fix conflicts

2020-12-21 13:43:40 +00:00 · 2020-12-21 13:43:40 +00:00 · 315c07d560
parent 551377a013 0ec11a29c6
commit 315c07d560
37 changed files with 5228 additions and 5094 deletions
--- a/PPOCRLabel/PPOCRLabel.py
+++ b/PPOCRLabel/PPOCRLabel.py
@ -106,7 +106,7 @@ 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=True, lang=lang)
+        self.ocr = PaddleOCR(use_pdserving=False, use_angle_cls=True, det=True, cls=True, use_gpu=False, lang=lang)

        if os.path.exists('./data/paddle.png'):
            result = self.ocr.ocr('./data/paddle.png', cls=True, det=True)
@ -274,6 +274,7 @@ class MainWindow(QMainWindow, WindowMixin):
        self.preButton.setIconSize(QSize(40, 100))
        self.preButton.clicked.connect(self.openPrevImg)
        self.preButton.setStyleSheet('border: none;')
+        self.preButton.setShortcut('a')
        self.iconlist = QListWidget()
        self.iconlist.setViewMode(QListView.IconMode)
        self.iconlist.setFlow(QListView.TopToBottom)
@ -289,12 +290,12 @@ class MainWindow(QMainWindow, WindowMixin):
        self.nextButton.setIconSize(QSize(40, 100))
        self.nextButton.setStyleSheet('border: none;')
        self.nextButton.clicked.connect(self.openNextImg)
+        self.nextButton.setShortcut('d')
        
        hlayout.addWidget(self.preButton)
        hlayout.addWidget(self.iconlist)
        hlayout.addWidget(self.nextButton)

-        # self.setLayout(hlayout)

        iconListContainer = QWidget()
        iconListContainer.setLayout(hlayout)
@ -359,11 +360,6 @@ class MainWindow(QMainWindow, WindowMixin):
        opendir = action(getStr('openDir'), self.openDirDialog,
                         'Ctrl+u', 'open', getStr('openDir'))

-        openNextImg = action(getStr('nextImg'), self.openNextImg,
-                             'd', 'next', getStr('nextImgDetail'))
-
-        openPrevImg = action(getStr('prevImg'), self.openPrevImg,
-                             'a', 'prev', getStr('prevImgDetail'))

        save = action(getStr('save'), self.saveFile,
                      'Ctrl+V', 'verify', getStr('saveDetail'), enabled=False)
@ -371,7 +367,7 @@ class MainWindow(QMainWindow, WindowMixin):
        alcm = action(getStr('choosemodel'), self.autolcm,
                                        'Ctrl+M', 'next', getStr('tipchoosemodel'))

-        deleteImg = action(getStr('deleteImg'), self.deleteImg, 'Ctrl+D', 'close', getStr('deleteImgDetail'),
+        deleteImg = action(getStr('deleteImg'), self.deleteImg, 'Ctrl+Shift+D', 'close', getStr('deleteImgDetail'),
                           enabled=True)

        resetAll = action(getStr('resetAll'), self.resetAll, None, 'resetall', getStr('resetAllDetail'))
@ -388,7 +384,7 @@ class MainWindow(QMainWindow, WindowMixin):
                        'w', 'new', getStr('crtBoxDetail'), enabled=False)

        delete = action(getStr('delBox'), self.deleteSelectedShape,
-                        'Delete', 'delete', getStr('delBoxDetail'), enabled=False)
+                        'backspace', 'delete', getStr('delBoxDetail'), enabled=False)
        copy = action(getStr('dupBox'), self.copySelectedShape,
                      'Ctrl+C', 'copy', getStr('dupBoxDetail'),
                      enabled=False)
@ -446,8 +442,11 @@ class MainWindow(QMainWindow, WindowMixin):
        reRec = action(getStr('reRecognition'), self.reRecognition, 
                      'Ctrl+Shift+R', 'reRec', getStr('reRecognition'), enabled=False)

+        singleRere = action(getStr('singleRe'), self.singleRerecognition,
+                            'Ctrl+R', 'reRec', getStr('singleRe'), enabled=False)
+
        createpoly = action(getStr('creatPolygon'), self.createPolygon,
-                            'p', 'new', 'Creat Polygon', enabled=True)
+                            'q', 'new', 'Creat Polygon', enabled=True)

        saveRec = action(getStr('saveRec'), self.saveRecResult,
                            '', 'save', getStr('saveRec'), enabled=False)
@ -491,6 +490,7 @@ class MainWindow(QMainWindow, WindowMixin):
                                icon='color', tip=getStr('shapeFillColorDetail'),
                                enabled=False)

+
        # Label list context menu.
        labelMenu = QMenu()
        addActions(labelMenu, (edit, delete))
@ -501,7 +501,6 @@ class MainWindow(QMainWindow, WindowMixin):

        # Draw squares/rectangles
        self.drawSquaresOption = QAction(getStr('drawSquares'), self)
-        self.drawSquaresOption.setShortcut('Ctrl+Shift+R')
        self.drawSquaresOption.setCheckable(True)
        self.drawSquaresOption.setChecked(settings.get(SETTING_DRAW_SQUARE, False))
        self.drawSquaresOption.triggered.connect(self.toogleDrawSquare)
@ -509,7 +508,7 @@ class MainWindow(QMainWindow, WindowMixin):
        # Store actions for further handling.
        self.actions = struct(save=save,  open=open,  resetAll=resetAll, deleteImg=deleteImg,
                              lineColor=color1, create=create, delete=delete, edit=edit, copy=copy,
-                              saveRec=saveRec,
+                              saveRec=saveRec, singleRere=singleRere,AutoRec=AutoRec,reRec=reRec,
                              createMode=createMode, editMode=editMode,
                              shapeLineColor=shapeLineColor, shapeFillColor=shapeFillColor,
                              zoom=zoom, zoomIn=zoomIn, zoomOut=zoomOut, zoomOrg=zoomOrg,
@ -518,9 +517,9 @@ class MainWindow(QMainWindow, WindowMixin):
                              fileMenuActions=(
                                  open, opendir, saveLabel,  resetAll, quit),
                              beginner=(), advanced=(),
-                              editMenu=(createpoly, edit, copy, delete,
+                              editMenu=(createpoly, edit, copy, delete,singleRere,
                                        None, color1, self.drawSquaresOption),
-                              beginnerContext=(create, edit, copy, delete),
+                              beginnerContext=(create, edit, copy, delete, singleRere),
                              advancedContext=(createMode, editMode, edit, copy,
                                               delete, shapeLineColor, shapeFillColor),
                              onLoadActive=(
@ -562,7 +561,7 @@ class MainWindow(QMainWindow, WindowMixin):
            zoomIn, zoomOut, zoomOrg, None,
            fitWindow, fitWidth))

-        addActions(self.menus.autolabel, (alcm, None, help)) #
+        addActions(self.menus.autolabel, (AutoRec, reRec, alcm, None, help)) #

        self.menus.file.aboutToShow.connect(self.updateFileMenu)

@ -572,6 +571,7 @@ class MainWindow(QMainWindow, WindowMixin):
            action('&Copy here', self.copyShape),
            action('&Move here', self.moveShape)))

+
        self.statusBar().showMessage('%s started.' % __appname__)
        self.statusBar().show()

@ -919,6 +919,7 @@ class MainWindow(QMainWindow, WindowMixin):
        self.actions.edit.setEnabled(selected)
        self.actions.shapeLineColor.setEnabled(selected)
        self.actions.shapeFillColor.setEnabled(selected)
+        self.actions.singleRere.setEnabled(selected)

    def addLabel(self, shape):
        shape.paintLabel = self.displayLabelOption.isChecked()
@ -988,6 +989,19 @@ class MainWindow(QMainWindow, WindowMixin):
        self.updateComboBox()
        self.canvas.loadShapes(s)

+    def singleLabel(self, shape):
+        if shape is None:
+            # print('rm empty label')
+            return
+        item = self.shapesToItems[shape]
+        item.setText(shape.label)
+        self.updateComboBox()
+
+        # ADD:
+        item = self.shapesToItemsbox[shape]
+        item.setText(str([(int(p.x()), int(p.y())) for p in shape.points]))
+        self.updateComboBox()
+
    def updateComboBox(self):
        # Get the unique labels and add them to the Combobox.
        itemsTextList = [str(self.labelList.item(i).text()) for i in range(self.labelList.count())]
@ -1441,6 +1455,8 @@ class MainWindow(QMainWindow, WindowMixin):
        self.haveAutoReced = False
        self.AutoRecognition.setEnabled(True)
        self.reRecogButton.setEnabled(True)
+        self.actions.AutoRec.setEnabled(True)
+        self.actions.reRec.setEnabled(True)
        self.actions.saveLabel.setEnabled(True)


@ -1755,6 +1771,7 @@ class MainWindow(QMainWindow, WindowMixin):
        self.loadFile(self.filePath) # ADD
        self.haveAutoReced = True
        self.AutoRecognition.setEnabled(False)
+        self.actions.AutoRec.setEnabled(False)
        self.setDirty()
        self.saveCacheLabel()

@ -1794,6 +1811,27 @@ class MainWindow(QMainWindow, WindowMixin):
        else:
            QMessageBox.information(self, "Information", "Draw a box!")

+    def singleRerecognition(self):
+        img = cv2.imread(self.filePath)
+        shape = self.canvas.selectedShape
+        box = [[int(p.x()), int(p.y())] for p in shape.points]
+        assert len(box) == 4
+        img_crop = get_rotate_crop_image(img, np.array(box, np.float32))
+        if img_crop is None:
+            msg = 'Can not recognise the detection box in ' + self.filePath + '. Please change manually'
+            QMessageBox.information(self, "Information", msg)
+            return
+        result = self.ocr.ocr(img_crop, cls=True, det=False)
+        if result[0][0] is not '':
+            result.insert(0, box)
+            print('result in reRec is ', result)
+            if result[1][0] == shape.label:
+                print('label no change')
+            else:
+                shape.label = result[1][0]
+            self.singleLabel(shape)
+            self.setDirty()
+        print(box)

    def autolcm(self):
        vbox = QVBoxLayout()
@ -1825,6 +1863,7 @@ class MainWindow(QMainWindow, WindowMixin):
        self.dialog.exec_()
        if self.filePath:
            self.AutoRecognition.setEnabled(True)
+            self.actions.AutoRec.setEnabled(True)


    def modelChoose(self):
--- a/PPOCRLabel/README.md
+++ b/PPOCRLabel/README.md
@ -6,6 +6,10 @@ PPOCRLabel is a semi-automatic graphic annotation tool suitable for OCR field. I

 <img src="./data/gif/steps_en.gif" width="100%"/>

+### Recent Update
+
+- 2020.12.18: Support re-recognition of a single label box (by [ninetailskim](https://github.com/ninetailskim) ), perfect shortcut keys.
+
 ## Installation

 ### 1. Install PaddleOCR
@ -92,11 +96,30 @@ Therefore, if the recognition result has been manually changed before, it may ch

 ## Explanation

+### Shortcut keys
+
+| Shortcut keys    | Description                                      |
+| ---------------- | ------------------------------------------------ |
+| Ctrl + shift + A | Automatically label all unchecked images         |
+| Ctrl + shift + R | Re-recognize all the labels of the current image |
+| W                | Create a rect box                                |
+| Q                | Create a four-points box                         |
+| Ctrl + E         | Edit label of the selected box                   |
+| Ctrl + R         | Re-recognize the selected box                    |
+| Backspace        | Delete the selected box                          |
+| Ctrl + V         | Check image                                      |
+| Ctrl + Shift + d | Delete image                                     |
+| D                | Next image                                       |
+| A                | Previous image                                   |
+| Ctrl++           | Zoom in                                          |
+| Ctrl--           | Zoom out                                         |
+| ↑→↓←             | Move selected box                                |
+
 ### Built-in Model

 - Default model: PPOCRLabel uses the Chinese and English ultra-lightweight OCR model in PaddleOCR by default, supports Chinese, English and number recognition, and multiple language detection.

- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languagesinclude French, German, Korean, and Japanese.
+- Model language switching: Changing the built-in model language is supportable by clicking "PaddleOCR"-"Choose OCR Model" in the menu bar. Currently supported languagesinclude French, German, Korean, and Japanese. 
  For specific model download links, please refer to [PaddleOCR Model List](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md#multilingual-recognition-modelupdating)

 - Custom model: The model trained by users can be replaced by modifying PPOCRLabel.py in [PaddleOCR class instantiation](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/PPOCRLabel/PPOCRLabel.py#L110) referring [Custom Model Code](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/whl_en.md#use-custom-model)
--- a/PPOCRLabel/README_ch.md
+++ b/PPOCRLabel/README_ch.md
@ -6,6 +6,10 @@ PPOCRLabel是一款适用于OCR领域的半自动化图形标注工具，使用p

 <img src="./data/gif/steps.gif" width="100%"/>

+#### 近期更新
+
+- 2020.12.18: 支持对单个标记框进行重新识别（by [ninetailskim](https://github.com/ninetailskim) ），完善快捷键。
+
 ## 安装

 ### 1. 安装PaddleOCR
@ -72,6 +76,26 @@ python3 PPOCRLabel.py --lang ch
 |   crop_img    |   识别数据。按照检测框切割后的图片。与rec_gt.txt同时产生。   |

 ## 说明
+
+### 快捷键
+
+| 快捷键           | 说明                         |
+| ---------------- | ---------------------------- |
+| Ctrl + shift + A | 自动标注所有未确认过的图片   |
+| Ctrl + shift + R | 对当前图片的所有标记重新识别 |
+| W                | 新建矩形框                   |
+| Q                | 新建四点框                   |
+| Ctrl + E         | 编辑所选框标签               |
+| Ctrl + R         | 重新识别所选标记             |
+| Backspace        | 删除所选框                   |
+| Ctrl + V         | 确认本张图片标记             |
+| Ctrl + Shift + d | 删除本张图片                 |
+| D                | 下一张图片                   |
+| A                | 上一张图片                   |
+| Ctrl++           | 缩小                         |
+| Ctrl--           | 放大                         |
+| ↑→↓←             | 移动标记框                   |
+
 ### 内置模型

 - 默认模型：PPOCRLabel默认使用PaddleOCR中的中英文超轻量OCR模型，支持中英文与数字识别，多种语言检测。
--- a/PPOCRLabel/libs/autoDialog.py
+++ b/PPOCRLabel/libs/autoDialog.py
@ -46,8 +46,9 @@ class Worker(QThread):
                            chars = res[1][0]
                            cond = res[1][1]
                            posi = res[0]
-                            strs += "Transcription: " + chars + " Probability: " + str(
-                                cond) + " Location: " + json.dumps(posi) + '\n'
+                            strs += "Transcription: " + chars + " Probability: " + str(cond) + \
+                                    " Location: " + json.dumps(posi) +'\n'
+                        # Sending large amounts of data repeatedly through pyqtSignal may affect the program efficiency
                        self.listValue.emit(strs)
                        self.mainThread.result_dic = self.result_dic
                        self.mainThread.filePath = Imgpath
--- a/PPOCRLabel/libs/resources.py
+++ b/PPOCRLabel/libs/resources.py
--- a/PPOCRLabel/resources/strings/strings-zh-CN.properties
+++ b/PPOCRLabel/resources/strings/strings-zh-CN.properties
@ -94,4 +94,5 @@ ok=确认
 autolabeling=自动标注中
 hideBox=隐藏所有标注
 showBox=显示所有标注
-saveLabel=保存标记结果
+saveLabel=保存标记结果
+singleRe=重识别此区块
--- a/PPOCRLabel/resources/strings/strings-zh-TW.properties
+++ b/PPOCRLabel/resources/strings/strings-zh-TW.properties
@ -1,70 +0,0 @@
-saveAsDetail=將標籤保存到其他文件
-changeSaveDir=改變存放目錄
-openFile=開啟檔案
-shapeLineColorDetail=更改線條顏色
-resetAll=重置
-crtBox=創建區塊
-crtBoxDetail=畫一個區塊
-dupBoxDetail=複製區塊
-verifyImg=驗證圖像
-zoominDetail=放大
-verifyImgDetail=驗證圖像
-saveDetail=將標籤存到
-openFileDetail=打開圖像
-fitWidthDetail=調整到窗口寬度
-tutorial=YouTube教學
-editLabel=編輯標籤
-openAnnotationDetail=打開標籤文件
-quit=結束
-shapeFillColorDetail=更改填充顏色
-closeCurDetail=關閉目前檔案
-closeCur=關閉
-deleteImg=刪除圖像
-deleteImgDetail=刪除目前圖像
-fitWin=調整到跟窗口一樣大小
-delBox=刪除選取區塊
-boxLineColorDetail=選擇框線顏色
-originalsize=原始大小
-resetAllDetail=重設所有設定
-zoomoutDetail=畫面放大
-save=儲存
-saveAs=另存為
-fitWinDetail=縮放到窗口一樣
-openDir=開啟目錄
-copyPrevBounding=複製當前圖像中的上一個邊界框
-showHide=顯示/隱藏標籤
-changeSaveFormat=更改儲存格式
-shapeFillColor=填充顏色
-quitApp=離開本程式
-dupBox=複製區塊
-delBoxDetail=刪除區塊
-zoomin=放大畫面
-info=資訊
-openAnnotation=開啟標籤
-prevImgDetail=上一個圖像
-fitWidth=縮放到跟畫面一樣寬
-zoomout=縮小畫面
-changeSavedAnnotationDir=更改預設標籤存的目錄
-nextImgDetail=下一個圖像
-originalsizeDetail=放大到原始大小
-prevImg=上一個圖像
-tutorialDetail=顯示示範內容
-shapeLineColor=形狀線條顏色
-boxLineColor=日期分隔線顏色
-editLabelDetail=修改所選區塊的標籤
-nextImg=下一張圖片
-useDefaultLabel=使用預設標籤
-useDifficult=有難度的
-boxLabelText=區塊的標籤
-labels=標籤
-autoSaveMode=自動儲存模式
-singleClsMode=單一類別模式
-displayLabel=顯示類別
-fileList=檔案清單
-files=檔案
-iconList=XX
-icon=XX
-advancedMode=進階模式
-advancedModeDetail=切到進階模式
-showAllBoxDetail=顯示所有區塊
-hideAllBoxDetail=隱藏所有區塊
--- a/PPOCRLabel/resources/strings/strings.properties
+++ b/PPOCRLabel/resources/strings/strings.properties
@ -94,4 +94,5 @@ ok=OK
 autolabeling=Automatic Labeling
 hideBox=Hide All Box
 showBox=Show All Box
-saveLabel=Save Label
+saveLabel=Save Label
+singleRe=Re-recognition RectBox
--- a/README.md
+++ b/README.md
@ -122,8 +122,7 @@ For a new language request, please refer to [Guideline for new language_requests
    <img src="./doc/ppocr_framework.png" width="800">
 </div>

-PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner and PACT quantization is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
-
+PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection[2], detection frame correction and CRNN text recognition[7]. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner [8] and PACT quantization [9] is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).


 ## Visualization [more](./doc/doc_en/visualization_en.md)
--- a/README_ch.md
+++ b/README_ch.md
@ -8,8 +8,8 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 - 静态图版本：develop分支

 **近期更新**
+- 2020.12.21 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数132个，每周一都会更新，欢迎大家持续关注。
 - 2020.12.15 更新数据合成工具[Style-Text](./StyleText/README_ch.md)，可以批量合成大量与目标场景类似的图像，在多个场景验证，效果明显提升。
- 2020.12.14 [FAQ](./doc/doc_ch/FAQ.md)新增5个高频问题，总数127个，每周一都会更新，欢迎大家持续关注。
 - 2020.11.25 更新半自动标注工具[PPOCRLabel](./PPOCRLabel/README_ch.md)，辅助开发者高效完成标注任务，输出格式与PP-OCR训练任务完美衔接。
 - 2020.9.22 更新PP-OCR技术文章，https://arxiv.org/abs/2009.09941
 - [More](./doc/doc_ch/update.md)
@ -101,8 +101,8 @@ PaddleOCR同时支持动态图与静态图两种编程范式
 - [效果展示](#效果展示)
 - FAQ
    - [【精选】OCR精选10个问题](./doc/doc_ch/FAQ.md)
-    - [【理论篇】OCR通用30个问题](./doc/doc_ch/FAQ.md)
-    - [【实战篇】PaddleOCR实战84个问题](./doc/doc_ch/FAQ.md)
+    - [【理论篇】OCR通用31个问题](./doc/doc_ch/FAQ.md)
+    - [【实战篇】PaddleOCR实战91个问题](./doc/doc_ch/FAQ.md)
 - [技术交流群](#欢迎加入PaddleOCR技术交流群)
 - [参考文献](./doc/doc_ch/reference.md)
 - [许可证书](#许可证书)
@ -115,7 +115,7 @@ PaddleOCR同时支持动态图与静态图两种编程范式
    <img src="./doc/ppocr_framework.png" width="800">
 </div>

-PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测、检测框矫正和CRNN文本识别三部分组成。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941 。其中FPGM裁剪器和PACT量化的实现可以参考[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)。
+PP-OCR是一个实用的超轻量OCR系统。主要由DB文本检测[2]、检测框矫正和CRNN文本识别三部分组成[7]。该系统从骨干网络选择和调整、预测头部的设计、数据增强、学习率变换策略、正则化参数选择、预训练模型使用以及模型自动裁剪量化8个方面，采用19个有效策略，对各个模块的模型进行效果调优和瘦身，最终得到整体大小为3.5M的超轻量中英文OCR和2.8M的英文数字OCR。更多细节请参考PP-OCR技术方案 https://arxiv.org/abs/2009.09941 。其中FPGM裁剪器[8]和PACT量化[9]的实现可以参考[PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim)。

 <a name="效果展示"></a>
 ## 效果展示 [more](./doc/doc_ch/visualization.md)
--- a/StyleText/README.md
+++ b/StyleText/README.md
@ -22,7 +22,7 @@ English | [简体中文](README_ch.md)
 </div>


-The Style-Text data synthesis tool is a tool based on Baidu's self-developed text editing algorithm "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047).
+The Style-Text data synthesis tool is a tool based on Baidu and HUST cooperation research work, "Editing Text in the Wild" [https://arxiv.org/abs/1908.03047](https://arxiv.org/abs/1908.03047).

 Different from the commonly used GAN-based data synthesis tools, the main framework of Style-Text includes:
 * (1) Text foreground style transfer module.
@ -78,6 +78,7 @@ python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_
 * Note 3: You can modify `use_gpu` in `configs/config.yml` to determine whether to use GPU for prediction.


+
 For example, enter the following image and corpus `PaddleOCR`.

 <div align="center">
@ -142,6 +143,7 @@ We provide a general dataset containing Chinese, English and Korean (50,000 imag
   ``` bash
   python3 tools/synth_dataset.py -c configs/dataset_config.yml
   ```
+
 We also provide example corpus and images in `examples` folder.
    <div align="center">
        <img src="examples/style_images/1.jpg" width="300">
--- a/StyleText/README_ch.md
+++ b/StyleText/README_ch.md
@ -21,7 +21,7 @@
 </div>


-Style-Text数据合成工具是基于百度自研的文本编辑算法《Editing Text in the Wild》https://arxiv.org/abs/1908.03047
+Style-Text数据合成工具是基于百度和华科合作研发的文本编辑算法《Editing Text in the Wild》https://arxiv.org/abs/1908.03047

 不同于常用的基于GAN的数据合成工具，Style-Text主要框架包括：1.文本前景风格迁移模块 2.背景抽取模块 3.融合模块。经过这样三步，就可以迅速实现图像文本风格迁移。下图是一些该数据合成工具效果图。

@ -128,7 +128,7 @@ python3 tools/synth_image.py -c configs/config.yml --style_image examples/style_
 2. 运行`tools/synth_dataset`合成数据：

   ``` bash
-   python tools/synth_dataset.py -c configs/dataset_config.yml
+   python3 tools/synth_dataset.py -c configs/dataset_config.yml
   ```
   我们在examples目录下提供了样例图片和语料。
    <div align="center">
--- a/deploy/cpp_infer/readme_en.md
+++ b/deploy/cpp_infer/readme_en.md
@ -107,10 +107,10 @@ make inference_lib_dist
 For more compilation parameter options, please refer to the official website of the Paddle C++ inference library:[https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html](https://www.paddlepaddle.org.cn/documentation/docs/en/advanced_guide/inference_deployment/inference/build_and_install_lib_en.html).


-* After the compilation process, you can see the following files in the folder of `build/fluid_inference_install_dir/`.
+* After the compilation process, you can see the following files in the folder of `build/paddle_inference_install_dir/`.

 ```
-build/fluid_inference_install_dir/
+build/paddle_inference_install_dir/
 |-- CMakeCache.txt
 |-- paddle
 |-- third_party
--- a/deploy/cpp_infer/src/preprocess_op.cpp
+++ b/deploy/cpp_infer/src/preprocess_op.cpp
@ -81,14 +81,14 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
  else if (resize_h / 32 < 1 + 1e-5)
    resize_h = 32;
  else
-    resize_h = (resize_h / 32 - 1) * 32;
+    resize_h = (resize_h / 32) * 32;

  if (resize_w % 32 == 0)
    resize_w = resize_w;
  else if (resize_w / 32 < 1 + 1e-5)
    resize_w = 32;
  else
-    resize_w = (resize_w / 32 - 1) * 32;
+    resize_w = (resize_w / 32) * 32;

  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));

--- a/deploy/cpp_infer/tools/config.txt
+++ b/deploy/cpp_infer/tools/config.txt
@ -10,7 +10,7 @@ max_side_len  960
 det_db_thresh  0.3
 det_db_box_thresh  0.5
 det_db_unclip_ratio  2.0
-det_model_dir  ../../../deploy/cpp_infer/inference/ch_ppocr_mobile_v2.0_det_infer/
+det_model_dir  ./inference/ch_ppocr_mobile_v2.0_det_infer/

 # cls config
 use_angle_cls 0
--- a/doc/doc_ch/FAQ.md
+++ b/doc/doc_ch/FAQ.md
@ -9,42 +9,55 @@

 ## PaddleOCR常见问题汇总(持续更新)

-* [近期更新（2020.12.14）](#近期更新)
+* [近期更新（2020.12.21）](#近期更新)
 * [【精选】OCR精选10个问题](#OCR精选10个问题)
-* [【理论篇】OCR通用30个问题](#OCR通用问题)
+* [【理论篇】OCR通用31个问题](#OCR通用问题)
  * [基础知识7题](#基础知识)
  * [数据集7题](#数据集2)
-  * [模型训练调优7题](#模型训练调优2)
-  * [预测部署9题](#预测部署2)
-* [【实战篇】PaddleOCR实战87个问题](#PaddleOCR实战问题)
-  * [使用咨询21题](#使用咨询)
+  * [模型训练调优17题](#模型训练调优2)
+* [【实战篇】PaddleOCR实战91个问题](#PaddleOCR实战问题)
+  * [使用咨询24题](#使用咨询)
  * [数据集17题](#数据集3)
  * [模型训练调优25题](#模型训练调优3)
-  * [预测部署24题](#预测部署3)
+  * [预测部署25题](#预测部署3)


 <a name="近期更新"></a>
-## 近期更新（2020.12.14）
+## 近期更新（2020.12.21）

-#### Q3.1.21：PaddleOCR支持动态图吗？
+#### Q2.3.17: StyleText 合成数据效果不好？
+**A**： StyleText模型生成的数据主要用于OCR识别模型的训练。PaddleOCR目前识别模型的输入为32 x N，因此当前版本模型主要适用高度为32的数据。
+建议要合成的数据尺寸设置为32 x N。尺寸相差不多的数据也可以生成，尺寸很大或很小的数据效果确实不佳。

-**A**：动态图版本正在紧锣密鼓开发中，将于2020年12月16日发布，敬请关注。
+#### Q3.1.22： ModuleNotFoundError: No module named 'paddle.nn'，
+**A**： paddle.nn是Paddle2.0版本特有的功能，请安装大于等于Paddle 2.0.0rc1的版本，安装方式为
+```
+python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
+```

-#### Q3.3.23：检测模型训练或预测时出现elementwise_add报错
+#### Q3.1.23： ImportError: /usr/lib/x86_64_linux-gnu/libstdc++.so.6:version `CXXABI_1.3.11` not found (required by /usr/lib/python3.6/site-package/paddle/fluid/core+avx.so)
+**A**：这个问题是glibc版本不足导致的，Paddle2.0rc1版本对gcc版本和glib版本有更高的要求，推荐gcc版本为8.2，glibc版本2.12以上。
+如果您的环境不满足这个要求，或者使用的docker镜像为:
+`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`
+`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`，安装Paddle2.0rc版本可能会出现上述错误，
+2.0版本推荐使用新的docker镜像 `paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82`。
+或者访问[dockerhub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获得与您机器适配的镜像。

-**A**：设置的输入尺寸必须是32的倍数，否则在网络多次下采样和上采样后，feature map会产生1个像素的diff，从而导致elementwise_add时报shape不匹配的错误。
+#### Q3.1.24:  PaddleOCR develop分支和dygraph分支的区别？
+**A**：目前PaddleOCR有四个分支，分别是：
+- develop：基于Paddle静态图开发的分支，推荐使用paddle1.8 或者2.0版本，该分支具备完善的模型训练、预测、推理部署、量化裁剪等功能，领先于release/1.1分支。
+- release/1.1：PaddleOCR 发布的第一个稳定版本，基于静态图开发，具备完善的训练、预测、推理部署、量化裁剪等功能。
+- dygraph：基于Paddle动态图开发的分支，未来将作为主要开发分支，运行要求使用Paddle2.0rc1版本，目前仍在开发中。
+- release/2.0-rc1-0：PaddleOCR发布的第二个稳定版本，基于动态图和paddle2.0版本开发，动态图开发的工程更易于调试，目前支，支持模型训练、预测，暂不支持移动端部署。

-#### Q3.3.24: DB检测训练输入尺寸640，可以改大一些吗？
+如果您已经上手过PaddleOCR，并且希望在各种环境上部署PaddleOCR，目前建议使用静态图分支，develop或者release/1.1分支。如果您是初学者，想快速训练，调试PaddleOCR中的算法，建议尝鲜PaddleOCR dygraph分支。

-**A**: 不建议改大。检测模型训练输入尺寸是预处理中random crop后的尺寸，并非直接将原图进行resize，多数场景下这个尺寸并不小了，改大后可能反而并不合适，而且训练会变慢。另外，代码里可能有的地方参数按照预设输入尺寸适配的，改大后可能有隐藏风险。
+**注意**：develop和dygraph分支要求的Paddle版本、本地环境有差别，请注意不同分支环境安装部分的差异。

-#### Q3.3.25: 识别模型训练时，loss能正常下降，但acc一直为0

-**A**: 识别模型训练初期acc为0是正常的，多训一段时间指标就上来了。
+#### Q3.4.25 : PaddleOCR模型Python端预测和C++预测结果不一致？
+**A**：正常来说，python端预测和C++预测文本是一致的，如果预测结果差异较大，建议首先排查diff出现在检测模型还是识别模型，或者尝试换其他模型是否有类似的问题。其次，检查python端和C++端数据处理部分是否存在差异，建议保存环境，更新PaddleOCR代码再试下。如果更新代码或者更新代码都没能解决，建议在PaddleOCR群里或者issue中抛出您的问题。

-#### Q3.4.24：DB模型能正确推理预测，但换成EAST或SAST模型时报错或结果不正确
-
-**A**：使用EAST或SAST模型进行推理预测时，需要在命令中指定参数--det_algorithm="EAST" 或 --det_algorithm="SAST"，使用DB时不用指定是因为该参数默认值是"DB"：https://github.com/PaddlePaddle/PaddleOCR/blob/e7a708e9fdaf413ed7a14da8e4a7b4ac0b211e42/tools/infer/utility.py#L43

 <a name="OCR精选10个问题"></a>
 ## 【精选】OCR精选10个问题
@ -238,18 +251,15 @@

 （2）调大系统的[l2 dcay值](https://github.com/PaddlePaddle/PaddleOCR/blob/a501603d54ff5513fc4fc760319472e59da25424/configs/rec/ch_ppocr_v1.1/rec_chinese_lite_train_v1.1.yml#L47)

-<a name="预测部署2"></a>
-### 预测部署
-
-#### Q2.4.1：请问对于图片中的密集文字，有什么好的处理办法吗？
+#### Q2.3.8：请问对于图片中的密集文字，有什么好的处理办法吗？

 **A**：可以先试用预训练模型测试一下，例如DB+CRNN，判断下密集文字图片中是检测还是识别的问题，然后针对性的改善。还有一种是如果图象中密集文字较小，可以尝试增大图像分辨率，对图像进行一定范围内的拉伸，将文字稀疏化，提高识别效果。

-#### Q2.4.2：对于一些在识别时稍微模糊的文本，有没有一些图像增强的方式？
+#### Q2.3.9：对于一些在识别时稍微模糊的文本，有没有一些图像增强的方式？

 **A**：在人类肉眼可以识别的前提下，可以考虑图像处理中的均值滤波、中值滤波或者高斯滤波等模糊算子尝试。也可以尝试从数据扩增扰动来强化模型鲁棒性，另外新的思路有对抗性训练和超分SR思路，可以尝试借鉴。但目前业界尚无普遍认可的最优方案，建议优先在数据采集阶段增加一些限制提升图片质量。

-#### Q2.4.3：对于特定文字检测，例如身份证只检测姓名，检测指定区域文字更好，还是检测全部区域再筛选更好？
+#### Q2.3.10：对于特定文字检测，例如身份证只检测姓名，检测指定区域文字更好，还是检测全部区域再筛选更好？

 **A**：两个角度来说明一般检测全部区域再筛选更好。

@ -257,11 +267,11 @@

 （2）产品的需求可能是变化的，不排除后续对于模型需求变化的可能性（比如又需要增加一个字段），相比于训练模型，后处理的逻辑会更容易调整。

-#### Q2.4.4：对于小白如何快速入门中文OCR项目实践？
+#### Q2.3.11：对于小白如何快速入门中文OCR项目实践？

 **A**：建议可以先了解OCR方向的基础知识，大概了解基础的检测和识别模型算法。然后在Github上可以查看OCR方向相关的repo。目前来看，从内容的完备性来看，PaddleOCR的中英文双语教程文档是有明显优势的，在数据集、模型训练、预测部署文档详实，可以快速入手。而且还有微信用户群答疑，非常适合学习实践。项目地址：[PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)

-#### Q2.4.5：如何识别带空格的英文行文本图像？
+#### Q3.12：如何识别带空格的英文行文本图像？

 **A**：空格识别可以考虑以下两种方案：

@ -269,22 +279,25 @@

 (2)优化文本识别算法。在识别字典里面引入空格字符，然后在识别的训练数据中，如果用空行，进行标注。此外，合成数据时，通过拼接训练数据，生成含有空格的文本。

-#### Q2.4.6：中英文一起识别时也可以加空格字符来训练吗
+#### Q2.3.13：中英文一起识别时也可以加空格字符来训练吗

 **A**：中文识别可以加空格当做分隔符训练，具体的效果如何没法给出直接评判，根据实际业务数据训练来判断。

-#### Q2.4.7：低像素文字或者字号比较小的文字有什么超分辨率方法吗
+#### Q2.3.14：低像素文字或者字号比较小的文字有什么超分辨率方法吗

 **A**：超分辨率方法分为传统方法和基于深度学习的方法。基于深度学习的方法中，比较经典的有SRCNN，另外CVPR2020也有一篇超分辨率的工作可以参考文章：Unpaired Image Super-Resolution using Pseudo-Supervision，但是没有充分的实践验证过，需要看实际场景下的效果。

-#### Q2.4.8：表格识别有什么好的模型 或者论文推荐么
+#### Q2.3.15：表格识别有什么好的模型 或者论文推荐么

 **A**：表格目前学术界比较成熟的解决方案不多 ，可以尝试下分割的论文方案。

-#### Q2.4.9：弯曲文本有试过opencv的TPS进行弯曲校正吗？
+#### Q2.3.16：弯曲文本有试过opencv的TPS进行弯曲校正吗？

 **A**：opencv的tps需要标出上下边界对应的点，这个点很难通过传统方法或者深度学习方法获取。PaddleOCR里StarNet网络中的tps模块实现了自动学点，自动校正，可以直接尝试这个。

+#### Q2.3.17: StyleText 合成数据效果不好？
+**A**：StyleText模型生成的数据主要用于OCR识别模型的训练。PaddleOCR目前识别模型的输入为32 x N，因此当前版本模型主要适用高度为32的数据。
+建议要合成的数据尺寸设置为32 x N。尺寸相差不多的数据也可以生成，尺寸很大或很小的数据效果确实不佳。


 <a name="PaddleOCR实战问题"></a>
@ -392,6 +405,34 @@

 **A**：动态图版本正在紧锣密鼓开发中，将于2020年12月16日发布，敬请关注。

+#### Q3.1.22：ModuleNotFoundError: No module named 'paddle.nn'，
+**A**：paddle.nn是Paddle2.0版本特有的功能，请安装大于等于Paddle 2.0.0rc1的版本，安装方式为
+```
+python3 -m pip install paddlepaddle-gpu==2.0.0rc1 -i https://mirror.baidu.com/pypi/simple
+```
+
+#### Q3.1.23： ImportError: /usr/lib/x86_64_linux-gnu/libstdc++.so.6:version `CXXABI_1.3.11` not found (required by /usr/lib/python3.6/site-package/paddle/fluid/core+avx.so)
+**A**：这个问题是glibc版本不足导致的，Paddle2.0rc1版本对gcc版本和glib版本有更高的要求，推荐gcc版本为8.2，glibc版本2.12以上。
+如果您的环境不满足这个要求，或者使用的docker镜像为:
+`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`
+`hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev`，安装Paddle2.0rc版本可能会出现上述错误，2.0版本推荐使用新的docker镜像 `paddlepaddle/paddle:latest-dev-cuda10.1-cudnn7-gcc82`。
+或者访问[dockerhub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获得与您机器适配的镜像。
+
+
+#### Q3.1.24: PaddleOCR develop分支和dygraph分支的区别？
+**A** 目前PaddleOCR有四个分支，分别是：
+
+- develop：基于Paddle静态图开发的分支，推荐使用paddle1.8 或者2.0版本，该分支具备完善的模型训练、预测、推理部署、量化裁剪等功能，领先于release/1.1分支。
+- release/1.1：PaddleOCR 发布的第一个稳定版本，基于静态图开发，具备完善的训练、预测、推理部署、量化裁剪等功能。
+- dygraph：基于Paddle动态图开发的分支，目前仍在开发中，未来将作为主要开发分支，运行要求使用Paddle2.0rc1版本，目前仍在开发中。
+- release/2.0-rc1-0：PaddleOCR发布的第二个稳定版本，基于动态图和paddle2.0版本开发，动态图开发的工程更易于调试，目前支，支持模型训练、预测，暂不支持移动端部署。
+
+如果您已经上手过PaddleOCR，并且希望在各种环境上部署PaddleOCR，目前建议使用静态图分支，develop或者release/1.1分支。如果您是初学者，想快速训练，调试PaddleOCR中的算法，建议尝鲜PaddleOCR dygraph分支。
+
+**注意**：develop和dygraph分支要求的Paddle版本、本地环境有差别，请注意不同分支环境安装部分的差异。
+
+
+
 <a name="数据集3"></a>
 ### 数据集

@ -594,11 +635,11 @@ ps -axu | grep train.py | awk '{print $2}' | xargs kill -9

 #### Q3.3.20: 文字检测时怎么模糊的数据增强？

-**A**: 模糊的数据增强需要修改代码进行添加，以DB为例，参考[Normalize](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppocr/data/imaug/operators.py#L60) ,添加模糊的增强就行 
+**A**: 模糊的数据增强需要修改代码进行添加，以DB为例，参考[Normalize](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppocr/data/imaug/operators.py#L60) ,添加模糊的增强就行

 #### Q3.3.21: 文字检测时怎么更改图片旋转的角度，实现360度任意旋转？

-**A**: 将[这里](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppocr/data/imaug/iaa_augment.py#L64) 的(-10,10) 改为(-180,180)即可 
+**A**: 将[这里](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/ppocr/data/imaug/iaa_augment.py#L64) 的(-10,10) 改为(-180,180)即可

 #### Q3.3.22: 训练数据的长宽比过大怎么修改shape

@ -728,4 +769,10 @@ ps -axu | grep train.py | awk '{print $2}' | xargs kill -9

 #### Q3.4.24：DB模型能正确推理预测，但换成EAST或SAST模型时报错或结果不正确

-**A**：使用EAST或SAST模型进行推理预测时，需要在命令中指定参数--det_algorithm="EAST" 或 --det_algorithm="SAST"，使用DB时不用指定是因为该参数默认值是"DB"：https://github.com/PaddlePaddle/PaddleOCR/blob/e7a708e9fdaf413ed7a14da8e4a7b4ac0b211e42/tools/infer/utility.py#L43
+**A**：使用EAST或SAST模型进行推理预测时，需要在命令中指定参数--det_algorithm="EAST" 或 --det_algorithm="SAST"，使用DB时不用指定是因为该参数默认值是"DB"：https://github.com/PaddlePaddle/PaddleOCR/blob/e7a708e9fdaf413ed7a14da8e4a7b4ac0b211e42/tools/infer/utility.py#L43
+
+#### Q3.4.25 : PaddleOCR模型Python端预测和C++预测结果不一致？
+正常来说，python端预测和C++预测文本是一致的，如果预测结果差异较大，
+建议首先排查diff出现在检测模型还是识别模型，或者尝试换其他模型是否有类似的问题。
+其次，检查python端和C++端数据处理部分是否存在差异，建议保存环境，更新PaddleOCR代码再试下。
+如果更新代码或者更新代码都没能解决，建议在PaddleOCR微信群里或者issue中抛出您的问题。
--- a/doc/doc_ch/algorithm_overview.md
+++ b/doc/doc_ch/algorithm_overview.md
@ -9,9 +9,9 @@
 ### 1.文本检测算法

 PaddleOCR开源的文本检测算法列表：
- [x]  DB([paper]( https://arxiv.org/abs/1911.08947) )（ppocr推荐）
- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))
+- [x]  DB([paper]( https://arxiv.org/abs/1911.08947)) [2]（ppocr推荐）
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[1]
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]

 在ICDAR2015文本检测公开数据集上，算法效果如下：

@ -38,13 +38,13 @@ PaddleOCR文本检测算法的训练和使用请参考文档教程中[模型训
 ### 2.文本识别算法

 PaddleOCR基于动态图开源的文本识别算法列表：
- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717) )（ppocr推荐）
- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [ ]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) coming soon
- [ ]  RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon
- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294)) coming soon
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]（ppocr推荐）
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
+- [ ]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] coming soon
+- [ ]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
+- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon

-参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
+参考[DTRB][3](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：

 |模型|骨干网络|Avg Accuracy|模型存储命名|下载链接|
 |-|-|-|-|-|
--- a/doc/doc_ch/angle_class.md
+++ b/doc/doc_ch/angle_class.md
@ -117,7 +117,7 @@ python3 tools/eval.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/

 ```
 # 预测分类结果
-python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
 ```

 预测图片：
--- a/doc/doc_ch/detection.md
+++ b/doc/doc_ch/detection.md
@ -120,16 +120,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{pat

 测试单张图像的检测效果
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
 ```

 测试DB模型时，调整后处理阈值，
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```


 测试文件夹下所有图像的检测效果
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
 ```
--- a/doc/doc_ch/inference.md
+++ b/doc/doc_ch/inference.md
@ -245,7 +245,10 @@ python3 tools/infer/predict_det.py --det_algorithm="SAST" --image_dir="./doc/img
 超轻量中文识别模型推理，可以执行如下命令：

 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/"
+# 下载超轻量中文识别模型：
+wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
+tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
 ```

 ![](../imgs_words/ch/word_4.jpg)
@ -266,7 +269,6 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)

 ```
 python3 tools/export_model.py -c configs/rec/rec_r34_vd_none_bilstm_ctc.yml -o Global.pretrained_model=./rec_r34_vd_none_bilstm_ctc_v2.0_train/best_accuracy Global.load_static_weights=False Global.save_inference_dir=./inference/rec_crnn
-
 ```

 CRNN 文本识别模型推理，可以执行如下命令：
@ -327,7 +329,10 @@ Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
 方向分类模型推理，可以执行如下命令：

 ```
-python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="./inference/cls/"
+# 下载超轻量中文方向分类器模型：
+wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
 ```

 ![](../imgs_words/ch/word_1.jpg)
--- a/doc/doc_ch/recognition.md
+++ b/doc/doc_ch/recognition.md
@ -324,7 +324,6 @@ Eval:

 评估数据集可以通过 `configs/rec/rec_icdar15_train.yml`  修改Eval中的 `label_file_path` 设置。

-*注意* 评估时必须确保配置文件中 infer_img 字段为空
 ```
 # GPU 评估， Global.checkpoints 为待测权重
 python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
@ -342,7 +341,7 @@ python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec

 ```
 # 预测英文结果
-python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
+python3 tools/infer_rec.py -c configs/rec/rec_icdar15_train.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.png
 ```

 预测图片：
@ -361,7 +360,7 @@ infer_img: doc/imgs_words/en/word_1.png

 ```
 # 预测中文结果
-python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
 ```

 预测图片：
--- a/doc/doc_ch/reference.md
+++ b/doc/doc_ch/reference.md
@ -11,11 +11,12 @@
 }

 2. DB:
-@article{liao2019real,
-  title={Real-time Scene Text Detection with Differentiable Binarization},
+@inproceedings{liao2020real,
+  title={Real-Time Scene Text Detection with Differentiable Binarization.},
  author={Liao, Minghui and Wan, Zhaoyi and Yao, Cong and Chen, Kai and Bai, Xiang},
-  journal={arXiv preprint arXiv:1911.08947},
-  year={2019}
+  booktitle={AAAI},
+  pages={11474--11481},
+  year={2020}
 }

 3. DTRB:
@ -37,10 +38,11 @@
 }

 5. SRN:
-@article{yu2020towards,
-  title={Towards Accurate Scene Text Recognition with Semantic Reasoning Networks},
-  author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Han, Junyu and Liu, Jingtuo and Ding, Errui},
-  journal={arXiv preprint arXiv:2003.12294},
+@inproceedings{yu2020towards,
+  title={Towards accurate scene text recognition with semantic reasoning networks},
+  author={Yu, Deli and Li, Xuan and Zhang, Chengquan and Liu, Tao and Han, Junyu and Liu, Jingtuo and Ding, Errui},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={12113--12122},
  year={2020}
 }

@ -52,4 +54,62 @@
  pages={9086--9095},
  year={2019}
 }
-```
+
+7. CRNN:
+@article{shi2016end,
+  title={An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition},
+  author={Shi, Baoguang and Bai, Xiang and Yao, Cong},
+  journal={IEEE transactions on pattern analysis and machine intelligence},
+  volume={39},
+  number={11},
+  pages={2298--2304},
+  year={2016},
+  publisher={IEEE}
+}
+
+8. FPGM:
+@inproceedings{he2019filter,
+  title={Filter pruning via geometric median for deep convolutional neural networks acceleration},
+  author={He, Yang and Liu, Ping and Wang, Ziwei and Hu, Zhilan and Yang, Yi},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={4340--4349},
+  year={2019}
+}
+
+9. PACT:
+@article{choi2018pact,
+  title={Pact: Parameterized clipping activation for quantized neural networks},
+  author={Choi, Jungwook and Wang, Zhuo and Venkataramani, Swagath and Chuang, Pierce I-Jen and Srinivasan, Vijayalakshmi and Gopalakrishnan, Kailash},
+  journal={arXiv preprint arXiv:1805.06085},
+  year={2018}
+}
+
+10.Rosetta
+@inproceedings{borisyuk2018rosetta,
+  title={Rosetta: Large scale system for text detection and recognition in images},
+  author={Borisyuk, Fedor and Gordo, Albert and Sivakumar, Viswanath},
+  booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
+  pages={71--79},
+  year={2018}
+}
+
+11.STAR-Net
+@inproceedings{liu2016star,
+  title={STAR-Net: A SpaTial Attention Residue Network for Scene Text Recognition.},
+  author={Liu, Wei and Chen, Chaofeng and Wong, Kwan-Yee K and Su, Zhizhong and Han, Junyu},
+  booktitle={BMVC},
+  volume={2},
+  pages={7},
+  year={2016}
+}
+
+12.RARE
+@inproceedings{shi2016robust,
+  title={Robust scene text recognition with automatic rectification},
+  author={Shi, Baoguang and Wang, Xinggang and Lyu, Pengyuan and Yao, Cong and Bai, Xiang},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={4168--4176},
+  year={2016}
+}
+
+```
--- a/doc/doc_en/algorithm_overview_en.md
+++ b/doc/doc_en/algorithm_overview_en.md
@ -11,9 +11,9 @@ This tutorial lists the text detection algorithms and text recognition algorithm
 ### 1. Text Detection Algorithm

 PaddleOCR open source text detection algorithms list:
- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))
- [x]  DB([paper](https://arxiv.org/abs/1911.08947))
- [x]  SAST([paper](https://arxiv.org/abs/1908.05498) )(Baidu Self-Research)
+- [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[2]
+- [x]  DB([paper](https://arxiv.org/abs/1911.08947))[1]
+- [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]

 On the ICDAR2015 dataset, the text detection result is as follows:

@ -39,11 +39,11 @@ For the training guide and use of PaddleOCR text detection algorithms, please re
 ### 2. Text Recognition Algorithm

 PaddleOCR open-source text recognition algorithms list:
- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))
- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
- [ ]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html)) coming soon
- [ ]  RARE([paper](https://arxiv.org/abs/1603.03915v1)) coming soon
- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294) )(Baidu Self-Research) coming soon
+- [x]  CRNN([paper](https://arxiv.org/abs/1507.05717))[7]
+- [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))[10]
+- [ ]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))[11] coming soon
+- [ ]  RARE([paper](https://arxiv.org/abs/1603.03915v1))[12] coming soon
+- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294))[5] coming soon

 Refer to [DTRB](https://arxiv.org/abs/1904.01906), the training and evaluation result of these above text recognition (using MJSynth and SynthText for training, evaluate on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE) is as follow:

--- a/doc/doc_en/angle_class_en.md
+++ b/doc/doc_en/angle_class_en.md
@ -119,7 +119,7 @@ Use `Global.infer_img` to specify the path of the predicted picture or folder, a

 ```
 # Predict English results
-python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.checkpoints={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_10.png
+python3 tools/infer_cls.py -c configs/cls/cls_mv3.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words_en/word_10.png
 ```

 Input image:
--- a/doc/doc_en/detection_en.md
+++ b/doc/doc_en/detection_en.md
@ -113,16 +113,16 @@ python3 tools/eval.py -c configs/det/det_mv3_db.yml  -o Global.checkpoints="{pat

 Test the detection result on a single image:
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy"
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
 ```

 When testing the DB model, adjust the post-processing threshold:
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.checkpoints="./output/det_db/best_accuracy" PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/img_10.jpg" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false PostProcess.box_thresh=0.6 PostProcess.unclip_ratio=1.5
 ```


 Test the detection result on all images in the folder:
 ```shell
-python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.checkpoints="./output/det_db/best_accuracy"
+python3 tools/infer_det.py -c configs/det/det_mv3_db.yml -o Global.infer_img="./doc/imgs_en/" Global.pretrained_model="./output/det_db/best_accuracy" Global.load_static_weights=false
 ```
--- a/doc/doc_en/inference_en.md
+++ b/doc/doc_en/inference_en.md
@ -255,15 +255,18 @@ The following will introduce the lightweight Chinese recognition model inference
 For lightweight Chinese recognition model inference, you can execute the following commands:

 ```
-python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/ch/word_4.jpg" --rec_model_dir="./inference/rec_crnn/"
+# download CRNN text recognition inference model
+wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar
+tar xf ch_ppocr_mobile_v2.0_rec_infer.tar
+python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words_en/word_10.png" --rec_model_dir="ch_ppocr_mobile_v2.0_rec_infer"
 ```

-![](../imgs_words/ch/word_4.jpg)
+![](../imgs_words_en/word_10.png)

 After executing the command, the prediction results (recognized text and score) of the above image will be printed on the screen.

 ```bash
-Predicts of ./doc/imgs_words/ch/word_4.jpg:('实力活力', 0.98458153)
+Predicts of ./doc/imgs_words_en/word_10.png:('PAIN', 0.9897658)
 ```

 <a name="CTC-BASED_RECOGNITION"></a>
@ -339,7 +342,12 @@ For angle classification model inference, you can execute the following commands
 ```
 python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="./inference/cls/"
 ```
-
+```
+# download text angle class inference model：
+wget  https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar xf ch_ppocr_mobile_v2.0_cls_infer.tar
+python3 tools/infer/predict_cls.py --image_dir="./doc/imgs_words_en/word_10.png" --cls_model_dir="ch_ppocr_mobile_v2.0_cls_infer"
+```
 ![](../imgs_words_en/word_10.png)

 After executing the command, the prediction results (classification angle and score) of the above image will be printed on the screen.
--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@ -317,11 +317,11 @@ Eval:
 <a name="EVALUATION"></a>
 ### EVALUATION

-The evaluation data set can be modified via `configs/rec/rec_icdar15_reader.yml` setting of `label_file_path` in EvalReader.
+The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.

 ```
 # GPU evaluation, Global.checkpoints is the weight to be tested
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_reader.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
 ```

 <a name="PREDICTION"></a>
@ -336,7 +336,7 @@ The default prediction picture is stored in `infer_img`, and the weight is speci

 ```
 # Predict English results
-python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/en/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.jpg
 ```

 Input image:
@ -354,7 +354,7 @@ The configuration file used for prediction must be consistent with the training.

 ```
 # Predict Chinese results
-python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.checkpoints={path/to/weights}/best_accuracy TestReader.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
 ```

 Input image:
--- a/paddleocr.py
+++ b/paddleocr.py
@ -262,8 +262,8 @@ class PaddleOCR(predict_system.TextSystem):
            logger.error('rec_algorithm must in {}'.format(SUPPORT_REC_MODEL))
            sys.exit(0)

-        postprocess_params.rec_char_dict_path = Path(
-            __file__).parent / postprocess_params.rec_char_dict_path
+        postprocess_params.rec_char_dict_path = str(
+            Path(__file__).parent / postprocess_params.rec_char_dict_path)

        # init det_model and rec_model
        super().__init__(postprocess_params)
--- a/ppocr/losses/det_basic_loss.py
+++ b/ppocr/losses/det_basic_loss.py
@ -45,7 +45,6 @@ class BalanceLoss(nn.Layer):
        self.balance_loss = balance_loss
        self.main_loss_type = main_loss_type
        self.negative_ratio = negative_ratio
-        self.main_loss_type = main_loss_type
        self.return_origin = return_origin
        self.eps = eps

--- a/ppocr/losses/det_sast_loss.py
+++ b/ppocr/losses/det_sast_loss.py
@ -19,7 +19,6 @@ from __future__ import print_function
 import paddle
 from paddle import nn
 from .det_basic_loss import DiceLoss
-import paddle.fluid as fluid
 import numpy as np


@ -27,9 +26,7 @@ class SASTLoss(nn.Layer):
    """
    """

-    def __init__(self,
-                 eps=1e-6,
-                 **kwargs):
+    def __init__(self, eps=1e-6, **kwargs):
        super(SASTLoss, self).__init__()
        self.dice_loss = DiceLoss(eps=eps)

@ -39,7 +36,7 @@ class SASTLoss(nn.Layer):
        tcl_mask: N x 128 x 1
        tcl_label: N x X list or LoDTensor
        """
-                
+
        f_score = predicts['f_score']
        f_border = predicts['f_border']
        f_tvo = predicts['f_tvo']
@ -53,15 +50,17 @@ class SASTLoss(nn.Layer):
        score_loss = 1.0 - 2 * intersection / (union + 1e-5)

        #border loss
-        l_border_split, l_border_norm = paddle.split(l_border, num_or_sections=[4, 1], axis=1)
+        l_border_split, l_border_norm = paddle.split(
+            l_border, num_or_sections=[4, 1], axis=1)
        f_border_split = f_border
        border_ex_shape = l_border_norm.shape * np.array([1, 4, 1, 1])
-        l_border_norm_split = paddle.expand(x=l_border_norm, shape=border_ex_shape)
-        l_border_score = paddle.expand(x=l_score, shape=border_ex_shape)   
-        l_border_mask = paddle.expand(x=l_mask, shape=border_ex_shape)  
+        l_border_norm_split = paddle.expand(
+            x=l_border_norm, shape=border_ex_shape)
+        l_border_score = paddle.expand(x=l_score, shape=border_ex_shape)
+        l_border_mask = paddle.expand(x=l_mask, shape=border_ex_shape)

        border_diff = l_border_split - f_border_split
-        abs_border_diff = paddle.abs(border_diff) 
+        abs_border_diff = paddle.abs(border_diff)
        border_sign = abs_border_diff < 1.0
        border_sign = paddle.cast(border_sign, dtype='float32')
        border_sign.stop_gradient = True
@ -72,15 +71,16 @@ class SASTLoss(nn.Layer):
                    (paddle.sum(l_border_score * l_border_mask) + 1e-5)

        #tvo_loss
-        l_tvo_split, l_tvo_norm = paddle.split(l_tvo, num_or_sections=[8, 1], axis=1)
+        l_tvo_split, l_tvo_norm = paddle.split(
+            l_tvo, num_or_sections=[8, 1], axis=1)
        f_tvo_split = f_tvo
        tvo_ex_shape = l_tvo_norm.shape * np.array([1, 8, 1, 1])
        l_tvo_norm_split = paddle.expand(x=l_tvo_norm, shape=tvo_ex_shape)
-        l_tvo_score = paddle.expand(x=l_score, shape=tvo_ex_shape)   
-        l_tvo_mask = paddle.expand(x=l_mask, shape=tvo_ex_shape)   
+        l_tvo_score = paddle.expand(x=l_score, shape=tvo_ex_shape)
+        l_tvo_mask = paddle.expand(x=l_mask, shape=tvo_ex_shape)
        #
        tvo_geo_diff = l_tvo_split - f_tvo_split
-        abs_tvo_geo_diff = paddle.abs(tvo_geo_diff) 
+        abs_tvo_geo_diff = paddle.abs(tvo_geo_diff)
        tvo_sign = abs_tvo_geo_diff < 1.0
        tvo_sign = paddle.cast(tvo_sign, dtype='float32')
        tvo_sign.stop_gradient = True
@ -91,15 +91,16 @@ class SASTLoss(nn.Layer):
                    (paddle.sum(l_tvo_score * l_tvo_mask) + 1e-5)

        #tco_loss
-        l_tco_split, l_tco_norm = paddle.split(l_tco, num_or_sections=[2, 1], axis=1)
+        l_tco_split, l_tco_norm = paddle.split(
+            l_tco, num_or_sections=[2, 1], axis=1)
        f_tco_split = f_tco
        tco_ex_shape = l_tco_norm.shape * np.array([1, 2, 1, 1])
        l_tco_norm_split = paddle.expand(x=l_tco_norm, shape=tco_ex_shape)
-        l_tco_score = paddle.expand(x=l_score, shape=tco_ex_shape)   
-        l_tco_mask = paddle.expand(x=l_mask, shape=tco_ex_shape) 
-        
+        l_tco_score = paddle.expand(x=l_score, shape=tco_ex_shape)
+        l_tco_mask = paddle.expand(x=l_mask, shape=tco_ex_shape)
+
        tco_geo_diff = l_tco_split - f_tco_split
-        abs_tco_geo_diff = paddle.abs(tco_geo_diff) 
+        abs_tco_geo_diff = paddle.abs(tco_geo_diff)
        tco_sign = abs_tco_geo_diff < 1.0
        tco_sign = paddle.cast(tco_sign, dtype='float32')
        tco_sign.stop_gradient = True
@ -109,13 +110,12 @@ class SASTLoss(nn.Layer):
        tco_loss = paddle.sum(tco_out_loss * l_tco_score * l_tco_mask) / \
                    (paddle.sum(l_tco_score * l_tco_mask) + 1e-5)

-
        # total loss
        tvo_lw, tco_lw = 1.5, 1.5
        score_lw, border_lw = 1.0, 1.0
        total_loss = score_loss * score_lw + border_loss * border_lw + \
                    tvo_loss * tvo_lw + tco_loss * tco_lw
-                    
+
        losses = {'loss':total_loss, "score_loss":score_loss,\
            "border_loss":border_loss, 'tvo_loss':tvo_loss, 'tco_loss':tco_loss}
-        return losses
+        return losses
--- a/setup.py
+++ b/setup.py
@ -32,7 +32,7 @@ setup(
    package_dir={'paddleocr': ''},
    include_package_data=True,
    entry_points={"console_scripts": ["paddleocr= paddleocr.paddleocr:main"]},
-    version='2.0.1',
+    version='2.0.2',
    install_requires=requirements,
    license='Apache License 2.0',
    description='Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices',
--- a/tools/infer/predict_cls.py
+++ b/tools/infer/predict_cls.py
@ -24,7 +24,6 @@ import numpy as np
 import math
 import time
 import traceback
-import paddle.fluid as fluid

 import tools.infer.utility as utility
 from ppocr.postprocess import build_post_process
@ -39,7 +38,6 @@ class TextClassifier(object):
        self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
        self.cls_batch_num = args.cls_batch_num
        self.cls_thresh = args.cls_thresh
-        self.use_zero_copy_run = args.use_zero_copy_run
        postprocess_params = {
            'name': 'ClsPostProcess',
            "label_list": args.label_list,
@ -99,12 +97,8 @@ class TextClassifier(object):
            norm_img_batch = norm_img_batch.copy()
            starttime = time.time()

-            if self.use_zero_copy_run:
-                self.input_tensor.copy_from_cpu(norm_img_batch)
-                self.predictor.zero_copy_run()
-            else:
-                norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
-                self.predictor.run([norm_img_batch])
+            self.input_tensor.copy_from_cpu(norm_img_batch)
+            self.predictor.run()
            prob_out = self.output_tensors[0].copy_to_cpu()
            cls_result = self.postprocess_op(prob_out)
            elapse += time.time() - starttime
@ -143,10 +137,11 @@ def main(args):
            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
        exit()
    for ino in range(len(img_list)):
-        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], cls_res[
-            ino]))
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
+                                               cls_res[ino]))
    logger.info("Total predict time for {} images, cost: {:.3f}".format(
        len(img_list), predict_time))

+
 if __name__ == "__main__":
    main(utility.parse_args())
--- a/tools/infer/predict_det.py
+++ b/tools/infer/predict_det.py
@ -22,7 +22,6 @@ import cv2
 import numpy as np
 import time
 import sys
-import paddle

 import tools.infer.utility as utility
 from ppocr.utils.logging import get_logger
@ -35,8 +34,8 @@ logger = get_logger()

 class TextDetector(object):
    def __init__(self, args):
+        self.args = args
        self.det_algorithm = args.det_algorithm
-        self.use_zero_copy_run = args.use_zero_copy_run
        pre_process_list = [{
            'DetResizeForTest': {
                'limit_side_len': args.det_limit_side_len,
@ -70,6 +69,11 @@ class TextDetector(object):
            postprocess_params["cover_thresh"] = args.det_east_cover_thresh
            postprocess_params["nms_thresh"] = args.det_east_nms_thresh
        elif self.det_algorithm == "SAST":
+            pre_process_list[0] = {
+                'DetResizeForTest': {
+                    'resize_long': args.det_limit_side_len
+                }
+            }
            postprocess_params['name'] = 'SASTPostProcess'
            postprocess_params["score_thresh"] = args.det_sast_score_thresh
            postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
@ -157,12 +161,8 @@ class TextDetector(object):
        img = img.copy()
        starttime = time.time()

-        if self.use_zero_copy_run:
-            self.input_tensor.copy_from_cpu(img)
-            self.predictor.zero_copy_run()
-        else:
-            im = paddle.fluid.core.PaddleTensor(img)
-            self.predictor.run([im])
+        self.input_tensor.copy_from_cpu(img)
+        self.predictor.run()
        outputs = []
        for output_tensor in self.output_tensors:
            output = output_tensor.copy_to_cpu()
--- a/tools/infer/predict_rec.py
+++ b/tools/infer/predict_rec.py
@ -23,7 +23,6 @@ import numpy as np
 import math
 import time
 import traceback
-import paddle.fluid as fluid

 import tools.infer.utility as utility
 from ppocr.postprocess import build_post_process
@ -39,7 +38,6 @@ class TextRecognizer(object):
        self.character_type = args.rec_char_type
        self.rec_batch_num = args.rec_batch_num
        self.rec_algorithm = args.rec_algorithm
-        self.use_zero_copy_run = args.use_zero_copy_run
        postprocess_params = {
            'name': 'CTCLabelDecode',
            "character_type": args.rec_char_type,
@ -101,12 +99,8 @@ class TextRecognizer(object):
            norm_img_batch = np.concatenate(norm_img_batch)
            norm_img_batch = norm_img_batch.copy()
            starttime = time.time()
-            if self.use_zero_copy_run:
-                self.input_tensor.copy_from_cpu(norm_img_batch)
-                self.predictor.zero_copy_run()
-            else:
-                norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
-                self.predictor.run([norm_img_batch])
+            self.input_tensor.copy_from_cpu(norm_img_batch)
+            self.predictor.run()
            outputs = []
            for output_tensor in self.output_tensors:
                output = output_tensor.copy_to_cpu()
@ -145,8 +139,8 @@ def main(args):
            "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
        exit()
    for ino in range(len(img_list)):
-        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[
-            ino]))
+        logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
+                                               rec_res[ino]))
    logger.info("Total predict time for {} images, cost: {:.3f}".format(
        len(img_list), predict_time))

--- a/tools/infer/utility.py
+++ b/tools/infer/utility.py
@ -20,8 +20,7 @@ import numpy as np
 import json
 from PIL import Image, ImageDraw, ImageFont
 import math
-from paddle.fluid.core import AnalysisConfig
-from paddle.fluid.core import create_paddle_predictor
+from paddle import inference


 def parse_args():
@ -33,6 +32,7 @@ def parse_args():
    parser.add_argument("--use_gpu", type=str2bool, default=True)
    parser.add_argument("--ir_optim", type=str2bool, default=True)
    parser.add_argument("--use_tensorrt", type=str2bool, default=False)
+    parser.add_argument("--use_fp16", type=str2bool, default=False)
    parser.add_argument("--gpu_mem", type=int, default=8000)

    # params for text detector
@ -46,7 +46,7 @@ def parse_args():
    parser.add_argument("--det_db_thresh", type=float, default=0.3)
    parser.add_argument("--det_db_box_thresh", type=float, default=0.5)
    parser.add_argument("--det_db_unclip_ratio", type=float, default=1.6)
-
+    parser.add_argument("--max_batch_size", type=int, default=10)
    # EAST parmas
    parser.add_argument("--det_east_score_thresh", type=float, default=0.8)
    parser.add_argument("--det_east_cover_thresh", type=float, default=0.1)
@ -62,7 +62,7 @@ def parse_args():
    parser.add_argument("--rec_model_dir", type=str)
    parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
    parser.add_argument("--rec_char_type", type=str, default='ch')
-    parser.add_argument("--rec_batch_num", type=int, default=6)
+    parser.add_argument("--rec_batch_num", type=int, default=1)
    parser.add_argument("--max_text_length", type=int, default=25)
    parser.add_argument(
        "--rec_char_dict_path",
@ -78,12 +78,10 @@ def parse_args():
    parser.add_argument("--cls_model_dir", type=str)
    parser.add_argument("--cls_image_shape", type=str, default="3, 48, 192")
    parser.add_argument("--label_list", type=list, default=['0', '180'])
-    parser.add_argument("--cls_batch_num", type=int, default=30)
+    parser.add_argument("--cls_batch_num", type=int, default=6)
    parser.add_argument("--cls_thresh", type=float, default=0.9)

    parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
-    parser.add_argument("--use_zero_copy_run", type=str2bool, default=False)
-
    parser.add_argument("--use_pdserving", type=str2bool, default=False)

    return parser.parse_args()
@ -109,10 +107,15 @@ def create_predictor(args, mode, logger):
        logger.info("not find params file path {}".format(params_file_path))
        sys.exit(0)

-    config = AnalysisConfig(model_file_path, params_file_path)
+    config = inference.Config(model_file_path, params_file_path)

    if args.use_gpu:
        config.enable_use_gpu(args.gpu_mem, 0)
+        if args.use_tensorrt:
+            config.enable_tensorrt_engine(
+                precision_mode=inference.PrecisionType.Half
+                if args.use_fp16 else inference.PrecisionType.Float32,
+                max_batch_size=args.max_batch_size)
    else:
        config.disable_gpu()
        config.set_cpu_math_library_num_threads(6)
@ -124,20 +127,18 @@ def create_predictor(args, mode, logger):
    # config.enable_memory_optim()
    config.disable_glog_info()

-    if args.use_zero_copy_run:
-        config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
-        config.switch_use_feed_fetch_ops(False)
-    else:
-        config.switch_use_feed_fetch_ops(True)
+    config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
+    config.switch_use_feed_fetch_ops(False)

-    predictor = create_paddle_predictor(config)
+    # create predictor
+    predictor = inference.create_predictor(config)
    input_names = predictor.get_input_names()
    for name in input_names:
-        input_tensor = predictor.get_input_tensor(name)
+        input_tensor = predictor.get_input_handle(name)
    output_names = predictor.get_output_names()
    output_tensors = []
    for output_name in output_names:
-        output_tensor = predictor.get_output_tensor(output_name)
+        output_tensor = predictor.get_output_handle(output_name)
        output_tensors.append(output_tensor)
    return predictor, input_tensor, output_tensors

--- a/tools/program.py
+++ b/tools/program.py
@ -131,7 +131,7 @@ def check_gpu(use_gpu):
          "model on CPU"

    try:
-        if use_gpu and not paddle.fluid.is_compiled_with_cuda():
+        if use_gpu and not paddle.is_compiled_with_cuda():
            print(err)
            sys.exit(1)
    except Exception as e:
@ -332,7 +332,7 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
    return metirc


-def preprocess():
+def preprocess(is_train=False):
    FLAGS = ArgsParser().parse_args()
    config = load_config(FLAGS.config)
    merge_config(FLAGS.opt)
@ -350,15 +350,17 @@ def preprocess():
    device = paddle.set_device(device)

    config['Global']['distributed'] = dist.get_world_size() != 1
-
-    # save_config
-    save_model_dir = config['Global']['save_model_dir']
-    os.makedirs(save_model_dir, exist_ok=True)
-    with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
-        yaml.dump(dict(config), f, default_flow_style=False, sort_keys=False)
-
-    logger = get_logger(
-        name='root', log_file='{}/train.log'.format(save_model_dir))
+    if is_train:
+        # save_config
+        save_model_dir = config['Global']['save_model_dir']
+        os.makedirs(save_model_dir, exist_ok=True)
+        with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
+            yaml.dump(
+                dict(config), f, default_flow_style=False, sort_keys=False)
+        log_file = '{}/train.log'.format(save_model_dir)
+    else:
+        log_file = None
+    logger = get_logger(name='root', log_file=log_file)
    if config['Global']['use_visualdl']:
        from visualdl import LogWriter
        vdl_writer_path = '{}/vdl/'.format(save_model_dir)
--- a/tools/train.py
+++ b/tools/train.py
@ -110,6 +110,6 @@ def test_reader(config, device, logger):


 if __name__ == '__main__':
-    config, device, logger, vdl_writer = program.preprocess()
+    config, device, logger, vdl_writer = program.preprocess(is_train=True)
    main(config, device, logger, vdl_writer)
    # test_reader(config, device, logger)