:
-        return None
     cmd = "env | grep CUDA_VISIBLE_DEVICES"
     env_cuda = os.popen(cmd).readlines()
     if len(env_cuda) == 0:
@@ -589,5 +603,12 @@ def get_rotate_crop_image(img, points):
     return dst_img
 
 
+def check_gpu(use_gpu):
+    if use_gpu and not paddle.is_compiled_with_cuda():
+
+        use_gpu = False
+    return use_gpu
+
+
 if __name__ == '__main__':
     pass

tools/infer_cls.py

@@ -32,7 +32,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -47,7 +47,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # create data ops
     transforms = []

tools/infer_det.py

@@ -34,7 +34,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model, load_dygraph_params
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -59,7 +59,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    _ = load_dygraph_params(config, model, logger, None)
+    load_model(config, model)
     # build post process
     post_process_class = build_post_process(config['PostProcess'])
 

tools/infer_e2e.py

@@ -34,7 +34,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -68,7 +68,7 @@ def main():
     # build model
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # build post process
     post_process_class = build_post_process(config['PostProcess'],

tools/infer_rec.py

@@ -33,7 +33,7 @@ import paddle
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 
@@ -58,7 +58,7 @@ def main():
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model)
+    load_model(config, model)
 
     # create data ops
     transforms = []
@@ -75,9 +75,7 @@ def main():
                     'gsrm_slf_attn_bias1', 'gsrm_slf_attn_bias2'
                 ]
             elif config['Architecture']['algorithm'] == "SAR":
-                op[op_name]['keep_keys'] = [
-                    'image', 'valid_ratio'
-                ]
+                op[op_name]['keep_keys'] = ['image', 'valid_ratio']
             else:
                 op[op_name]['keep_keys'] = ['image']
         transforms.append(op)

tools/infer_table.py

@@ -34,11 +34,12 @@ from paddle.jit import to_static
 from ppocr.data import create_operators, transform
 from ppocr.modeling.architectures import build_model
 from ppocr.postprocess import build_post_process
-from ppocr.utils.save_load import init_model
+from ppocr.utils.save_load import load_model
 from ppocr.utils.utility import get_image_file_list
 import tools.program as program
 import cv2
 
+
 def main(config, device, logger, vdl_writer):
     global_config = config['Global']
 
@@ -53,7 +54,7 @@ def main(config, device, logger, vdl_writer):
 
     model = build_model(config['Architecture'])
 
-    init_model(config, model, logger)
+    load_model(config, model)
 
     # create data ops
     transforms = []
@@ -104,4 +105,3 @@ def main(config, device, logger, vdl_writer):
 if __name__ == '__main__':
     config, device, logger, vdl_writer = program.preprocess()
     main(config, device, logger, vdl_writer)
-

tools/program.py

@@ -159,7 +159,8 @@ def train(config,
           eval_class,
           pre_best_model_dict,
           logger,
-          vdl_writer=None):
+          vdl_writer=None,
+          scaler=None):
     cal_metric_during_train = config['Global'].get('cal_metric_during_train',
                                                    False)
     log_smooth_window = config['Global']['log_smooth_window']
@@ -211,15 +212,15 @@ def train(config,
     for epoch in range(start_epoch, epoch_num + 1):
         train_dataloader = build_dataloader(
             config, 'Train', device, logger, seed=epoch)
-        train_batch_cost = 0.0
         train_reader_cost = 0.0
-        batch_sum = 0
-        batch_start = time.time()
+        train_run_cost = 0.0
+        total_samples = 0
+        reader_start = time.time()
         max_iter = len(train_dataloader) - 1 if platform.system(
         ) == "Windows" else len(train_dataloader)
         for idx, batch in enumerate(train_dataloader):
             profiler.add_profiler_step(profiler_options)
-            train_reader_cost += time.time() - batch_start
+            train_reader_cost += time.time() - reader_start
             if idx >= max_iter:
                 break
             lr = optimizer.get_lr()
@@ -230,16 +231,34 @@ def train(config,
                 preds = model(images, data=batch[1:])
             if model_type == "kie":
                 preds = model(batch)
+
+            train_start = time.time()
+            # use amp
+            if scaler:
+                with paddle.amp.auto_cast():
+                    if model_type == 'table' or extra_input:
+                        preds = model(images, data=batch[1:])
+                    else:
+                        preds = model(images)
             else:
-                preds = model(images)
+                if model_type == 'table' or extra_input:
+                    preds = model(images, data=batch[1:])
+                else:
+                    preds = model(images)
             loss = loss_class(preds, batch)
             avg_loss = loss['loss']
-            avg_loss.backward()
-            optimizer.step()
+
+            if scaler:
+                scaled_avg_loss = scaler.scale(avg_loss)
+                scaled_avg_loss.backward()
+                scaler.minimize(optimizer, scaled_avg_loss)
+            else:
+                avg_loss.backward()
+                optimizer.step()
             optimizer.clear_grad()
 
-            train_batch_cost += time.time() - batch_start
-            batch_sum += len(images)
+            train_run_cost += time.time() - train_start
+            total_samples += len(images)
 
             if not isinstance(lr_scheduler, float):
                 lr_scheduler.step()
@@ -270,12 +289,13 @@ def train(config,
                 logs = train_stats.log()
                 strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
                     epoch, epoch_num, global_step, logs, train_reader_cost /
-                    print_batch_step, train_batch_cost / print_batch_step,
-                    batch_sum, batch_sum / train_batch_cost)
+                    print_batch_step, (train_reader_cost + train_run_cost) /
+                    print_batch_step, total_samples,
+                    total_samples / (train_reader_cost + train_run_cost))
                 logger.info(strs)
-                train_batch_cost = 0.0
                 train_reader_cost = 0.0
-                batch_sum = 0
+                train_run_cost = 0.0
+                total_samples = 0
             # eval
             if global_step > start_eval_step and \
                     (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
@@ -328,7 +348,7 @@ def train(config,
                                           global_step)
             global_step += 1
             optimizer.clear_grad()
-            batch_start = time.time()
+            reader_start = time.time()
         if dist.get_rank() == 0:
             save_model(
                 model,
@@ -369,7 +389,11 @@ def eval(model,
     with paddle.no_grad():
         total_frame = 0.0
         total_time = 0.0
-        pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
+        pbar = tqdm(
+            total=len(valid_dataloader),
+            desc='eval model:',
+            position=0,
+            leave=True)
         max_iter = len(valid_dataloader) - 1 if platform.system(
         ) == "Windows" else len(valid_dataloader)
         for idx, batch in enumerate(valid_dataloader):
@@ -404,6 +428,55 @@ def eval(model,
     return metric
 
 
+def update_center(char_center, post_result, preds):
+    result, label = post_result
+    feats, logits = preds
+    logits = paddle.argmax(logits, axis=-1)
+    feats = feats.numpy()
+    logits = logits.numpy()
+
+    for idx_sample in range(len(label)):
+        if result[idx_sample][0] == label[idx_sample][0]:
+            feat = feats[idx_sample]
+            logit = logits[idx_sample]
+            for idx_time in range(len(logit)):
+                index = logit[idx_time]
+                if index in char_center.keys():
+                    char_center[index][0] = (
+                        char_center[index][0] * char_center[index][1] +
+                        feat[idx_time]) / (char_center[index][1] + 1)
+                    char_center[index][1] += 1
+                else:
+                    char_center[index] = [feat[idx_time], 1]
+    return char_center
+
+
+def get_center(model, eval_dataloader, post_process_class):
+    pbar = tqdm(total=len(eval_dataloader), desc='get center:')
+    max_iter = len(eval_dataloader) - 1 if platform.system(
+    ) == "Windows" else len(eval_dataloader)
+    char_center = dict()
+    for idx, batch in enumerate(eval_dataloader):
+        if idx >= max_iter:
+            break
+        images = batch[0]
+        start = time.time()
+        preds = model(images)
+
+        batch = [item.numpy() for item in batch]
+        # Obtain usable results from post-processing methods
+        post_result = post_process_class(preds, batch[1])
+
+        #update char_center
+        char_center = update_center(char_center, post_result, preds)
+        pbar.update(1)
+
+    pbar.close()
+    for key in char_center.keys():
+        char_center[key] = char_center[key][0]
+    return char_center
+
+
 def preprocess(is_train=False):
     FLAGS = ArgsParser().parse_args()
     profiler_options = FLAGS.profiler_options

tools/train.py

@@ -35,7 +35,7 @@ from ppocr.losses import build_loss
 from ppocr.optimizer import build_optimizer
 from ppocr.postprocess import build_post_process
 from ppocr.metrics import build_metric
-from ppocr.utils.save_load import init_model, load_dygraph_params
+from ppocr.utils.save_load import load_model
 import tools.program as program
 
 dist.get_world_size()
@@ -97,15 +97,32 @@ def main(config, device, logger, vdl_writer):
     # build metric
     eval_class = build_metric(config['Metric'])
     # load pretrain model
-    pre_best_model_dict = load_dygraph_params(config, model, logger, optimizer)
+    pre_best_model_dict = load_model(config, model, optimizer)
     logger.info('train dataloader has {} iters'.format(len(train_dataloader)))
     if valid_dataloader is not None:
         logger.info('valid dataloader has {} iters'.format(
             len(valid_dataloader)))
+
+    use_amp = config["Global"].get("use_amp", False)
+    if use_amp:
+        AMP_RELATED_FLAGS_SETTING = {
+            'FLAGS_cudnn_batchnorm_spatial_persistent': 1,
+            'FLAGS_max_inplace_grad_add': 8,
+        }
+        paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING)
+        scale_loss = config["Global"].get("scale_loss", 1.0)
+        use_dynamic_loss_scaling = config["Global"].get(
+            "use_dynamic_loss_scaling", False)
+        scaler = paddle.amp.GradScaler(
+            init_loss_scaling=scale_loss,
+            use_dynamic_loss_scaling=use_dynamic_loss_scaling)
+    else:
+        scaler = None
+
     # start train
     program.train(config, train_dataloader, valid_dataloader, device, model,
                   loss_class, optimizer, lr_scheduler, post_process_class,
-                  eval_class, pre_best_model_dict, logger, vdl_writer)
+                  eval_class, pre_best_model_dict, logger, vdl_writer, scaler)
 
 
 def test_reader(config, device, logger):