Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into dygraph

76274121 · tink2123 · 39c584af · 55d54dfc · 76274121 · 76274121
138 changed file
--- a/PPOCRLabel/PPOCRLabel.py
+++ b/PPOCRLabel/PPOCRLabel.py
@@ -114,7 +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)
@@ -1389,7 +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

--- a/PPOCRLabel/README.md
+++ b/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)):
@@ -21,11 +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.
-## 1. Installation
-### 1.1 Environment Preparation
-#### **Install PaddlePaddle 2.0**
+## 1. Installation and Run
+### 1.1 Install PaddlePaddle
 ```bash
 pip3 install --upgrade pip
@@ -39,58 +42,54 @@ 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
+pip install PPOCRLabel  # install
-pip3 install -r requirements.txt
+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.
+> 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)
+> Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
+>
-### 1.2 Install PPOCRLabel
+#### Ubuntu Linux
-#### Windows
+```bash
+pip3 install PPOCRLabel
+pip3 install trash-cli
+PPOCRLabel
+```
+#### MacOS
 ```bash
-pip install pyqt5
+pip3 install PPOCRLabel
-cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
+pip3 install opencv-contrib-python-headless==4.2.0.32
-python PPOCRLabel.py
+PPOCRLabel # run
 ```
-#### Ubuntu Linux
+#### 1.2.2 Build and Install the Whl Package Locally
 ```bash
-pip3 install pyqt5
+cd PaddleOCR/PPOCRLabel
-pip3 install trash-cli
+python3 setup.py bdist_wheel 
-cd ./PPOCRLabel # Change the directory to the PPOCRLabel folder
+pip3 install dist/PPOCRLabel-1.0.0-py2.py3-none-any.whl
-python3 PPOCRLabel.py
 ```
-#### MacOS
+#### 1.2.3 Run PPOCRLabel by Python Script
 ```bash
-pip3 install pyqt5
+cd ./PPOCRLabel  # Switch to the PPOCRLabel directory
-pip3 uninstall opencv-python # Uninstall opencv manually as it conflicts with pyqt
+python PPOCRLabel.py --lang ch
-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
 ```
 ## 2. Usage
 ### 2.1 Steps
@@ -119,7 +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".
@@ -137,6 +136,8 @@ 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* |
 ## 3. Explanation
 ### 3.1 Shortcut keys
@@ -189,7 +190,26 @@ For some data that are difficult to recognize, the recognition results will not
 > *Note: The status of the checkboxes in the recognition results still needs to be saved manually by clicking Save Button.*
-### 3.5 Error message
+### 3.5 Dataset division
+- Enter the following command in the terminal to execute the dataset division script:
+  ```
+  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.
@@ -207,24 +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
    ```
-### Dataset division
- Enter the following command in the terminal to execute the dataset division script:
-    ```
-    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
-### Related
+### 4. Related
 1.[Tzutalin. LabelImg. Git code (2015)](https://github.com/tzutalin/labelImg)
\ No newline at end of file
--- a/PPOCRLabel/README_ch.md
+++ b/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。
@@ -24,73 +27,69 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，内置P
 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）
-## 1. 安装
-### 1.1 环境搭建
+## 1. 安装与运行
-#### 安装PaddlePaddle
+### 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
+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
-【推荐】git clone https://github.com/PaddlePaddle/PaddleOCR
-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
 ```
+> 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
-#### 安装第三方库
+##### Ubuntu Linux
 ```bash
-cd PaddleOCR
+pip3 install PPOCRLabel
-pip3 install -r requirements.txt
+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`。
+##### MacOS
-### 1.2 安装PPOCRLabel
-#### Windows
 ```bash
-pip install pyqt5
+pip3 install PPOCRLabel
-cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+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"
-python PPOCRLabel.py --lang ch
+PPOCRLabel --lang ch # 启动
 ```
-#### Ubuntu Linux
+#### 1.2.2 本地构建whl包并安装
 ```bash
-pip3 install pyqt5
+cd PaddleOCR/PPOCRLabel
-pip3 install trash-cli
+python3 setup.py bdist_wheel 
-cd ./PPOCRLabel # 将目录切换到PPOCRLabel文件夹下
+pip3 install dist/PPOCRLabel-1.0.0-py2.py3-none-any.whl -i https://mirror.baidu.com/pypi/simple
-python3 PPOCRLabel.py --lang ch
 ```
-#### 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
+cd ./PPOCRLabel  # 切换到PPOCRLabel目录
-pip3 uninstall opencv-python # 由于mac版本的opencv与pyqt有冲突，需先手动卸载opencv
+python PPOCRLabel.py --lang ch
-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
 ```
 ## 2. 使用
 ### 2.1 操作步骤
@@ -106,7 +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。
@@ -124,6 +123,8 @@ 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。   |
 ## 3. 说明
 ### 3.1 快捷键
@@ -152,9 +153,7 @@ python3 PPOCRLabel.py --lang ch
 - 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**：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) :
+ - **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。 
-   `self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=gpu, lang=lang) `，在 `det_model_dir` 中传入  自己的模型即可。 
 ### 3.3 导出标记结果
@@ -174,7 +173,27 @@ PPOCRLabel支持三种导出方式：
 > *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 错误提示
+### 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`
+- `labelRootPath` 是PPOCRLabel标注的数据集存放路径，默认是`../train_data/label`
+- `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
 - 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。
@@ -193,21 +212,7 @@ PPOCRLabel支持三种导出方式：
    ```
    pip install opencv-contrib-python-headless==4.2.0.32
    ```
-### 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，根据你的实际情况设定，默认是6:2:2
-    labelRootPath是PPOCRLabel标注的数据集存放路径，默认是../train_data/label
-    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
 ### 4. 参考资料

--- a/PPOCRLabel/libs/resources.py
+++ b/PPOCRLabel/libs/resources.py
--- a/PPOCRLabel/libs/stringBundle.py
+++ b/PPOCRLabel/libs/stringBundle.py
@@ -60,14 +60,14 @@ 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
            tags = re.split('[^a-zA-Z]', localeStr)
            for tag in tags:
                lastPath = resultPaths[-1]
-                resultPaths[-1] = lastPath + '-' + tag
+                resultPaths.append(lastPath + '-' + tag)
            resultPaths[-1] = __dirpath__ + resultPaths[-1] + ".properties"
        return resultPaths

--- a/PPOCRLabel/libs/utils.py
+++ b/PPOCRLabel/libs/utils.py
@@ -33,9 +33,9 @@ except ImportError:
 def newIcon(icon, iconSize=None):
    if iconSize is not None:
-        return QIcon(QIcon(__iconpath__ + "\\" + icon + ".png").pixmap(iconSize,iconSize))
+        return QIcon(QIcon(__iconpath__ + "/" + icon + ".png").pixmap(iconSize,iconSize))
    else:
-        return QIcon(__iconpath__ + "\\" + icon + ".png")
+        return QIcon(__iconpath__ + "/" + icon + ".png")
 def newButton(text, icon=None, slot=None):

--- a/PPOCRLabel/requirements.txt
+++ b/PPOCRLabel/requirements.txt
-shapely
+pyqt5
-scikit-image==0.17.2
+paddleocr
-imgaug==0.4.0
\ No newline at end of file
-pyclipper
-lmdb
-tqdm
-numpy
-visualdl
-python-Levenshtein
-opencv-contrib-python==4.2.0.32
-PaddleOCR
\ No newline at end of file
--- a/PPOCRLabel/resources.qrc
+++ b/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>
--- a/benchmark/run_benchmark_det.sh
+++ b/benchmark/run_benchmark_det.sh
@@ -6,27 +6,35 @@ 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:-"10"}       # 可选，如果需要修改代码提前中断
+    max_epoch=${4:-"10"}         # 可选，如果需要修改代码提前中断
-    model_name=${5:-"model_name"}
+    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=${model_item}_bs${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=${max_iter} Global.eval_batch_step=[0,20000] Global.print_batch_step=2"   
+    train_cmd="-c configs/det/${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
@@ -46,17 +54,7 @@ function _train(){
    fi
 }
-function _analysis_log(){
+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
-    analysis_cmd="python3.7 benchmark/analysis.py --filename ${log_file}  --mission_name ${model_name} --run_mode ${run_mode} --direction_id 0 --keyword 'ips:' --base_batch_size ${batch_size} --skip_steps 1 --gpu_num ${num_gpu_devices}  --index 1  --model_mode=-1  --ips_unit=samples/sec"
-    eval $analysis_cmd
-}
-function _kill_process(){
-    kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'`
-}
 _set_params $@
-_train
+#_train      # 如果只想产出训练log,不解析,可取消注释
-_analysis_log
+_run         # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开
-_kill_process
\ No newline at end of file
--- a/benchmark/run_det.sh
+++ b/benchmark/run_det.sh
+#!/bin/bash
 # 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7  paddle=2.1.2  py=37
 # 执行目录: ./PaddleOCR
 # 1 安装该模型需要的依赖 (如需开启优化策略请注明)
-python3.7 -m pip install -r requirements.txt
+log_path=${LOG_PATH_INDEX_DIR:-$(pwd)}
+python -m pip install -r requirements.txt
 # 2 拷贝该模型需要数据、预训练模型
 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
@@ -11,17 +13,23 @@ wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dyg
 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
      for fp_item in ${fp_item_list[@]}; do
+          if [ ${model_mode} == "det_r50_vd_east" ]; then
+              bs_list=(16)
+          else
+              bs_list=(8 16)
+          fi
          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} 2 ${model_mode}     #  (5min)
+            log_name=ocr_${model_mode}_bs${bs_item}_${fp_item}_${run_mode}
+            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} 2 ${model_mode} 
+            log_name=ocr_${model_mode}_bs${bs_item}_${fp_item}_${run_mode}
+            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

--- a/deploy/cpp_infer/readme.md
+++ b/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
+wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
-tar -xf 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
+rm -rf ${build_dir}
-mkdir build
+mkdir ${build_dir}
-cd build
+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|

--- a/deploy/cpp_infer/readme_en.md
+++ b/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
+wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
-tar -xf 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.

--- a/deploy/cpp_infer/tools/build_opencv.sh
+++ b/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
--- a/deploy/lite/ocr_db_crnn.cc
+++ b/deploy/lite/ocr_db_crnn.cc
@@ -172,7 +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);
@@ -191,7 +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
@@ -199,8 +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;
@@ -224,7 +231,20 @@ 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>>>
@@ -312,7 +332,6 @@ std::shared_ptr<PaddlePredictor> loadModel(std::string model_file, int num_threa
  config.set_model_from_file(model_file);
  config.set_threads(num_threads);
  std::shared_ptr<PaddlePredictor> predictor =
      CreatePaddlePredictor<MobileConfig>(config);
  return predictor;
@@ -434,6 +453,9 @@ void system(char **argv){
  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};
  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);
@@ -460,7 +482,37 @@ void system(char **argv){
    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();
  }
 }
@@ -503,15 +555,15 @@ void det(int argc, char **argv) {
    auto img_vis = Visualization(srcimg, boxes);
    std::cout << boxes.size() << " bboxes have detected:" << std::endl;
-    // for (int i=0; i<boxes.size(); i++){
+    for (int i=0; i<boxes.size(); i++){
-    //   std::cout << "The " << i << " box:" << std::endl;
+      std::cout << "The " << i << " box:" << std::endl;
-    //   for (int j=0; j<4; j++){
+      for (int j=0; j<4; j++){
-    //     for (int k=0; k<2; k++){
+        for (int k=0; k<2; k++){
-    //       std::cout << boxes[i][j][k] << "\t";
+          std::cout << boxes[i][j][k] << "\t";
-    //     }
+        }
-    //   }
+      }
-    //   std::cout << std::endl;
+      std::cout << std::endl;
-    // }
+    }
    time_info[0] += times[0];
    time_info[1] += times[1];
    time_info[2] += times[2];
@@ -585,6 +637,9 @@ void rec(int argc, char **argv) {
      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) {

--- a/deploy/slim/prune/export_prune_model.py
+++ b/deploy/slim/prune/export_prune_model.py
@@ -52,12 +52,17 @@ def main(config, device, logger, vdl_writer):
        config['Architecture']["Head"]['out_channels'] = char_num
    model = build_model(config['Architecture'])
-    flops = paddle.flops(model, [1, 3, 640, 640])
+    if config['Architecture']['model_type'] == 'det':
-    logger.info(f"FLOPs before pruning: {flops}")
+        input_shape = [1, 3, 640, 640]
+    elif config['Architecture']['model_type'] == 'rec':
+        input_shape = [1, 3, 32, 320]
+    flops = paddle.flops(model, input_shape)
+    logger.info("FLOPs before pruning: {}".format(flops))
    from paddleslim.dygraph import FPGMFilterPruner
    model.train()
-    pruner = FPGMFilterPruner(model, [1, 3, 640, 640])
+    pruner = FPGMFilterPruner(model, input_shape)
    # build metric
    eval_class = build_metric(config['Metric'])
@@ -65,8 +70,13 @@ def main(config, device, logger, vdl_writer):
    def eval_fn():
        metric = program.eval(model, valid_dataloader, post_process_class,
                              eval_class)
-        logger.info(f"metric['hmean']: {metric['hmean']}")
+        if config['Architecture']['model_type'] == 'det':
-        return metric['hmean']
+            main_indicator = 'hmean'
+        else:
+            main_indicator = 'acc'
+        logger.info("metric[{}]: {}".format(main_indicator, metric[
+            main_indicator]))
+        return metric[main_indicator]
    params_sensitive = pruner.sensitive(
        eval_func=eval_fn,
@@ -81,18 +91,22 @@ def main(config, device, logger, vdl_writer):
    # calculate pruned params's ratio
    params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
    for key in params_sensitive.keys():
-        logger.info(f"{key}, {params_sensitive[key]}")
+        logger.info("{}, {}".format(key, params_sensitive[key]))
    plan = pruner.prune_vars(params_sensitive, [0])
-    flops = paddle.flops(model, [1, 3, 640, 640])
+    flops = paddle.flops(model, input_shape)
-    logger.info(f"FLOPs after pruning: {flops}")
+    logger.info("FLOPs after pruning: {}".format(flops))
    # load pretrain model
    load_model(config, model)
    metric = program.eval(model, valid_dataloader, post_process_class,
                          eval_class)
-    logger.info(f"metric['hmean']: {metric['hmean']}")
+    if config['Architecture']['model_type'] == 'det':
+        main_indicator = 'hmean'
+    else:
+        main_indicator = 'acc'
+    logger.info("metric['']: {}".format(main_indicator, metric[main_indicator]))
    # start export model
    from paddle.jit import to_static

--- a/deploy/slim/prune/sensitivity_anal.py
+++ b/deploy/slim/prune/sensitivity_anal.py
@@ -73,13 +73,18 @@ def main(config, device, logger, vdl_writer):
        char_num = len(getattr(post_process_class, 'character'))
        config['Architecture']["Head"]['out_channels'] = char_num
    model = build_model(config['Architecture'])
+    if config['Architecture']['model_type'] == 'det':
+        input_shape = [1, 3, 640, 640]
+    elif config['Architecture']['model_type'] == 'rec':
+        input_shape = [1, 3, 32, 320]
+    flops = paddle.flops(model, input_shape)
-    flops = paddle.flops(model, [1, 3, 640, 640])
    logger.info("FLOPs before pruning: {}".format(flops))
    from paddleslim.dygraph import FPGMFilterPruner
    model.train()
-    pruner = FPGMFilterPruner(model, [1, 3, 640, 640])
+    pruner = FPGMFilterPruner(model, input_shape)
    # build loss
    loss_class = build_loss(config['Loss'])
@@ -107,8 +112,14 @@ def main(config, device, logger, vdl_writer):
    def eval_fn():
        metric = program.eval(model, valid_dataloader, post_process_class,
                              eval_class, False)
-        logger.info("metric['hmean']: {}".format(metric['hmean']))
+        if config['Architecture']['model_type'] == 'det':
-        return metric['hmean']
+            main_indicator = 'hmean'
+        else:
+            main_indicator = 'acc'
+        logger.info("metric[{}]: {}".format(main_indicator, metric[
+            main_indicator]))
+        return metric[main_indicator]
    run_sensitive_analysis = False
    """
@@ -149,7 +160,7 @@ def main(config, device, logger, vdl_writer):
    plan = pruner.prune_vars(params_sensitive, [0])
-    flops = paddle.flops(model, [1, 3, 640, 640])
+    flops = paddle.flops(model, input_shape)
    logger.info("FLOPs after pruning: {}".format(flops))
    # start train

--- a/doc/doc_ch/code_and_doc.md
+++ b/doc/doc_ch/code_and_doc.md
+# 附录
+本附录包含了Python、文档规范以及Pull Request流程，请各位开发者遵循相关内容
+- [附录1：Python代码规范](#附录1)
+- [附录2：文档规范](#附录2)
+- [附录3：Pull Request说明](#附录3)
+<a name="附录1"></a>
+## 附录1：Python代码规范
+PaddleOCR的Python代码遵循 [PEP8规范](https://www.python.org/dev/peps/pep-0008/)，其中一些关注的重点包括如下内容
+- 空格 
+  - 空格应该加在逗号、分号、冒号前，而非他们的后面
+    ```python
+    # 正确：
+    print(x, y)
+    # 错误：
+    print(x , y)
+    ```
+  - 在函数中指定关键字参数或默认参数值时, 不要在其两侧使用空格
+    ```python
+    # 正确：
+    def complex(real, imag=0.0)
+    # 错误：
+    def complex(real, imag = 0.0)
+    ```
+- 注释
+  - 行内注释：行内注释使用 `#` 号表示，在代码与 `#` 之间需要空两个空格， `#`  与注释之间应当空一个空格，例如
+    ```python
+    x = x + 1  # Compensate for border
+    ```
+  - 函数和方法：每个函数的定义后的描述应该包括以下内容：
+    - 函数描述：函数的作用，输入输出的
+    - Args：每个参数的名字以及对该参数的描述
+    - Returns：返回值的含义和类型
+    ```python
+    def fetch_bigtable_rows(big_table, keys, other_silly_variable=None):
+        """Fetches rows from a Bigtable.
+        Retrieves rows pertaining to the given keys from the Table instance
+        represented by big_table.  Silly things may happen if
+        other_silly_variable is not None.
+        Args:
+            big_table: An open Bigtable Table instance.
+            keys: A sequence of strings representing the key of each table row
+                to fetch.
+            other_silly_variable: Another optional variable, that has a much
+                longer name than the other args, and which does nothing.
+        Returns:
+            A dict mapping keys to the corresponding table row data
+            fetched. Each row is represented as a tuple of strings. For
+            example:
+            {'Serak': ('Rigel VII', 'Preparer'),
+             'Zim': ('Irk', 'Invader'),
+             'Lrrr': ('Omicron Persei 8', 'Emperor')}
+            If a key from the keys argument is missing from the dictionary,
+            then that row was not found in the table.
+        """
+        pass
+    ```
+<a name="附录2"></a>
+## 附录2：文档规范
+### 2.1 总体说明
+- 文档位置：如果您增加的新功能可以补充在原有的Markdown文件中，请**不要重新新建**一个文件。如果您对添加的位置不清楚，可以先PR代码，然后在commit中询问官方人员。
+- 新增Markdown文档名称：使用英文描述文档内容，一般由小写字母与下划线组合而成，例如  `add_new_algorithm.md`
+- 新增Markdown文档格式：目录 - 正文 - FAQ
+  > 目录生成方法可以使用 [此网站](https://ecotrust-canada.github.io/markdown-toc/) 将md内容复制之后自动提取目录，然后在md文件的每个标题前添加 `<a name="XXXX"></a>` 
+- 中英双语：任何对文档的改动或新增都需要分别在中文和英文文档上进行。
+### 2.2 格式规范
+- 标题格式：文档标题格式按照：阿拉伯数字小数点组合 - 空格 - 标题的格式（例如 `2.1 XXXX` ， `2. XXXX`）
+- 代码块：通过代码块格式展示需要运行的代码，在代码块前描述命令参数的含义。例如：
+  > 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。
+  >
+  > ```
+  > paddleocr --image_dir ./imgs/11.jpg --use_angle_cls true
+  > ```
+- 变量引用：如果在行内引用到代码变量或命令参数，需要用行内代码表示，例如上方  `--use_angle_cls true` ，并在前后各空一格
+- 补充说明：通过引用格式 `>` 补充说明，或对注意事项进行说明
+- 图片：如果在说明文档中增加了图片，请规范图片的命名形式（描述图片内容），并将图片添加在 `doc/` 下
+<a name="附录3"></a>
+## 附录3：Pull Request说明
+### 3.1 PaddleOCR分支说明
+PaddleOCR未来将维护2种分支，分别为：
+- release/x.x系列分支：为稳定的发行版本分支，也是默认分支。PaddleOCR会根据功能更新情况发布新的release分支，同时适配Paddle的release版本。随着版本迭代，release/x.x系列分支会越来越多，默认维护最新版本的release分支。
+- dygraph分支：为开发分支，适配Paddle动态图的dygraph版本，主要用于开发新功能。如果有同学需要进行二次开发，请选择dygraph分支。为了保证dygraph分支能在需要的时候拉出release/x.x分支，dygraph分支的代码只能使用Paddle最新release分支中有效的api。也就是说，如果Paddle dygraph分支中开发了新的api，但尚未出现在release分支代码中，那么请不要在PaddleOCR中使用。除此之外，对于不涉及api的性能优化、参数调整、策略更新等，都可以正常进行开发。
+PaddleOCR的历史分支，未来将不再维护。考虑到一些同学可能仍在使用，这些分支还会继续保留：
+- develop分支：这个分支曾用于静态图的开发与测试，目前兼容>=1.7版本的Paddle。如果有特殊需求，要适配旧版本的Paddle，那还可以使用这个分支，但除了修复bug外不再更新代码。
+PaddleOCR欢迎大家向repo中积极贡献代码，下面给出一些贡献代码的基本流程。
+### 3.2 PaddleOCR代码提交流程与规范
+> 如果你熟悉Git使用，可以直接跳转到 [3.2.10 提交代码的一些约定](#提交代码的一些约定)
+#### 3.2.1 创建你的 `远程仓库`
+- 在PaddleOCR的 [GitHub首页](https://github.com/PaddlePaddle/PaddleOCR)，点击左上角 `Fork`  按钮，在你的个人目录下创建 `远程仓库`，比如`https://github.com/{your_name}/PaddleOCR`。
+![banner](/Users/zhulingfeng01/OCR/PaddleOCR/doc/banner.png)
+- 将 `远程仓库` Clone到本地
+```
+# 拉取develop分支的代码
+git clone https://github.com/{your_name}/PaddleOCR.git -b dygraph
+cd PaddleOCR
+```
+> 多数情况下clone失败是由于网络原因，请稍后重试或配置代理
+#### 3.2.2 和 `远程仓库` 建立连接
+首先查看当前 `远程仓库` 的信息。
+```
+git remote -v
+# origin    https://github.com/{your_name}/PaddleOCR.git (fetch)
+# origin    https://github.com/{your_name}/PaddleOCR.git (push)
+```
+只有clone的 `远程仓库` 的信息，也就是自己用户名下的 PaddleOCR，接下来我们创建一个原始 PaddleOCR 仓库的远程主机，命名为 upstream。
+```
+git remote add upstream https://github.com/PaddlePaddle/PaddleOCR.git
+```
+使用 `git remote -v` 查看当前 `远程仓库` 的信息，输出如下，发现包括了origin和upstream 2个 `远程仓库` 。
+```
+origin    https://github.com/{your_name}/PaddleOCR.git (fetch)
+origin    https://github.com/{your_name}/PaddleOCR.git (push)
+upstream    https://github.com/PaddlePaddle/PaddleOCR.git (fetch)
+upstream    https://github.com/PaddlePaddle/PaddleOCR.git (push)
+```
+这主要是为了后续在提交pull request(PR)时，始终保持本地仓库最新。
+#### 3.2.3 创建本地分支
+可以基于当前分支创建新的本地分支，命令如下。
+```
+git checkout -b new_branch
+```
+也可以基于远程或者上游的分支创建新的分支，命令如下。
+```
+# 基于用户远程仓库(origin)的develop创建new_branch分支
+git checkout -b new_branch origin/develop
+# 基于上游远程仓库(upstream)的develop创建new_branch分支
+# 如果需要从upstream创建新的分支，需要首先使用git fetch upstream获取上游代码
+git checkout -b new_branch upstream/develop
+```
+最终会显示切换到新的分支，输出信息如下
+```
+Branch new_branch set up to track remote branch develop from upstream.
+Switched to a new branch 'new_branch'
+```
+#### 3.2.4 使用pre-commit勾子
+Paddle 开发人员使用 pre-commit 工具来管理 Git 预提交钩子。 它可以帮助我们格式化源代码（C++，Python），在提交（commit）前自动检查一些基本事宜（如每个文件只有一个 EOL，Git 中不要添加大文件等）。
+pre-commit测试是 Travis-CI 中单元测试的一部分，不满足钩子的 PR 不能被提交到 PaddleOCR，首先安装并在当前目录运行它：
+```
+pip install pre-commit
+pre-commit install
+```
+ >  1. Paddle 使用 clang-format 来调整 C/C++ 源代码格式，请确保 `clang-format` 版本在 3.8 以上。
+ >
+ >  2. 通过pip install pre-commit和conda install -c conda-forge pre-commit安装的yapf稍有不同的，PaddleOCR 开发人员使用的是 `pip install pre-commit`。
+#### 3.2.5 修改与提交代码
+ 假设对PaddleOCR的 `README.md` 做了一些修改，可以通过 `git status` 查看改动的文件，然后使用 `git add` 添加改动文件。
+```
+git status # 查看改动文件
+git add README.md
+pre-commit
+```
+重复上述步骤，直到pre-comit格式检查不报错。如下所示。
+[![img](https://github.com/PaddlePaddle/PaddleClas/raw/release/2.3/docs/images/quick_start/community/003_precommit_pass.png)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.3/docs/images/quick_start/community/003_precommit_pass.png)
+使用下面的命令完成提交。
+```
+git commit -m "your commit info"
+```
+#### 3.2.6 保持本地仓库最新
+获取 upstream 的最新代码并更新当前分支。这里的upstream来自于2.2节的`和远程仓库建立连接`部分。
+```
+git fetch upstream
+# 如果是希望提交到其他分支，则需要从upstream的其他分支pull代码，这里是develop
+git pull upstream develop
+```
+#### 3.2.7 push到远程仓库
+```
+git push origin new_branch
+```
+#### 3.2.7 提交Pull Request
+点击new pull request，选择本地分支和目标分支，如下图所示。在PR的描述说明中，填写该PR所完成的功能。接下来等待review，如果有需要修改的地方，参照上述步骤更新 origin 中的对应分支即可。
+![banner](/Users/zhulingfeng01/OCR/PaddleOCR/doc/pr.png)
+#### 3.2.8 签署CLA协议和通过单元测试
+- 签署CLA 在首次向PaddlePaddle提交Pull Request时，您需要您签署一次CLA(Contributor License Agreement)协议，以保证您的代码可以被合入，具体签署方式如下：
+  1. 请您查看PR中的Check部分，找到license/cla，并点击右侧detail，进入CLA网站
+  2. 点击CLA网站中的“Sign in with GitHub to agree”,点击完成后将会跳转回您的Pull Request页面
+#### 3.2.9 删除分支
+- 删除远程分支
+  在 PR 被 merge 进主仓库后，我们可以在 PR 的页面删除远程仓库的分支。
+  也可以使用 `git push origin :分支名` 删除远程分支，如：
+  ```
+  git push origin :new_branch
+  ```
+- 删除本地分支
+  ```
+  # 切换到develop分支，否则无法删除当前分支
+  git checkout develop
+  # 删除new_branch分支
+  git branch -D new_branch
+  ```
+<a name="提交代码的一些约定"></a>
+#### 3.2.10 提交代码的一些约定
+为了使官方维护人员在评审代码时更好地专注于代码本身，请您每次提交代码时，遵守以下约定：
+1）请保证Travis-CI 中单元测试能顺利通过。如果没过，说明提交的代码存在问题，官方维护人员一般不做评审。
+2）提交Pull Request前：
+- 请注意commit的数量。
+  原因：如果仅仅修改一个文件但提交了十几个commit，每个commit只做了少量的修改，这会给评审人带来很大困扰。评审人需要逐一查看每个commit才能知道做了哪些修改，且不排除commit之间的修改存在相互覆盖的情况。
+  建议：每次提交时，保持尽量少的commit，可以通过git commit --amend补充上次的commit。对已经Push到远程仓库的多个commit，可以参考[squash commits after push](https://stackoverflow.com/questions/5667884/how-to-squash-commits-in-git-after-they-have-been-pushed)。
+- 请注意每个commit的名称：应能反映当前commit的内容，不能太随意。
+3）如果解决了某个Issue的问题，请在该Pull Request的第一个评论框中加上：fix #issue_number，这样当该Pull Request被合并后，会自动关闭对应的Issue。关键词包括：close, closes, closed, fix, fixes, fixed, resolve, resolves, resolved，请选择合适的词汇。详细可参考[Closing issues via commit messages](https://help.github.com/articles/closing-issues-via-commit-messages)。
+此外，在回复评审人意见时，请您遵守以下约定：
+1）官方维护人员的每一个review意见都希望得到回复，这样会更好地提升开源社区的贡献。
+- 对评审意见同意且按其修改完的，给个简单的Done即可；
+- 对评审意见不同意的，请给出您自己的反驳理由。
+2）如果评审意见比较多:
+- 请给出总体的修改情况。
+- 请采用`start a review`进行回复，而非直接回复的方式。原因是每个回复都会发送一封邮件，会造成邮件灾难。
\ No newline at end of file
--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@@ -247,3 +247,7 @@ 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。
+Q1: 训练EAST模型提示找不到lanms库？
+**A**：执行pip3 install lanms-nova 即可。
--- a/doc/doc_ch/inference.md
+++ b/doc/doc_ch/inference.md
@@ -34,6 +34,8 @@ inference 模型（`paddle.jit.save`保存的模型）
    - [1. 超轻量中文OCR模型推理](#超轻量中文OCR模型推理)
    - [2. 其他模型推理](#其他模型推理)
+- [六、参数解释](参数解释)
 <a name="训练模型转inference模型"></a>
 ## 一、训练模型转inference模型
@@ -394,3 +396,127 @@ python3 tools/infer/predict_system.py --image_dir="./doc/imgs_en/img_10.jpg" --d
 执行命令后，识别结果图像如下：
 ![](../imgs_results/img_10_east_starnet.jpg)
+<a name="参数解释"></a>
+# 六、参数解释
+更多关于预测过程的参数解释如下所示。
+* 全局信息
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  image_dir | str | 无，必须显式指定 | 图像或者文件夹路径 |
+|  vis_font_path | str | "./doc/fonts/simfang.ttf" | 用于可视化的字体路径 |
+|  drop_score | float | 0.5 | 识别得分小于该值的结果会被丢弃，不会作为返回结果 |
+|  use_pdserving | bool | False | 是否使用Paddle Serving进行预测 |
+|  warmup | bool | False | 是否开启warmup，在统计预测耗时的时候，可以使用这种方法 |
+|  draw_img_save_dir | str | "./inference_results" | 系统串联预测OCR结果的保存文件夹 |
+|  save_crop_res | bool | False  | 是否保存OCR的识别文本图像 |
+|  crop_res_save_dir | str | "./output" | 保存OCR识别出来的文本图像路径 |
+|  use_mp | bool | False | 是否开启多进程预测  |
+|  total_process_num | int | 6 | 开启的进城数，`use_mp`为`True`时生效  |
+|  process_id | int | 0 | 当前进程的id号，无需自己修改  |
+|  benchmark | bool | False | 是否开启benchmark，对预测速度、显存占用等进行统计  |
+|  save_log_path | str | "./log_output/" | 开启`benchmark`时，日志结果的保存文件夹 |
+|  show_log | bool | True | 是否显示预测中的日志信息  |
+|  use_onnx | bool | False | 是否开启onnx预测 |
+* 预测引擎相关
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  use_gpu | bool | True | 是否使用GPU进行预测 |
+|  ir_optim | bool | True | 是否对计算图进行分析与优化，开启后可以加速预测过程 |
+|  use_tensorrt | bool | False | 是否开启tensorrt |
+|  min_subgraph_size | int | 15 | tensorrt中最小子图size，当子图的size大于该值时，才会尝试对该子图使用trt engine计算 |
+|  precision | str | fp32 | 预测的精度，支持`fp32`, `fp16`, `int8` 3种输入 |
+|  enable_mkldnn | bool | True | 是否开启mkldnn |
+|  cpu_threads | int | 10 | 开启mkldnn时，cpu预测的线程数 |
+* 文本检测模型相关
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_algorithm | str | "DB" | 文本检测算法名称，目前支持`DB`, `EAST`, `SAST`, `PSE`  |
+|  det_model_dir | str | xx | 检测inference模型路径 |
+|  det_limit_side_len | int | 960 | 检测的图像边长限制 |
+|  det_limit_type | str | "max" | 检测的变成限制类型，目前支持`min`, `max`，`min`表示保证图像最短边不小于`det_limit_side_len`，`max`表示保证图像最长边不大于`det_limit_side_len` |
+其中，DB算法相关参数如下
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_db_thresh | float | 0.3 | DB输出的概率图中，得分大于该阈值的像素点才会被认为是文字像素点 |
+|  det_db_box_thresh | float | 0.6 | 检测结果边框内，所有像素点的平均得分大于该阈值时，该结果会被认为是文字区域 |
+|  det_db_unclip_ratio | float | 1.5 | `Vatti clipping`算法的扩张系数，使用该方法对文字区域进行扩张 |
+|  max_batch_size | int | 10 | 预测的batch size |
+|  use_dilation | bool | False | 是否对分割结果进行膨胀以获取更优检测效果 |
+|  det_db_score_mode | str | "fast" | DB的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
+EAST算法相关参数如下
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_east_score_thresh | float | 0.8 | EAST后处理中score map的阈值 |
+|  det_east_cover_thresh | float | 0.1 | EAST后处理中文本框的平均得分阈值 |
+|  det_east_nms_thresh | float | 0.2 | EAST后处理中nms的阈值 |
+SAST算法相关参数如下
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_sast_score_thresh | float | 0.5 | SAST后处理中的得分阈值 |
+|  det_sast_nms_thresh | float | 0.5 | SAST后处理中nms的阈值 |
+|  det_sast_polygon | bool | False | 是否多边形检测，弯曲文本场景（如Total-Text）设置为True |
+PSE算法相关参数如下
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  det_pse_thresh | float | 0.0 | 对输出图做二值化的阈值 |
+|  det_pse_box_thresh | float | 0.85 | 对box进行过滤的阈值，低于此阈值的丢弃 |
+|  det_pse_min_area | float | 16 | box的最小面积，低于此阈值的丢弃 |
+|  det_pse_box_type | str | "box" | 返回框的类型，box:四点坐标，poly: 弯曲文本的所有点坐标 |
+|  det_pse_scale | int | 1 | 输入图像相对于进后处理的图的比例，如`640*640`的图像，网络输出为`160*160`，scale为2的情况下，进后处理的图片shape为`320*320`。这个值调大可以加快后处理速度，但是会带来精度的下降 |
+* 文本识别模型相关
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  rec_algorithm | str | "CRNN" | 文本识别算法名称，目前支持`CRNN`, `SRN`, `RARE`, `NETR`, `SAR` |
+|  rec_model_dir | str | 无，如果使用识别模型，该项是必填项 | 识别inference模型路径 |
+|  rec_image_shape | list | [3, 32, 320] | 识别时的图像尺寸， |
+|  rec_batch_num | int | 6 | 识别的batch size |
+|  max_text_length | int | 25 | 识别结果最大长度，在`SRN`中有效 |
+|  rec_char_dict_path | str | "./ppocr/utils/ppocr_keys_v1.txt" | 识别的字符字典文件 |
+|  use_space_char | bool | True | 是否包含空格，如果为`True`，则会在最后字符字典中补充`空格`字符 |
+* 端到端文本检测与识别模型相关
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  e2e_algorithm | str | "PGNet" | 端到端算法名称，目前支持`PGNet` |
+|  e2e_model_dir | str | 无，如果使用端到端模型，该项是必填项 | 端到端模型inference模型路径 |
+|  e2e_limit_side_len | int | 768 | 端到端的输入图像边长限制 |
+|  e2e_limit_type | str | "max" | 端到端的边长限制类型，目前支持`min`, `max`，`min`表示保证图像最短边不小于`e2e_limit_side_len`，`max`表示保证图像最长边不大于`e2e_limit_side_len` |
+|  e2e_pgnet_score_thresh | float | xx | xx |
+|  e2e_char_dict_path | str | "./ppocr/utils/ic15_dict.txt" | 识别的字典文件路径 |
+|  e2e_pgnet_valid_set | str | "totaltext" | 验证集名称，目前支持`totaltext`, `partvgg`，不同数据集对应的后处理方式不同，与训练过程保持一致即可 |
+|  e2e_pgnet_mode | str | "fast" | PGNet的检测结果得分计算方法，支持`fast`和`slow`，`fast`是根据polygon的外接矩形边框内的所有像素计算平均得分，`slow`是根据原始polygon内的所有像素计算平均得分，计算速度相对较慢一些，但是更加准确一些。 |
+* 方向分类器模型相关
+| 参数名称 | 类型 | 默认值 | 含义 |
+| :--: | :--: | :--: | :--: |
+|  use_angle_cls | bool | False | 是否使用方向分类器 |
+|  cls_model_dir | str | 无，如果需要使用，则必须显式指定路径 | 方向分类器inference模型路径 |
+|  cls_image_shape | list | [3, 48, 192] | 预测尺度 |
+|  label_list | list | ['0', '180'] | class id对应的角度值 |
+|  cls_batch_num | int | 6 | 方向分类器预测的batch size |
+|  cls_thresh | float | 0.9 | 预测阈值，模型预测结果为180度，且得分大于该阈值时，认为最终预测结果为180度，需要翻转 |
--- a/doc/doc_ch/thirdparty.md
+++ b/doc/doc_ch/thirdparty.md
@@ -19,9 +19,15 @@ 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
 - 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开发的工具
+### 1.2 基于PaddleOCR的社区贡献
- 【最新】[包建强](https://gitee.com/BaoJianQiang) 使用PaddleOCR构建了完整的C#版本标注工具 [FastOCRLabel](https://gitee.com/BaoJianQiang/FastOCRLabel)
+- 【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
@@ -37,14 +43,12 @@ 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
 - 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))。
-<a name="贡献说明"></a>
 ## 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usedby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。*
@@ -59,18 +63,18 @@ PaddleOCR非常欢迎社区贡献以PaddleOCR为核心的各种服务、部署
 - 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`【improvement】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理**。
+**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`【improvement】` , 在说明中@Evezerest，拥有此标签的PR将会被高优处理。**
+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、表格）。
+- 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。
+- 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
@@ -78,7 +82,7 @@ PaddleOCR非常欢迎广大开发者在有意向贡献前与我们联系，这
 我们推荐的贡献流程是：
- 通过  `【improvement】` 标记的github issue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。
+- 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。
@@ -86,4 +90,4 @@ PaddleOCR非常欢迎广大开发者在有意向贡献前与我们联系，这
  - 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，请按照同样的方式联系我们。
+  - **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。**
--- a/doc/joinus.PNG
+++ b/doc/joinus.PNG
--- a/ppocr/modeling/backbones/__init__.py
+++ b/ppocr/modeling/backbones/__init__.py
@@ -16,7 +16,7 @@ __all__ = ["build_backbone"]
 def build_backbone(config, model_type):
-    if model_type == "det":
+    if model_type == "det" or model_type == "table":
        from .det_mobilenet_v3 import MobileNetV3
        from .det_resnet_vd import ResNet
        from .det_resnet_vd_sast import ResNet_SAST
@@ -36,10 +36,6 @@ def build_backbone(config, model_type):
    elif model_type == "e2e":
        from .e2e_resnet_vd_pg import ResNet
        support_dict = ["ResNet"]
-    elif model_type == "table":
-        from .table_resnet_vd import ResNet
-        from .table_mobilenet_v3 import MobileNetV3
-        support_dict = ["ResNet", "MobileNetV3"]
    else:
        raise NotImplementedError

--- a/ppocr/modeling/backbones/rec_mobilenet_v3.py
+++ b/ppocr/modeling/backbones/rec_mobilenet_v3.py
@@ -26,8 +26,10 @@ class MobileNetV3(nn.Layer):
                 scale=0.5,
                 large_stride=None,
                 small_stride=None,
+                 disable_se=False,
                 **kwargs):
        super(MobileNetV3, self).__init__()
+        self.disable_se = disable_se
        if small_stride is None:
            small_stride = [2, 2, 2, 2]
        if large_stride is None:
@@ -101,6 +103,7 @@ class MobileNetV3(nn.Layer):
        block_list = []
        inplanes = make_divisible(inplanes * scale)
        for (k, exp, c, se, nl, s) in cfg:
+            se = se and not self.disable_se
            block_list.append(
                ResidualUnit(
                    in_channels=inplanes,

--- a/ppocr/modeling/backbones/table_mobilenet_v3.py
+++ b/ppocr/modeling/backbones/table_mobilenet_v3.py
-# 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.
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import paddle
-from paddle import nn
-import paddle.nn.functional as F
-from paddle import ParamAttr
-__all__ = ['MobileNetV3']
-def make_divisible(v, divisor=8, min_value=None):
-    if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
-class MobileNetV3(nn.Layer):
-    def __init__(self,
-                 in_channels=3,
-                 model_name='large',
-                 scale=0.5,
-                 disable_se=False,
-                 **kwargs):
-        """
-        the MobilenetV3 backbone network for detection module.
-        Args:
-            params(dict): the super parameters for build network
-        """
-        super(MobileNetV3, self).__init__()
-        self.disable_se = disable_se
-        if model_name == "large":
-            cfg = [
-                # k, exp, c,  se,     nl,  s,
-                [3, 16, 16, False, 'relu', 1],
-                [3, 64, 24, False, 'relu', 2],
-                [3, 72, 24, False, 'relu', 1],
-                [5, 72, 40, True, 'relu', 2],
-                [5, 120, 40, True, 'relu', 1],
-                [5, 120, 40, True, 'relu', 1],
-                [3, 240, 80, False, 'hardswish', 2],
-                [3, 200, 80, False, 'hardswish', 1],
-                [3, 184, 80, False, 'hardswish', 1],
-                [3, 184, 80, False, 'hardswish', 1],
-                [3, 480, 112, True, 'hardswish', 1],
-                [3, 672, 112, True, 'hardswish', 1],
-                [5, 672, 160, True, 'hardswish', 2],
-                [5, 960, 160, True, 'hardswish', 1],
-                [5, 960, 160, True, 'hardswish', 1],
-            ]
-            cls_ch_squeeze = 960
-        elif model_name == "small":
-            cfg = [
-                # k, exp, c,  se,     nl,  s,
-                [3, 16, 16, True, 'relu', 2],
-                [3, 72, 24, False, 'relu', 2],
-                [3, 88, 24, False, 'relu', 1],
-                [5, 96, 40, True, 'hardswish', 2],
-                [5, 240, 40, True, 'hardswish', 1],
-                [5, 240, 40, True, 'hardswish', 1],
-                [5, 120, 48, True, 'hardswish', 1],
-                [5, 144, 48, True, 'hardswish', 1],
-                [5, 288, 96, True, 'hardswish', 2],
-                [5, 576, 96, True, 'hardswish', 1],
-                [5, 576, 96, True, 'hardswish', 1],
-            ]
-            cls_ch_squeeze = 576
-        else:
-            raise NotImplementedError("mode[" + model_name +
-                                      "_model] is not implemented!")
-        supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
-        assert scale in supported_scale, \
-            "supported scale are {} but input scale is {}".format(supported_scale, scale)
-        inplanes = 16
-        # conv1
-        self.conv = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=make_divisible(inplanes * scale),
-            kernel_size=3,
-            stride=2,
-            padding=1,
-            groups=1,
-            if_act=True,
-            act='hardswish',
-            name='conv1')
-        self.stages = []
-        self.out_channels = []
-        block_list = []
-        i = 0
-        inplanes = make_divisible(inplanes * scale)
-        for (k, exp, c, se, nl, s) in cfg:
-            se = se and not self.disable_se
-            start_idx = 2 if model_name == 'large' else 0
-            if s == 2 and i > start_idx:
-                self.out_channels.append(inplanes)
-                self.stages.append(nn.Sequential(*block_list))
-                block_list = []
-            block_list.append(
-                ResidualUnit(
-                    in_channels=inplanes,
-                    mid_channels=make_divisible(scale * exp),
-                    out_channels=make_divisible(scale * c),
-                    kernel_size=k,
-                    stride=s,
-                    use_se=se,
-                    act=nl,
-                    name="conv" + str(i + 2)))
-            inplanes = make_divisible(scale * c)
-            i += 1
-        block_list.append(
-            ConvBNLayer(
-                in_channels=inplanes,
-                out_channels=make_divisible(scale * cls_ch_squeeze),
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                groups=1,
-                if_act=True,
-                act='hardswish',
-                name='conv_last'))
-        self.stages.append(nn.Sequential(*block_list))
-        self.out_channels.append(make_divisible(scale * cls_ch_squeeze))
-        for i, stage in enumerate(self.stages):
-            self.add_sublayer(sublayer=stage, name="stage{}".format(i))
-    def forward(self, x):
-        x = self.conv(x)
-        out_list = []
-        for stage in self.stages:
-            x = stage(x)
-            out_list.append(x)
-        return out_list
-class ConvBNLayer(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride,
-                 padding,
-                 groups=1,
-                 if_act=True,
-                 act=None,
-                 name=None):
-        super(ConvBNLayer, self).__init__()
-        self.if_act = if_act
-        self.act = act
-        self.conv = nn.Conv2D(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            groups=groups,
-            weight_attr=ParamAttr(name=name + '_weights'),
-            bias_attr=False)
-        self.bn = nn.BatchNorm(
-            num_channels=out_channels,
-            act=None,
-            param_attr=ParamAttr(name=name + "_bn_scale"),
-            bias_attr=ParamAttr(name=name + "_bn_offset"),
-            moving_mean_name=name + "_bn_mean",
-            moving_variance_name=name + "_bn_variance")
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        if self.if_act:
-            if self.act == "relu":
-                x = F.relu(x)
-            elif self.act == "hardswish":
-                x = F.hardswish(x)
-            else:
-                print("The activation function({}) is selected incorrectly.".
-                      format(self.act))
-                exit()
-        return x
-class ResidualUnit(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 mid_channels,
-                 out_channels,
-                 kernel_size,
-                 stride,
-                 use_se,
-                 act=None,
-                 name=''):
-        super(ResidualUnit, self).__init__()
-        self.if_shortcut = stride == 1 and in_channels == out_channels
-        self.if_se = use_se
-        self.expand_conv = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=mid_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            if_act=True,
-            act=act,
-            name=name + "_expand")
-        self.bottleneck_conv = ConvBNLayer(
-            in_channels=mid_channels,
-            out_channels=mid_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=int((kernel_size - 1) // 2),
-            groups=mid_channels,
-            if_act=True,
-            act=act,
-            name=name + "_depthwise")
-        if self.if_se:
-            self.mid_se = SEModule(mid_channels, name=name + "_se")
-        self.linear_conv = ConvBNLayer(
-            in_channels=mid_channels,
-            out_channels=out_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            if_act=False,
-            act=None,
-            name=name + "_linear")
-    def forward(self, inputs):
-        x = self.expand_conv(inputs)
-        x = self.bottleneck_conv(x)
-        if self.if_se:
-            x = self.mid_se(x)
-        x = self.linear_conv(x)
-        if self.if_shortcut:
-            x = paddle.add(inputs, x)
-        return x
-class SEModule(nn.Layer):
-    def __init__(self, in_channels, reduction=4, name=""):
-        super(SEModule, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2D(1)
-        self.conv1 = nn.Conv2D(
-            in_channels=in_channels,
-            out_channels=in_channels // reduction,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            weight_attr=ParamAttr(name=name + "_1_weights"),
-            bias_attr=ParamAttr(name=name + "_1_offset"))
-        self.conv2 = nn.Conv2D(
-            in_channels=in_channels // reduction,
-            out_channels=in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            weight_attr=ParamAttr(name + "_2_weights"),
-            bias_attr=ParamAttr(name=name + "_2_offset"))
-    def forward(self, inputs):
-        outputs = self.avg_pool(inputs)
-        outputs = self.conv1(outputs)
-        outputs = F.relu(outputs)
-        outputs = self.conv2(outputs)
-        outputs = F.hardsigmoid(outputs, slope=0.2, offset=0.5)
-        return inputs * outputs
\ No newline at end of file
--- a/ppocr/modeling/backbones/table_resnet_vd.py
+++ b/ppocr/modeling/backbones/table_resnet_vd.py
-# 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.
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import paddle
-from paddle import ParamAttr
-import paddle.nn as nn
-import paddle.nn.functional as F
-__all__ = ["ResNet"]
-class ConvBNLayer(nn.Layer):
-    def __init__(
-            self,
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=1,
-            groups=1,
-            is_vd_mode=False,
-            act=None,
-            name=None, ):
-        super(ConvBNLayer, self).__init__()
-        self.is_vd_mode = is_vd_mode
-        self._pool2d_avg = nn.AvgPool2D(
-            kernel_size=2, stride=2, padding=0, ceil_mode=True)
-        self._conv = nn.Conv2D(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=(kernel_size - 1) // 2,
-            groups=groups,
-            weight_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        self._batch_norm = nn.BatchNorm(
-            out_channels,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
-    def forward(self, inputs):
-        if self.is_vd_mode:
-            inputs = self._pool2d_avg(inputs)
-        y = self._conv(inputs)
-        y = self._batch_norm(y)
-        return y
-class BottleneckBlock(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 stride,
-                 shortcut=True,
-                 if_first=False,
-                 name=None):
-        super(BottleneckBlock, self).__init__()
-        self.conv0 = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=1,
-            act='relu',
-            name=name + "_branch2a")
-        self.conv1 = ConvBNLayer(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=3,
-            stride=stride,
-            act='relu',
-            name=name + "_branch2b")
-        self.conv2 = ConvBNLayer(
-            in_channels=out_channels,
-            out_channels=out_channels * 4,
-            kernel_size=1,
-            act=None,
-            name=name + "_branch2c")
-        if not shortcut:
-            self.short = ConvBNLayer(
-                in_channels=in_channels,
-                out_channels=out_channels * 4,
-                kernel_size=1,
-                stride=1,
-                is_vd_mode=False if if_first else True,
-                name=name + "_branch1")
-        self.shortcut = shortcut
-    def forward(self, inputs):
-        y = self.conv0(inputs)
-        conv1 = self.conv1(y)
-        conv2 = self.conv2(conv1)
-        if self.shortcut:
-            short = inputs
-        else:
-            short = self.short(inputs)
-        y = paddle.add(x=short, y=conv2)
-        y = F.relu(y)
-        return y
-class BasicBlock(nn.Layer):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 stride,
-                 shortcut=True,
-                 if_first=False,
-                 name=None):
-        super(BasicBlock, self).__init__()
-        self.stride = stride
-        self.conv0 = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=3,
-            stride=stride,
-            act='relu',
-            name=name + "_branch2a")
-        self.conv1 = ConvBNLayer(
-            in_channels=out_channels,
-            out_channels=out_channels,
-            kernel_size=3,
-            act=None,
-            name=name + "_branch2b")
-        if not shortcut:
-            self.short = ConvBNLayer(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=1,
-                stride=1,
-                is_vd_mode=False if if_first else True,
-                name=name + "_branch1")
-        self.shortcut = shortcut
-    def forward(self, inputs):
-        y = self.conv0(inputs)
-        conv1 = self.conv1(y)
-        if self.shortcut:
-            short = inputs
-        else:
-            short = self.short(inputs)
-        y = paddle.add(x=short, y=conv1)
-        y = F.relu(y)
-        return y
-class ResNet(nn.Layer):
-    def __init__(self, in_channels=3, layers=50, **kwargs):
-        super(ResNet, self).__init__()
-        self.layers = layers
-        supported_layers = [18, 34, 50, 101, 152, 200]
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(
-                supported_layers, layers)
-        if layers == 18:
-            depth = [2, 2, 2, 2]
-        elif layers == 34 or layers == 50:
-            depth = [3, 4, 6, 3]
-        elif layers == 101:
-            depth = [3, 4, 23, 3]
-        elif layers == 152:
-            depth = [3, 8, 36, 3]
-        elif layers == 200:
-            depth = [3, 12, 48, 3]
-        num_channels = [64, 256, 512,
-                        1024] if layers >= 50 else [64, 64, 128, 256]
-        num_filters = [64, 128, 256, 512]
-        self.conv1_1 = ConvBNLayer(
-            in_channels=in_channels,
-            out_channels=32,
-            kernel_size=3,
-            stride=2,
-            act='relu',
-            name="conv1_1")
-        self.conv1_2 = ConvBNLayer(
-            in_channels=32,
-            out_channels=32,
-            kernel_size=3,
-            stride=1,
-            act='relu',
-            name="conv1_2")
-        self.conv1_3 = ConvBNLayer(
-            in_channels=32,
-            out_channels=64,
-            kernel_size=3,
-            stride=1,
-            act='relu',
-            name="conv1_3")
-        self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
-        self.stages = []
-        self.out_channels = []
-        if layers >= 50:
-            for block in range(len(depth)):
-                block_list = []
-                shortcut = False
-                for i in range(depth[block]):
-                    if layers in [101, 152] and block == 2:
-                        if i == 0:
-                            conv_name = "res" + str(block + 2) + "a"
-                        else:
-                            conv_name = "res" + str(block + 2) + "b" + str(i)
-                    else:
-                        conv_name = "res" + str(block + 2) + chr(97 + i)
-                    bottleneck_block = self.add_sublayer(
-                        'bb_%d_%d' % (block, i),
-                        BottleneckBlock(
-                            in_channels=num_channels[block]
-                            if i == 0 else num_filters[block] * 4,
-                            out_channels=num_filters[block],
-                            stride=2 if i == 0 and block != 0 else 1,
-                            shortcut=shortcut,
-                            if_first=block == i == 0,
-                            name=conv_name))
-                    shortcut = True
-                    block_list.append(bottleneck_block)
-                self.out_channels.append(num_filters[block] * 4)
-                self.stages.append(nn.Sequential(*block_list))
-        else:
-            for block in range(len(depth)):
-                block_list = []
-                shortcut = False
-                for i in range(depth[block]):
-                    conv_name = "res" + str(block + 2) + chr(97 + i)
-                    basic_block = self.add_sublayer(
-                        'bb_%d_%d' % (block, i),
-                        BasicBlock(
-                            in_channels=num_channels[block]
-                            if i == 0 else num_filters[block],
-                            out_channels=num_filters[block],
-                            stride=2 if i == 0 and block != 0 else 1,
-                            shortcut=shortcut,
-                            if_first=block == i == 0,
-                            name=conv_name))
-                    shortcut = True
-                    block_list.append(basic_block)
-                self.out_channels.append(num_filters[block])
-                self.stages.append(nn.Sequential(*block_list))
-    def forward(self, inputs):
-        y = self.conv1_1(inputs)
-        y = self.conv1_2(y)
-        y = self.conv1_3(y)
-        y = self.pool2d_max(y)
-        out = []
-        for block in self.stages:
-            y = block(y)
-            out.append(y)
-        return out
--- a/ppocr/modeling/heads/rec_att_head.py
+++ b/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
@@ -75,6 +74,7 @@ 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

--- a/ppocr/modeling/transforms/tps_spatial_transformer.py
+++ b/ppocr/modeling/transforms/tps_spatial_transformer.py
@@ -53,7 +53,7 @@ def compute_partial_repr(input_points, control_points):
                                                                         1]
    repr_matrix = 0.5 * pairwise_dist * paddle.log(pairwise_dist)
    # fix numerical error for 0 * log(0), substitute all nan with 0
-    mask = repr_matrix != repr_matrix
+    mask = np.array(repr_matrix != repr_matrix)
    repr_matrix[mask] = 0
    return repr_matrix

--- a/ppocr/postprocess/east_postprocess.py
+++ b/ppocr/postprocess/east_postprocess.py
@@ -29,6 +29,7 @@ class EASTPostProcess(object):
    """
    The post process for EAST.
    """
    def __init__(self,
                 score_thresh=0.8,
                 cover_thresh=0.1,
@@ -39,11 +40,6 @@ class EASTPostProcess(object):
        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):
        """
        Restore rectangle from quadrangle.
@@ -64,6 +60,7 @@ class EASTPostProcess(object):
        """
        restore text boxes from score map and geo map
        """
        score_map = score_map[0]
        geo_map = np.swapaxes(geo_map, 1, 0)
        geo_map = np.swapaxes(geo_map, 1, 2)
@@ -79,10 +76,14 @@ 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:
+        try:
            import lanms
            boxes = lanms.merge_quadrangle_n9(boxes, nms_thresh)
-        else:
+        except:
+            print(
+                'you should install lanms by pip3 install lanms-nova to speed up nms_locality'
+            )
            boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
        if boxes.shape[0] == 0:
            return []

--- a/ppocr/utils/save_load.py
+++ b/ppocr/utils/save_load.py
@@ -54,14 +54,37 @@ def load_model(config, model, optimizer=None):
    pretrained_model = global_config.get('pretrained_model')
    best_model_dict = {}
    if checkpoints:
-        if checkpoints.endswith('pdparams'):
+        if checkpoints.endswith('.pdparams'):
            checkpoints = checkpoints.replace('.pdparams', '')
-        assert os.path.exists(checkpoints + ".pdopt"), \
+        assert os.path.exists(checkpoints + ".pdparams"), \
-            f"The {checkpoints}.pdopt does not exists!"
+            "The {}.pdparams does not exists!".format(checkpoints)
-        load_pretrained_params(model, checkpoints)
-        optim_dict = paddle.load(checkpoints + '.pdopt')
+        # 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()))
+                continue
+            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)
        if optimizer is not None:
+            if os.path.exists(checkpoints + '.pdopt'):
+                optim_dict = paddle.load(checkpoints + '.pdopt')
                optimizer.set_state_dict(optim_dict)
+            else:
+                logger.warning(
+                    "{}.pdopt is not exists, params of optimizer is not loaded".
+                    format(checkpoints))
        if os.path.exists(checkpoints + '.states'):
            with open(checkpoints + '.states', 'rb') as f:
@@ -80,10 +103,10 @@ def load_model(config, model, optimizer=None):
 def load_pretrained_params(model, path):
    logger = get_logger()
-    if path.endswith('pdparams'):
+    if path.endswith('.pdparams'):
        path = path.replace('.pdparams', '')
    assert os.path.exists(path + ".pdparams"), \
-        f"The {path}.pdparams does not exists!"
+        "The {}.pdparams does not exists!".format(path)
    params = paddle.load(path + '.pdparams')
    state_dict = model.state_dict()
@@ -92,11 +115,11 @@ def load_pretrained_params(model, path):
        if list(state_dict[k1].shape) == list(params[k2].shape):
            new_state_dict[k1] = params[k2]
        else:
-            logger.info(
+            logger.warning(
-                f"The shape of model params {k1} {state_dict[k1].shape} not matched with loaded params {k2} {params[k2].shape} !"
+                "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)
-    logger.info(f"load pretrain successful from {path}")
+    logger.info("load pretrain successful from {}".format(path))
    return model

--- a/test_tipc/common_func.sh
+++ b/test_tipc/common_func.sh
@@ -30,6 +30,7 @@ function func_set_params(){
 function func_parser_params(){
    strs=$1
+    MODE=$2
    IFS=":"
    array=(${strs})
    key=${array[0]}

--- a/test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+===========================ch_PP-OCRv2===========================
+model_name:ch_PP-OCRv2
+python:python3.7
+infer_model:./inference/ch_PP-OCRv2_det_infer/
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_system.py
+--use_gpu:False|True
+--enable_mkldnn:False|True
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp32
+--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
--- a/test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+++ b/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
--- a/test_tipc/configs/ch_PP-OCRv2/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
+++ b/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
--- a/test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+++ b/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
--- a/test_tipc/configs/ch_PP-OCRv2_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
+++ b/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
--- a/test_tipc/configs/ppocrv2_det_mobile/train_infer_python.txt
+++ b/test_tipc/configs/ppocrv2_det_mobile/train_infer_python.txt
 ===========================train_params===========================
-model_name:PPOCRv2_ocr_det
+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_infer=1|whole_train_infer=500
+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_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 configs/det/ch_PP-OCRv2/ch_PP-OCR_det_cml.yml -o 
+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-OCR_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
@@ -27,8 +27,8 @@ 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-OCR_det_cml.yml -o 
+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-OCR_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

--- a/test_tipc/configs/ch_PP-OCRv2_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+++ b/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
+Global.pretrained_model:null
+Global.save_inference_dir:null
+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|True
+--enable_mkldnn:True
+--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
--- a/test_tipc/configs/ch_PP-OCRv2_det_PACT/train_infer_python.txt
+++ b/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
--- a/test_tipc/configs/ch_PP-OCRv2_rec/ch_PP-OCRv2_rec_distillation.yml
+++ b/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
--- a/test_tipc/configs/ch_PP-OCRv2_rec/train_infer_python.txt
+++ b/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
--- a/test_tipc/configs/ch_PP-OCRv2_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+++ b/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
+Global.pretrained_model:null
+Global.save_inference_dir:null
+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|True
+--enable_mkldnn:False|True
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True
+--precision:int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+null:null
+--benchmark:True
+null:null
+null:null
--- a/test_tipc/configs/ch_PP-OCRv2_rec_PACT/train_infer_python.txt
+++ b/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
--- a/test_tipc/configs/ch_ppocr_mobile_V2.0_det_FPGM/train_infer_python.txt
+++ b/test_tipc/configs/ch_ppocr_mobile_V2.0_det_FPGM/train_infer_python.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_lite_infer=5|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:fpgm_train
+norm_train:null
+pact_train:null
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.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:null
+quant_export:null
+fpgm_export:deploy/slim/prune/export_prune_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:null
+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
\ No newline at end of file
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+++ b/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:False
+inference:tools/infer/predict_system.py
+--use_gpu:False|True
+--enable_mkldnn:False|True
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:fp32
+--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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+++ b/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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
+++ b/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
--- a/test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml
+++ b/test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml
--- a/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
--- a/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_python_jetson.txt
+++ b/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_python_jetson.txt
--- a/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
 ===========================lite_params===========================
 inference:./ocr_db_crnn det
-infer_model:ch_PP-OCRv2_det_infer|ch_PP-OCRv2_det_slim_quant_infer
 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
--rec_batch_size:1
+null:null
--system_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
+null:null
 --benchmark:True
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/model_linux_gpu_normal_normal_lite_cpp_arm_gpu_opencl.txt
+++ b/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
--- a/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
--- a/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
--- a/test_tipc/configs/ppocr_det_mobile/train_infer_python.txt
+++ b/test_tipc/configs/ppocr_det_mobile/train_infer_python.txt
 ===========================train_params===========================
-model_name:ocr_det
+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.epoch_num:lite_train_lite_infer=5|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
@@ -12,10 +12,10 @@ 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
+trainer:norm_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
+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:deploy/slim/quantization/quant.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o
+pact_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
+fpgm_train:null
 distill_train:null
 null:null
 null:null
@@ -27,9 +27,9 @@ 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 
+norm_export:tools/export_model.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
-quant_export:deploy/slim/quantization/export_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+quant_export:null
-fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ppocr_det_mobile/det_mv3_db.yml -o 
+fpgm_export:null
 distill_export:null
 export1:null
 export2:null

--- a/test_tipc/configs/ppocr_det_mobile/train_linux_dcu_normal_normal_infer_python_dcu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/train_linux_dcu_normal_normal_infer_python_dcu.txt
--- a/test_tipc/configs/ppocr_det_mobile/train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/train_linux_gpu_fleet_amp_infer_python_linux_gpu_cpu.txt
--- a/test_tipc/configs/ppocr_det_mobile/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/train_linux_gpu_normal_amp_infer_python_linux_gpu_cpu.txt
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt
+++ b/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=5|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: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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt
+++ b/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:python
+gpu_list:0
+Global.use_gpu:True
+Global.auto_cast:fp32|amp
+Global.epoch_num:lite_train_lite_infer=5|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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+++ b/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:ch_ppocr_mobile_v2.0_det_KL
+python:python3.7
+Global.pretrained_model:null
+Global.save_inference_dir:null
+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|True
+--enable_mkldnn:True
+--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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_mac_cpu.txt
+++ b/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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_KL/model_linux_gpu_normal_normal_infer_python_windows_gpu_cpu.txt
+++ b/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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/train_infer_python.txt
+++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_det_PACT/train_infer_python.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_lite_infer=5|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:pact_train
+norm_train:null
+pact_train:deploy/slim/quantization/quant.py -c configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.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_ppocr_v2.0/ch_det_mv3_db_v2.0.yml -o 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:null
+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
\ No newline at end of file
--- a/test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
+++ b/test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
--- a/test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ppocr_rec_mobile/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_infer_python.txt
+++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_rec/train_infer_python.txt
+===========================train_params===========================
+model_name:ch_ppocr_mobile_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_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
+norm_train:tools/train.py -c configs/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 configs/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 configs/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 configs/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" --rec_algorithm="RARE"
+--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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml
+++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml
+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_lite_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  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
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+    disable_se: True
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+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
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - 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, 320]
+      - 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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_infer_python.txt
+++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/train_infer_python.txt
+===========================train_params===========================
+model_name:ch_ppocr_mobile_v2.0_rec_FPGM
+python:python3.7
+gpu_list:0
+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=128|whole_train_whole_infer=128
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/ic15_data/test/word_1.png
+null:null
+##
+trainer:fpgm_train
+norm_train:null
+pact_train:null
+fpgm_train:deploy/slim/prune/sensitivity_anal.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./pretrain_models/ch_ppocr_mobile_v2.0_rec_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:null
+quant_export:null
+fpgm_export:deploy/slim/prune/export_prune_model.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_FPGM/rec_chinese_lite_train_v2.0.yml -o 
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:null
+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
+--use_tensorrt:False|True
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+null:null
+--benchmark:True
+null:null
\ No newline at end of file
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+===========================kl_quant_params===========================
+model_name:ch_ppocr_mobile_v2.0_rec_KL
+python:python3.7
+Global.pretrained_model:null
+Global.save_inference_dir:null
+infer_model:./inference/ch_ppocr_mobile_v2.0_rec_infer/
+infer_export:deploy/slim/quantization/quant_kl.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/rec_chinese_lite_train_v2.0.yml -o
+infer_quant:True
+inference:tools/infer/predict_rec.py
+--use_gpu:False|True
+--enable_mkldnn:True
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:False|True
+--precision:int8
+--det_model_dir:
+--image_dir:./inference/rec_inference
+null:null
+--benchmark:True
+null:null
+null:null
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/rec_chinese_lite_train_v2.0.yml
+++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_KL/rec_chinese_lite_train_v2.0.yml
+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_lite_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  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
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+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
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - 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, 320]
+      - 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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml
+++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml
+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec_chinese_lite_v2.0
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  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
+  # for data or label process
+  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: True
+  save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+Architecture:
+  model_type: rec
+  algorithm: CRNN
+  Transform:
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: small
+    small_stride: [1, 2, 2, 2]
+  Neck:
+    name: SequenceEncoder
+    encoder_type: rnn
+    hidden_size: 48
+  Head:
+    name: CTCHead
+    fc_decay: 0.00001
+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
+      - RecAug: 
+      - CTCLabelEncode: # Class handling label
+      - RecResizeImg:
+          image_shape: [3, 32, 320]
+      - 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, 320]
+      - 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
--- a/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/train_infer_python.txt
+++ b/test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/train_infer_python.txt
+===========================train_params===========================
+model_name:ch_ppocr_mobile_v2.0_rec_PACT
+python:python3.7
+gpu_list:0
+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=128|whole_train_whole_infer=128
+Global.checkpoints:null
+train_model_name:latest
+train_infer_img_dir:./train_data/ic15_data/test/word_1.png
+null:null
+##
+trainer:pact_train
+norm_train:null
+pact_train:deploy/slim/quantization/quant.py -c test_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.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.checkpoints:
+norm_export:null
+quant_export:deploy/slim/quantization/export_model.py -ctest_tipc/configs/ch_ppocr_mobile_v2.0_rec_PACT/rec_chinese_lite_train_v2.0.yml -o 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+inference_dir:null
+train_model:null
+infer_export:null
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ppocr_keys_v1.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:False|True
+--precision:fp32|fp16|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
\ No newline at end of file
--- a/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ch_ppocr_server_v2.0/model_linux_gpu_normal_normal_infer_python_linux_gpu_cpu.txt
+===========================ch_ppocr_server_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|True
+--enable_mkldnn:False|True
+--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/
+--rec_model_dir:./inference/ch_ppocr_server_v2.0_rec_infer/
+--benchmark:True
+null:null
+null:null
--- a/test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml
+++ b/test_tipc/configs/ppocr_det_server/det_r50_vd_db.yml
--- a/test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
+++ b/test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
--- a/test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ppocr_det_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
--- a/test_tipc/configs/ppocr_det_server/train_infer_python.txt
+++ b/test_tipc/configs/ppocr_det_server/train_infer_python.txt
 ===========================train_params===========================
-model_name:ocr_server_det
+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_infer=2|whole_train_infer=300
+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_infer=2|whole_train_infer=4
+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/

--- a/test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
+++ b/test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_paddle2onnx_python_linux_cpu.txt
--- a/test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
+++ b/test_tipc/configs/ppocr_rec_server/model_linux_gpu_normal_normal_serving_python_linux_gpu_cpu.txt
--- a/test_tipc/configs/ppocr_rec_server/rec_icdar15_r34_train.yml
+++ b/test_tipc/configs/ppocr_rec_server/rec_icdar15_r34_train.yml
--- a/test_tipc/configs/ch_ppocr_server_v2.0_rec/train_infer_python.txt
+++ b/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
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/det_mv3_db_v2.0/train_infer_python.txt
+++ b/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
--- a/test_tipc/configs/det_mv3_east_v2.0/det_mv3_east.yml
+++ b/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
--- a/test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/det_mv3_east_v2.0/train_infer_python.txt
+===========================train_params===========================
+model_name:det_mv3_east_v2.0
+python:python3.7
+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: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_mv3_east_v2.0/det_mv3_east.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/det_mv3_east_v2.0/det_mv3_east.yml -o 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+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: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
+--det_algorithm:EAST
--- a/test_tipc/configs/det_mv3_pse_v2.0/det_mv3_pse.yml
+++ b/test_tipc/configs/det_mv3_pse_v2.0/det_mv3_pse.yml
+Global:
+  use_gpu: true
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_mv3_pse/
+  save_epoch_step: 600
+  # evaluation is run every 63 iterations
+  eval_batch_step: [ 0,1000 ]
+  cal_metric_during_train: False
+  pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
+  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_pse/predicts_pse.txt
+Architecture:
+  model_type: det
+  algorithm: PSE
+  Transform: null
+  Backbone:
+    name: MobileNetV3
+    scale: 0.5
+    model_name: large
+  Neck:
+    name: FPN
+    out_channels: 96
+  Head:
+    name: PSEHead
+    hidden_dim: 96
+    out_channels: 7
+Loss:
+  name: PSELoss
+  alpha: 0.7
+  ohem_ratio: 3
+  kernel_sample_mask: pred
+  reduction: none
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Step
+    learning_rate: 0.001
+    step_size: 200
+    gamma: 0.1
+  regularizer:
+    name: 'L2'
+    factor: 0.0005
+PostProcess:
+  name: PSEPostProcess
+  thresh: 0
+  box_thresh: 0.85
+  min_area: 16
+  box_type: box # 'box' or 'poly'
+  scale: 1
+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
+      - ColorJitter:
+          brightness: 0.12549019607843137
+          saturation: 0.5
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
+      - MakePseGt:
+          kernel_num: 7
+          min_shrink_ratio: 0.4
+          size: 640
+      - RandomCropImgMask:
+          size: [ 640,640 ]
+          main_key: gt_text
+          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
+      - 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', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
+  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
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 736
+          limit_type: min
+      - 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: 8
\ No newline at end of file
--- a/test_tipc/configs/det_mv3_pse_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/det_mv3_pse_v2.0/train_infer_python.txt
+===========================train_params===========================
+model_name:det_mv3_pse_v2.0
+python:python3.7
+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: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_mv3_pse_v2.0/det_mv3_pse.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/det_mv3_pse_v2.0/det_mv3_pse.yml -o 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/det_mv3_pse/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/cconfigs/det_mv3_pse_v2.0/det_mv3_pse.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
+--det_algorithm:PSE
--- a/test_tipc/configs/det_r50_db_v2.0/train_infer_python.txt
+++ b/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
--- a/test_tipc/configs/det_r50_vd_east_v2.0/det_r50_vd_east.yml
+++ b/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
--- a/test_tipc/configs/det_r50_vd_east_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/det_r50_vd_east_v2.0/train_infer_python.txt
+===========================train_params===========================
+model_name:det_r50_vd_east_v2.0
+python:python3.7
+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: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_east_v2.0/det_r50_vd_east.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/det_r50_vd_east_v2.0/det_r50_vd_east.yml -o 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/det_r50_vd_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_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
+--det_algorithm:EAST
--- a/test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml
+++ b/test_tipc/configs/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml
+Global:
+  use_gpu: true
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/det_r50_vd_pse/
+  save_epoch_step: 600
+  # evaluation is run every 125 iterations
+  eval_batch_step: [ 0,1000 ]
+  cal_metric_during_train: False
+  pretrained_model: 
+  checkpoints: #./output/det_r50_vd_pse_batch8_ColorJitter/best_accuracy
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_en/img_10.jpg
+  save_res_path: ./output/det_pse/predicts_pse.txt
+Architecture:
+  model_type: det
+  algorithm: PSE
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: FPN
+    out_channels: 256
+  Head:
+    name: PSEHead
+    hidden_dim: 256
+    out_channels: 7
+Loss:
+  name: PSELoss
+  alpha: 0.7
+  ohem_ratio: 3
+  kernel_sample_mask: pred
+  reduction: none
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Step
+    learning_rate: 0.0001
+    step_size: 200
+    gamma: 0.1
+  regularizer:
+    name: 'L2'
+    factor: 0.0005
+PostProcess:
+  name: PSEPostProcess
+  thresh: 0
+  box_thresh: 0.85
+  min_area: 16
+  box_type: box # 'box' or 'poly'
+  scale: 1
+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
+      - ColorJitter:
+          brightness: 0.12549019607843137
+          saturation: 0.5
+      - IaaAugment:
+          augmenter_args:
+            - { 'type': Resize, 'args': { 'size': [ 0.5, 3 ] } }
+            - { 'type': Fliplr, 'args': { 'p': 0.5 } }
+            - { 'type': Affine, 'args': { 'rotate': [ -10, 10 ] } }
+      - MakePseGt:
+          kernel_num: 7
+          min_shrink_ratio: 0.4
+          size: 640
+      - RandomCropImgMask:
+          size: [ 640,640 ]
+          main_key: gt_text
+          crop_keys: [ 'image', 'gt_text', 'gt_kernels', 'mask' ]
+      - 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', 'gt_text', 'gt_kernels', 'mask' ] # the order of the dataloader list
+  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
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - DetLabelEncode: # Class handling label
+      - DetResizeForTest:
+          limit_side_len: 736
+          limit_type: min
+      - 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: 8
\ No newline at end of file
--- a/test_tipc/configs/det_r50_vd_pse_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/det_r50_vd_pse_v2.0/train_infer_python.txt
+===========================train_params===========================
+model_name:det_r50_vd_pse_v2.0
+python:python3.7
+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: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_pse_v2.0/det_r50_vd_pse.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/det_r50_vd_pse_v2.0/det_r50_vd_pse.yml -o 
+quant_export:null 
+fpgm_export:null
+distill_export:null
+export1:null
+export2:null
+##
+train_model:./inference/det_r50_vd_pse/best_accuracy
+infer_export:tools/export_model.py -c test_tipc/cconfigs/det_r50_vd_pse_v2.0/det_r50_vd_pse.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
+--det_algorithm:PSE
--- a/test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/det_r50_vd_sast_icdar2015.yml
+++ b/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
--- a/test_tipc/configs/det_r50_vd_sast_icdar15_v2.0/train_infer_python.txt
+++ b/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
--- a/test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/det_r50_vd_sast_totaltext.yml
+++ b/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
--- a/test_tipc/configs/det_r50_vd_sast_totaltext_v2.0/train_infer_python.txt
+++ b/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
--- a/test_tipc/configs/en_server_pgnetA/train_infer_python.txt
+++ b/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
+--e2e_model_dir:
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+null:null
+--benchmark:True
+null:null
--- a/test_tipc/configs/ppocr_det_mobile/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/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
-===========================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 det
--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
-===========================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
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
-serving_dir:./deploy/pdserving
-web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
-op.det.local_service_conf.devices:null|0
-op.det.local_service_conf.use_mkldnn:True|False
-op.det.local_service_conf.thread_num:1|6
-op.det.local_service_conf.use_trt:False|True
-op.det.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py|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: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
--- a/test_tipc/configs/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt
+++ b/test_tipc/configs/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt
-===========================train_params===========================
-model_name:ocr_det
-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_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: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
-===========================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 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
-===========================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
--params_filename:inference.pdiparams
--serving_server:./deploy/pdserving/ppocr_det_mobile_2.0_serving/
--serving_client:./deploy/pdserving/ppocr_det_mobile_2.0_client/
-serving_dir:./deploy/pdserving
-web_service:web_service_det.py --config=config.yml --opt op.det.concurrency=1
-op.det.local_service_conf.devices:null|0
-op.det.local_service_conf.use_mkldnn:True|False
-op.det.local_service_conf.thread_num:1|6
-op.det.local_service_conf.use_trt:False|True
-op.det.local_service_conf.precision:fp32|fp16|int8
-pipline:pipeline_http_client.py|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
--- a/test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml
+++ b/test_tipc/configs/rec_mtb_nrtr/rec_mtb_nrtr.yml
+Global:
+  use_gpu: True
+  epoch_num: 21
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/nrtr/
+  save_epoch_step: 1
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_10.png
+  # for data or label process
+  character_dict_path: ppocr/utils/EN_symbol_dict.txt
+  max_text_length: 25
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_nrtr.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.99
+  clip_norm: 5.0
+  lr:
+    name: Cosine
+    learning_rate: 0.0005
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0.
+Architecture:
+  model_type: rec
+  algorithm: NRTR
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: MTB
+    cnn_num: 2
+  Head:
+    name: Transformer
+    d_model: 512
+    num_encoder_layers: 6
+    beam_size: -1 # When Beam size is greater than 0, it means to use beam search when evaluation.
+Loss:
+  name: NRTRLoss
+  smoothing: True
+PostProcess:
+  name: NRTRLabelDecode
+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
+      - NRTRLabelEncode: # Class handling label
+      - NRTRRecResizeImg:
+          image_shape: [100, 32]
+          resize_type: PIL # PIL or OpenCV
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 512
+    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
+      - NRTRLabelEncode: # Class handling label
+      - NRTRRecResizeImg:
+          image_shape: [100, 32]
+          resize_type: PIL # PIL or OpenCV
+      - 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: 1
+    use_shared_memory: False
--- a/test_tipc/configs/rec_mtb_nrtr/train_infer_python.txt
+++ b/test_tipc/configs/rec_mtb_nrtr/train_infer_python.txt
+===========================train_params===========================
+model_name:rec_mtb_nrtr
+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=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_mtb_nrtr/rec_mtb_nrtr.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_mtb_nrtr/rec_mtb_nrtr.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_mtb_nrtr/rec_mtb_nrtr.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_mtb_nrtr/rec_mtb_nrtr.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/EN_symbol_dict.txt --rec_image_shape="1,32,100" --rec_algorithm="NRTR"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/rec_icdar15_train.yml
+++ b/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
--- a/test_tipc/configs/rec_mv3_none_bilstm_ctc_v2.0/train_infer_python.txt
+++ b/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|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_mv3_none_none_ctc_v2.0/rec_icdar15_train.yml
+++ b/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
--- a/test_tipc/configs/rec_mv3_none_none_ctc_v2.0/train_infer_python.txt
+++ b/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|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml
+++ b/test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/rec_mv3_tps_bilstm_att.yml
+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/rec_mv3_tps_bilstm_att/
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  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
+  # 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_att.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0.00001
+Architecture:
+  model_type: rec
+  algorithm: RARE
+  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: AttentionHead  
+    hidden_size: 96
+Loss:
+  name: AttentionLoss
+PostProcess:
+  name: AttnLabelDecode
+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
+      - AttnLabelEncode: # 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
+      - AttnLabelEncode: # 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: 1
--- a/test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/rec_mv3_tps_bilstm_att_v2.0/train_infer_python.txt
+===========================train_params===========================
+model_name:rec_mv3_tps_bilstm_att_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=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_att_v2.0/rec_mv3_tps_bilstm_att.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_att_v2.0/rec_mv3_tps_bilstm_att.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_att_v2.0/rec_mv3_tps_bilstm_att.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_att_v2.0/rec_mv3_tps_bilstm_att.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" --rec_algorithm="RARE"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml
+++ b/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
--- a/test_tipc/configs/rec_mv3_tps_bilstm_ctc_v2.0/train_infer_python.txt
+++ b/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|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_r31_sar/rec_r31_sar.yml
+++ b/test_tipc/configs/rec_r31_sar/rec_r31_sar.yml
+Global:
+  use_gpu: true
+  epoch_num: 5
+  log_smooth_window: 20
+  print_batch_step: 20
+  save_model_dir: ./sar_rec
+  save_epoch_step: 1
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 2000]
+  cal_metric_during_train: True
+  pretrained_model:
+  checkpoints: 
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: 
+  # for data or label process
+  character_dict_path: ppocr/utils/dict90.txt
+  max_text_length: 30
+  infer_mode: False
+  use_space_char: False
+  rm_symbol: True
+  save_res_path: ./output/rec/predicts_sar.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Piecewise
+    decay_epochs: [3, 4]
+    values: [0.001, 0.0001, 0.00001] 
+  regularizer:
+    name: 'L2'
+    factor: 0
+Architecture:
+  model_type: rec
+  algorithm: SAR
+  Transform:
+  Backbone:
+    name: ResNet31
+  Head:
+    name: SARHead
+Loss:
+  name: SARLoss
+PostProcess:
+  name: SARLabelDecode
+Metric:
+  name: RecMetric
+Train:
+  dataset:
+    name: SimpleDataSet
+    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
+      - SARLabelEncode: # Class handling label
+      - SARRecResizeImg:
+          image_shape: [3, 48, 48, 160] # h:48 w:[48,160]
+          width_downsample_ratio: 0.25
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    batch_size_per_card: 64
+    drop_last: True
+    num_workers: 8
+    use_shared_memory: False
+Eval:
+  dataset:
+    name: 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
+      - SARLabelEncode: # Class handling label
+      - SARRecResizeImg:
+          image_shape: [3, 48, 48, 160]
+          width_downsample_ratio: 0.25
+      - KeepKeys:
+          keep_keys: ['image', 'label', 'valid_ratio'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 64
+    num_workers: 4
+    use_shared_memory: False
--- a/test_tipc/configs/rec_r31_sar/train_infer_python.txt
+++ b/test_tipc/configs/rec_r31_sar/train_infer_python.txt
+===========================train_params===========================
+model_name:rec_r31_sar
+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=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_r31_sar/rec_r31_sar.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_r31_sar/rec_r31_sar.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_r31_sar/rec_r31_sar.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_r31_sar/rec_r31_sar.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/dict90.txt --rec_image_shape="3,48,48,160" --rec_algorithm="SAR"
+--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
--- a/test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/rec_icdar15_train.yml
+++ b/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
--- a/test_tipc/configs/rec_r34_vd_none_bilstm_ctc_v2.0/train_infer_python.txt
+++ b/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|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/rec_icdar15_train.yml
+++ b/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
--- a/test_tipc/configs/rec_r34_vd_none_none_ctc_v2.0/train_infer_python.txt
+++ b/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|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml
+++ b/test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/rec_r34_vd_tps_bilstm_att.yml
+Global:
+  use_gpu: True
+  epoch_num: 400
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/rec/b3_rare_r34_none_gru/
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  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
+  # 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_b3_rare_r34_none_gru.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    learning_rate: 0.0005
+  regularizer:
+    name: 'L2'
+    factor: 0.00000
+Architecture:
+  model_type: rec
+  algorithm: RARE
+  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 #96
+  Head:
+    name: AttentionHead  # AttentionHead
+    hidden_size: 256 #
+    l2_decay: 0.00001
+Loss:
+  name: AttentionLoss
+PostProcess:
+  name: AttnLabelDecode
+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
+      - AttnLabelEncode: # 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
+      - AttnLabelEncode: # 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
--- a/test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt
+++ b/test_tipc/configs/rec_r34_vd_tps_bilstm_att_v2.0/train_infer_python.txt
+===========================train_params===========================
+model_name:rec_r34_vd_tps_bilstm_att_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=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_att_v2.0/rec_r34_vd_tps_bilstm_att.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_att_v2.0/rec_r34_vd_tps_bilstm_att.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_att_v2.0/rec_r34_vd_tps_bilstm_att.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_att_v2.0/rec_r34_vd_tps_bilstm_att.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" --rec_algorithm="RARE"
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/rec_icdar15_train.yml
+++ b/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
--- a/test_tipc/configs/rec_r34_vd_tps_bilstm_ctc_v2.0/train_infer_python.txt
+++ b/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|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml
+++ b/test_tipc/configs/rec_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml
+Global:
+  use_gpu: True
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 5
+  save_model_dir: ./output/rec/srn_new
+  save_epoch_step: 3
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [0, 5000]
+  cal_metric_during_train: True
+  pretrained_model: 
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img: doc/imgs_words/ch/word_1.jpg
+  # for data or label process
+  character_dict_path:
+  max_text_length: 25
+  num_heads: 8
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/rec/predicts_srn.txt
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  clip_norm: 10.0
+  lr:
+    learning_rate: 0.0001
+Architecture:
+  model_type: rec
+  algorithm: SRN
+  in_channels: 1
+  Transform:
+  Backbone:
+    name: ResNetFPN
+  Head:
+    name: SRNHead
+    max_text_length: 25
+    num_heads: 8
+    num_encoder_TUs: 2
+    num_decoder_TUs: 4
+    hidden_dims: 512
+Loss:
+  name: SRNLoss
+PostProcess:
+  name: SRNLabelDecode
+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
+      - SRNLabelEncode: # Class handling label
+      - SRNRecResizeImg:
+          image_shape: [1, 64, 256]
+      - KeepKeys:
+          keep_keys: ['image',
+                      'label',
+                      'length',
+                      'encoder_word_pos',
+                      'gsrm_word_pos',
+                      'gsrm_slf_attn_bias1',
+                      'gsrm_slf_attn_bias2'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 64
+    drop_last: False
+    num_workers: 4
+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
+      - SRNLabelEncode: # Class handling label
+      - SRNRecResizeImg:
+          image_shape: [1, 64, 256]
+      - KeepKeys:
+          keep_keys: ['image',
+                      'label',
+                      'length',
+                      'encoder_word_pos',
+                      'gsrm_word_pos',
+                      'gsrm_slf_attn_bias1',
+                      'gsrm_slf_attn_bias2'] 
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 32
+    num_workers: 4
--- a/test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt
+++ b/test_tipc/configs/rec_r50_fpn_vd_none_srn/train_infer_python.txt
+===========================train_params===========================
+model_name:rec_r50_fpn_vd_none_srn
+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=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_r50_fpn_vd_none_srn/rec_r50_fpn_srn.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_r50_fpn_vd_none_srn/rec_r50_fpn_srn.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_r50_fpn_vd_none_srn/rec_r50_fpn_srn.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_r50_fpn_vd_none_srn/rec_r50_fpn_srn.yml -o
+infer_quant:False
+inference:tools/infer/predict_rec.py --rec_char_dict_path=./ppocr/utils/ic15_dict.txt --rec_image_shape="1,64,256" --rec_algorithm="SRN" --use_space_char=False
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1|6
+--use_tensorrt:True|False
+--precision:fp32|int8
+--rec_model_dir:
+--image_dir:./inference/rec_inference
+--save_log_path:./test/output/
+--benchmark:True
+null:null
--- a/test_tipc/docs/jeston_test_train_inference_python.md
+++ b/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。
+Jeston端基础训练预测功能测试的主程序为`test_inference_inference.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
@@ -40,21 +40,21 @@ Jeston端基础训练预测功能测试的主程序为`test_train_inference_pyth
 ### 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。
+先运行`prepare.sh`准备数据和模型，然后运行`test_inference_inference.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：
+`test_inference_inference.sh`仅有一个模式`whole_infer`，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/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer'
+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/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer'
+bash test_tipc/test_inference_inference.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/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_python_jetson.txt 'whole_infer' '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' '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：
+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`whole_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

--- a/test_tipc/docs/mac_test_train_inference_python.md
+++ b/test_tipc/docs/mac_test_train_inference_python.md
@@ -57,36 +57,35 @@ Mac端无GPU，环境准备只需要Python环境即可，安装PaddlePaddle等
 ```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/ppocr_det_mobile/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'lite_train_lite_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'lite_train_whole_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'whole_infer'
+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/ppocr_det_mobile/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt '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_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/ppocr_det_mobile/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'whole_infer' '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' '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/ppocr_det_mobile/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'whole_train_whole_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_mac_cpu_normal_normal_infer_python_mac_cpu.txt 'klquant_whole_infer'
+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'
-bash test_tipc/test_train_inference_python.sh test_tipc/configs/ppocr_det_mobile/train_mac_cpu_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：

--- a/test_tipc/docs/test_inference_cpp.md
+++ b/test_tipc/docs/test_inference_cpp.md
@@ -20,12 +20,12 @@ C++预测功能测试的主程序为`test_inference_cpp.sh`，可以测试基于
 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/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt "cpp_infer"
+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/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt
+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/ppocr_det_mobile/model_linux_gpu_normal_normal_infer_cpp_linux_gpu_cpu.txt '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 '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：

--- a/test_tipc/docs/test_lite_arm_cpu_cpp.md
+++ b/test_tipc/docs/test_lite_arm_cpu_cpp.md
-# Lite\_arm\_cpu\_cpp预测功能测试
+# Lite\_arm\_cpp预测功能测试
-Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`，可以在ARM CPU上基于Lite预测库测试模型的C++推理功能。
+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
@@ -10,12 +10,13 @@ Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`
 - 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：包括多predictor预测和单predictor预测
+- 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 |
+| 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 |  1 | 下载方式 |
+| 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
@@ -23,21 +24,44 @@ Lite\_arm\_cpu\_cpp预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`
 ### 2.1 功能测试
-先运行`prepare_lite.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_cpu_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。
+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
-# 数据和模型准备
+# 数据、模型、Paddle-Lite预测库准备
-bash test_tipc/prepare_lite.sh ./test_tipc/configs/ppocr_det_mobile/model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+#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_cpu_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.txt
+bash test_lite_arm_cpp.sh model_linux_gpu_normal_normal_lite_cpp_arm_cpu.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)。
+#### 2.1.2 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)。
-#### 运行结果
+### 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：

--- a/test_tipc/docs/test_train_inference_python.md
+++ b/test_tipc/docs/test_train_inference_python.md
@@ -64,35 +64,35 @@ Linux端基础训练预测功能测试的主程序为`test_train_inference_pytho
 - 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/ppocr_det_mobile/train_infer_python.txt 'lite_train_lite_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_infer_python.txt  'lite_train_whole_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_infer_python.txt 'whole_infer'
+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/ppocr_det_mobile/train_infer_python.txt 'whole_infer'
+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/ppocr_det_mobile/train_infer_python.txt 'whole_infer' '1'
+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/ppocr_det_mobile/train_infer_python.txt 'whole_train_whole_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_infer_python.txt  'klquant_whole_infer'
+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/ppocr_det_mobile/train_infer_python.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：

--- a/test_tipc/docs/win_test_train_inference_python.md
+++ b/test_tipc/docs/win_test_train_inference_python.md
@@ -58,35 +58,35 @@ Windows端基础训练预测功能测试的主程序为`test_train_inference_pyt
 - 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/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'lite_train_lite_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'lite_train_whole_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'whole_infer'
+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/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  '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_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/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'whole_infer' '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' '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/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'whole_train_whole_infer'
+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/ppocr_det_mobile/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/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.txt  'klquant_whole_infer'
+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/ppocr_det_mobile/train_windows_gpu_normal_normal_infer_python_windows_cpu_gpu.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'
 ```

--- a/test_tipc/prepare.sh
+++ b/test_tipc/prepare.sh
@@ -25,7 +25,7 @@ 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  --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} == "PPOCRv2_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
@@ -41,6 +41,31 @@ if [ ${MODE} = "lite_train_lite_infer" ];then
    ln -s ./icdar2015_lite ./icdar2015
    cd ../
    cd ./inference && tar xf rec_inference.tar && cd ../
+    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_lite 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
+    if [ ${model_name} == "ch_ppocr_mobile_v2.0_rec_FPGM" ]; then
+        wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar --no-check-certificate
+        cd ./pretrain_models/ && tar xf ch_ppocr_mobile_v2.0_rec_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
@@ -48,10 +73,21 @@ elif [ ${MODE} = "whole_train_whole_infer" ];then
    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 ../
-    if [ ${model_name} == "PPOCRv2_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
+    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
@@ -61,65 +97,89 @@ elif [ ${MODE} = "lite_train_whole_infer" ];then
    cd ./train_data/ && tar xf icdar2015_infer.tar && tar xf ic15_data.tar
    ln -s ./icdar2015_infer ./icdar2015
    cd ../
-    if [ ${model_name} == "PPOCRv2_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
-    if [ ${model_name} = "ocr_det" ]; 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 --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} = "ocr_server_det" ]; then
+    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} = "ocr_system_mobile" ]; then
+    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
+    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
+    elif [ ${model_name} = "ch_ppocr_mobile_v2.0_rec" ]; then
-        rm -rf ./train_data/ic15_data
        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 --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
+    elif [ ${model_name} = "ch_ppocr_server_v2.0_rec" ]; then
-        rm -rf ./train_data/ic15_data
        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 --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 
+    if [ ${model_name} = "ch_PPOCRv2_det" ]; then
-    elif [ ${model_name} = "PPOCRv2_ocr_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} == "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} = "ocr_det" ]; then
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar --no-check-certificate
+    cd ./train_data/ && tar xf icdar2015_lite.tar
+    ln -s ./icdar2015_lite ./icdar2015 && cd ../
+    if [ ${model_name} = "ch_ppocr_mobile_v2.0_det_KL" ]; 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} = "PPOCRv2_ocr_det" ]; 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_ppocr_mobile_v2.0_rec_KL" ]; then
+        wget -nc -P ./inference/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.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 ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ic15_data.tar --no-check-certificate
+        cd ./train_data/ && tar xf ic15_data.tar && cd ../
+        cd ./inference && tar xf ch_ppocr_mobile_v2.0_rec_infer.tar && tar xf rec_inference.tar &&  cd ../ 
+    fi 
 fi
 if [ ${MODE} = "cpp_infer" ];then
@@ -127,7 +187,7 @@ if [ ${MODE} = "cpp_infer" ];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/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
+    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 ../

--- a/test_tipc/prepare_lite.sh
+++ b/test_tipc/prepare_lite.sh
-#!/bin/bash
-source ./test_tipc/common_func.sh
-FILENAME=$1
-dataline=$(cat ${FILENAME})
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-IFS=$'\n'
-lite_model_list=$(func_parser_value "${lines[2]}")
-# prepare lite .nb model
-pip install paddlelite==2.9
-current_dir=${PWD}
-IFS="|"
-model_path=./inference_models
-for model in ${lite_model_list[*]}; do
-    inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
-    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=arm --optimize_out=${model_dir}_opt
-done
-# prepare test data
-data_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
-model_path=./inference_models
-inference_model=${inference_model_url##*/}
-data_file=${data_url##*/}
-wget -nc  -P ./inference_models ${inference_model_url}
-wget -nc  -P ./test_data ${data_url}
-cd ./inference_models && tar -xf ${inference_model} && cd ../
-cd ./test_data && tar -xf ${data_file} && rm ${data_file} && cd ../
-# prepare lite env
-paddlelite_url=https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.9/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz
-paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
-paddlelite_file=${paddlelite_zipfile:0:66}
-wget ${paddlelite_url} && tar -xf ${paddlelite_zipfile}
-mkdir -p  ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp -r ${model_path}/*_opt.nb test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp -r ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
-cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp ${FILENAME} test_tipc/test_lite_arm_cpu_cpp.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
-cd ${paddlelite_file}/demo/cxx/ocr/
-git clone https://github.com/cuicheng01/AutoLog.git
-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_file}* && rm -rf ${model_path}
--- a/test_tipc/prepare_lite_cpp.sh
+++ b/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}
--- a/test_tipc/readme.md
+++ b/test_tipc/readme.md
-# 飞桨训推一体认证
+# 飞桨训推一体全流程（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。
+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全流程（Training and Inference Pipeline Criterion(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">
@@ -23,14 +23,17 @@
 | 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 | FPGM裁剪 <br> 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| DB     |ch_ppocr_mobile_v2.0_det | 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_server_v2.0_det | 检测  | 支持 | 多机多卡 <br> 混合精度 | FPGM裁剪 <br> 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| DB     |ch_ppocr_mobile_v2.0_det_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 | PACT量化 <br> 离线量化| Paddle Inference: C++ <br> Paddle Serving: Python, C++ <br> Paddle-Lite: <br> (1) ARM CPU(C++) |
+| 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 | PACT量化 <br> 离线量化| 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++ <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  |
@@ -54,24 +57,23 @@
-## 3. 一键测试工具使用
+## 3. 测试工具简介
 ### 目录介绍
 ```shell
 test_tipc/
 ├── configs/  # 配置文件目录
-    ├── ppocr_det_mobile             # ppocr_det_mobile模型的测试配置文件目录
+    ├── ch_ppocr_mobile_v2.0_det    # ch_ppocr_mobile_v2.0_det模型的测试配置文件目录
-        ├── det_mv3_db.yml                  # 测试mobile版ppocr检测模型训练的yml文件
+        ├── train_infer_python.txt      # 测试Linux上python训练预测（基础训练预测）的配置文件
-        ├── train_infer_python.txt.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
        ├── ...  
-    ├── ppocr_det_server               # ppocr_det_server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
        ├── ...  
-    ├── ppocr_rec_mobile               # ppocr_rec_mobile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
        ├── ...  
-    ├── ppocr_rec_server               # ppocr_rec_server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
@@ -89,19 +91,9 @@ test_tipc/
 └── readme.md                         # 使用文档
 ```
-### 配置文件命名规范
+### 测试流程概述
-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_fleet_amp_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_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，可以直接从配置文件名看出对应的测试场景和功能。
+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：
-### 测试流程
-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，测试流程如下：
 <div align="center">
    <img src="docs/test.png" width="800">
 </div>
@@ -110,18 +102,43 @@ test_tipc/
 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）。
-其中，有4个测试主程序，功能如下：
+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：
- `test_train_inference_python.sh`：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。
+```shell
- `test_inference_cpp.sh`：测试基于C++的模型推理。
+# 功能：准备数据
- `test_serving.sh`：测试基于Paddle Serving的服务化部署功能。
+# 格式：bash + 运行脚本 + 参数1: 配置文件选择 + 参数2: 模式选择
- `test_lite_arm_cpu_cpp.sh`：测试基于Paddle-Lite的ARM CPU端c++预测部署功能。
+bash test_tipc/prepare.sh  configs/[model_name]/[params_file_name]  [Mode]
- `test_paddle2onnx.sh`：测试Paddle2ONNX的模型转化功能，并验证正确性。
+# 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>
-#### 更多教程
+## 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)  
+- [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)  
+- [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)  
+- [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)  
+- [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)  
+- [test_paddle2onnx 使用](docs/test_paddle2onnx.md)：测试Paddle2ONNX的模型转化功能，并验证正确性。
--- a/test_tipc/test_inference_jeston.sh
+++ b/test_tipc/test_inference_jeston.sh
 #!/bin/bash
 source test_tipc/common_func.sh
-source test_tipc/test_train_inference_python.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}'  $FILENAME)
+dataline=$(awk 'NR==1, NR==20{print}'  $FILENAME)
 # parser params
 IFS=$'\n'
@@ -35,18 +35,100 @@ 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]}")
+rec_model_key=$(func_parser_key "${lines[15]}")
+rec_model_value=$(func_parser_value "${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"
+function func_inference(){
+    IFS='|'
+    _python=$1
+    _script=$2
+    _model_dir=$3
+    _log_path=$4
+    _img_dir=$5
+    _flag_quant=$6
+    # inference 
+    for use_gpu in ${use_gpu_list[*]}; do
+        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
+            for use_mkldnn in ${use_mkldnn_list[*]}; do
+                if [ ${use_mkldnn} = "False" ] && [ ${_flag_quant} = "True" ]; then
+                    continue
+                fi
+                for threads in ${cpu_threads_list[*]}; do
+                    for batch_size in ${batch_size_list[*]}; do
+                        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_params0=$(func_set_params "${rec_model_key}" "${rec_model_value}")
+                            set_infer_params1=$(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_params0} ${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
+        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
+            for use_trt in ${use_trt_list[*]}; do
+                for precision in ${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 ${batch_size_list[*]}; do
+                        _save_log_path="${_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}")
+                        set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
+                        set_precision=$(func_set_params "${precision_key}" "${precision}")
+                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                        set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}")
+                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} ${set_infer_params0} > ${_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} = "whole_infer" ]; then
    GPUID=$3
    if [ ${#GPUID} -le 0 ];then
@@ -68,7 +150,6 @@ if [ ${MODE} = "whole_infer" ]; then
            set_save_infer_key=$(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
            eval $export_cmd
            status_export=$?
            status_check $status_export "${export_cmd}" "${status_log}"
@@ -85,3 +166,4 @@ if [ ${MODE} = "whole_infer" ]; then
    done
 fi
--- a/test_tipc/test_lite_arm_cpp.sh
+++ b/test_tipc/test_lite_arm_cpp.sh
+#!/bin/bash
+source ./common_func.sh
+export LD_LIBRARY_PATH=${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}" "${rec_dict_dir}"
+        done
+    done
+fi
--- a/test_tipc/test_lite_arm_cpu_cpp.sh
+++ b/test_tipc/test_lite_arm_cpu_cpp.sh
-#!/bin/bash
-source ./common_func.sh
-export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
-FILENAME=$1
-dataline=$(cat $FILENAME)
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-# parser lite inference
-lite_inference_cmd=$(func_parser_value "${lines[1]}")
-lite_model_dir_list=$(func_parser_value "${lines[2]}")
-runtime_device=$(func_parser_value "${lines[3]}")
-lite_cpu_threads_list=$(func_parser_value "${lines[4]}")
-lite_batch_size_list=$(func_parser_value "${lines[5]}")
-lite_infer_img_dir_list=$(func_parser_value "${lines[8]}")
-lite_config_dir=$(func_parser_value "${lines[9]}")
-lite_rec_dict_dir=$(func_parser_value "${lines[10]}")
-lite_benchmark_value=$(func_parser_value "${lines[11]}")
-LOG_PATH="./output"
-mkdir -p ${LOG_PATH}
-status_log="${LOG_PATH}/results.log"
-function func_lite(){
-    IFS='|'
-    _script=$1
-    _lite_model=$2
-    _log_path=$3
-    _img_dir=$4
-    _config=$5
-    if [[ $lite_model =~ "slim" ]]; then
-        precision="INT8"
-    else
-        precision="FP32"
-    fi
-    # lite inference
-    for num_threads in ${lite_cpu_threads_list[*]}; do
-	for batchsize in ${lite_batch_size_list[*]}; do
-            _save_log_path="${_log_path}/lite_${_lite_model}_runtime_device_${runtime_device}_precision_${precision}_batchsize_${batchsize}_threads_${num_threads}.log"
-            command="${_script} ${_lite_model} ${runtime_device} ${precision} ${num_threads} ${batchsize}  ${_img_dir} ${_config} ${lite_benchmark_value} > ${_save_log_path} 2>&1"
-            eval ${command}
-            status_check $? "${command}" "${status_log}"
-        done
-    done
-}
-echo "################### run test ###################"
-IFS="|"
-for lite_model in ${lite_model_dir_list[*]}; do
-    #run lite inference
-    for img_dir in ${lite_infer_img_dir_list[*]}; do
-        func_lite "${lite_inference_cmd}" "${lite_model}_opt.nb" "${LOG_PATH}" "${img_dir}" "${lite_config_dir}"
-    done
-done
--- a/test_tipc/test_train_inference_python.sh
+++ b/test_tipc/test_train_inference_python.sh
@@ -20,10 +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]}")
@@ -90,34 +90,39 @@ infer_value1=$(func_parser_value "${lines[50]}")
 # parser klquant_infer
 if [ ${MODE} = "klquant_whole_infer" ]; then
-    dataline=$(awk 'NR==85 NR==101{print}'  $FILENAME)
+    dataline=$(awk 'NR==1, NR==17{print}'  $FILENAME)
    lines=(${dataline})
+    model_name=$(func_parser_value "${lines[1]}")
+    python=$(func_parser_value "${lines[2]}")
+    export_weight=$(func_parser_key "${lines[3]}")
+    save_infer_key=$(func_parser_key "${lines[4]}")
    # parser inference model 
-    infer_model_dir_list=$(func_parser_value "${lines[1]}")
+    infer_model_dir_list=$(func_parser_value "${lines[5]}")
-    infer_export_list=$(func_parser_value "${lines[2]}")
+    infer_export_list=$(func_parser_value "${lines[6]}")
-    infer_is_quant=$(func_parser_value "${lines[3]}")
+    infer_is_quant=$(func_parser_value "${lines[7]}")
    # parser inference 
-    inference_py=$(func_parser_value "${lines[4]}")
+    inference_py=$(func_parser_value "${lines[8]}")
-    use_gpu_key=$(func_parser_key "${lines[5]}")
+    use_gpu_key=$(func_parser_key "${lines[9]}")
-    use_gpu_list=$(func_parser_value "${lines[5]}")
+    use_gpu_list=$(func_parser_value "${lines[9]}")
-    use_mkldnn_key=$(func_parser_key "${lines[6]}")
+    use_mkldnn_key=$(func_parser_key "${lines[10]}")
-    use_mkldnn_list=$(func_parser_value "${lines[6]}")
+    use_mkldnn_list=$(func_parser_value "${lines[10]}")
-    cpu_threads_key=$(func_parser_key "${lines[7]}")
+    cpu_threads_key=$(func_parser_key "${lines[11]}")
-    cpu_threads_list=$(func_parser_value "${lines[7]}")
+    cpu_threads_list=$(func_parser_value "${lines[11]}")
-    batch_size_key=$(func_parser_key "${lines[8]}")
+    batch_size_key=$(func_parser_key "${lines[12]}")
-    batch_size_list=$(func_parser_value "${lines[8]}")
+    batch_size_list=$(func_parser_value "${lines[12]}")
-    use_trt_key=$(func_parser_key "${lines[9]}")
+    use_trt_key=$(func_parser_key "${lines[13]}")
-    use_trt_list=$(func_parser_value "${lines[9]}")
+    use_trt_list=$(func_parser_value "${lines[13]}")
-    precision_key=$(func_parser_key "${lines[10]}")
+    precision_key=$(func_parser_key "${lines[14]}")
-    precision_list=$(func_parser_value "${lines[10]}")
+    precision_list=$(func_parser_value "${lines[14]}")
-    infer_model_key=$(func_parser_key "${lines[11]}")
+    infer_model_key=$(func_parser_key "${lines[15]}")
-    image_dir_key=$(func_parser_key "${lines[12]}")
+    image_dir_key=$(func_parser_key "${lines[16]}")
-    infer_img_dir=$(func_parser_value "${lines[12]}")
+    infer_img_dir=$(func_parser_value "${lines[16]}")
-    save_log_key=$(func_parser_key "${lines[13]}")
+    save_log_key=$(func_parser_key "${lines[17]}")
-    benchmark_key=$(func_parser_key "${lines[14]}")
+    save_log_value=$(func_parser_value "${lines[17]}")
-    benchmark_value=$(func_parser_value "${lines[14]}")
+    benchmark_key=$(func_parser_key "${lines[18]}")
-    infer_key1=$(func_parser_key "${lines[15]}")
+    benchmark_value=$(func_parser_value "${lines[18]}")
-    infer_value1=$(func_parser_value "${lines[15]}")
+    infer_key1=$(func_parser_key "${lines[19]}")
+    infer_value1=$(func_parser_value "${lines[19]}")
 fi
 LOG_PATH="./test_tipc/output"
@@ -157,8 +162,9 @@ function func_inference(){
                            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}" "${_model_dir}")
+                            set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}")
                            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} > ${_save_log_path} 2>&1 "
+                            command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_params0} ${set_infer_data} ${set_benchmark} ${set_precision} ${set_infer_params1} > ${_save_log_path} 2>&1 "
                            eval $command
                            last_status=${PIPESTATUS[0]}
                            eval "cat ${_save_log_path}"
@@ -187,8 +193,9 @@ function func_inference(){
                        set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
                        set_precision=$(func_set_params "${precision_key}" "${precision}")
                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                        set_infer_params0=$(func_set_params "${save_log_key}" "${save_log_value}")
                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} ${set_infer_params0} > ${_save_log_path} 2>&1 "
                        eval $command
                        last_status=${PIPESTATUS[0]}
                        eval "cat ${_save_log_path}"
@@ -233,7 +240,7 @@ if [ ${MODE} = "whole_infer" ] || [ ${MODE} = "klquant_whole_infer" ]; then
        fi
        #run inference
        is_quant=${infer_quant_flag[Count]}
-        if [ ${MODE} = "klquant_infer" ]; then
+        if [ ${MODE} = "klquant_whole_infer" ]; then
            is_quant="True"
        fi
        func_inference "${python}" "${inference_py}" "${save_infer_dir}" "${LOG_PATH}" "${infer_img_dir}" ${is_quant}
@@ -334,7 +341,7 @@ else
                set_eval_pretrain=$(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} ] && [ ${nodes} -le 1]; then
+                if [ ${trainer} = ${trainer_norm} ] && [ ${nodes} -le 1 ]; then
                    load_norm_train_model=${set_eval_pretrain}
                fi
                # run eval 
@@ -357,7 +364,7 @@ else
                    #run inference
                    eval $env
                    save_infer_path="${save_log}"
-                    if [ ${inference_dir} != "null" ] && [ ${inference_dir} != '##' ]; then
+                    if [[ ${inference_dir} != "null" ]] && [[ ${inference_dir} != '##' ]]; then
                        infer_model_dir="${save_infer_path}/${inference_dir}"
                    else
                        infer_model_dir=${save_infer_path}

--- a/tools/infer/predict_e2e.py
+++ b/tools/infer/predict_e2e.py
@@ -68,7 +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)

--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@@ -91,7 +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):

--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@@ -96,7 +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
@@ -191,6 +190,7 @@ def create_predictor(args, mode, logger):
            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)
@@ -211,7 +211,7 @@ def create_predictor(args, mode, logger):
                    "nearest_interp_v2_0.tmp_0": [1, 256, 2, 2]
                }
                max_input_shape = {
-                    "x": [1, 3, 1280, 1280],
+                    "x": [1, 3, 1536, 1536],
                    "conv2d_92.tmp_0": [1, 120, 400, 400],
                    "conv2d_91.tmp_0": [1, 24, 200, 200],
                    "conv2d_59.tmp_0": [1, 96, 400, 400],
@@ -261,7 +261,7 @@ def create_predictor(args, mode, logger):
                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]}
+                max_input_shape = {"x": [args.rec_batch_num, 3, 32, 1536]}
                opt_input_shape = {"x": [args.rec_batch_num, 3, 32, 320]}
            elif mode == "cls":
                min_input_shape = {"x": [1, 3, 48, 10]}
@@ -311,10 +311,6 @@ def create_predictor(args, mode, logger):
 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:

--- a/tools/infer_det.py
+++ b/tools/infer_det.py
@@ -53,6 +53,7 @@ def draw_det_res(dt_boxes, config, img, img_name, save_path):
        logger.info("The detected Image saved in {}".format(save_path))
+@paddle.no_grad()
 def main():
    global_config = config['Global']